title: Colocation Data Center Demand Response Using Nash Bargaining Theory

abstract: The huge yet flexible power consumption of data centers makes them promising resources for demand response, particularly for emergency demand response (EDR) which requires a certain amount of load curtailment during emergencies. However, current data centers often participate in EDR by starting up their backup diesel generators, resulting in both high costs and large carbon emissions. In this paper, we focus on cost-effective and eco-friendly demand response in colocation data centers by designing economic incentives for tenants to reduce their loads during emergency periods for EDR. In particular, we model and analyze the interaction among the data center operator and tenants by using Nash bargaining theory, and derive the optimal solutions for the load reduction and reimbursement for each tenant under two different bargaining protocols (i.e., sequential bargaining and concurrent bargaining). We prove that the derived solutions are Pareto-efficient and fair, and therefore self-enforcing and satisfactory for all entities. Numerical results based on trace-driven simulations show that the proposed bargaining approach is beneficial to both the data center operator and tenants, while also reducing the carbon emissions to the environment from data center demand response.


title: Is citation analysis a legitimate evaluation tool?

abstract: A comprehensive discussion on the use of citation analysis to rate scientific performance and the controversy surrounding it. The general adverse criticism that citation counts include an excessive number of negative citations (citations to incorrect results worthy of attack), self-citations (citations to the works of the citing authors), and citations to methodological papers is analyzed. Included are a discussion of measurement problems such as counting citations for multiauthored papers, distinguishing between more than one person with the same last name (homographs), and what it is that citation analysis actually measures. It is concluded that as the scientific enterprise becomes larger and more complex, and its role in society more critical, it will become more difficult, expensive and necessary to evaluate and identify the largest contributors. When properly used, citation analysis can introduce a useful measure of objectivity into the evaluation process at relatively low financial cost.


title: An integrated multichannel neural recording analog front-end ASIC with area-efficient driven right leg circuit

abstract: This paper describes an integrated multichannel neural recording analog front end (AFE) with a novel area-efficient driven right leg (DRL) circuit to improve the system common mode rejection ratio (CMRR). The proposed AFE consists of an AC-coupled low-noise programmable-gain amplifier, an area-efficient DRL block and a 10-bit SAR ADC. Compared to conventional DRL circuit, the proposed capacitor-less DRL design achieves 90% chip area reduction with enhanced CMRR performance, making it ideal for multichannel biomedical recording applications. The AFE circuit has been designed in a standard 0.18-µm CMOS process. Post-layout simulation results show that the AFE provides two gain settings of 54dB/60dB while consuming 1 µA per channel under a supply voltage of 1 V. The input-referred noise of the AFE integrated from 1 Hz to 10k Hz is only 4 µVrms and the CMRR is 110 dB.


title: Detection of changes in mitochondrial hydrogen sulfide in vivo in the fish model Poecilia mexicana (Poeciliidae)

abstract: ABSTRACT In this paper, we outline the use of a mitochondria-targeted ratiometric mass spectrometry probe, MitoA, to detect in vivo changes in mitochondrial hydrogen sulfide (H2S) in Poecilia mexicana (family Poeciliidae). MitoA is introduced via intraperitoneal injection into the animal and is taken up by mitochondria, where it reacts with H2S to form the product MitoN. The MitoN/MitoA ratio can be used to assess relative changes in the amounts of mitochondrial H2S produced over time. We describe the use of MitoA in the fish species P. mexicana to illustrate the steps for adopting the use of MitoA in a new organism, including extraction and purification of MitoA and MitoN from tissues followed by tandem mass spectrometry. In this proof-of-concept study we exposed H2S tolerant P. mexicana to 59 µM free H2S for 5 h, which resulted in increased MitoN/MitoA in brain and gills, but not in liver or muscle, demonstrating increased mitochondrial H2S levels in select tissues following whole-animal H2S exposure. This is the first time that accumulation of H2S has been observed in vivo during whole-animal exposure to free H2S using MitoA. ::: This article has an associated First Person interview with the first author of the paper.


title: Individualism and Collectivism in Trade Agents

abstract: Agent-Based Modeling can contribute to the understanding of international trade processes. Models for the effects of culture and cultural differences on agent behavior are required for realistic agent-based simulation of international trade. This paper makes a step toward modeling of culture in agents. The focus is one of the five dimensions of culture according to Hofstede: individualism versus collectivism. The paper presents an analysis based on social science literature about national cultures. For cultural differentiation of agent behavior, rules are formulated for individualist versus collectivist agent behavior with respect to negotiations, cooperation or defection in the delivery phase of transactions, trade partner selection, and trust. Example computations demonstrate the feasibility in multi-agent simulations.


title: Identification of patients with cancer with a high risk to develop delirium

abstract: Delirium deteriorates the quality of life in patients with cancer, but is frequently underdiagnosed and not adequately treated. In this study, we evaluated the occurrence of delirium and its risk factors in patients admitted to the hospital for treatment or palliative care in order to develop a prediction model to identify patients at high risk for delirium. In a period of 1.5 years, we evaluated the risk of developing delirium in 574 consecutively admitted patients with cancer to our academic oncology department with the Delirium Observation Screening Scale. Risk factors for delirium were extracted from the patient's chart. A delirium prediction algorithm was constructed using tree analysis, and validated with fivefold cross-validation. A total of 574 patients with cancer were acutely (42%) or electively (58%) admitted 1733 times. The incidence rate of delirium was 3.5 per 100 admittances. Tree analysis revealed that the predisposing factors of an unscheduled admittance and a metabolic imbalance accurately predicted the development of delirium. In this group the incidence rate of delirium was 33 per 100 patients (1:3). The AUC of the model was 0.81, and 0.65 after fivefold cross-validation. We identified that especially patients undergoing an unscheduled admittance with a metabolic imbalance do have a clinically relevant high risk to develop a delirium. Based on these factors, we propose to evaluate preventive treatment of these patients when admitted to the hospital in order to improve their quality of life.


title: Fault Diagnostic System for a Multilevel Inverter Using a Neural Network

abstract: In this paper, a fault diagnostic system in a multilevel-inverter using a neural network is developed. It is difficult to diagnose a multilevel-inverter drive (MLID) system using a mathematical model because MLID systems consist of many switching devices and their system complexity has a nonlinear factor. Therefore, a neural network classification is applied to the fault diagnosis of a MLID system. Five multilayer perceptron (MLP) networks are used to identify the type and location of occurring faults from inverter output voltage measurement. The neural network design process is clearly described. The classification performance of the proposed network between normal and abnormal condition is about 90%, and the classification performance among fault features is about 85%. Thus, by utilizing the proposed neural network fault diagnostic system, a better understanding about fault behaviors, diagnostics, and detections of a multilevel inverter drive system can be accomplished. The results of this analysis are identified in percentage tabular form of faults and switch locations


title: Multiview Partitioning via Tensor Methods

abstract: Clustering by integrating multiview representations has become a crucial issue for knowledge discovery in heterogeneous environments. However, most prior approaches assume that the multiple representations share the same dimension, limiting their applicability to homogeneous environments. In this paper, we present a novel tensor-based framework for integrating heterogeneous multiview data in the context of spectral clustering. Our framework includes two novel formulations; that is multiview clustering based on the integration of the Frobenius-norm objective function (MC-FR-OI) and that based on matrix integration in the Frobenius-norm objective function (MC-FR-MI). We show that the solutions for both formulations can be computed by tensor decompositions. We evaluated our methods on synthetic data and two real-world data sets in comparison with baseline methods. Experimental results demonstrate that the proposed formulations are effective in integrating multiview data in heterogeneous environments.


Segmenting Chinese Microtext: Joint Informal-Word Detection and Segmentation with Neural Networks


title: Geocast Routing Protocols for VANETs: Survey and Geometry-Driven Scheme Proposal

abstract: Vehicular Ad Hoc Networks (VANETs) are characterized by highly speed nodes, highly dynamic topology and frequent link disconnections. This raises a number of challenges especially in the field of data dissemination. Our study focuses on Geocast routing which consists of routing a message from a unique source vehicle to all vehicles located in a well geographically defined destination area called ZOR (Zone Of Relevance). In this work, we introduce some existing Geocast routing protocols for VANETs as well as a classification of these protocols based on the relay selection technique they use. We then provide a comparison of these protocols according to different criteria. While in literature ZORs are often assumed to be of any form and still chosen according to the scenarios and motivation needs of the authors [1], we consider a ZOR as a set of sub-ZORs and we choose simple geometrical forms for each sub-ZOR so that they would be easy to implement and to represent mathematically. We provide a geometrical vision angles based technique to define if two sub-ZORs are in the same direction in order to send them a single message, and hence, reduce messages overhead. Finally, we introduce a new routing protocol in Sub-ZORs (GeoSUZ) for VANETs based on our geometrical vision angles and greedy forwarding techniques. We compare GeoSUZ to GPSR routing protocol [3] and some numerical results show a significant gain in term of number of messages sent over the network.


