title: Eventdisplay: An Analysis and Reconstruction Package for Ground-based Gamma-ray Astronomy

abstract: Eventdisplay is a software package for the analysis and reconstruction of data and Monte Carlo events from ground-based gamma-ray observatories such as VERITAS and CTA. It was originally developed as a display tool for data from the VERITAS prototype telescope, but evolved into a full analysis package with routines for calibration, FADC trace integration, image and stereo parameter analysis, response function calculation, and high-level analysis steps. Eventdisplay makes use of an image parameter analysis combined with gamma-hadron separation methods based on multivariate algorithms. An overview of the reconstruction methods and some selected results are presented in this contribution.


title: Automatic Planning of Manipulator Transfer Movements

abstract: The class of problems that involve finding where to place or how to move a solid object in the presence of obstacles is discussed. The solution to this class of problems is essential to the automatic planning of manipulator transfer movements, i.e., the motions to grasp a part and place it at some destination. For example, planning transfer movements requires the ability to plan paths for the manipulator that avoid collisions with objects in the workspace and the ability to choose safe grasp points on objects. The approach to these problems described here is based on a method of computing an explicit representation of the manipulator configurations that would bring about a collision.


title: BSLIC: SLIC Superpixels Based on Boundary Term

abstract: A modified method for better superpixel generation based on simple linear iterative clustering (SLIC) is presented and named BSLIC in this paper. By initializing cluster centers in hexagon distribution and performing k-means clustering in a limited region, the generated superpixels are shaped into regular and compact hexagons. The additional cluster centers are initialized as edge pixels to improve boundary adherence, which is further promoted by incorporating the boundary term into the distance calculation of the k-means clustering. Berkeley Segmentation Dataset BSDS500 is used to qualitatively and quantitatively evaluate the proposed BSLIC method. Experimental results show that BSLIC achieves an excellent compromise between boundary adherence and regularity of size and shape. In comparison with SLIC, the boundary adherence of BSLIC is increased by at most 12.43% for boundary recall and 3.51% for under segmentation error.


Designing Virtual Worlds as Architecture


title: CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learning

abstract: It is known that the inconsistent distributions and representations of different modalities, such as image and text, cause the heterogeneity gap, which makes it very challenging to correlate heterogeneous data and measure their similarities. Recently, generative adversarial networks (GANs) have been proposed and have shown their strong ability to model data distribution and learn discriminative representation. It has also been shown that adversarial learning can be fully exploited to learn discriminative common representations for bridging the heterogeneity gap. Inspired by this, we aim to effectively correlate large-scale heterogeneous data of different modalities with the power of GANs to model cross-modal joint distribution. In this article, we propose Cross-modal Generative Adversarial Networks (CM-GANs) with the following contributions. First, a cross-modal GAN architecture is proposed to model joint distribution over the data of different modalities. The inter-modality and intra-modality correlation can be explored simultaneously in generative and discriminative models. Both compete with each other to promote cross-modal correlation learning. Second, the cross-modal convolutional autoencoders with weight-sharing constraint are proposed to form the generative model. They not only exploit the cross-modal correlation for learning the common representations but also preserve reconstruction information for capturing the semantic consistency within each modality. Third, a cross-modal adversarial training mechanism is proposed, which uses two kinds of discriminative models to simultaneously conduct intra-modality and inter-modality discrimination. They can mutually boost to make the generated common representations more discriminative by the adversarial training process. In summary, our proposed CM-GAN approach can use GANs to perform cross-modal common representation learning by which the heterogeneous data can be effectively correlated. Extensive experiments are conducted to verify the performance of CM-GANs on cross-modal retrieval compared with 13 state-of-the-art methods on 4 cross-modal datasets.


title: Improved Strategies of Multi-objective Differential Evolution (MODE) for Multi-objective Optimization

abstract: Multi-objective optimization using an evolutionary computation technique is used extensively for solving conflicting multi-objective optimization problems. In this work, an improved strategy of multi-objective differential evolution (MODE) where the mutation strategy is changed to a trigonometric mutation approach is proposed. The proposed strategy along with other well known strategies of MODE is used to compare the performance metrics (such as convergence and divergence) with other evolutionary algorithms from the literature. The Pareto optimal solutions are obtained for benchmark test functions and are compared using several strategies of MODE. Improved strategies of MODE show a competitive performance when compared with other evolutionary multi-objective optimization algorithms (EMOAs).


title: Automated texture mapping of 3D city models with oblique aerial imagery

abstract: This work describes an approach to texture mapping a 3D city model obtained from aerial and ground-based laser scans with oblique aerial imagery. First, the images are automatically registered by matching 2D image lines with projections of 3D lines from the city model. Then, for each triangle in the model, the optimal image is selected by taking into account occlusion, image resolution, surface normal orientation, and coherence with neighboring triangles. Finally, the utilized texture patches from all images are combined into one texture atlas for compact representation and efficient rendering. We evaluate our approach on a data set of downtown Berkeley.


Galaxy Workflows for Web-based Bioinformatics Analysis of Aptamer High-throughput Sequencing Data


title: Bio-inspired design and dynamic maneuverability of a minimally actuated six-legged robot

abstract: Rapidly running arthropods like cockroaches make use of passive dynamics to achieve remarkable locomotion performance with regard to stability, speed, and maneuverability. In this work, we take inspiration from these organisms to design, fabricate, and control a 10cm, 24 gram underactuated hexapedal robot capable of running at 14 body lengths per second and performing dynamic turning maneuvers. Our design relies on parallel kinematic mechanisms fabricated using the scaled smart composite microstructures (SCM) process and viscoelastic polymer legs with tunable stiffness. In addition to the novel robot design, we present experimental validation of the lateral leg spring (LLS) locomotion model's prediction that dynamic turning can be achieved by modulating leg stiffness. Finally, we present and validate a leg design for active stiffness control using shape memory alloy and demonstrate the ability of the robot to execute near-gymnastic 90° turns in the span of five strides.


title: MEMBERSHIP TURNOVER AND COLLABORATION SUCCESS IN ONLINE COMMUNITIES: EXPLAINING RISES AND FALLS FROM GRACE IN WIKIPEDIA 1

abstract: Firms increasingly turn to online communities to create valuable information. These communities are empowered by new information technology-enabled collaborative tools, tools such as blogs, wikis, and social networks. Collaboration on these platforms is characterized by considerable membership turnover, which could have significant effects on collaborative outcomes. We hypothesize that membership retention relates in a curvilinear fashion to effective collaboration: positively up to a threshold and negatively thereafter. The longitudinal history of 2,065 featured articles on Wikipedia offers support for this hypotheses: Contributions from a mixture of new and experienced participants both increases the likelihood that an article will be promoted to featured article status and decreases the risk it will be demoted after having been promoted. These findings imply that, contrary to many of the assumptions in previous research, participant retention does not have a strictly positive effect on emerging collaborative environments. Further analysis of our data provides empirical evidence that knowledge creation and knowledge retention are actually distinct phases of communitybased peer production, and that communities may on average experience more turnover than ideal during the knowledge retention phase.


title: Large-Margin Classification in Infinite Neural Networks

abstract: We introduce a new family of positive-definite kernels for large margin classification in support vector machines (SVMs). These kernels mimic the computation in large neural networks with one layer of hidden units. We also show how to derive new kernels, by recursive composition, that may be viewed as mapping their inputs through a series of nonlinear feature spaces. These recursively derived kernels mimic the computation in deep networks with multiple hidden layers. We evaluate SVMs with these kernels on problems designed to illustrate the advantages of deep architectures. Compared to previous benchmarks, we find that on some problems, these SVMs yield state-of-the-art results, beating not only other SVMs but also deep belief nets.


title: Fusing audio, visual and textual clues for sentiment analysis from multimodal content

abstract: A huge number of videos are posted every day on social media platforms such as Facebook and YouTube. This makes the Internet an unlimited source of information. In the coming decades, coping with such information and mining useful knowledge from it will be an increasingly difficult task. In this paper, we propose a novel methodology for multimodal sentiment analysis, which consists in harvesting sentiments from Web videos by demonstrating a model that uses audio, visual and textual modalities as sources of information. We used both feature- and decision-level fusion methods to merge affective information extracted from multiple modalities. A thorough comparison with existing works in this area is carried out throughout the paper, which demonstrates the novelty of our approach. Preliminary comparative experiments with the YouTube dataset show that the proposed multimodal system achieves an accuracy of nearly 80%, outperforming all state-of-the-art systems by more than 20%.


title: Near Optimal Behavior via Approximate State Abstraction

abstract: The combinatorial explosion that plagues planning and reinforcement learning (RL) algorithms can be moderated using state abstraction. Prohibitively large task representations can be condensed such that essential information is preserved, and consequently, solutions are tractably computable. However, exact abstractions, which treat only fully-identical situations as equivalent, fail to present opportunities for abstraction in environments where no two situations are exactly alike. In this work, we investigate approximate state abstractions, which treat nearly-identical situations as equivalent. We present theoretical guarantees of the quality of behaviors derived from four types of approximate abstractions. Additionally, we empirically demonstrate that approximate abstractions lead to reduction in task complexity and bounded loss of optimality of behavior in a variety of environments.


