title: Focusing “Hot” or Focusing “Cool”: Attentional Mechanisms in Sexual Arousal in Men and Women

abstract: ABSTRACT Introduction Knowledge about the regulation of sexual emotion may add to the understanding of sexual problems such as diminished sexual desire and hypersexuality. Aim To investigate the regulation of sexual arousal by means of attentional focus in healthy sexually functional men and women. Method Using a habituation design with attentional strategies, it was investigated whether a focus on hot, emotional information of sexual stimuli would sustain or amplify sexual responses, whereas a focus on cool, cognitive information would weaken sexual responses. Main Outcome Measures Genital response (in women measured by vaginal photoplethysmography assessing vaginal pulse amplitude, and in men measured by mechanical penile strain gauge assessing penile circumference) and subjective report of sexual arousal and absorption. Results Attenuation of sexual feelings by attentional focus was observed, with stronger sexual feelings under the hot focus condition than under the cool focus condition. Also, sexual feelings diminished during repeated erotic stimulation, and increased with the introduction of novel stimulation, indicating habituation and novelty effects. Contrary to the expectations, the hot attentional focus did not preclude habituation of sexual arousal. Conclusions Attentional focus has substantial regulatory effects on subjective sexual arousal. Taking a participant and emotion‐oriented focus rather than a spectator and stimulus‐oriented focus while viewing erotic stimuli, enhances feelings of sexual arousal. Implications for the treatment of hypoactive sexual desire, sexual arousal disorder, and hypersexuality are discussed, as well as future directions for studying regulation of sexual emotion. Both S, Laan E, and Everaerd W. Focusing “hot” or focusing “cool”: Attentional mechanisms in sexual arousal in men and women.


Rebuilding Big Pharma ’ s Business Model


title: Soft switching three level inverter (S3L inverter)

abstract: The Soft Switching Three Level Inverter - abbreviated to S3L Inverter - was first introduced in 2011. It is a novel circuit topology for PWM inverters, whose areas of application include electrical drives and grid-tie inverters for photovoltaic installations and wind power plants, and for power supplies. It is of very simple design and therefore inexpensive. It is implemented completely as soft switching and hence offers very low switching losses. This means that its efficiency is very high, and it can achieve very high values for the switching frequency. This paper begins by describing how the device functions. It then goes on to discuss specialised methods of control. A variant of the S3L Inverter with a deactivatable snubber circuit is presented. A quantitative comparison between a well-known hard switching NPC 3-level inverter and the novel S3L Inverter using 90 kVA (three-phase) prototypes illustrates the advantages of the S3L inverter.


title: New Synchronization Algorithm Based on Delta Synchronization for Compressed Files in the Mobile Cloud Environment

abstract: The fast growing market of mobile device adoption and cloud computing has led to exploitation of mobile devices utilizing cloud services. One major chal-lenge facing the usage of mobile devices in the cloud environment is mobile synchronization to the cloud, e.g., synchronizing contacts, text messages, imag-es, and videos. Owing to the expected high volume of traffic and high time complexity required for synchronization, an appropriate synchronization algo-rithm needs to be developed. Delta synchronization is one method of synchro-nizing compressed files that requires uploading the whole file, even when no changes were made or if it was only partially changed. In the present study, we proposed an algorithm, based on Delta synchronization, to solve the problem of synchronizing compressed files under various forms of modification (e.g., not modified, partially modified, or completely modified). To measure the effi-ciency of our proposed algorithm, we compared it to the Dropbox application algorithm. The results demonstrated that our algorithm outperformed the regular Dropbox synchronization mechanism by reducing the synchronization time, cost, and traffic load between clients and the cloud service provider.


title: A property oriented fault detection approach for link state routing protocol

abstract: This paper proposes a new approach to fault detection for a link state routing system-property oriented analysis and detection (POD). A routing system is modeled as a set of distributed processes. A property is defined as state predicate(s) over system variables. For the link state routing protocol, the high-level overall converging property P is defined as the "synchronization" among routing information bases maintained by all processes. We decompose the routing protocol into different computation phases. For each phase, we use invariant state predicates (safety property) and the liveness property as our guide for observation and analysis. The goal of the detection algorithm is to construct a validation path based on the history to determine if the fault is natural or malicious once the stable property P is rendered invalid by faults. The contribution of this paper is twofold: first, a new detection approach is proposed that differs from traditional signature-based or profile-based intrusion detection paradigms in the sense that it utilizes the stable property as a starting point, and correlates the history and future to validate changes in the system; second, by exploring the primary concerned system properties, we show that detection effort can be conducted in a more focused and systematic fashion.