title: Implementation of noise cancellation using least mean square in GNU radio

abstract: GNU Radio is open frame work, based on C++ and Python which can be used to develop real time signal processing application, even in low cost credit card sized single board computer named Raspberry Pi. Now a days demand for Wireless Communication system design is rapidly increasing. Whenever information is transmitting through a wireless channel there is a more probability that information is effected by the noise over the channel. We need to cancel that noise in order to achieve the reliable communication. In this way Noise Cancellation has a prominent role in Digital communication. In this paper, we implemented Noise Cancellation by using LMS adaptive filter in GNU radio. In this paper, we analysis and synthesis of signals has most importance for the detection of cardiac abnormalities. The ECG signals are mixed with random noise from sources. Noises signals are mixed with basic ECG signals are power line interference, baseline wander interference, noises due to respirational movements. There are different types of adaptive filter algorithms have been reduced these noises. In our paper we used Least Mean Square algorithm (LMS) is an adaptive filtering algorithm which is defined as the minimization of the sum of the squares of the difference between the original signal and the filter output.


title: Awe the Audience: How the Narrative Trajectories Affect Audience Perception in Public Speaking

abstract: Telling a great story often involves a deliberate alteration of emotions. In this paper, we objectively measure and analyze the narrative trajectories of stories in public speaking and their impact on subjective ratings. We conduct the analysis using the transcripts of over 2000 TED talks and estimate potential audience response using over 5 million spontaneous annotations from the viewers. We use IBM Watson Tone Analyzer to extract sentence-wise emotion, language, and social scores. Our study indicates that it is possible to predict (with AUC as high as 0.88) the subjective ratings of the audience by analyzing the narrative trajectories. Additionally, we find that some trajectories (for example, a flat trajectory of joy) correlate well with some specific ratings (e.g. "Longwinded') assigned by the viewers. Such an association could be useful in forecasting audience responses using objective analysis.


title: Impact of Matching Network on Bandwidth of Compact Antenna Arrays

abstract: We analyze the impact of the matching network on compact multiple-input multiple-output systems. Existing studies have found that the matching network has a significant influence on the performance of multiple antenna systems when the antennas are in close proximity. However, none has examined the wide-band case. In this paper, we investigate the wide-band performance of four different matching networks for multiple dipole antennas. The performance of the matching networks is given in terms of the bandwidths of correlation and matching efficiency, which are extensions of the single-antenna concept of bandwidth to multiple antenna systems. We also investigate the impact of the propagation conditions on the matching and bandwidth. For a uniform two-dimensional (2-D) angular power spectrum, we find that while individual-port matching can achieve in excess of 3% fractional correlation bandwidth for envelope correlation of 0.5 at an antenna separation of 0.01lambda, multiport matching is required for efficiency bandwidth to exist for a return loss of -6 dB. Moreover, even with multiport matching, both correlation and efficiency bandwidths decrease drastically at small antenna separations. At 0.01lambda, the correlation and efficiency bandwidths are 0.4% and 0.2%, respectively. Similar evaluations were performed for measured outdoor-to-indoor channels with moderate to small 2-D angular spreads. We find that the efficiency advantage of multiport matching over individual-port matching diminishes with decreasing angular spread


Pantoprazole: A new proton pump inhibitor in the management of upper gastrointestinal disease


title: Hot-Carrier-Induced Degradations and Optimizations for Lateral DMOS Transistor With Multiple Floating Poly-Gate Field Plates

abstract: The electrical parameters degradations of lateral double-diffused MOS with multiple floating poly-gate field plates under different stress conditions have been investigated experimentally. For the maximum substrate current ( ${I}_{{\text {submax}}})$ stress, the increased interface states at the bird’s beak mainly result in an on-resistance ( ${R}_{ \mathrm{\scriptscriptstyle ON}})$ increase at the beginning of the stress, while hot holes injection and trapping into the oxide beneath the edge of real poly-gate turns out to be the dominating degradation mechanism after around 800-s stress, making the ${R}_{{ \mathrm{\scriptscriptstyle ON}}}$ decrease. For the maximum operating gate voltage ( ${V}_{{\text {gmax}}})$ stress, the trapped hot electrons in the channel region bring an increase in threshold voltage ( ${V}_{{\text {th}}})$ and ${R}_{{ \mathrm{\scriptscriptstyle ON}}}$ , while the generation of large numbers of interface states at the bird’s beak further dramatically increases the ${R}_{{ \mathrm{\scriptscriptstyle ON}}}$ . A novel device structurewith a poly-gate partly recessed into the field oxide has been presented to decrease the hot-carrier-induced degradations.


title: Leg mechanism design for SLIP model of hydraulic quadruped robot

abstract: The Spring Loaded Inverted Pendulum (SLIP) describes the dynamic walking of humans and animals in a simplified manner. However, realizing such movements by means of a combination of typical articulated legs and linear actuators has some limitations. This paper proposes a leg mechanism that accurately reflects the SLIP based on its mechanical constitution. The SLIP design based leg is able to do decoupled swing motion and extension motion. Also this study has focused on improving the straightness of extension motion.


title: Mechanical property of a articulated in-pipe locomotion robot

abstract: This paper focuses on mechanical property of a articulated multi-unit wheel type in-pipe locomotion robot system. Through establishing the posture model of the robot system, can get the coordinates of wheel center and its corresponding contact point with pipe wall of each wheel for robot unit. Based on the posture model, the mechanical model of the robot unit is presented and the analysis is carried out in details. To confirm the effectiveness of the proposed theoretical analysis, an example about statics of the pipe robot is calculated, and the calculation results basically reflect the actual characteristics of the pipe robot. This provide theoretical basis for the selection of driving mechanism design and control mode of wheel type pipe robot.


Monza : Image Classification of Vehicle Make and Model Using Convolutional Neural Networks and Transfer Learning


title: Investigating the effect of website quality on e-business success: an analytic hierarchy process (AHP) approach

abstract: This study investigates website quality factors, their relative importance in selecting the most preferred website, and the relationship between website preference and financial performance. DeLone and McLean's IS success model extended through applying an analytic hierarchy process is used. A field study with 156 online customers and 34 managers/designers of e-business companies was performed. The study identified different relative importance of each website quality factor and priority of alternative websites across e-business domains and between stakeholders. This study also found that the website with the highest quality produced the highest business performance. The findings of this study provide decision makers of e-business companies with useful insights to enhance their website quality.


title: Comparison of different methods of antioxidant activity evaluation of green and roast C. Arabica and C. Robusta coffee beans

abstract: Coffee beans contain a large amount of antioxidants, which are subjected to various changes during roasting. In this study, antioxidant potential of raw and roasted to different degree (light, medium, dark) C. arabica and C. robusta coffee beans was evaluated by the four antioxidant assay methods, TPC, FRAP, TEAC, and DPPH˙. The obtained results revealed significant differences between the coffee types, roasting degree, and antioxidant activity assessment methods. FRAP and TPC appeared to be the most appropriate methods for revealing the differences in antioxidant potential of different coffee types and the effects of roasting. The results obtained by these methods were in good correlation. ABTS and DPPH? methods are not enough sensitive for the determination of roasting degrees. In general, based on statistical data evaluation, antioxidant activity is more dependent on the coffee type than on the degree of roasting, however, the selection of analytical method may also be significant.


title: Smart technologies for personalized experiences: a case study in the hospitality domain

abstract: Recent advances in the field of technology have led to the emergence of innovative technological smart solutions providing unprecedented opportunities for application in the tourism and hospitality industry. With intensified competition in the tourism market place, it has become paramount for businesses to explore the potential of technologies, not only to optimize existing processes but facilitate the creation of more meaningful and personalized services and experiences. This study aims to bridge the current knowledge gap between smart technologies and experience personalization to understand how smart mobile technologies can facilitate personalized experiences in the context of the hospitality industry. By adopting a qualitative case study approach, this paper makes a two-fold contribution; it a) identifies the requirements of smart technologies for experience creation, including information aggregation, ubiquitous mobile connectedness and real time synchronization and b) highlights how smart technology integration can lead to two distinct levels of personalized tourism experiences. The paper concludes with the development of a model depicting the dynamic process of experience personalization and a discussion of the strategic implications for tourism and hospitality management and research.


title: Recent trends in neuromorphic engineering

abstract: Neuromorphic Engineering has emerged as an exciting research area, primarily owing to the paradigm shift from conventional computing architectures to data-driven, cognitive computing. There is a diversity of work in the literature pertaining to neuromorphic systems, devices and circuits. This review looks at recent trends in neuromorphic engineering and its sub-domains, with an attempt to identify key research directions that would assume significance in the future. We hope that this review would serve as a handy reference to both beginners and experts, and provide a glimpse into the broad spectrum of applications of neuromorphic hardware and algorithms. Our survey indicates that neuromorphic engineering holds a promising future, particularly with growing data volumes, and the imminent need for intelligent, versatile computing.


title: Towards Creation of a Corpus for Argumentation Mining the Biomedical Genetics Research Literature

abstract: Argumentation mining involves automatically identifying the premises, conclusion, and type of each argument as well as relationships between pairs of arguments in a document. We describe our plan to create a corpus from the biomedical genetics research literature, annotated to support argumentation mining research. We discuss the argumentation elements to be annotated, theoretical challenges, and practical issues in creating such a corpus.


title: The Network Weather Service � A Distributed Resource Performance Forecasting Service for Metacomputing Rich Wolski

abstract: Abstract The goal of the Network Weather Service is to provide accurate forecasts of dynamically changing performance characteristics from a distributed set of metacomputing resources. Providing a ubiquitous service that can both track dynamic performance changes and remain stable in spite of them requires adaptive programming techniques, an architectural design that supports extensibility, and internal abstractions that can be implemented efficiently and portably. In this paper, we describe the current implementation of the NWS for Unix and TCP/IP sockets and provide examples of its performance monitoring and forecasting capabilities.