title: Privacy in mini-drone based video surveillance

abstract: Mini-drones are increasingly used in video surveillance. Their areal mobility and ability to carry video cameras provide new perspectives in visual surveillance which can impact privacy in ways that have not been considered in a typical surveillance scenario. To better understand and analyze them, we have created a publicly available video dataset of typical drone-based surveillance sequences in a car parking. Using the sequences from this dataset, we have assessed five privacy protection filters via a crowdsourcing evaluation. We asked crowdsourcing workers several privacy- and surveillance-related questions to determine the tradeoff between intelligibility of the scene and privacy, and we present conclusions of this evaluation in this paper.


title: Using Forward Snowballing to update Systematic Reviews in Software Engineering

abstract: Background: A Systematic Literature Review (SLR) is a methodology used to aggregate relevant evidence related to one or more research questions. Whenever new evidence is published after the completion of a SLR, this SLR should be updated in order to preserve its value. However, updating SLRs involves significant effort. Objective: The goal of this paper is to investigate the application of forward snowballing to support the update of SLRs. Method: We compare outcomes of an update achieved using the forward snowballing versus a published update using the search-based approach, i.e., searching for studies in electronic databases using a search string. Results: Forward snowballing showed a higher precision and a slightly lower recall. It reduced in more than five times the number of primary studies to filter however missed one relevant study. Conclusions: Due to its high precision, we believe that the use of forward snowballing considerably reduces the effort in updating SLRs in Software Engineering; however the risk of missing relevant papers should not be underrated.


title: CPW-fed wideband printed planar dipole antenna for Digital TV

abstract: In recent years, Digital TV (DTV) terrestrial broadcasting has been widely spread in the world. Additionally, it is expected to smoothly replace all the conventional analog TV broadcasting. However, in order to meet good receiving performance, a DTV antenna with an ultra-wide band and a high gain is generally investigated. In the literature associated with Co-Planar Waveguide (CPW)-fed wideband printed antennas [1], [2], the printed monopole antenna with a step-sloped ground plane was developed to extend the bandwidth and the printed monopole antenna was successfully implemented for WLAN/WIMAX applications. In this work, we use the CPW-fed mechanism on the dipole antenna in which multiple loops are constructed. Since each loop corresponds to a specific frequency response, multiple loops can yield the integrated frequency response. Restated, all frequency responses of multiple loops are coupled to reach an ultra-wide band. Hence, the proposed CPW-fed wideband printed planar dipole antenna can have a large bandwidth for DTV applications.


Adaptive and Intelligent Technologies for Webbased Education


title: FRASAD: A framework for model-driven IoT Application Development

abstract: This paper presents FRASAD, an effective model-driven software development framework to manage the complexity of Internet of Things (IoT) applications. We propose a node-centric software architecture and a rule-based programming model that allow designers to describe their application using only sensor node domain concepts. The final application code is successively generated from the initial models through an automatic model transformation process. The evaluation results show that our framework enables a fast way to develop IoT applications by reducing the cost of dealing with the heterogeneity and complexity exhibited by sensor nodes and their operating systems.


title: Beyond the PDP-11: Architectural Support for a Memory-Safe C Abstract Machine

abstract: We propose a new memory-safe interpretation of the C abstract machine that provides stronger protection to benefit security and debugging. Despite ambiguities in the specification intended to provide implementation flexibility, contemporary implementations of C have converged on a memory model similar to the PDP-11, the original target for C. This model lacks support for memory safety despite well-documented impacts on security and reliability. Attempts to change this model are often hampered by assumptions embedded in a large body of existing C code, dating back to the memory model exposed by the original C compiler for the PDP-11. Our experience with attempting to implement a memory-safe variant of C on the CHERI experimental microprocessor led us to identify a number of problematic idioms. We describe these as well as their interaction with existing memory safety schemes and the assumptions that they make beyond the requirements of the C specification. Finally, we refine the CHERI ISA and abstract model for C, by combining elements of the CHERI capability model and fat pointers, and present a softcore CPU that implements a C abstract machine that can run legacy C code with strong memory protection guarantees.


title: Optimal Power Management of Residential Customers in the Smart Grid

abstract: Recently intensive efforts have been made on the transformation of the world's largest physical system, the power grid, into a “smart grid” by incorporating extensive information and communication infrastructures. Key features in such a “smart grid” include high penetration of renewable and distributed energy sources, large-scale energy storage, market-based online electricity pricing, and widespread demand response programs. From the perspective of residential customers, we can investigate how to minimize the expected electricity cost with real-time electricity pricing, which is the focus of this paper. By jointly considering energy storage, local distributed generation such as photovoltaic (PV) modules or small wind turbines, and inelastic or elastic energy demands, we mathematically formulate this problem as a stochastic optimization problem and approximately solve it by using the Lyapunov optimization approach. From the theoretical analysis, we have also found a good tradeoff between cost saving and storage capacity. A salient feature of our proposed approach is that it can operate without any future knowledge on the related stochastic models (e.g., the distribution) and is easy to implement in real time. We have also evaluated our proposed solution with practical data sets and validated its effectiveness.


title: A new efficient objective function for routing in Internet of Things paradigm

abstract: Low Power and Lossy Networks (LLNs) have a crucial role in the Internet of Things (IoT) paradigm. For the IPv6 Routing Protocol for LLNs (RPL), two objective functions (OF) have been standardized so far, namely (Objective Function zero (OF0), and the Minimum Rank with Hysteresis Objective Function (MRHOF). However, these particular objective functions are used to build a topology where bottleneck nodes may suffer from an excessive unbalanced traffic load. As a result, a part of the network may be disconnected as the energy of the overburdened preferred parent node will drain much faster than other candidate parent nodes. Thus, in this paper, we propose a new objective function that balances the number of children nodes for the overburdened nodes to ensure node lifetime maximization. To implement this OF, we amended the DIO message format. By using a new utilization technique, we also mitigated any potential extra overhead. Finally, a new RPL metric has been introduced to balance the traffic load over the network. Simulation experiments have been conducted to examine the performance of our new approach. Collected results corroborate the superiority of our objective function over the existing ones in terms of lifetime, power consumption and packet delivery ratio.


title: Efficient identification of hot data for flash memory storage systems

abstract: Hot data identification for flash memory storage systems not only imposes great impacts on flash memory garbage collection but also strongly affects the performance of flash memory access and its lifetime (due to wear-levelling). This research proposes a highly efficient method for on-line hot data identification with limited space requirements. Different from past work, multiple independent hash functions are adopted to reduce the chance of false identification of hot data and to provide predictable and excellent performance for hot data identification. This research not only offers an efficient implementation for the proposed framework, but also presents an analytic study on the chance of false hot data identification. A series of experiments was conducted to verify the performance of the proposed method, and very encouraging results are presented.


title: C-FOREST: Parallel Shortest Path Planning With Superlinear Speedup

abstract: In (Otte and Correll 2013) we present C-FOREST, a parallelization framework for single-query sampling-based shortest-path planning algorithms. C-FOREST has been observed to have super linear speedup on many problems, e.g., paths of quality Ltarget are found 350X faster by 64 CPUs working in parallel than by 1 CPU. In (Otte and Correll 2013) C-FOREST is tested in conjunction with the RRT* algorithm. In the current work we perform additional experiments that show C-FOREST provides similar advantages when used conjunction with the SPRT algorithm. This reinforces our original claim that C-FOREST is generally applicable to a wide range of sampling based motion planning algorithms.


title: Knee rehabilitation using an intelligent robotic system

abstract: There is an increasing trend in using robots for medical purposes. One specific area is the rehabilitation. There are some commercial exercise machines used for rehabilitation purposes. However, these machines have limited use because of their insufficient motion freedom. In addition, these types of machines are not actively controlled and therefore can not accommodate complicated exercises required during rehabilitation. In this study, a rule based intelligent control methodology is proposed to imitate the faculties of an experienced physiotherapist. These involve interpretation of patient reactions, storing the information received, acting according to the available data, and learning from the previous experiences. Robot manipulator is driven by a servo motor and controlled by a computer using force/torque and position sensor information. Impedance control technique is selected for the force control.


title: Characterization of microsatellite markers in silver pomfret, Pampus argenteus (Perciformes: Stromateidae) through cross-species amplification and population genetic applications

abstract: Pampus argenteus (Euphrasen 1788) is one of the commercially ::: important marine food fish in India, having high value ::: and demand in both domestic and export market. Pomfrets ::: in Indian waters showed a steady decline in catch due to ::: capture of undersized, juvenile fishes. Baseline information ::: on the genetic stock structure is important for conservation ::: and sustainable harvest of this species. Fish stock structure ::: and genetic variation can be evaluated/identified through ::: microsatellite molecular markers. An alternative for finding ::: polymorphic microsatellites is cross-species amplification ::: between genetically closely related species.


title: Motion Control of Robot Manipulators with MATLAB •

abstract: This article presents an open-source MATLAB toolbox for the motion control of KUKA robot manipulators. The KUKA Control Toolbox (KCT) is a collection of MATLAB functions developed at the Univer sity of Siena. The toolbox, which is compatible with all 6 degrees of freedom (DoF) small and low-payload KUKA robots that use the Eth.RSIXML, runs on a remote computer connected with the KUKA controller via transmission control Protocol/Internet Protocol (TCP/IP). KCT includes more than 40 functions, spanning operations such as forward and inverse kine matics computation, point-to-point joint and Cartesian control, trajectory gen eration, graphical display, three-dimensional (3-D) animation and diagnostics. Applicative examples show the flexibility of KCT and its easy interfacing with other toolboxes and external devices.