Combined Group and Exclusive Sparsity for Deep Neural Networks


title: Deep Online Video Stabilization

abstract: Video stabilization technique is essential for most hand-held captured videos due to high-frequency shakes. Several 2D-, 2.5D- and 3D-based stabilization techniques are well studied, but to our knowledge, no solutions based on deep neural networks had been proposed. The reason for this is mostly the shortage of training data, as well as the challenge of modeling the problem using neural networks. In this paper, we solve the video stabilization problem using a convolutional neural network (ConvNet). Instead of dealing with offline holistic camera path smoothing based on feature matching, we focus on low-latency real-time camera path smoothing without explicitly representing the camera path. Our network, called StabNet, learns a transformation for each input unsteady frame progressively along the time-line, while creating a more stable latent camera path. To train the network, we create a dataset of synchronized steady/unsteady video pairs via a well designed hand-held hardware. Experimental results shows that the proposed online method (without using future frames) performs comparatively to traditional offline video stabilization methods, while running about 30 times faster. Further, the proposed StabNet is able to handle night-time and blurry videos, where existing methods fail in robust feature matching.


title: A Bottom-Up Approach To Sentence Ordering For Multi-Document Summarization

abstract: Ordering information is a difficult but important task for applications generating natural language texts such as multi-document summarization, question answering, and concept-to-text generation. In multi-document summarization, information is selected from a set of source documents. However, improper ordering of information in a summary can confuse the reader and deteriorate the readability of the summary. Therefore, it is vital to properly order the information in multi-document summarization. We present a bottom-up approach to arrange sentences extracted for multi-document summarization. To capture the association and order of two textual segments (e.g. sentences), we define four criteria: chronology, topical-closeness, precedence, and succession. These criteria are integrated into a criterion by a supervised learning approach. We repeatedly concatenate two textual segments into one segment based on the criterion, until we obtain the overall segment with all sentences arranged. We evaluate the sentence orderings produced by the proposed method and numerous baselines using subjective gradings as well as automatic evaluation measures. We introduce the average continuity, an automatic evaluation measure of sentence ordering in a summary, and investigate its appropriateness for this task.


title: Supervised Topic Models

abstract: We introduce supervised latent Dirichlet allocation (sLDA), a statistical model of labelled documents. The model accommodates a variety of response types. We derive a maximum-likelihood procedure for parameter estimation, which relies on variational approximations to handle intractable posterior expectations. Prediction problems motivate this research: we use the fitted model to predict response values for new documents. We test sLDA on two real-world problems: movie ratings predicted from reviews, and web page popularity predicted from text descriptions. We illustrate the benefits of sLDA versus modern regularized regression, as well as versus an unsupervised LDA analysis followed by a separate regression.


title: Error Link Detection and Correction in Wikipedia

abstract: The hyperlink structure of Wikipedia forms a rich semantic network connecting entities and concepts, enabling it as a valuable source for knowledge harvesting. Wikipedia, as crowd-sourced data, faces various data quality issues which significantly impacts knowledge systems depending on it as the information source. One such issue occurs when an anchor text in a Wikipage links to a wrong Wikipage, causing the error link problem. While much of previous work has focused on leveraging Wikipedia for entity linking, little has been done to detect error links. In this paper, we address the error link problem, and propose algorithms to detect and correct error links. We introduce an efficient method to generate candidate error links based on iterative ranking in an Anchor Text Semantic Network. This greatly reduces the problem space. A more accurate pairwise learning model was used to detect error links from the reduced candidate error link set, while suggesting correct links in the same time. This approach is effective when data sparsity is a challenging issue. The experiments on both English and Chinese Wikipedia illustrate the effectiveness of our approach. We also provide a preliminary analysis on possible causes of error links in English and Chinese Wikipedia.