title: Comparison of Non-Intrusive Polynomial Chaos and Stochastic Collocation Methods for Uncertainty Quantification

abstract: Non-intrusive polynomial chaos expansion (PCE) and stochastic collocation (SC) methods are attractive techniques for uncertainty quantification (UQ) due to their strong mathematical basis and ability to produce functional representations of stochastic variability. PCE estimates coefficients for known orthogonal polynomial basis functions based on a set of response function evaluations, using sampling, linear regression, tensor-product quadrature, or Smolyak sparse grid approaches. SC, on the other hand, forms interpolation functions for known coefficients, and requires the use of structured collocation point sets derived from tensor-products or sparse grids. When tailoring the basis functions or interpolation grids to match the forms of the input uncertainties, exponential convergence rates can be achieved with both techniques for general probabilistic analysis problems. In this paper, we explore relative performance of these methods using a number of simple algebraic test problems, and analyze observed differences. In these computational experiments, performance of PCE and SC is shown to be very similar, although when differences are evident, SC is the consistent winner over traditional PCE formulations. This stems from the practical difficulty of optimally synchronizing the formof the PCE with the integration approach being employed, resulting in slight over- or under-integration of prescribed expansion form. With additional nontraditional tailoring of PCE form, it is shown that this performance gap can be reduced, and in some cases, eliminated.


title: Freesound Datasets: A Platform for the Creation of Open Audio Datasets

abstract: Comunicacio presentada al 18th International Society for Music Information Retrieval Conference celebrada a Suzhou, Xina, del 23 al 27 d'cotubre de 2017.


title: Optimal charging operation of battery swapping stations with QoS guarantee

abstract: Electric Vehicle (EV) drivers have an urgent demand for fast battery refueling methods for long distance trip and emergency drive. A well-planned battery swapping station (BSS) network can be a promising solution to offer timely refueling services. However, an inappropriate battery recharging process in the BSS may not only violate the stabilization of the power grid by their large power consumption, but also increase the charging cost from the BSS operators' point of view. In this paper, we aim to obtain the optimal charging policy to minimize the charging cost while ensuring the quality of service (QoS) of the BSS. A novel queueing network model is proposed to capture the operation nature for an individual BSS. Based on practical assumptions, we formulate the charging schedule problem as a stochastic control problem and achieve the optimal charging policy by dynamic programming. Monte Carlo simulation is used to evaluate the performance of different policies for both stationary and non-stationary EV arrival cases. Numerical results show the importance of determining the number of total batteries and charging outlets held in the BSS. Our work gives insight for the future infrastructure planning and operational management of BSS network.


Bayesian Theory of Mind


title: Augmented Reality Guidance for Needle Biopsies: A Randomized, Controlled Trial in Phantoms

abstract: We report the results of a randomized, controlled tnal to compare the accuracy of standard ultrasound-guided needle biopsy to biopsies performed using a 3D Augmented Reality (AR) guidance system. Fifty core biopsies of breast phantoms were conducted by a board-certified radiologist, with each set of five biopsies randomly assigned to one of the methods. The raw ultrasound data from each biopsy was recorded. Another board-certified radiologist, blinded to the actual biopsy guidance mechanism, evaluated the ultrasound recordings and determined the distance of the biopsy from the ideal position. A repeated measures analysis of variance indicated that the head-mounted display method led to a statistically significantly smaller mean deviation from the desired target than did the CRT display method. (2.48mm for control versus 1.62mm for augmented reality, p < 0.02). This result suggests that AR systems can offer improved accuracy over traditional biopsy guidance methods.


title: "What do they snapchat about?" Patterns of use in time-limited instant messaging service

abstract: The use of Snapchat - a time-limited instant messaging service - has been rapidly rising amongst adolescents. However, the exact nature of Snapchat use remains difficult to examine due to the self-destructive nature of content sent and received via this service. We report an online survey conducted with the use of a memory sampling method to enquire about the specific details of the very last image or video each participant sent and received via Snapchat. We found that users mainly share 'selfies', typically embed text and 'doodles' with photos they share, use it mostly at home, and primarily for communication with close friends and family as an 'easier and funnier' alternative to other instant messaging services. We also found that high intensity of Snapchat use was more associated with bonding rather than bridging social capital. We discuss those findings in the context of existing studies on the use of instant messaging services and social networking sites. Online survey with memory sampling method was used to examine the last image sent and received via Snapchat.Snapchat is easy-to-use and highly playful alternative to other IM services.Snapchat is used to privately communicate and share content - primarily selfies with doodles.Snapchat is used with a small group of "strong ties" - close friends, partners and family.Snapchat appears to be more useful for bonding rather than bridging of social capital.


title: Brain waves for automatic biometric-based user recognition

abstract: Brain signals have been investigated within the medical field for more than a century to study brain diseases like epilepsy, spinal cord injuries, Alzheimer's, Parkinson's, schizophrenia, and stroke among others. They are also used in both brain computer and brain machine interface systems with assistance, rehabilitative, and entertainment applications. Despite the broad interest in clinical applications, the use of brain signals has been only recently investigated by the scientific community as a biometric characteristic to be used in automatic people recognition systems. However, brain signals present some peculiarities, not shared by the most commonly used biometrics, such as face, iris, and fingerprints, with reference to privacy compliance, robustness against spoofing attacks, possibility to perform continuous identification, intrinsic liveness detection, and universality. These peculiarities make the use of brain signals appealing. On the other hand, there are many challenges which need to be properly addressed. The understanding of the level of uniqueness and permanence of brain responses, the design of elicitation protocols, and the invasiveness of the acquisition process are only few of the challenges which need to be tackled. In this paper, we further speculate on those issues, which represent an obstacle toward the deployment of biometric systems based on the analysis of brain activity in real life applications and intend to provide a critical and comprehensive review of state-of-the-art methods for electroencephalogram-based automatic user recognition, also reporting neurophysiological evidences related to the performed claims.


title: Bug isolation via remote program sampling

abstract: We propose a low-overhead sampling infrastructure for gathering information from the executions experienced by a program's user community. Several example applications illustrate ways to use sampled instrumentation to isolate bugs. Assertion-dense code can be transformed to share the cost of assertions among many users. Lacking assertions, broad guesses can be made about predicates that predict program errors and a process of elimination used to whittle these down to the true bug. Finally, even for non-deterministic bugs such as memory corruption, statistical modeling based on logistic regression allows us to identify program behaviors that are strongly correlated with failure and are therefore likely places to look for the error.


title: Bandwidth enhancement of HF antennas mounted on military platforms using a Characteristic-Modes-Based Design Approach

abstract: Narrow bandwidth is a challenging problem for designing platform-mounted electrically small antennas in the high-frequency (HF) band. In this paper, we demonstrate a process that can be used to improve the bandwidth of a platform-mounted HF antenna or antennas by taking advantage of the presence of the platform. Specifically, we examine a simplified model for the Expeditionary Fighting Vehicle (EFV) and examine how the mounted antennas can be used to excite a desired natural resonant mode of this platform for bandwidth enhancement. The proposed approach is employed to successfully enhance the bandwidth of horizontally-polarized HF loop antennas from 0.64% to 0.79% and 0.89%, by using respectively one, two, or four half loop antennas to excite the desired mode of the EFV.


title: Augmented Chemistry: Interactive Education System

abstract: knowledge delivery methods in education should move away from memory based learning to more motivated and creative education. This paper will emphasize on the advantages tangible interaction can bring to education. Augmented Chemistry provides an efficient way for designing and interacting with the molecules to understand the spatial relations between molecules. For Students it is very informative to see actual molecules representation 3D environment, inspect molecules from multiple viewpoints and control the interaction of molecules.We present in this paper an Augmented Reality system for teaching spatial relationships and chemical-reaction problem-solving skills to school-level students based on the VSEPR theory. Our system is based on inexpensive webcams and open-source software. We hope this willgenerate more ideas for educators and researcher to explore Augmented Reality technology in the field of interactive education.


title: Audience Analysis for Competing Memes in Social Media

abstract: Existing tools for exploratory analysis of information diffusion in social media focus on the message senders who actively diffuse the meme. We develop a tool for audience analysis, focusing on the people who are passively exposed to the messages, with a special emphasis on competing memes such as propagations and corrections of a rumor. In such competing meme diffusions, important questions include which meme reached a bigger total audience, the overlap in audiences of the two, and whether exposure to one meme inhibited propagation of the other. We track audience members’ states of interaction, such as having been exposed to one meme or another or both. We analyze the marginal impact of each message in terms of the number of people who transition between states as a result of that message. These marginal impacts can be computed efficiently, even for diffusions involving thousands of senders and millions of receivers. The marginal impacts provide the raw material for an interactive tool, RumorLens, that includes a Sankey diagram and a network diagram. We validate the utility of the tool through a case study of nine rumor diffusions. We validate the usability of the tool through a user study, showing that nonexperts are able to use it to answer audience analysis questions.