title: Abstract AdaBoost Gabor Feature Selection for Classification

abstract: This paper describes an application of the AdaBoost algorithm for selecting Gabor features for classification. Gabor wavelets are powerful image descriptors but they often result in very high dimensional feature vectors, which rend them impractical for real applications. We have trained a classifier using the AdaBoost algorithm with a set of Gabor features extracted from images. Compared with the huge number of features used by typical classifiers using Gabor features, our classifier selects only about one hundred features. Whilst significant memory and computation cost has been saved, our classifier still achieves very high classification accuracy. Two image datasets have been used to test our system, and only 20 features are required to achieve zero error rates on the car image dataset.


title: Adiposis dolorosa (Dercum's disease): MRI and ultrasound appearances

abstract: Aim To describe ultrasound and magnetic resonance imaging (MRI) features of adiposis dolorosa, Dercum's disease, and to evaluate the MRI features prospectively against a large number of MRI examinations. Materials and methods Institutional review board approval for this study was obtained. The imaging features at MRI and ultrasound of 13 cases of adiposis dolorosa (nine female, four male; age range 32–72 years) were reviewed. MRI findings typical for adiposis dolorosa were proposed and prospectively evaluated on 6247 MRI examinations performed over a period of 8 months. Results Adiposis dolorosa demonstrates multiple, oblong, fatty lesions in the superficial subcutaneous fatty tissue. They are mostly Discussion Adiposis dolorosa, Dercum's disease, should be suggested in the presence of multiple (many) small, oblong, fatty lesions in the subcutaneous fatty tissue in adult patients if they are hyperechoic on ultrasound imaging or blush-like at unenhanced MRI; typically a small number of these lesions are tender/painful. Imaging does not demonstrate inflammation or oedema in relation to these lesions. These MRI features should suggest the diagnosis and are likely to be pathognomonic. The radiologist is often the first to suggest the diagnosis based on the imaging features.


title: Achieving Centimeter-Accuracy Indoor Localization on WiFi Platforms: A Frequency Hopping Approach

abstract: Indoor positioning systems (IPSs) are attracting more and more attention from the academia and industry recently. Among them, approaches based on WiFi techniques are more favorable since they are built upon the WiFi infrastructures available in most indoor spaces. However, due to the bandwidth limit in mainstream WiFi systems, the IPS leveraging WiFi can hardly achieve centimeter localization accuracy under strong nonline-of-sight (NLOS) conditions which is common for indoor environment. In this paper, to achieve the centimeter-level accuracy, we present a WiFi-based IPS that exploits the frequency diversity via frequency hopping. In the offline phase, the system collects channel frequency responses (CFRs) from multiple channels and from a number of locations-of-interest. Then, the CFRs are post-processed to mitigate the synchronization errors as well as interference from other WiFi networks. Then, using bandwidth concatenation, the CFRs from multiple channels are combined into location fingerprints which are stored into a local database. During the online phase, CFRs are formulated into the location fingerprint and is compared against the fingerprints in the database via the time-reversal resonating strength (TRRS). Finally, the IPS determines the location according to the TRRS. Extensive experiment results demonstrate a perfect centimeter-level accuracy in an office environment with strong NLOS using only one pair of single-antenna WiFi devices.


title: SOFTWARE ENGINEERING JOB PRODUCTIVITY — A SYSTEMATIC REVIEW

abstract: Productivity is a key element in organizational management. Although it can be measured at different levels (country, sector, organization …) this research focuses on productivity at the job level. The aim of this paper is to obtain an overview of the state of the art in productivity measurement in software engineering, including the inputs and outputs of the production process used for this measurement at the job level in the workplace. To do so, a systematic literature review protocol was adapted from literature review protocol standards, and subsequently carried out. The objective is to assess the current inputs and outputs present in the literature in order to create new productivity measures for software practitioners. This paper reveals that two different measures are used to assess software engineering professionals: traditional SLOC/Time and planning projects units per time unit.


title: Modeling Analysis and Parameters Calculation of Permanent Magnet Linear Synchronous Motor

abstract: Permanent magnet linear synchronous motor (PMLSM) has several advantages, such as simple structure, small volume, high force, and so on. It is a research focus and difficulties in the research field of motor. Because PMLSM is different from the traditional rotary motors in structure and running state, it is difficult for to build its precise mathematical model by analytical method. The asymmetry of the three-phase winding electromagnetic and electrical parameters of open PMLSM in a low-speed working zone is impossible to take into account. In this paper, the difference between the permanent magnet linear synchronous motor and traditional motors are analyzed. And then, a non-linear, asymmetric and variable parameter space state model is built, considering the structural and running characteristics of PMLSM. The proposed model needs all kinds of parameters of PMLSM, including self- inductance, mutual inductance, flux linkage, etc. So, a finite element calculation and actual measurement of the required parameters of a prototype PMLSM are done, respectively. The influence of air-gap on the inductance parameters was analyzed.


title: Language Identification in Short Utterances Using Long Short-Term Memory (LSTM) Recurrent Neural Networks

abstract: Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs) have recently outperformed other state-of-the-art approaches, such as i-vector and Deep Neural Networks (DNNs), in automatic Language Identification (LID), particularly when dealing with very short utterances (∼3s). In this contribution we present an open-source, end-to-end, LSTM RNN system running on limited computational resources (a single GPU) that outperforms a reference i-vector system on a subset of the NIST Language Recognition Evaluation (8 target languages, 3s task) by up to a 26%. This result is in line with previously published research using proprietary LSTM implementations and huge computational resources, which made these former results hardly reproducible. Further, we extend those previous experiments modeling unseen languages (out of set, OOS, modeling), which is crucial in real applications. Results show that a LSTM RNN with OOS modeling is able to detect these languages and generalizes robustly to unseen OOS languages. Finally, we also analyze the effect of even more limited test data (from 2.25s to 0.1s) proving that with as little as 0.5s an accuracy of over 50% can be achieved.


title: Involvement of Flt-1 (VEGF receptor-1) in cancer and preeclampsia

abstract: We previously isolated a novel tyrosine kinase receptor, Flt-1, now known as VEGF-receptor (VEGFR)-1. The VEGF–VEGFR system plays a pivotal role in not only physiological but also pathological angiogenesis. We examined the role of Flt-1 in carcinogenesis using Flt-1-signal-deficient (Flt-1 TK−/−) mice, and found that this receptor stimulates tumor growth and metastasis most likely via macrophages, making it an important potential target in the treatment of cancer. In addition to the full-length receptor, the Flt-1 gene produces a soluble protein, sFlt-1, an endogenous VEGF-inhibitor. sFlt-1 is expressed in trophoblasts of the placenta between fetal and maternal blood vessels, suggesting it to be a barrier against extreme VEGF-signaling. Abnormally high expression of sFlt-1 occurs in most preeclampsia patients, whose main symptoms are hypertension and proteinurea. In cancer patients, strong suppression of VEGF–VEGFR by drugs induces similar side effects including hypertension. These results indicate a close relationship between abnormal VEGF-block and hypertension/proteinurea. sFlt-1 is an attractive target for the control of preeclampsia.


title: AllInOneNews: development and evaluation of a large-scale news metasearch engine

abstract: AllInOneNews is the largest news metasearch engine in the world, connecting to over 1,000 news sites over 150 countries. Implementing a large-scale metasearch engine like AllInOneNews needs to overcome unique challenges not faced by building small metasearch engines such as developing highly scalable search engine selection techniques. In this paper, we discuss these unique challenges and our solutions to these challenges. We also discuss some novel features of AllInOneNews such as highly automated solution and semantic query match. This paper also reports the results of a comparative evaluation of three commercial news search systems, one search engine - Google News and two metasearch engines - Mamma News and AllInOneNews. Several measures such as effectiveness, diversity and time-sensitivity are used to perform the comparison. Another contribution of this paper is that we introduce a novel scheme to compare multiple news search systems in a combined measure that takes both relevance and time-sensitivity of retrieved information into consideration.


title: Human-Robot Interaction for Cooperative Manipulation: Handing Objects to One Another

abstract: For manipulation tasks, the transfer of objects between humans and robots is a fundamental way to coordinate activity and cooperatively perform useful work. Within this paper we demonstrate that robots and people can effectively and intuitively work together by directly handing objects to one another. First, we present experimental results that demonstrate that subjects without explicit instructions or robotics expertise can successfully hand objects to a robot and take objects from a robot in response to reaching gestures. Moreover, when handing an object to the robot, subjects control the object's position and orientation to match the configuration of the robot's hand, thereby simplifying robotic grasping and offering opportunities to simplify the manipulation task. Second, we present a robotic application that relies on this form of human-robot interaction. This application enables a humanoid robot to help a user place objects on a shelf, perform bimanual insertion tasks, and hold a box within which the user can place objects. By handing appropriate objects to the robot, the human directly and intuitively controls the robot. Through this interaction, the human and robot complement one another's abilities and work together to achieve results.


title: Multi-view Dense Match for Forest Area

abstract: LIDAR (Light Detection And Ranging) is widely used in forestry applications to obtain information about tree density, composition, change, etc. An advantage of LIDAR is its ability to get this information in a 3D structure. However, the density of LIDAR data is low, the acquisition of LIDAR data is often very expensive, and it is difficult to be utilised in small areas. In this article we eavluate different methods by using multi-view to acquire high resolution images of the forest. Using the dense match method a dense point cloud can be generated. Our analysis shows that this method can provide a good alternative to using LIDAR in situations such as these.


title: Firework: Big Data Sharing and Processing in Collaborative Edge Environment

abstract: Cloud computing, arguably, has become the de facto computing platform for the big data processing by researchers and practitioners for the last decade, and enabled different stakeholders to discover valuable information from large scale data. At the same time, in the decade, we have witnessed the fast growing deployment of billions of sensors and actuators in multiple applications domains, such as transportation, manufacturing, connected/wearable health care, smart city and so on, stimulating the emerging of Edge Computing (a.k.a., fog computing, cloudlet). However, data, as the core of both cloud computing and edge computing, is still owned by each stakeholder and rarely shared due to privacy concern and formidable cost of data transportation, which significantly limits Internet of Things (IoT) applications that need data input from multiple stakeholders (e.g., video analytics collects data from cameras owned by police department, transportation department, retailer stores, etc.). In this paper, we envision that in the era of IoT the demand of distributed big data sharing and processing applications will dramatically increase since the data producing and consuming are pushed to the edge of the network. Data processing in collaborative edge environment needs to fuse data owned by multiple stakeholders, while keeping the computation within stakeholders' data facilities. To attack this challenge, we propose a new computing paradigm, Firework, which is designed for big data processing in collaborative edge environment (CEE). Firework fuses geographically distributed data by creating virtual shared data views that are exposed to end users via predefined interfaces by data owners. The interfaces are provided in the form of a set of datasets and a set of functions, where the functions are privacy preserved and bound to the datasets. Firework targets to share data while ensuring data privacy and integrity for stakeholders. By pushing the data processing as close as to data sources, Firework also aims to avoid data movement from the edge of the network to the cloud and improve the response latency.


title: A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas

abstract: Cortical activity has distinct features across scales, from the spiking statistics of individual cells to global resting-state networks. We here describe the first full-density multi-area spiking network model of cortex, using macaque visual cortex as a test system. The model represents each area by a microcircuit with area-specific architecture and features layer- and population-resolved connectivity between areas. Simulations reveal a structured asynchronous irregular ground state. In a metastable regime, the network reproduces spiking statistics from electrophysiological recordings and cortico-cortical interaction patterns in fMRI functional connectivity under resting-state conditions. Stable inter-area propagation is supported by cortico-cortical synapses that are moderately strong onto excitatory neurons and stronger onto inhibitory neurons. Causal interactions depend on both cortical structure and the dynamical state of populations. Activity propagates mainly in the feedback direction, similar to experimental results associated with visual imagery and sleep. The model unifies local and large-scale accounts of cortex, and clarifies how the detailed connectivity of cortex shapes its dynamics on multiple scales. Based on our simulations, we hypothesize that in the spontaneous condition the brain operates in a metastable regime where cortico-cortical projections target excitatory and inhibitory populations in a balanced manner that produces substantial inter-area interactions while maintaining global stability.


title: A Tangible, Story-Construction Process Employing Spatial, Computational-Thinking

abstract: The outcome of a multidisciplinary and iterative process, CyberPLAYce is a tangible, interactive, cyber-physical learning tool for children supporting computational thinking and, particularly, playful storytelling. CyberPLAYce finds inspiration in the concept of child-computer interaction, where meaning is constructed through spatially reconfiguring the physical environment. The novel aspect of CyberPLAYce is its extension of cyber-learning to the dimension of space where children construct meaning at a larger physical scale. This paper outlines the motivations for CyberPLAYce, focuses on the full arc of design and evaluation activities concerning computational thinking (CT) practices that engaged 8-12-year-old storytellers, and concludes with a consideration of future work focusing on spatial thinking with CyberPLAYce. Results from our empirical study suggest that cyber-physical play afforded by CyberPLAYce scaffolds computational thinking, creating, and sharing in children. Particularly for IDC researchers in the educational domain, CyberPLAYce represents a Research-through-design exemplar supporting children's enjoyment of learning and meaning-construction.


title: Direct Active and Reactive Power Regulation of Grid-Connected DC/AC Converters Using Sliding Mode Control Approach

abstract: This paper proposes a new direct active and reactive power control (DPC) for the three-phase grid connected dc/ac converters. The proposed DPC strategy employs a nonlinear sliding mode control (SMC) scheme to directly calculate the required converter's control voltage so as to eliminate the instantaneous errors of active and reactive powers without involving any rotating coordinate transformations. Meanwhile, there are no extra current control loops involved, which simplifies the system design and enhances the transient performance. Constant converter switching frequency is achieved by using space vector modulation, which eases the design of the ac harmonic filter. Simulation and experimental results are provided and compared with those of the classic voltage-oriented vector control (VC) and conventional lookup table (LUT) DPC strategies. The proposed SMC-DPC is capable of providing enhanced transient performance similar to that of the LUT-DPC, and keeps the steady-state harmonic spectra at the same level as those of the VC scheme. The robustness of the proposed DPC to line inductance variations is also inspected during active and reactive power changes.


title: Generalized End-to-End Loss for Speaker Verification

abstract: In this paper, we propose a new loss function called generalized end-to-end (GE2E) loss, which makes the training of speaker verification models more efficient than our previous tuple-based end-to-end (TE2E) loss function. Unlike TE2E, the GE2E loss function updates the network in a way that emphasizes examples that are difficult to verify at each step of the training process. Additionally, the GE2E loss does not require an initial stage of example selection. With these properties, our model with the new loss function decreases speaker verification EER by more than 10%, while reducing the training time by 60% at the same time. We also introduce the MultiReader technique, which allows us to do domain adaptation - training a more accurate model that supports multiple keywords (i.e. "OK Google" and "Hey Google") as well as multiple dialects.


title: A quasi-Z-source integrated multi-port power converter with reduced capacitance for switched reluctance motor drives

abstract: This paper presents a quasi Z-source integrated multiport converter (ZIMPC) for switched reluctance motor (SRM) drives to reduce the dc link capacitance. In conventional SRM drives, employing multi-phase asymmetrical H-bridge (ASHB) topology, large capacitors are necessary to absorb the transient energy during phase current commutation. However, electrolytic capacitors would affect the lifetime, cost and power density of the drive system. With switch multiplexing technique, a Z-source integrated multiport power converter is derived to achieve power ripple reduction using relatively small capacitance. Corresponding control method is designed and developed for the proposed SRM drive. Also, the ZIMPC can boost the equivalent phase exciting voltage and widen the constant power speed range (CPSR). At last, simulation and experimental results verify the feasibility of the proposed ZIMPC and its superior performance with smaller capacitance as compared with ASHB.


title: Semi-Supervised Learning with GANs: Revisiting Manifold Regularization

abstract: GANS are powerful generative models that are able to model the manifold of natural images. We leverage this property to perform manifold regularization by approximating the Laplacian norm using a Monte Carlo approximation that is easily computed with the GAN. When incorporated into the feature-matching GAN of Improved GAN, we achieve state-of-the-art results for GAN-based semi-supervised learning on the CIFAR-10 dataset, with a method that is significantly easier to implement than competing methods.


title: Field trials and testing of the OctArm continuum manipulator

abstract: This paper describes the results of field trials and associated testing of the OctArm series of multi-section continuous backbone "continuum" robots. This novel series of manipulators has recently (Spring 2005) undergone a series of trials including open-air and in-water field tests. Outcomes of the trials, in which the manipulators demonstrated the ability for adaptive and novel manipulation in challenging environments, are described. Implications for the deployment of continuum robots in a variety of applications are discussed


title: Mind-Set Interventions Are a Scalable Treatment for Academic Underachievement

abstract: The efficacy of academic-mind-set interventions has been demonstrated by small-scale, proof-of-concept interventions, generally delivered in person in one school at a time. Whether this approach could be a practical way to raise school achievement on a large scale remains unknown. We therefore delivered brief growth-mind-set and sense-of-purpose interventions through online modules to 1,594 students in 13 geographically diverse high schools. Both interventions were intended to help students persist when they experienced academic difficulty; thus, both were predicted to be most beneficial for poorly performing students. This was the case. Among students at risk of dropping out of high school (one third of the sample), each intervention raised students’ semester grade point averages in core academic courses and increased the rate at which students performed satisfactorily in core courses by 6.4 percentage points. We discuss implications for the pipeline from theory to practice and for education reform.


title: Cultural diversity of quality of information on Wikipedias

abstract: This article explores the relationship between linguistic culture and the preferred standards of presenting information based on article representation in major Wikipedias. Using primary research analysis of the number of images, references, internal links, external links, words, and characters, as well as their proportions in Good and Featured articles on the eight largest Wikipedias, we discover a high diversity of approaches and format preferences, correlating with culture. We demonstrate that high-quality standards in information presentation are not globally shared and that in many aspects, the language culture's influence determines what is perceived to be proper, desirable, and exemplary for encyclopedic entries. As a result, we demonstrate that standards for encyclopedic knowledge are not globally agreed-upon and "objective" but local and very subjective.