title: A machine learning approach to predict movie box-office success

abstract: Predicting society's reaction to a new product in the sense of popularity and adaption rate has become an emerging field of data analysis. The motion picture industry is a multi-billion-dollar business, and there is a massive amount of data related to movies is available over the internet. This study proposes a decision support system for movie investment sector using machine learning techniques. This research helps investors associated with this business for avoiding investment risks. The system predicts an approximate success rate of a movie based on its profitability by analyzing historical data from different sources like IMDb, Rotten Tomatoes, Box Office Mojo and Metacritic. Using Support Vector Machine (SVM), Neural Network and Natural Language Processing the system predicts a movie box office profit based on some pre-released features and post-released features. This paper shows Neural Network gives an accuracy of 84.1% for pre-released features and 89.27% for all features while SVM has 83.44% and 88.87% accuracy for pre-released features and all features respectively when one away prediction is considered. Moreover, we figure out that budget, IMDb votes and no. of screens are the most important features which play a vital role while predicting a movie's box-office success.


title: Heuristic Search Value Iteration for POMDPs

abstract: We present a novel POMDP planning algorithm called heuristic search value iteration (HSVI).HSVI is an anytime algorithm that returns a policy and a provable bound on its regret with respect to the optimal policy. HSVI gets its power by combining two well-known techniques: attention-focusing search heuristics and piecewise linear convex representations of the value function. HSVI's soundness and convergence have been proven. On some benchmark problems from the literature, HSVI displays speedups of greater than 100 with respect to other state-of-the-art POMDP value iteration algorithms. We also apply HSVI to a new rover exploration problem 10 times larger than most POMDP problems in the literature.


title: RGB-D Face Recognition With Texture and Attribute Features

abstract: Face recognition algorithms generally utilize 2D images for feature extraction and matching. To achieve higher resilience toward covariates, such as expression, illumination, and pose, 3D face recognition algorithms are developed. While it is challenging to use specialized 3D sensors due to high cost, RGB-D images can be captured by low-cost sensors such as Kinect. This research introduces a novel face recognition algorithm using RGB-D images. The proposed algorithm computes a descriptor based on the entropy of RGB-D faces along with the saliency feature obtained from a 2D face. Geometric facial attributes are also extracted from the depth image and face recognition is performed by fusing both the descriptor and attribute match scores. The experimental results indicate that the proposed algorithm achieves high face recognition accuracy on RGB-D images obtained using Kinect compared with existing 2D and 3D approaches.


title: Image classification for content-based indexing

abstract: Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Using binary Bayesian classifiers, we attempt to capture high-level concepts from low-level image features under the constraint that the test image does belong to one of the classes. Specifically, we consider the hierarchical classification of vacation images; at the highest level, images are classified as indoor or outdoor; outdoor images are further classified as city or landscape; finally, a subset of landscape images is classified into sunset, forest, and mountain classes. We demonstrate that a small vector quantizer (whose optimal size is selected using a modified MDL criterion) can be used to model the class-conditional densities of the features, required by the Bayesian methodology. The classifiers have been designed and evaluated on a database of 6931 vacation photographs. Our system achieved a classification accuracy of 90.5% for indoor/outdoor, 95.3% for city/landscape, 96.6% for sunset/forest and mountain, and 96% for forest/mountain classification problems. We further develop a learning method to incrementally train the classifiers as additional data become available. We also show preliminary results for feature reduction using clustering techniques. Our goal is to combine multiple two-class classifiers into a single hierarchical classifier.


title: GROPING: Geomagnetism and cROwdsensing Powered Indoor NaviGation

abstract: Although a large number of WiFi fingerprinting based indoor localization systems have been proposed, our field experience with Google Maps Indoor (GMI), the only system available for public testing, shows that it is far from mature for indoor navigation. In this paper, we first report our field studies with GMI, as well as experiment results aiming to explain our unsatisfactory GMI experience. Then motivated by the obtained insights, we propose GROPING as a self-contained indoor navigation system independent of any infrastructural support. GROPING relies on geomagnetic fingerprints that are far more stable than WiFi fingerprints, and it exploits crowdsensing to construct floor maps rather than expecting individual venues to supply digitized maps. Based on our experiments with 20 participants in various floors of a big shopping mall, GROPING is able to deliver a sufficient accuracy for localization and thus provides smooth navigation experience.


title: Efficient multitasking: parallel versus serial processing of multiple tasks

abstract: In the context of performance optimizations in multitasking, a central debate has unfolded in multitasking research around whether cognitive processes related to different tasks proceed only sequentially (one at a time), or can operate in parallel (simultaneously). This review features a discussion of theoretical considerations and empirical evidence regarding parallel versus serial task processing in multitasking. In addition, we highlight how methodological differences and theoretical conceptions determine the extent to which parallel processing in multitasking can be detected, to guide their employment in future research. Parallel and serial processing of multiple tasks are not mutually exclusive. Therefore, questions focusing exclusively on either task-processing mode are too simplified. We review empirical evidence and demonstrate that shifting between more parallel and more serial task processing critically depends on the conditions under which multiple tasks are performed. We conclude that efficient multitasking is reflected by the ability of individuals to adjust multitasking performance to environmental demands by flexibly shifting between different processing strategies of multiple task-component scheduling.


title: A deep language model for software code

abstract: Existing language models such as n-grams for software code often fail to capture a long context where dependent code elements scatter far apart. In this paper, we propose a novel approach to build a language model for software code to address this particular issue. Our language model, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term dependencies which occur frequently in software code. Results from our intrinsic evaluation on a corpus of Java projects have demonstrated the effectiveness of our language model. This work contributes to realizing our vision for DeepSoft, an end-to-end, generic deep learning-based framework for modeling software and its development process.


title: A Biobank of Breast Cancer Explants with Preserved Intra-tumor Heterogeneity to Screen Anticancer Compounds

abstract: Summary The inter- and intra-tumor heterogeneity of breast cancer needs to be adequately captured in pre-clinical models. We have created a large collection of breast cancer patient-derived tumor xenografts (PDTXs), in which the morphological and molecular characteristics of the originating tumor are preserved through passaging in the mouse. An integrated platform combining in vivo maintenance of these PDTXs along with short-term cultures of PDTX-derived tumor cells (PDTCs) was optimized. Remarkably, the intra-tumor genomic clonal architecture present in the originating breast cancers was mostly preserved upon serial passaging in xenografts and in short-term cultured PDTCs. We assessed drug responses in PDTCs on a high-throughput platform and validated several ex vivo responses in vivo. The biobank represents a powerful resource for pre-clinical breast cancer pharmacogenomic studies (http://caldaslab.cruk.cam.ac.uk/bcape), including identification of biomarkers of response or resistance.


title: Translating Embeddings for Modeling Multi-relational Data

abstract: We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Despite its simplicity, this assumption proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Besides, it can be successfully trained on a large scale data set with 1M entities, 25k relationships and more than 17M training samples.


Direct manipulation systems offer the satisfying experience of operating on visible objects . The computer becomes transparent , and users can concentrate on their tasks . Direct Manipulation : A Step Beyond Programming Languages


title: Cross-language plagiarism detection

abstract: Cross-language plagiarism detection deals with the automatic identification and extraction of plagiarism in a multilingual setting. In this setting, a suspicious document is given, and the task is to retrieve all sections from the document that originate from a large, multilingual document collection. Our contributions in this field are as follows: (1) a comprehensive retrieval process for cross-language plagiarism detection is introduced, highlighting the differences to monolingual plagiarism detection, (2) state-of-the-art solutions for two important subtasks are reviewed, (3) retrieval models for the assessment of cross-language similarity are surveyed, and, (4) the three models CL-CNG, CL-ESA and CL-ASA are compared. Our evaluation is of realistic scale: it relies on 120,000 test documents which are selected from the corpora JRC-Acquis and Wikipedia, so that for each test document highly similar documents are available in all of the six languages English, German, Spanish, French, Dutch, and Polish. The models are employed in a series of ranking tasks, and more than 100 million similarities are computed with each model. The results of our evaluation indicate that CL-CNG, despite its simple approach, is the best choice to rank and compare texts across languages if they are syntactically related. CL-ESA almost matches the performance of CL-CNG, but on arbitrary pairs of languages. CL-ASA works best on "exact" translations but does not generalize well.


title: A New Approach for Large-Scale Localization and Mapping: Hybrid Metric-Topological SLAM

abstract: Most successful works in simultaneous localization and mapping (SLAM) aim to build a metric map under a probabilistic viewpoint employing Bayesian filtering techniques. This work introduces a new hybrid metric-topological approach, where the aim is to reconstruct the path of the robot in a hybrid continuous-discrete state space which naturally combines metric and topological maps. Our fundamental contributions are: (i) the estimation of the topological path, an improvement similar to that of Rao-Blackwellized particle filters (RBPF) and FastSLAM in the field of metric map building; and (ii) the application of grounded methods to the abstraction of topology (including loop closure) from raw sensor readings. It is remarkable that our approach could be still represented as a Bayesian inference problem, becoming an extension of purely metric SLAM. Besides providing the formal definitions and the basics for our approach, we also describe a practical implementation aimed to real-time operation. Promising experimental results mapping large environments with multiple nested loops (~30.000 m2, ~2Km robot path) validate our work.