title: An algorithm for ranking the nodes of an urban network based on the concept of PageRank vector

abstract: Abstract This paper presents a new method to establish a ranking of nodes in an urban network. In the original PageRank algorithm, a single PageRank vector is computed to determine the relative importance of Web pages, independent of any particular search query. We follow a similar reasoning, adapting the concept of PageRank vector to urban networks. We translate an urban network to graph theory language, where the nodes represent crossings or squares and the edges represent the connections between nodes. In this scenario, our main goal is to establish a ranking of importance of the different nodes in the graph. Unlike the PageRank model which only takes into account the connections between the Web pages, in our method we must consider other external factors to carry out the classification. These external factors may have some characteristics associated with the nodes and they are different according to the problem we are working. These characteristics are usually related to the presence of some facilities or endowments in the nodes of the network, like for example the presence of restaurants, bars, shopping centers, stores, and so on. A data matrix will collect the quantification of each of the characteristics studied for each node and will play an important role in the process of classification. Considering the influence of all these characteristics, we construct a matrix with some interesting algebraic features which allow us to compute an eigenvector associated to the dominant eigenvalue λ = 1 . This vector constitutes the solution to our problem of ranking the nodes of the network. The model is applied to a real example, in which we consider a part (the old town) of the city of Murcia (Spain). For this example, we apply the PageRank model, as well as the proposed model in this paper, in order to perform a comparative study of both models. This example clearly shows the importance of considering external factors to quantify the urban network nodes.


title: A Real-Time Parking Prediction System for Smart Cities

abstract: A methodological framework for multiple steps ahead parking availability prediction is presented. Two different types of predictions are provided: the probability of a free space to continue being free in subsequent time intervals, and the short-term parking occupancy prediction in selected regions of an urban road network. The available data come from a wide network of on-street parking sensors in the “smart” city of Santander, Spain. The sensor network is segmented in four different regions, and then survival and neural network models are developed for each region separately. Findings show that the Weibull parametric models best describe the probability of a parking space to continue to be free in the forthcoming time intervals. Moreover, simple genetically optimized multilayer perceptrons accurately predict region parking occupancy rates up to 30 minutes in the future by exploiting 1-minute data. Finally, the real time, Web-based, implementation of the proposed parking prediction availability system is...


title: Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection

abstract: In built infrastructure monitoring, an efficient path planning algorithm is essential for robotic inspection of large surfaces using computer vision. In this work, we first formulate the inspection path planning problem as an extended travelling salesman problem (TSP) in which both the coverage and obstacle avoidance were taken into account. An enhanced discrete particle swarm optimization (DPSO) algorithm is then proposed to solve the TSP, with performance improvement by using deterministic initialization, random mutation, and edge exchange. Finally, we take advantage of parallel computing to implement the DPSO in a GPU-based framework so that the computation time can be significantly reduced while keeping the hardware requirement unchanged. To show the effectiveness of the proposed algorithm, experimental results are included for datasets obtained from UAV inspection of an office building and a bridge.


title: A Cloud-based Service for Real-Time Performance Evaluation of NoSQL Databases

abstract: We have created a cloud-based service that allows the end users to run tests on multiple different databases to find which databases are most suitable for their project. From our research, we could not find another application that enables the user to test several databases to gauge the difference between them. This application allows the user to choose which type of test to perform and which databases to target. The application also displays the results of different tests that were run by other users previously. There is also a map to show the location where all the tests are run to give the user an estimate of the location. Unlike the orthodox static tests and reports conducted to evaluate NoSQL databases, we have created a web application to run and analyze these tests in real time. This web application evaluates the performance of several NoSQL databases. The databases covered are MongoDB, DynamoDB, CouchDB, and Firebase. The web service is accessible from: nosqldb.nextproject.ca.


title: A monofilar spiral antenna and its array above a ground plane-formation of a circularly polarized tilted fan beam

abstract: A monofilar spiral antenna is analyzed in the presence of a conducting plane reflector, using the method of moments. The circumference of the spiral antenna is chosen to be 2.3 wavelengths. A tilted beam of circular polarization is realized by superposing the fields from two active regions. The gain of the tilted beam is approximately 8 dB. The frequency bandwidths for 1-dB gain drop and 3-dB axial ratio criterions are 12% and 23%, respectively. An array consisting of the four monofilar spiral antennas is also analyzed, where the array element spacing is chosen to be 0.8 wavelength at a design frequency f/sub 0/. The input impedances of the four spirals are almost the same as the impedance of the single monofilar spiral antenna at f/sub 0/. The array radiates a tilted fan beam with a gain increase of approximately 6 dB from the gain of the single spiral antenna at f/sub 0/. The frequency bandwidth for a 3-dB axial ratio criterion is almost the same as that of the single spiral antenna.


title: Interactive directional subsurface scattering and transport of emergent light

abstract: Existing techniques for interactive rendering of deformable translucent objects can accurately compute diffuse but not directional subsurface scattering effects. It is currently a common practice to gain efficiency by storing maps of transmitted irradiance. This is, however, not efficient if we need to store elements of irradiance from specific directions. To include changes in subsurface scattering due to changes in the direction of the incident light, we instead sample incident radiance and store scattered radiosity. This enables us to accommodate not only the common distance-based analytical models for subsurface scattering but also directional models. In addition, our method enables easy extraction of virtual point lights for transporting emergent light to the rest of the scene. Our method requires neither preprocessing nor texture parameterization of the translucent objects. To build our maps of scattered radiosity, we progressively render the model from different directions using an importance sampling pattern based on the optical properties of the material. We obtain interactive frame rates, our subsurface scattering results are close to ground truth, and our technique is the first to include interactive transport of emergent light from deformable translucent objects.


title: Ultrasound-Guided Genicular Nerve Block for Knee Osteoarthritis: A Double-Blind, Randomized Controlled Trial of Local Anesthetic Alone or in Combination with Corticosteroid.

abstract: BACKGROUND ::: Recently, several studies suggested that radiofrequency (RF) ablation of the genicular nerves is a safe and effective therapeutic procedure for intractable pain associated with chronic knee osteoarthritis (OA). Diagnostic genicular nerve block (GNB) with local anesthetic has been generally conducted before making decisions regarding RF ablation. Although GNB has been recently performed together with corticosteroid, the analgesic effects of corticosteroids for treating chronic pain remain controversial. ::: ::: ::: OBJECTIVES ::: The current study aims to assess the effects of combining corticosteroids and local anesthesia during ultrasound-guided GNB in patients with chronic knee OA. ::: ::: ::: STUDY DESIGN ::: A randomized, double-blinded institutional study. ::: ::: ::: SETTING ::: This study took place at Asan Medical Center in Seoul, Korea. ::: ::: ::: METHODS ::: Forty-eight patients with chronic knee OA were randomly assigned to either the lidocaine alone group (n = 24) or lidocaine plus triamcinolone (TA) group (n = 24) before ultrasound-guided GNB. Visual analog scale (VAS), Oxford Knee Score (OKS), and global perceived effects (7-point scale) were assessed at baseline and at 1, 2, 4, and 8 weeks after the procedure. ::: ::: ::: RESULTS ::: The VAS scores were significantly lower in the lidocaine plus TA group than in the lidocaine alone group at both 2 (P < 0.001) and 4 (P < 0.001) weeks after GNB. The alleviation of intense pain in the lidocaine plus TA group was sustained up to 2 weeks after the procedure, in accordance with the definition of a minimal clinically important improvement. Although a similar intergroup difference in OKSs was observed at 4 weeks (P < 0.001), the clinical improvement in functional capacity lasted for only one week after the reassessment of OKSs, in accordance with a minimal important change. No patient reported any postprocedural adverse events during the follow-up period. ::: ::: ::: LIMITATIONS ::: The emotional state of the patients, which might affect the perception of knee pain, was not evaluated. The follow-up period was 2 months; this period might be insufficient to validate the short-term effects of GNB. ::: ::: ::: CONCLUSIONS ::: Ultrasound-guided GNB, when combined with a local anesthetic and corticosteroid, can provide short-term pain relief. However, the clinical benefit of corticosteroid administration was not clear in comparison with local anesthesia alone. Given the potential adverse effects, corticosteroids might not be appropriate as adjuvants during a GNB for chronic knee OA.The study protocol was approved by our institutional review board (2012-0210), and written informed consent was obtained from all patients. The trial was registered with the Clinical Research Information Service (KCT 0001139). ::: ::: ::: KEY WORDS ::: Chronic pain, knee osteoarthritis, genicular nerve block, ultrasound, corticosteroid, local anesthetic, visual analog scale, Oxford Knee Score.


title: A two-stage short-term load forecasting approach using temperature daily profiles estimation

abstract: Electrical load forecasting plays an important role in the regular planning of power systems, in which load is influenced by several factors that must be analysed and identified prior to modelling in order to ensure better and instant load balancing between supply and demand. This paper proposes a two-stage approach for short-term electricity load forecasting. In the first stage, a set of day classes of load profiles are identified using K-means clustering algorithm alongside daily temperature estimation profiles. The proposed estimation method is particularly useful in case of lack of historical regular temperature data. While in the second stage, the stacked denoising autoencoders approach is used to build regression models able to forecast each day type independently. The obtained models are trained and evaluated using hourly electricity power data offered by Algeria’s National Electricity and Gas Company. Several models are investigated to substantiate the accuracy and effectiveness of the proposed approach.


title: Alibaba International: Building a Global Electronic Marketplace

abstract: Alibaba is the largest global electronic marketplace (GEM) in the world in terms of its participated users. It was successful due to the factors of China?s foreign trade regime, the image of low product cost of China, Alibaba?s contingent adaptation to users? changing requirements and e-commerce environments. Alibaba is not a technically mature GEM platform compared with the ideal functions of GEM. Suggested solution for Alibaba to continuing its success is to adopt a novel GEM connectivity model.