title: Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation

abstract: Inferior temporal (IT) cortex in human and nonhuman primates serves visual object recognition. Computational object-vision models, although continually improving, do not yet reach human performance. It is unclear to what extent the internal representations of computational models can explain the IT representation. Here we investigate a wide range of computational model representations (37 in total), testing their categorization performance and their ability to account for the IT representational geometry. The models include well-known neuroscientific object-recognition models (e.g. HMAX, VisNet) along with several models from computer vision (e.g. SIFT, GIST, self-similarity features, and a deep convolutional neural network). We compared the representational dissimilarity matrices (RDMs) of the model representations with the RDMs obtained from human IT (measured with fMRI) and monkey IT (measured with cell recording) for the same set of stimuli (not used in training the models). Better performing models were more similar to IT in that they showed greater clustering of representational patterns by category. In addition, better performing models also more strongly resembled IT in terms of their within-category representational dissimilarities. Representational geometries were significantly correlated between IT and many of the models. However, the categorical clustering observed in IT was largely unexplained by the unsupervised models. The deep convolutional network, which was trained by supervision with over a million category-labeled images, reached the highest categorization performance and also best explained IT, although it did not fully explain the IT data. Combining the features of this model with appropriate weights and adding linear combinations that maximize the margin between animate and inanimate objects and between faces and other objects yielded a representation that fully explained our IT data. Overall, our results suggest that explaining IT requires computational features trained through supervised learning to emphasize the behaviorally important categorical divisions prominently reflected in IT.


title: Saliency from hierarchical adaptation through decorrelation and variance normalization

abstract: This paper presents a novel approach to visual saliency that relies on a contextually adapted representation produced through adaptive whitening of color and scale features. Unlike previous models, the proposal is grounded on the specific adaptation of the basis of low level features to the statistical structure of the image. Adaptation is achieved through decorrelation and contrast normalization in several steps in a hierarchical approach, in compliance with coarse features described in biological visual systems. Saliency is simply computed as the square of the vector norm in the resulting representation. The performance of the model is compared with several state-of-the-art approaches, in predicting human fixations using three different eye-tracking datasets. Referring this measure to the performance of human priority maps, the model proves to be the only one able to keep the same behavior through different datasets, showing free of biases. Moreover, it is able to predict a wide set of relevant psychophysical observations, to our knowledge, not reproduced together by any other model before.


title: Towards an insider threat prediction specification language

abstract: Purpose – This paper presents the process of constructing a language tailored to describing insider threat incidents, for the purposes of mitigating threats originating from legitimate users in an IT infrastructure.Design/methodology/approach – Various information security surveys indicate that misuse by legitimate (insider) users has serious implications for the health of IT environments. A brief discussion of survey data and insider threat concepts is followed by an overview of existing research efforts to mitigate this particular problem. None of the existing insider threat mitigation frameworks provide facilities for systematically describing the elements of misuse incidents, and thus all threat mitigation frameworks could benefit from the existence of a domain specific language for describing legitimate user actions.Findings – The paper presents a language development methodology which centres upon ways to abstract the insider threat domain and approaches to encode the abstracted information into lan...


title: Latent factor models with additive and hierarchically-smoothed user preferences

abstract: Items in recommender systems are usually associated with annotated attributes: for e.g., brand and price for products; agency for news articles, etc. Such attributes are highly informative and must be exploited for accurate recommendation. While learning a user preference model over these attributes can result in an interpretable recommender system and can hands the cold start problem, it suffers from two major drawbacks: data sparsity and the inability to model random effects. On the other hand, latent-factor collaborative filtering models have shown great promise in recommender systems; however, its performance on rare items is poor. In this paper we propose a novel model LFUM, which provides the advantages of both of the above models. We learn user preferences (over the attributes) using a personalized Bayesian hierarchical model that uses a combination(additive model) of a globally learned preference model along with user-specific preferences. To combat data-sparsity, we smooth these preferences over the item-taxonomy using an efficient forward-filtering and backward-smoothing inference algorithm. Our inference algorithms can handle both discrete attributes (e.g., item brands) and continuous attributes (e.g., item prices). We combine the user preferences with the latent-factor models and train the resulting collaborative filtering system end-to-end using the successful BPR ranking algorithm. In our extensive experimental analysis, we show that our proposed model outperforms several commonly used baselines and we carry out an ablation study showing the benefits of each component of our model.


title: Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data

abstract: Natural language generation plays a critical role in spoken dialogue systems. We present a new approach to natural language generation for task-oriented dialogue using recurrent neural networks in an encoder-decoder framework. In contrast to previous work, our model uses both lexicalized and delexicalized components i.e. slot-value pairs for dialogue acts, with slots and corresponding values aligned together. This allows our model to learn from all available data including the slot-value pairing, rather than being restricted to delexicalized slots. We show that this helps our model generate more natural sentences with better grammar. We further improve our model's performance by transferring weights learnt from a pretrained sentence auto-encoder. Human evaluation of our best-performing model indicates that it generates sentences which users find more appealing.


title: Evaluating radial, axial and transverse flux topologies for 'in-wheel' motor

abstract: Three different motor topologies, (1) radial, (2) axial and (3) transverse flux, are investigated for 'in-wheel' electric propulsion system. The design constraints are: 350mm/spl times/100mm package limitation, 48 V DC bus restriction and per phase inverter current limited to 500 A/sup pk/. In addition, magnet pole pairs are fixed to 9 due to limitation in 'position sensor' capacity. The paper summarizes results obtained from analytical design followed by 2D and 3D static and transient finite element analysis accompanied with experimental verification for the radial flux topology.


Named Entity Recognition and Classification in Kannada Language


title: A Widely Tunable Compact Patch Antenna

abstract: In this letter, we propose a tunable patch antenna made of a slotted rectangular patch loaded by a number of posts close to the patch edge. The posts are short circuited to the ground plane via a set of PIN diode switches. Simulations and measurements verify the possibility of tuning the antenna in subbands from 620 to 1150 MHz. Good matching has been achieved over most of the bands. Other performed designs show that more than one octave can be achieved using the proposed structure.


title: TH17 cytokines induce human airway smooth muscle cell migration

abstract: Background Migration of airway smooth muscle cells (ASMCs) might contribute to increased airway smooth muscle mass in asthma. T H 17 cells and T H 17-associated cytokines are involved in the pathogenesis of asthma and might also contribute to airway remodeling. Objective We sought to explore the possibility that migration of ASMCs might contribute to airway remodeling through the action of T H 17-related cytokines. Methods The effect of exogenous T H 17 cytokines on ex vivo human ASMC migration was investigated by using a chemotaxis assay. The involvement of signaling pathways, including p38 mitogen-activated protein kinase (MAPK), extracellular signal-regulated kinase 1/2 MAPK, nuclear factor κB, and phosphoinositide 3-kinase, was also examined. Results We demonstrated that IL-17A, IL-17F, and IL-22 promote migration in a dose-dependent manner. We further demonstrated that ASMCs express receptors for IL-17RA, IL-17RC, and IL-22R1. Using mAbs directed against these receptors, we confirmed that T H 17-associated cytokine–induced migration was dependent on selective receptor activation. Moreover, IL-17A and IL-17F exert their effects through signaling pathways that are distinct from those used by IL-22. The p38 MAPK inhibitor BIRB0796 inhibited the migration induced by IL-17A and IL-17F. PS1145, an inhibitor of nuclear factor κB, abolished the IL-22–induced migration. Conclusion These data raise the possibility that T H 17-associated cytokines promote human ASMC migration in vivo and suggest an important new mechanism for the promotion of airway remodeling in asthma.


title: Emotion recognition and its application in software engineering

abstract: In this paper a novel application of multimodal emotion recognition algorithms in software engineering is described. Several application scenarios are proposed concerning program usability testing and software process improvement. Also a set of emotional states relevant in that application area is identified. The multimodal emotion recognition method that integrates video and depth channels, physiological signals and input devices usage patterns is proposed and some preliminary results on learning set creation are described.


title: 3D reconstruction based on underwater video from ROV Kiel 6000 considering underwater imaging conditions

abstract: This work presents a system for 3D reconstruction from underwater images or video. Aside from a camera in an underwater housing, no special equipment is required. However, if navigational data is available, it is utilized in the algorithm. The algorithm is in essence a classical structure from motion approach, which is adapted to account for the special imaging conditions. Hence, there is no need for the camera to follow a specialized trajectory. Adaptions to the underwater imaging environment include a special filtering of background and floating particles, which allows a robust estimation of the camera poses and a sparse set of 3D points. Based on the estimated camera track, dense image correspondence computation enables building a detailed 3D surface model. Once the 3D surface model is completed, the colors of the texture are corrected by a physical model for underwater light propagation, allowing to view the model without the effects of scattering and attenuation or to simulate the effects of water on light in a 3D viewer.


title: A Survey of Body Sensor Networks

abstract: The technology of sensor, pervasive computing, and intelligent information processing is widely used in Body Sensor Networks (BSNs), which are a branch of wireless sensor networks (WSNs). BSNs are playing an increasingly important role in the fields of medical treatment, social welfare and sports, and are changing the way humans use computers. Existing surveys have placed emphasis on the concept and architecture of BSNs, signal acquisition, context-aware sensing, and system technology, while this paper will focus on sensor, data fusion, and network communication. And we will introduce the research status of BSNs, the analysis of hotspots, and future development trends, the discussion of major challenges and technical problems facing currently. The typical research projects and practical application of BSNs are introduced as well. BSNs are progressing along the direction of multi-technology integration and intelligence. Although there are still many problems, the future of BSNs is fundamentally promising, profoundly changing the human-machine relationships and improving the quality of people's lives.


title: A serverless topic-based and content-based pub/sub broker: demo

abstract: Building scalable, highly available publish/subscribe (pub/sub) systems can require sophisticated algorithms and a tremendous amount of engineering effort. This paper demonstrates a way to build a pub/sub broker on top of the OpenWhisk serverless platform that performs topic-based and content-based matching. This approach radically simplifies the design and significantly reduces the amount of code while still achieving scalability targets. Furthermore, we present a publisher/subscriber client application to interact with the broker as well as an evaluator application that enforces heavy workload on the broker to measure the scalability and latency of the pub/sub system and discover the potential bottlenecks.


title: Evaluating collaborative filtering recommender systems

abstract: Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.


title: Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery

abstract: Many of today's drug discoveries require expertise knowledge and insanely expensive biological experiments for identifying the chemical molecular properties. However, despite the growing interests of using supervised machine learning algorithms to automatically identify those chemical molecular properties, there is little advancement of the performance and accuracy due to the limited amount of training data. In this paper, we propose a novel unsupervised molecular embedding method, providing a continuous feature vector for each molecule to perform further tasks, e.g., solubility classification. In the proposed method, a multi-layered Gated Recurrent Unit (GRU) network is used to map the input molecule into a continuous feature vector of fixed dimensionality, and then another deep GRU network is employed to decode the continuous vector back to the original molecule. As a result, the continuous encoding vector is expected to contain rigorous and enough information to recover the original molecule and predict its chemical properties. The proposed embedding method could utilize almost unlimited molecule data for the training phase. With sufficient information encoded in the vector, the proposed method is also robust and task-insensitive. The performance and robustness are confirmed and interpreted in our extensive experiments.


title: Improvement of bond pad crystal defect by new aluminum pad film stack

abstract: With the rapid development of very large scale integration (VLSI) and continuous scaling in the metal oxide semiconductor field effect transistor (MOSFET), pad corrosion in the aluminum (Al) pad surface has become practical concern in the semiconductor industry. This paper presents a new method to improve the pad corrosion on Al pad surface by using new Al/Ti/TiN film stack. The effects of different Al film stacks on the Al pad corrosion have been investigated. The experiment results show that the Al/Ti/TiN film stack could improve bond pad corrosion effectively comparing to Al/SiON film stack. Wafers processed with new Al film stack were stored up to 28 days and display no pad crystal (PDCY) defects on bond pad surfaces.


title: Design and Measurement Error Analysis of a Low-Friction, Lightweight Linear Series Elastic Actuator

abstract: Series elastic actuators (SEAs) have many benefits for force controlled robotic applications. Placing an elastic member in series with a rigid actuator output enables more-stable force control and the potential for energy storage while sacrificing position control bandwidth. This paper presents the design and measurement error analysis of a low-friction, lightweight linear SEA used in the Shipboard Autonomous Fire Fighting Robot (SAFFiR). The SAFFiR SEA pairs a stand-alone linear actuator with a configurable compliant member. Unlike most electric linear actuators, this actuator does not use a linear guide, which reduces friction and weight. Unlike other SEAs which measure the force by measuring the spring deflection, a tension and compression load cell is integrated into the design for accurate force measurements. The configurable compliant member is a titanium cantilever with manually adjustable length. The final SEA weighs 0.82[kg] with a maximum force of 1,000[N]. The configurable compliant mechanism has in a spring constant range of 145–512[kN/m]. Having no linear guide and incorporating the load cell into the universal joint both introduce measurement errors. The length error across a parallel ankle joint is less than 0.015[mm] and the force measurement error is less than 0.25% of the actual force. Finally, several changes are suggested for the next iteration of the SEA to improve its usability on future robots.© 2013 ASME


title: A Developer's Guide to Silhouette Algorithms for Polygonal Models

abstract: Silhouettes play an important role in shape recognition because they provide one of the main cues for figure-to-ground distinction. However, since silhouettes are view dependent, they need to be determined for every frame of an animation. Finding an efficient way to accomplish this is nontrivial. Indeed, a variety of different algorithms exist that compute silhouettes for geometric objects. This article provides a guideline for developers who need to choose between one of these algorithms for his or her application. We restrict ourselves to discussing only those algorithms that apply to polygonal models, because these are the most commonly used object representations in modern computer graphics. Thus, we can use all algorithms discussed to take a polygonal mesh as input and compute the visible part of the silhouette as output. Some algorithms, however, can also help compute the silhouette only, without additional visibility culling. The silhouette's representation might vary depending on the algorithm class - that is, the silhouette might take the form of a pixel matrix or a set of analytic stroke descriptions.


title: A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks

abstract: Cognitive radio wireless sensor networks (CR-WSNs) have attracted a great deal of attention recently due to the emerging spectrum scarcity issue. This work attempts to provide a detailed analysis of the role of node clustering in CR-WSNs. We outline the objectives, requirements, and advantages of node clustering in CR-WSNs. We describe how a CR-WSN with node clustering differs from conventional wireless sensor networks, and we discuss its characteristics, architecture, and topologies. We survey the existing clustering algorithms and compare their objectives and features. We suggest how clustering issues and challenges can be handled.


title: Convolution in Convolution for Network in Network

abstract: Network in network (NiN) is an effective instance and an important extension of deep convolutional neural network consisting of alternating convolutional layers and pooling layers. Instead of using a linear filter for convolution, NiN utilizes shallow multilayer perceptron (MLP), a nonlinear function, to replace the linear filter. Because of the powerfulness of MLP and $ 1\times 1 $ convolutions in spatial domain, NiN has stronger ability of feature representation and hence results in better recognition performance. However, MLP itself consists of fully connected layers that give rise to a large number of parameters. In this paper, we propose to replace dense shallow MLP with sparse shallow MLP. One or more layers of the sparse shallow MLP are sparely connected in the channel dimension or channel–spatial domain. The proposed method is implemented by applying unshared convolution across the channel dimension and applying shared convolution across the spatial dimension in some computational layers. The proposed method is called convolution in convolution (CiC). The experimental results on the CIFAR10 data set, augmented CIFAR10 data set, and CIFAR100 data set demonstrate the effectiveness of the proposed CiC method.


title: Sustainable, Effective Implementation of a Surgical Preprocedural Checklist: An “Attestation” Format for All Operating Team Members

abstract: Article-at-a-Glance Background Adoption of a preprocedural pause (PPP) associated with a checklist and a team briefing has been shown to improve teamwork function in operating rooms (ORs) and has resulted in improved outcomes. The format of the World Health Organization Safe Surgery Saves Lives checklist has been used as a template for a PPP. Performing a PPP, described as a "time-out," is one of the three principal components, along with a preprocedure verification process and marking the procedure site, of the Joint Commission's Universal Protocol for Preventing Wrong Site, Wrong Procedure, Wrong Person Surgery™. However, if the surgeon alone leads the pause, its effectiveness may be decreased by lack of input from other operating team members. Methods In this study, the PPP was assessed to measure participation and input from operating team members. On the basis of low participation levels, the pause was modified to include an attestation from each member of the team. Results Preliminary analysis of our surgeon-led pause revealed only 54% completion of all items, which increased to 97% after the intervention. With the new format, operating team members stopped for the pause in 96% of cases, compared with 78% before the change. Operating team members introduced themselves in 94% of cases, compared with 44% before the change. Follow-up analysis showed sustained performance at 18 months after implementation. Conclusions A preprocedural checklist format in which each member of the operating team provides a personal attestation can improve pause compliance and may contribute to improvements in the culture of teamwork within an OR. Successful online implementation of a PPP, which includes participation by all operating team members, requires little or no additional expense and only minimal formal coaching outside working situations.


title: FEATURE ARTICLE Single-Molecule Michaelis-Menten Equations

abstract: This paper summarizes our present theoretical understanding of single-molecule kinetics associated with the Michaelis−Menten mechanism of enzymatic reactions. Single-molecule enzymatic turnover experiments typically measure the probability density f(t) of the stochastic waiting time t for individual turnovers. While f(t) can be reconciled with ensemble kinetics, it contains more information than the ensemble data; in particular, it provides crucial information on dynamic disorder, the apparent fluctuation of the catalytic rates due to the interconversion among the enzyme's conformers with different catalytic rate constants. In the presence of dynamic disorder, f(t) exhibits a highly stretched multiexponential decay at high substrate concentrations and a monoexponential decay at low substrate concentrations. We derive a single-molecule Michaelis−Menten equation for the reciprocal of the first moment of f(t), 1/〈t〉, which shows a hyperbolic dependence on the substrate concentration [S], similar to the ensem...


title: Characterizing Privacy Risks of Mobile Apps with Sensitivity Analysis

abstract: Given the emerging concerns over app privacy-related risks, major app distribution providers (e.g., Microsoft) have been exploring approaches to help end users to make informed decision before installation. This is different from existing approaches of simply trusting users to make the right decision. We build on the direction of risk rating as the way to communicate app-specific privacy risks to end users. To this end, we propose to use sensitivity analysis to infer whether an app requests sensitive on-device resources/data that are not required for its expected functionality. Our system, Privet, addresses challenges in efficiently achieving test coverage and automated privacy risk assessment. Finally, we evaluate Privet with 1,000 Android apps released in the wild.