title: Contradiction Detection with Contradiction-Specific Word Embedding

abstract: Contradiction detection is a task to recognize contradiction relations between a pair of sentences. Despite the effectiveness of traditional context-based word embedding learning algorithms in many natural language processing tasks, such algorithms are not powerful enough for contradiction detection. Contrasting words such as “overfull” and “empty” are mostly mapped into close vectors in such embedding space. To solve this problem, we develop a tailored neural network to learn contradiction-specific word embedding (CWE). The method can separate antonyms in the opposite ends of a spectrum. CWE is learned from a training corpus which is automatically generated from the paraphrase database, and is naturally applied as features to carry out contradiction detection in SemEval 2014 benchmark dataset. Experimental results show that CWE outperforms traditional context-based word embedding in contradiction detection. The proposed model for contradiction detection performs comparably with the top-performing system in accuracy of three-category classification and enhances the accuracy from 75.97% to 82.08% in the contradiction category.


title: A Hellinger-based discretization method for numeric attributes in classification learning

abstract: Many classification algorithms require that training examples contain only discrete values. In order to use these algorithms when some attributes have continuous numeric values, the numeric attributes must be converted into discrete ones. This paper describes a new way of discretizing numeric values using information theory. Our method is context-sensitive in the sense that it takes into account the value of the target attribute. The amount of information each interval gives to the target attribute is measured using Hellinger divergence, and the interval boundaries are decided so that each interval contains as equal amount of information as possible. In order to compare our discretization method with some current discretization methods, several popular classification data sets are selected for discretization. We use naive Bayesian classifier and C4.5 as classification tools to compare the accuracy of our discretization method with that of other methods.


title: HYPERACCURATE ELLIPSE FITTING WITHOUT ITERATIONS

abstract: This paper presents a new method for fitting an ellipse to a point sequence extracted from images. It is widely known that the best fit is obtained by maximum likelihood. However, it requires iterations, which may not converge in the presence of large noise. Our approach is algebraic distance minimization; no iterations are required. Exploiting the fact that the solution depends on the way the scale is normalized, we analyze the accuracy to high order error terms with the scale normalization weight unspecified and determine it so that the bias is zero up to the second order. We demonstrate by experiments that our method is superior to the Taubin method, also algebraic and known to be highly accurate.


title: Surgical Anatomy of the Midcheek and Malar Mounds

abstract: The anatomy of the midcheek has not been satisfactorily described to adequately explain midcheek aging and malar mounds, nor has it suggested a logical approach to their correction or provided sufficient detail for safe surgery in this area. This cadaver study, which was complemented by many operative dissections, located a missing link: a glide plane space overlying the body of the zygoma. The space functions to allow mobility of the orbicularis oculi, where it overlies the zygoma and the origins of the elevator muscles to the upper lip. The space is a cleft between the sub-orbicularis oculi fat and the preperiosteal fat and is lined by a fine membrane. The anatomic boundaries are clearly defined by retaining ligaments, which correlate with the triangularity of the space. Several anatomic features provide the functional characteristics of the prezygomatic space, including the (1) absence of direct attachments between the orbicularis in the roof to the floor, (2) more rigid inferior boundary formed by the zygomatic ligaments, and (3) more mobile upper ligamentous boundary formed by the orbicularis retaining ligament (separating from the preseptal space of the lower lid). These components determine the characteristic aging changes that occur in this region and explain much about malar mounds. An appreciation of this anatomy has several surgical implications. The prezygomatic space is a junction area that can be approached from the temple, lower lid, and cheek. The zygomatic branches of the facial nerve to the orbicularis do not cross the space; rather, they course in the walls and in the sub-orbicularis fat within the roof of the space.


title: EFFICIENCY ENHANCEMENT OF BASE STATION POWER AMPLIFIERS USING DOHERTY TECHNIQUE

abstract: The power amplifiers are typically the most power-consuming block in wireless communication systems. Spectrum is expensive, and newer technologies demand transmission of maximum amount of data with minimum spectrum usage. This requires sophisticated modulation techniques, leading to wide, dynamic signals that require linear amplification. Although linear amplification is achievable, it always comes at the expense of efficiency. Most of the modern wireless applications such as WCDMA use non-constant envelope modulation techniques with a high peak to average ratio. Linearity being a critical issue, power amplifiers implemented in such applications are forced to operate at a backed off region from saturation. Therefore, in order to overcome the battery lifetime limitation, a design of a high efficiency power amplifier that can maintain the efficiency for a wider range of radio frequency input signal is the obvious solution. A new technique that improves the drain efficiency of a linear power amplifier such as Class A or AB, for a wider range of output power, has been investigated in this research. The Doherty technique consists of two amplifiers in parallel; in such a way that the combination enhances the power added efficiency of the main amplifier at 6dB back off from the maximum output power. The classes of operation of power amplifier (A, AB,B, C etc), and the design techniques are presented. Design of a 2.14 GHz Doherty power amplifier has been provided in chapter 4. This technique shows a 15% increase in power added efficiency at 6 dB back off from the compression point. This PA can be implemented in WCDMA base station transmitter.


title: Touch based digital ordering system on Android using GSM and Bluetooth for restaurants

abstract: Technology has entered almost every field in our life, but still its effect is not yet that evident in the food industry, especially the food serving outlets including restaurants, hotels. Even today, most of the restaurants in India follow the traditional pen and paper method to take orders from customers, which wastes a lot of time of both, the customer and the restaurant. This work aims to substitute the traditional pen and paper method by automating the food-ordering process in restaurant and thus improving the dining experience of the customer. This paper proposes an automated system that uses wireless communication, a centralized database, and an android application to place the order without even waiting for a waiter. The android application installed in the touch screen device, fitted at the table, contains all the menu details with pictures of each item. The ordered details are wirelessly sent to the chef and the cashier. The manager has his own android application that is used to update the menu that updates the central database, view and manage table wise customers' orders, and receive feedbacks from the customer. This system improves efficiency and accuracy for restaurants by saving time, eliminating human errors, getting customers feedback. As the system is automated, it becomes economical even from restaurants point of view, as it reduces manpower and it just requires one time investment in installing the devices at tables.


title: PROCEEDINGS of the HUMAN FACTORS AND ERGONOMICS SOCIETY 50th ANNUAL MEETING—2006 2547 MEASURING THE USABILITY OF PAPER BALLOTS: EFFICIENCY, EFFECTIVENESS, AND SATISFACTION

abstract: The Help America Vote Act (HAVA) of 2002 secured funding for improvements to election administration. Improvements include upgrading older voting systems to meet new guidelines. To determine whether the new voting systems are improvements over existing voting systems, information is needed on the usability of the older, traditional systems. This study was designed as a first step in addressing the need for usability data on existing voting systems. Three traditional paper ballots were empirically evaluated to collect baseline data that can later be compared to newer, electronic voting systems. Usability was evaluated using the thee International Organization for Standardization (ISO) metrics suggested by the National Institute of Standards and Technology (NIST): effectiveness, efficiency, and satisfaction. All three ballot types (bubble, arrow, and open response) produced reasonable levels of efficiency. The three ballot types did not produce different levels of effectiveness, but the overall error rate w...


title: An image encryption scheme based on elliptic curve pseudo random and Advanced Encryption System

abstract: Usage of EC operations as initial random generator and encrypting through AES to generate new pseudo random to mask whole image pixels.Simulations and analysis results show that cooperation of AES and EC random numbers makes great results in randomness and image encryption field.Comparable security and speed with other trend schemes like chaos based encryptions. Elliptic Curve Cryptography (ECC) has proven to be an effective cryptography. ECC has its own advantages such as efficient key size compared to other Public Key Infrastructures. This paper exploits the Elliptic curve random generator defined by National Institute of Standards and Technology (NIST) to generate a sequence of arbitrary numbers based on curves. The random generation phase is based on public shared key and a changing point G, which is a generator of a curve to obtain random sequences. Then, Advanced Encryption System is applied to these sequences acquiring arbitrary keys for encrypting image. Using AES alongside well distributed randoms provides a prominent encryption technique. Our experiments show that the proposed method fulfills the basics of cryptography including simpleness and correctness. Moreover, the results of the evaluation prove the effectiveness and security of the proposed method.


title: Projection Based Weight Normalization for Deep Neural Networks

abstract: Optimizing deep neural networks (DNNs) often suffers from the ill-conditioned problem. We observe that the scaling-based weight space symmetry property in rectified nonlinear network will cause this negative effect. Therefore, we propose to constrain the incoming weights of each neuron to be unit-norm, which is formulated as an optimization problem over Oblique manifold. A simple yet efficient method referred to as projection based weight normalization (PBWN) is also developed to solve this problem. PBWN executes standard gradient updates, followed by projecting the updated weight back to Oblique manifold. This proposed method has the property of regularization and collaborates well with the commonly used batch normalization technique. We conduct comprehensive experiments on several widely-used image datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet for supervised learning over the state-of-the-art convolutional neural networks, such as Inception, VGG and residual networks. The results show that our method is able to improve the performance of DNNs with different architectures consistently. We also apply our method to Ladder network for semi-supervised learning on permutation invariant MNIST dataset, and our method outperforms the state-of-the-art methods: we obtain test errors as 2.52%, 1.06%, and 0.91% with only 20, 50, and 100 labeled samples, respectively.