title: Semi-supervised Robust Dictionary Learning via Efficient l-Norms Minimization

abstract: Representing the raw input of a data set by a set of relevant codes is crucial to many computer vision applications. Due to the intrinsic sparse property of real-world data, dictionary learning, in which the linear decomposition of a data point uses a set of learned dictionary bases, i.e., codes, has demonstrated state-of-the-art performance. However, traditional dictionary learning methods suffer from three weaknesses: sensitivity to noisy and outlier samples, difficulty to determine the optimal dictionary size, and incapability to incorporate supervision information. In this paper, we address these weaknesses by learning a Semi-Supervised Robust Dictionary (SSR-D). Specifically, we use the l2,0+-norm as the loss function to improve the robustness against outliers, and develop a new structured sparse regularization to incorporate the supervision information in dictionary learning, without incurring additional parameters. Moreover, the optimal dictionary size is automatically learned from the input data. Minimizing the derived objective function is challenging because it involves many non-smooth l2,0+-norm terms. We present an efficient algorithm to solve the problem with a rigorous proof of the convergence of the algorithm. Extensive experiments are presented to show the superior performance of the proposed method.


title: Holographic Pattern Synthesis With Modulated Substrate Integrated Waveguide Line-Source Leaky-Wave Antennas

abstract: We present the synthesis of one-dimensional (line-source) leaky-wave antennas (LWAs) in substrate integrated waveguide (SIW) technology with modulated geometry, demonstrating the capability to flexibly tailor the radiated fields pattern, both in nearand far-field regimes. The synthesis technique is inspired in holographic concepts, which are related to the existence of modulated leaky waves. A systematic design algorithm to obtain the requested modulation of the SIW width and distance between posts to synthesize the desired radiation pattern is described. Several design examples operating at 15 GHz are reported and experimentally validated, showing the power and versatility of the proposed holographic SIW technology.


title: Impaired decision making following 49 h of sleep deprivation

abstract: Summary ::: Sleep deprivation reduces regional cerebral metabolism within the prefrontal cortex, the brain region most responsible for higher-order cognitive processes, including judgment and decision making. Accordingly, we hypothesized that two nights of sleep loss would impair decision making quality and lead to increased risk-taking behavior on the Iowa Gambling Task (IGT), which mimics real-world decision making under conditions of uncertainty. Thirty-four healthy participants completed the IGT at rested baseline and again following 49.5 h of sleep deprivation. At baseline, volunteers performed in a manner similar to that seen in most samples of healthy normal individuals, rapidly learning to avoid high-risk decks and selecting more frequently from advantageous low-risk decks as the game progressed. After sleep loss, however, volunteers showed a strikingly different pattern of performance. Relative to rested baseline, sleep-deprived individuals tended to choose more frequently from risky decks as the game progressed, a pattern similar to, though less severe than, previously published reports of patients with lesions to the ventromedial prefrontal cortex. Although risky decision making was not related to participant age when tested at rested baseline, age was negatively correlated with advantageous decision making on the IGT, when tested following sleep deprivation (i.e. older subjects made more risky choices). These findings suggest that cognitive functions known to be mediated by the ventromedial prefrontal cortex, including decision making under conditions of uncertainty, may be particularly vulnerable to sleep loss and that this vulnerability may become more pronounced with increased age.


title: The use of IMU MEMS-sensors for designing of motion capture system for control of robotic objects

abstract: Developed HMI is a system consisting of a set of sensors, placed in a special suit or item of clothing. Dressed in a special suit human operator is able to control mechatronic object in the distance. Mechatronic object (for example an anthropomorphic robot) imitates the motion of the human operator by performing the technological operations on removal. So, to do it motion sensors are installed in the body areas which are suitable to copy the human movements. Then the information from sensors is processed and going to a special unit which transmits control signals wirelessly to the control object. Human-machine interface is represented as a hardware and software suite. The hardware suite are INS (inertial modules attached to various body parts), the communication channel between the modules, processing channel and wireless data transmission modules on a desktop computer. INS contain three-axis MEMS gyroscope, accelerometer and magnetometer.


title: Artificial Intelligence Methodologies and Their Application to Diabetes

abstract: In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variab...


title: Credit Risk modeling for Companies Default Prediction using Neural Networks

abstract: The paper assesses the business default risk on a cross-national sample of 3000 companies applying for credit to an international bank operating in Romania. The structure of the sample replicates the structure of the general population of companies in Romania. Based on their past credit history, we have distributed the companies in seven classes plus the default, using and adapting the Standard & Poor’s categories: AAA (1020 companies, 34%) – no risk; AA (279 companies, 9.3%) – minimal risk; A (906 companies, 30.2%) – low risk; BBB (201 companies, 6.7%) – moderate risk; BB (123 companies, 4.1%) – acceptable risk; B (111 companies, 3.7%) – high risk; C (105 companies, 3.5%) – very high risk and D (255 companies, 8.5%) – default. We have then, estimated the one-step transitions probability for downgrading for one year, based on the present category, loan amount, size of company and sector of activity. Thus, although the approach is bottom-up and unconditioned, focusing on the companies, we have included the economic context, taking into account the type of company and the economic sector. We have performed the estimations first using logit regression, and then ANN (Artificial Neural Networks), and compared the results with Standard & Poor’s transition matrix for 2010. The results were compared in terms of predictive power, and arguments were given for choosing an ANN design.


title: Non-parametric similarity measures for unsupervised texture segmentation and image retrieval

abstract: In this paper we propose and examine non-parametric statistical tests to define similarity and homogeneity measures for textures. The statistical tests are applied to the coefficients of images filtered by a multi-scale Gabor filter bank. We demonstrate that these similarity measures are useful for both, texture based image retrieval and for unsupervised texture segmentation, and hence offer a unified approach to these closely related tasks. We present results on Brodatz-like micro-textures and a collection of real-word images.


title: Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

abstract: We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT using a single model. On the WMT’14 benchmarks, a single multilingual model achieves comparable performance for English→French and surpasses state-of-the-art results for English→German. Similarly, a single multilingual model surpasses state-of-the-art results for French→English and German→English on WMT’14 and WMT’15 benchmarks, respectively. On production corpora, multilingual models of up to twelve language pairs allow for better translation of many individual pairs. Our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation. Finally, we show analyses that hints at a universal interlingua representation in our models and show some interesting examples when mixing languages.


title: A character-based convolutional neural network for language-agnostic Twitter sentiment analysis

abstract: Most work on tweet sentiment analysis is mono-lingual and the models that are generated by machine learning strategies do not generalize across multiple languages. Cross-language sentiment analysis is usually performed through machine translation approaches that translate a given source language into the target language of choice. Machine translation is expensive and the results that are provided by theses strategies are limited by the quality of the translation that is performed. In this paper, we propose a language-agnostic translation-free method for Twitter sentiment analysis, which makes use of deep convolutional neural networks with character-level embeddings for pointing to the proper polarity of tweets that may be written in distinct (or multiple) languages. The proposed method is more accurate than several other deep neural architectures while requiring substantially less learnable parameters. The resulting model is capable of learning latent features from all languages that are employed during the training process in a straightforward fashion and it does not require any translation process to be performed whatsoever. We empirically evaluate the efficiency and effectiveness of the proposed approach in tweet corpora based on tweets from four different languages, showing that our approach comfortably outperforms the baselines. Moreover, we visualize the knowledge that is learned by our method to qualitatively validate its effectiveness for tweet sentiment classification.


title: Citation Sentiment Analysis in Clinical Trial Papers

abstract: In scientific writing, positive credits and negative criticisms can often be seen in the text mentioning the cited papers, providing useful information about whether a study can be reproduced or not. In this study, we focus on citation sentiment analysis, which aims to determine the sentiment polarity that the citation context carries towards the cited paper. A citation sentiment corpus was annotated first on clinical trial papers. The effectiveness of n-gram and sentiment lexicon features, and problem-specified structure features for citation sentiment analysis were then examined using the annotated corpus. The combined features from the word n-grams, the sentiment lexicons and the structure information achieved the highest Micro F-score of 0.860 and Macro-F score of 0.719, indicating that it is feasible to use machine learning methods for citation sentiment analysis in biomedical publications. A comprehensive comparison between citation sentiment analysis of clinical trial papers and other general domains were conducted, which additionally highlights the unique challenges within this domain.


title: Automatic malware classification and new malware detection using machine learning

abstract: The explosive growth of malware variants poses a major threat to information security. Traditional anti-virus systems based on signatures fail to classify unknown malware into their corresponding families and to detect new kinds of malware programs. Therefore, we propose a machine learning based malware analysis system, which is composed of three modules: data processing, decision making, and new malware detection. The data processing module deals with gray-scale images, Opcode n-gram, and import functions, which are employed to extract the features of the malware. The decision-making module uses the features to classify the malware and to identify suspicious malware. Finally, the detection module uses the shared nearest neighbor (SNN) clustering algorithm to discover new malware families. Our approach is evaluated on more than 20 000 malware instances, which were collected by Kingsoft, ESET NOD32, and Anubis. The results show that our system can effectively classify the unknown malware with a best accuracy of 98.9%, and successfully detects 86.7% of the new malware.