title: Massive MIMO and mmWave for 5G Wireless HetNet: Potential Benefits and Challenges

abstract: There has been active research worldwide to develop the next-generation, i.e., fifth-generation (5G), wireless network. The 5G network is expected to support a significantly large amount of mobile data traffic and a huge number of wireless connections and achieve better costand energy-efficiency as well as quality of service (QoS) in terms of communication delay, reliability, and security. To this end, the 5G wireless network should exploit the potential of new developments, including superdense and heterogeneous deployment of cells and massive antenna arrays [i.e., massive multiple-input, multiple-output (MIMO) technologies] and utilization of higher frequencies, particularly millimeter-wave (mmWave) frequencies. This article discusses the potential benefits and challenges of the 5G wireless heterogeneous network (HetNet) that incorporates massive MIMO and mmWave technologies.


title: Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition

abstract: This paper proposes an encoder-decoder network to disentangle shape features during 3D face reconstruction from single 2D images, such that the tasks of reconstructing accurate 3D face shapes and learning discriminative shape features for face recognition can be accomplished simultaneously. Unlike existing 3D face reconstruction methods, our proposed method directly regresses dense 3D face shapes from single 2D images, and tackles identity and residual (i.e., non-identity) components in 3D face shapes explicitly and separately based on a composite 3D face shape model with latent representations. We devise a training process for the proposed network with a joint loss measuring both face identification error and 3D face shape reconstruction error. To construct training data we develop a method for fitting 3D morphable model (3DMM) to multiple 2D images of a subject. Comprehensive experiments have been done on MICC, BU3DFE, LFW and YTF databases. The results show that our method expands the capacity of 3DMM for capturing discriminative shape features and facial detail, and thus outperforms existing methods both in 3D face reconstruction accuracy and in face recognition accuracy.


title: The effects of video game playing on attention, memory, and executive control

abstract: Expert video game players often outperform non-players on measures of basic attention and performance. Such differences might result from exposure to video games or they might reflect other group differences between those people who do or do not play video games. Recent research has suggested a causal relationship between playing action video games and improvements in a variety of visual and attentional skills (e.g., [Green, C. S., & Bavelier, D. (2003). Action video game modifies visual selective attention. Nature, 423, 534–537]). The current research sought to replicate and extend these results by examining both expert/non-gamer differences and the effects of video game playing on tasks tapping a wider range of cognitive abilities, including attention, memory, and executive control. Non-gamers played 20+ h of an action video game, a puzzle game, or a real-time strategy game. Expert gamers and non-gamers differed on a number of basic cognitive skills: experts could track objects moving at greater speeds, better detected changes to objects stored in visual short-term memory, switched more quickly from one task to another, and mentally rotated objects more efficiently. Strikingly, extensive video game practice did not substantially enhance performance for non-gamers on most cognitive tasks, although they did improve somewhat in mental rotation performance. Our results suggest that at least some differences between video game experts and non-gamers in basic cognitive performance result either from far more extensive video game experience or from pre-existing group differences in abilities that result in a self-selection effect.


title: Analysis and Design of a 3 dB Tunable Lumped-Element Directional Coupler

abstract: An analysis of a 3 dB lumped-element directional coupler (LEDC) based on arbitrary terminal impedance is described numerically. To solve the conflicted requirement for broad bandwidth and small size in LEDC, a new structure of coupler is introduced, which can significantly improve bandwidth and whose size is only 3 cm×4 cm on the conditions of the frequency domain of 410 MHz to 490 MHz. The measure results are in good agreement with simulations despite the unexpected resistor loss.


title: Enabling Smart Personalized Healthcare: A Hybrid Mobile-Cloud Approach for ECG Telemonitoring

abstract: The severe challenges of the skyrocketing healthcare expenditure and the fast aging population highlight the needs for innovative solutions supporting more accurate, affordable, flexible, and personalized medical diagnosis and treatment. Recent advances of mobile technologies have made mobile devices a promising tool to manage patients' own health status through services like telemedicine. However, the inherent limitations of mobile devices make them less effective in computation- or data-intensive tasks such as medical monitoring. In this study, we propose a new hybrid mobile-cloud computational solution to enable more effective personalized medical monitoring. To demonstrate the efficacy and efficiency of the proposed approach, we present a case study of mobile-cloud based electrocardiograph monitoring and analysis and develop a mobile-cloud prototype. The experimental results show that the proposed approach can significantly enhance the conventional mobile-based medical monitoring in terms of diagnostic accuracy, execution efficiency, and energy efficiency, and holds the potential in addressing future large-scale data analysis in personalized healthcare.


title: Detecting highly overlapping community structure by greedy clique expansion

abstract: In complex networks it is common for each node to belong to several communities, implying a highly overlapping community structure. Recent advances in benchmarking indicate that the existing community assignment algorithms that are capable of detecting overlapping communities perform well only when the extent of community overlap is kept to modest levels. To overcome this limitation, we introduce a new community assignment algorithm called Greedy Clique Expansion (GCE). The algorithm identifies distinct cliques as seeds and expands these seeds by greedily optimizing a local fitness function. We perform extensive benchmarks on synthetic data to demonstrate that GCE’s good performance is robust across diverse graph topologies. Significantly, GCE is the only algorithm to perform well on these synthetic graphs, in which every node belongs to multiple communities. Furthermore, when put to the task of identifying functional modules in protein interaction data, and college dorm assignments in Facebook friendship data, we find that GCE performs competitively.


title: Econometric Models of Limit-Order Executions

abstract: This paper attempts to assess whether money can generate persistent economic" fluctuations in dynamic general equilibrium models of the business cycle. We show that a small" nominal friction in the goods market can make the response of output to monetary shocks large" and persistent if it is amplified by real wage rigidity in the labor market. We also argue that" given the level of real wage rigidity that is observed in developed countries nominal stickiness might be sufficient for money to produce economic fluctuations as persistent" as those observed in the data.


title: Reaching Data Confidentiality and Model Accountability on the CalTrain

abstract: Distributed collaborative learning (DCL) paradigms enable building joint machine learning models from distrusted multi-party participants. Data confidentiality is guaranteed by retaining private training data on each participant's local infrastructure. However, this approach makes today's DCL design fundamentally vulnerable to data poisoning and backdoor attacks. It limits DCL's model accountability, which is key to backtracking problematic training data instances and their responsible contributors. In this paper, we introduce CALTRAIN, a centralized collaborative learning system that simultaneously achieves data confidentiality and model accountability. CALTRAIN enforces isolated computation via secure enclaves on centrally aggregated training data to guarantee data confidentiality. To support building accountable learning models, we securely maintain the links between training instances and their contributors. Our evaluation shows that the models generated by CALTRAIN can achieve the same prediction accuracy when compared to the models trained in non-protected environments. We also demonstrate that when malicious training participants tend to implant backdoors during model training, CALTRAIN can accurately and precisely discover the poisoned or mislabeled training data that lead to the runtime mispredictions.


title: Improving literacy in rural India: cellphone games in an after-school program

abstract: Literacy is one of the great challenges in the developing world. But universal education is an unattainable dream for those children who lack access to quality educational resources such as well-prepared teachers and schools. Worse, many of them do not attend school regularly due to their need to work for the family in the agricultural fields or households. This work commitment puts formal education far out of their reach. On the other hand, educational games on cellphones hold the promise of making learning more accessible and enjoyable. In our projects 4th year, we reached a stage where we could implement a semester-long pilot on cellphone-based learning. The pilot study took the form of an after-school program in a village in India. This paper reports on this summative learning assessment. While we found learning benefits across the board, it seemed that more of the gains accrued to those children who were better equipped to take advantage of this opportunity. We conclude with future directions for designing educational games that target less well-prepared children in developing regions.


title: Learning Deep Hierarchical Visual Feature Coding

abstract: In this paper, we propose a hybrid architecture that combines the image modeling strengths of the bag of words framework with the representational power and adaptability of learning deep architectures. Local gradient-based descriptors, such as SIFT, are encoded via a hierarchical coding scheme composed of spatial aggregating restricted Boltzmann machines (RBM). For each coding layer, we regularize the RBM by encouraging representations to fit both sparse and selective distributions. Supervised fine-tuning is used to enhance the quality of the visual representation for the categorization task. We performed a thorough experimental evaluation using three image categorization data sets. The hierarchical coding scheme achieved competitive categorization accuracies of 79.7% and 86.4% on the Caltech-101 and 15-Scenes data sets, respectively. The visual representations learned are compact and the model's inference is fast, as compared with sparse coding methods. The low-level representations of descriptors that were learned using this method result in generic features that we empirically found to be transferrable between different image data sets. Further analysis reveal the significance of supervised fine-tuning when the architecture has two layers of representations as opposed to a single layer.


title: mtDNA Variation Predicts Population Size in Humans and Reveals a Major Southern Asian Chapter in Human Prehistory

abstract: The relative timing and size of regional human population growth following our expansion from Africa remain unknown. Human mitochondrial DNA (mtDNA) diversity carries a legacy of our population history. Given a set of sequences, we can use coalescent theory to estimate past population size through time and draw inferences about human population history. However, recent work has challenged the validity of using mtDNA diversity to infer species population sizes. Here we use Bayesian coalescent inference methods, together with a global data set of 357 human mtDNA coding-region sequences, to infer human population sizes through time across 8 major geographic regions. Our estimates of relative population sizes show remarkable concordance with the contemporary regional distribution of humans across Africa, Eurasia, and the Americas, indicating that mtDNA diversity is a good predictor of population size in humans. Plots of population size through time show slow growth in sub-Saharan Africa beginning 143-193 kya, followed by a rapid expansion into Eurasia after the emergence of the first non-African mtDNA lineages 50-70 kya. Outside Africa, the earliest and fastest growth is inferred in Southern Asia approximately 52 kya, followed by a succession of growth phases in Northern and Central Asia (approximately 49 kya), Australia (approximately 48 kya), Europe (approximately 42 kya), the Middle East and North Africa (approximately 40 kya), New Guinea (approximately 39 kya), the Americas (approximately 18 kya), and a second expansion in Europe (approximately 10-15 kya). Comparisons of relative regional population sizes through time suggest that between approximately 45 and 20 kya most of humanity lived in Southern Asia. These findings not only support the use of mtDNA data for estimating human population size but also provide a unique picture of human prehistory and demonstrate the importance of Southern Asia to our recent evolutionary past.