A CTION-DEPENDENT C ONTROL V ARIATES FOR P OL-ICY O PTIMIZATION VIA S TEIN ’ S I DENTITY


title: Violent Scenes Detection Using Mid-Level Violence Clustering

abstract: This work proposes a novel system for Violent Scenes Detection, which is based on the combination of visual and audio features with machine learning at segment-level. Multiple Kernel Learning is applied so that multimodality of videos can be maximized. In particular, Mid-level Violence Clustering is proposed in order for mid-level concepts to be implicitly learned, without using manually tagged annotations. Finally a violence-score for each shot is calculated. The whole system is trained ona dataset from MediaEval 2013 Affect Task and evaluated by its official metric. The obtained results outperformed its best score.


title: Image and video colorization based on prioritized source propagation

abstract: An efficient colorization scheme for images and videos based on prioritized source propagation is proposed in this work. A user first scribbles colors on a set of source pixels in an image or the first frame of a movie. The proposed algorithm then propagates those colors to the other non-source pixels and the subsequent frames. Specifically, the proposed algorithm identifies the non-source pixel with the highest priority, which can be most reliably colorized. Then, its color is interpolated from the neighboring pixels. This is repeated until the whole image or movie is colorized. Simulation results demonstrate that the proposed algorithm yields more reliable colorization performance than the conventional algorithms.


title: OpenCog: A Software Framework for Integrative Artificial General

abstract: The OpenCog software development framework, for advancement of the development and testing of powerful and responsible integrative AGI, is described. The OpenCog Framework (OCF) 1.0, to be released in 2008 under the GPLv2, is comprised of a collection of portable libraries for OpenCog applications, plus an initial collection of cognitive algorithms that operate within the OpenCog framework. The OCF libraries include a flexible knowledge representation embodied in a scalable knowledge store, a cognitive process scheduler, and a plug-in architecture for allowing interaction between cognitive, perceptual, and control algorithms.


title: Level design as model transformation: a strategy for automated content generation

abstract: This paper frames the process of designing a level in a game as a series of model transformations. The transformations correspond to the application of particular design principles, such as the use of locks and keys to transform a linear mission into a branching space. It shows that by using rewrite systems, these transformations can be formalized and automated. The resulting automated process is highly controllable: it is a perfect match for a mixed-initiative approach to level generation where human and computer collaborate in designing levels. An experimental prototype that implements these ideas is presented.


title: A Data-Based Approach to Social Influence Maximization

abstract: Influence maximization is the problem of finding a set of users in a social network, such that by targeting this set, one maximizes the expected spread of influence in the network. Most of the literature on this topic has focused exclusively on the social graph, overlooking historical data, i.e., traces of past action propagations. In this paper, we study influence maximization from a novel data-based perspective. In particular, we introduce a new model, which we call credit distribution, that directly leverages available propagation traces to learn how influence flows in the network and uses this to estimate expected influence spread. Our approach also learns the different levels of influence-ability of users, and it is time-aware in the sense that it takes the temporal nature of influence into account. ::: ::: We show that influence maximization under the credit distribution model is NP-hard and that the function that defines expected spread under our model is submodular. Based on these, we develop an approximation algorithm for solving the influence maximization problem that at once enjoys high accuracy compared to the standard approach, while being several orders of magnitude faster and more scalable.


title: Extraction of Slum Areas From VHR Imagery Using GLCM Variance

abstract: Many cities in the global South are facing the emergence and growth of highly dynamic slum areas, but often lack detailed information on these developments. Available statistical data are commonly aggregated to large, heterogeneous administrative units that are geographically meaningless for informing effective pro-poor policies. General base information neither allows spatially disaggregated analysis of deprived areas nor monitoring of rapidly changing settlement dynamics, which characterize slums. This paper explores the utility of the gray-level co-occurrence matrix (GLCM) variance to distinguish between slums and formal built-up (formal) areas in very high spatial and spectral resolution satellite imagery such as WorldView-2, OrbView, Quickbird, and Resourcesat. Three geographically different cities are selected for this investigation: Mumbai and Ahmedabad, India and Kigali, Rwanda. The exploration of the utility and transferability of the GLCM shows that the variance of the GLCM combined with the normalized difference vegetation index (NDVI) is able to separate slums and formal areas. The overall accuracy achieved is 84% in Kigali, 87% in Mumbai, and 88% in Ahmedabad. Furthermore, combining spectral information with the GLCM variance within a random forest classifier results in a pixel-based classification accuracy of 90%. The final slum map, aggregated to homogenous urban patches (HUPs), shows an accuracy of 88%–95% for slum locations depending on the scale parameter.


title: Considering Supplier Relations and Monetization in Designing Recommendation Systems

abstract: E-commerce merchants need to optimize their recommendations and sort listings on multi-dimensional requirements beyond product attributes to include supplier considerations, long-term customer experience and the value of the sale to achieve long term success. Product recommendations for optimizing customer conversion can be modeled effectively with predictive analytic methodologies. However, supplier and customer experience elements are not easily modeled in the same manner. This paper outlines an algorithmic approach for these considerations from Expedia's experiences.


title: Handwriting Analysis based on Segmentation Method for Prediction of Human Personality using Support Vector Machine

abstract: Handwriting analysis is a method to predict personality of an author and to better understand the writer. Allograph and allograph combination analysis is a scientific method of writer identification and evaluating the behavior. To make this computerized we considered six main different types of features: (i) size of letters, (ii) slant of letters and words, (iii) baseline, (iv) pen pressure, (v) spacing between letters and (vi) spacing between words in a document to identify the personality of the writer. Segmentation is used to calculate the features from digital handwriting and is trained to SVM which outputs the behavior of the writer. For this experiment 100 different writers were used for different handwriting data samples. The proposed method gives about 94% of accuracy rate with RBF kernel. In this paper an automatic method has been proposed to predict the psychological personality of the writer. The system performance is measured under two different conditions with the same sample.


title: Classic and Adaptive Washout Comparison for a Low Cost Driving Simulator

abstract: The paper deals with control of small driving simulator SIM comparing the useless of adaptive and classic approaches. Driving simulators are considering as an interactive virtual reality tools, which take a considerable place in the human factors studies. The difficulty to reproduce in reality some drive situations mainly for risk and reproducibility reasons increases the interest of this tool. Nevertheless, the validation of the experiments carried out on driving simulator is closely related to embedding realism of the driver in the simulated world. In this article, we present the design of a low cost and small motion platform, which allow the restitution of 2 DOF movements. This overall system is considered as two independent systems linked mechanically. The first system consists in motorized rail for the longitudinal movement while the second system consists in motorized seat allowing either pitch movement of this one or just back seat inclination (this case would not be discussed in the rest of paper)


title: Discriminative Embeddings of Latent Variable Models for Structured Data

abstract: Kernel classifiers and regressors designed for structured data, such as sequences, trees and graphs, have significantly advanced a number of interdisciplinary areas such as computational biology and drug design. Typically, kernels are designed beforehand for a data type which either exploit statistics of the structures or make use of probabilistic generative models, and then a discriminative classifier is learned based on the kernels via convex optimization. However, such an elegant two-stage approach also limited kernel methods from scaling up to millions of data points, and exploiting discriminative information to learn feature representations. ::: ::: We propose, structure2vec, an effective and scalable approach for structured data representation based on the idea of embedding latent variable models into feature spaces, and learning such feature spaces using discriminative information. Interestingly, structure2vec extracts features by performing a sequence of function mappings in a way similar to graphical model inference procedures, such as mean field and belief propagation. In applications involving millions of data points, we showed that structure2vec runs 2 times faster, produces models which are 10, 000 times smaller, while at the same time achieving the state-of-the-art predictive performance.


title: Fast Mencius: Mencius with low commit latency

abstract: Mencius is a protocol for general state machine replication that tolerates crash failures. It has high performance in wide-area networks. However, the commit latency of Mencius is limited by the slowest replica. This paper presents Fast Mencius, a crash fault-tolerant state machine replication protocol, which enhances Mencius with Active Revoke and Multi-instance Propose. Active Revoke allows the non-slow replicas to proceed without being delayed by the slowest replica, while Multi-instance Propose enables the slow replicas to have their proposals chosen by the replicated state machine. Our evaluation shows that in presence of slow replicas, Fast Mencius's commit latency is significantly lower than that of Mencius, and it also achieves high throughput.


title: On the design and implementation of linear differential microphone arrays

abstract: Differential microphone array (DMA), a particular kind of sensor array that is responsive to the differential sound pressure field, has a broad range of applications in sound recording, noise reduction, signal separation, dereverberation, etc. Traditionally, an Nth-order DMA is formed by combining, in a linear manner, the outputs of a number of DMAs up to (including) the order of N - 1. This method, though simple and easy to implement, suffers from a number of drawbacks and practical limitations. This paper presents an approach to the design of linear DMAs. The proposed technique first transforms the microphone array signals into the short-time Fourier transform (STFT) domain and then converts the DMA beamforming design to simple linear systems to solve. It is shown that this approach is much more flexible as compared to the traditional methods in the design of different directivity patterns. Methods are also presented to deal with the white noise amplification problem that is considered to be the biggest hurdle for DMAs, particularly higher-order implementations.