title: Nonprofits Are Seen as Warm and For-Profits as Competent: Firm Stereotypes Matter

abstract: Consumers use warmth and competence, two fundamental dimensions that govern social judgments of people, to form perceptions of firms. Three experiments showed that consumers perceive non-profits as being warmer than for-profits, but as less competent. Further, consumers are less willing to buy a product made by a non-profit than a for-profit because of their perceptions that the firm lacks competence. Consequently, when perceived competence of a non-profit is boosted through subtle cues that connote credibility, discrepancies in willingness to buy disappear. In fact, when consumers perceive high levels of competence and warmth, they feel admiration for the firm--which translates to consumers' increased desire to buy. This work highlights the importance of consumer stereotypes about non-profit and for-profit companies that, at baseline, come with opposing advantages and disadvantages but that can be altered.


title: Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance

abstract: One approach to secure systems is through the analysis of audit trails. An audit trail is a record of all events that take place in a system and across a network, i.e., it provides a trace of user/system actions so that security events can be related to the actions of a specific individual or system component. Audit trails can be inspected for the presence or absence of certain patterns. This paper advocates the use of process mining techniques to analyze audit trails for security violations. It is shown how a specific algorithm, called the @a-algorithm, can be used to support security efforts at various levels ranging from low-level intrusion detection to high-level fraud prevention.


title: An Adaptive Fusion Algorithm for Visible and Infrared Videos Based on Entropy and the Cumulative Distribution of Gray Levels

abstract: Visible videos captured under different weather conditions may exhibit different characteristics, and thermal infrared videos are easily affected by ambient temperature variations; this sensitivity to environmental conditions makes the fusion of visible and thermal infrared videos a challenge. This paper proposes an adaptive fusion algorithm for visible and infrared videos, and uses cumulative distribution of gray levels and the entropy to adaptively retain infrared-hot targets and visible textures. The original visible and infrared frames are decomposed into two layers, namely, the base layer and the detail layer. The guided filter is employed to decompose frames due to its high efficiency. Two weight maps, one for the infrared base layer and one for the visible base layer, are adaptively generated based on the cumulative distribution of gray levels and the entropy, respectively. The visible base layer and the infrared base layer are fused based on their weight maps. The final fusion result is obtained by combining the fused base layer with the visible detail layer. Experimental results demonstrate that the proposed algorithm can achieve better fusion results compared with state-of-the-art methods.


title: Repeater for 5G wireless: A complementary contender for Spectrum Sensing intelligence

abstract: Exploring innovative cellular architectures to achieve enhanced system capacity and good coverage has become a critical issue towards realizing the fifth generation (5G) of wireless communications. In this context, this paper proposes a novel concept of an intelligent Amplify and Forward (AF) 5G repeater for enabling the densification of future cellular networks. The proposed repeater features a Spectrum Sensing (SS) intelligence capability and utilizes such intelligence in a complementary fashion in comparison to its existing counterpart (e.g., Cognitive Radio) by detecting the active channels within the assigned spectrum. This intelligence allows the proposed repeater to carry out selective amplification of the active channels in contrast to the full amplification in conventional AF repeaters. Furthermore, the performance of a Frequency Division Multiple Access (FDMA) based two hop cellular network utilizing the proposed repeater is evaluated in terms of the system throughput. Simulation results demonstrate up to 13 % increase when compared with the conventional repeaters. Moreover, the effect of SS errors on the system capacity is analyzed.


title: Accordion: Better Memory Organization for LSM Key-Value Stores

abstract: Log-structured merge (LSM) stores have emerged as the technology of choice for building scalable write-intensive key-value storage systems. An LSM store replaces random I/O with sequential I/O by accumulating large batches of writes in a memory store prior to flushing them to log-structured disk storage; the latter is continuously re-organized in the background through a compaction process for efficiency of reads. Though inherent to the LSM design, frequent compactions are a major pain point because they slow down data store operations, primarily writes, and also increase disk wear. Another performance bottleneck in today's state-of-the-art LSM stores, in particular ones that use managed languages like Java, is the fragmented memory layout of their dynamic memory store. ::: ::: In this paper we show that these pain points may be mitigated via better organization of the memory store. We present Accordion - an algorithm that addresses these problems by re-applying the LSM design principles to memory management. Accordion is implemented in the production code of Apache HBase, where it was extensively evaluated. We demonstrate Accordion's double-digit performance gains versus the baseline HBase implementation and discuss some unexpected lessons learned in the process.


title: The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics

abstract: AbstractThe emergence of big data has stimulated enormous investments into business analytics solutions, but large-scale and reliable empirical evidence about the business value of big data and analytics (BDA) remains scarce. This article presents the results of an econometric study that analyzes the direction, sign, and magnitude of the relationship between BDA and firm performance based on objective measurements of BDA assets. Using a unique panel data set that contains detailed information about BDA solutions owned by 814 companies during the time frame from 2008 to 2014, on the one hand, and their financial performance, on the other hand, we estimate the relationship between BDA assets and firm productivity and find that live BDA assets are associated with an average of 3–7 percent improvement in firm productivity. Yet we also find substantial differences in returns from BDA when we consider the industry in which a firm operates. While firms in information technology-intensive or highly competitive in...


title: Soil Classification From Large Imagery Databases Using a Neuro-Fuzzy Classifier

abstract: In this paper, we propose a neuro-fuzzy (NF) classification technique to determine various soil classes from large imagery soil databases. The technique looks at the feature-wise degree of belongings of the imagery databases to obtainable soil classes using a fuzzification method. The fuzzification method builds a membership matrix with an element count equal to the mathematical product of the number of data records and soil classes present. The elements of this matrix are the input to a neural network model. We apply our technique to three UCI databases, namely, Statlog Landsat Satellite, Forest Covertype, and Wilt for soil classification. The paper aims to find out soil classes using the proposed technique, and then compare its performance with four well-known classification algorithms, namely, radial basis function network, k-nearest neighbor, support vector machine, and adaptive NF inference system. Numerous measures, for example, root-mean-square error, kappa statistic, accuracy, false positive rate, true positive rate, precision, recall, F-measure, and area under the curve, are used for evaluating the quantitative analysis of the simulated results. All these evaluation measures approve the supremacy of the proposed NF method.


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title: A practical liquidity-sensitive automated market maker

abstract: Automated market makers are algorithmic agents that enable participation and information elicitation in electronic markets. They have been widely and successfully applied in artificial-money settings, like some Internet prediction markets. Automated market makers from the literature suffer from two problems that contribute to their impracticality and impair their use beyond artificial-money settings: first, they are unable to adapt to liquidity, so that trades cause prices to move the same amount in both heavily and lightly traded markets, and second, in typical circumstances, they run at a deficit. In this article, we construct a market maker that is both sensitive to liquidity and can run at a profit. Our market maker has bounded loss for any initial level of liquidity and, as the initial level of liquidity approaches zero, worst-case loss approaches zero. For any level of initial liquidity we can establish a boundary in market state space such that, if the market terminates within that boundary, the market maker books a profit regardless of the realized outcome.


title: Robust Face Recognition via Adaptive Sparse Representation

abstract: Sparse Representation (or coding) based Classification (SRC) has gained great success in face recognition in recent years. However, SRC emphasizes the sparsity too much and overlooks the correlation information which has been demonstrated to be critical in real-world face recognition problems. Besides, some work considers the correlation but overlooks the discriminative ability of sparsity. Different from these existing techniques, in this paper, we propose a framework called Adaptive Sparse Representation based Classification (ASRC) in which sparsity and correlation are jointly considered. Specifically, when the samples are of low correlation, ASRC selects the most discriminative samples for representation, like SRC; when the training samples are highly correlated, ASRC selects most of the correlated and discriminative samples for representation, rather than choosing some related samples randomly. In general, the representation model is adaptive to the correlation structure, which benefits from both $\ell_1$-norm and $\ell_2$-norm. Extensive experiments conducted on publicly available data sets verify the effectiveness and robustness of the proposed algorithm by comparing it with state-of-the-art methods.


title: Retrocausality at no extra cost

abstract: One obstacle faced by proposals of retrocausal influences in quantum mechanics is the perceived high conceptual cost of making such a proposal. I assemble here a metaphysical picture consistent with the possibility of retrocausality and not precluded by the known physical structure of our reality. This picture employs two relatively well-established positions—the block universe model of time and the interventionist account of causation—and requires the dismantling of our ordinary asymmetric causal intuition and our ordinary intuition about epistemic access to the past. The picture is then built upon an existing model of agent deliberation that permits us to strike a harmony between our causal intuitions and the fixity of the block universe view. I conclude that given the right mix of these reasonable metaphysical and epistemological ingredients there is no conceptual cost to such a retrocausal picture of quantum mechanics.


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