title: Detecting Arabic spammers and content polluters on Twitter

abstract: Spam is thriving on Arabic Twitter. With a large online population, a mounting political unrest, and an undersized and unspecialized response effort, the current state of Arabic online social networks (OSNs) offers a perfect target for the spam industry, bringing both abuse and manipulation to the scene. The result is a ubiquitous spam presence that redefines the signal to noise ratio, and makes spam a de facto component of the online social platforms. English spam on online social networks has been heavily studied in the literature. To date however, social spam in other languages has been largely ignored. Our own analysis of spam content on Arabic trending hashtags in Saudi Arabia results in an estimate of about three quarters of the total generated content. This alarming rate, backed by independent concurrent estimates, makes the development of adaptive spam detection techniques a very real and pressing need. In this study, we present a first attempt at detecting accounts that promote spam and content pollution on Arabic Twitter. Using a large crawled dataset of more than 23 million Arabic tweets, and a manually labeled sample of more than 5000 tweets, we analyze the spam content on Saudi Twitter, and assess the performance of previous spam detection features on our recently gathered dataset. We also adapt the previously proposed features to respond to spammers evading techniques, and use these features to build a new highly accurate data-driven detection system.


title: On the endogenesis of Twitter's Spritzer and Gardenhose sample streams

abstract: Many recent publications deal with trend analysis, event detection or opinion mining on social media data. Twitter, as the most important microblogging service, is often in the focus of these works, as it offers free access to big volumes of data. The free access, on that many publications rely, is composed of a random subset of the complete public status stream. Publications rely particularly on the uniform distribution of tweets in this sample stream, and therefore, till today, one has to trust in the statement of Twitter that the sample data is indeed uniformly distributed1. In our research on the technical properties of Twitter's streaming data, we found evidence for discovering the method used by Twitter to decide which tweets will show up in the random sample streams. A deeper insight into this process leads to the possible reasons of why Twitter chose the presented sampling method. For this purpose we provide an overview of how Twitter's unique tweet IDs are generated and explain the regularities of each part of a tweet ID. This results also in some information about the tweet ID generating infrastructure of Twitter and what kind of knowledge can possibly be derived from small features like the tweet ID.


title: Relating improvisational music therapy with severely and multiply disabled children to communication development.

abstract: The effect of different levels of preintentional and intentional communication development on musical interaction with children with severe and multiple disabilities has not been explored in the music therapy literature. Aside from stage of communication development, what are the particular influences of disability on musical interaction with children who have preintentional and early intentional communication? A qualitative research project explored these issues. Ten school-aged children with severe and multiple disabilities participated in the project. The most common medical diagnosis was cerebral palsy. Analysis of video recordings and other data confirmed that the children's level of communication development was reflected in individual music therapy. Specifically, children at different levels of communication development varied in their abilities to initiate, anticipate, and sustain participation in turn taking, and to maintain attention to and engagement in the interaction. Both turn taking and playing and singing together were found to be important forms of communication during music therapy. Communication problems related to disability included: difficulties in using objects as a focus of joint attention, difficulties in interpreting the interactive environment, being sufficiently motivated to communicate, severely limited means of interaction, attaining and maintaining an appropriate level of arousal, and lack of interest in interaction and the outside environment. Further study of how music therapy can be related to general issues in communication for individuals with severe and multiple disabilities is recommended.


title: A Linear Kalman Filter for MARG Orientation Estimation Using the Algebraic Quaternion Algorithm

abstract: Real-time orientation estimation using low-cost inertial sensors is essential for all the applications where size and power consumption are critical constraints. Such applications include robotics, human motion analysis, and mobile devices. This paper presents a linear Kalman filter for magnetic angular rate and gravity sensors that processes angular rate, acceleration, and magnetic field data to obtain an estimation of the orientation in quaternion representation. Acceleration and magnetic field observations are preprocessed through a novel external algorithm, which computes the quaternion orientation as the composition of two algebraic quaternions. The decoupled nature of the two quaternions makes the roll and pitch components of the orientation immune to magnetic disturbances. The external algorithm reduces the complexity of the filter, making the measurement equations linear. Real-time implementation and the test results of the Kalman filter are presented and compared against a typical quaternion-based extended Kalman filter and a constant gain filter based on the gradient-descent algorithm.


title: Content Adaptive Image Detail Enhancement

abstract: Detail enhancement is required by many problems in the fields of image processing and computational photography. Existing detail enhancement algorithms first decompose a source image into a base layer and a detail layer via an edge-preserving smoothing algorithm, and then amplify the detail layer to produce a detail-enhanced image. In this letter, we propose a new L ::: 0 ::: norm based detail enhancement algorithm which generates the detail-enhanced image directly. The proposed algorithm preserves sharp edges better than an existing L ::: 0 ::: norm based algorithm. Experimental results show that the proposed algorithm reduces color distortion in the detail-enhanced image, especially around sharp edges.


title: Scale-Aware Navigation of a Low-Cost Quadrocopter with a Monocular Camera

abstract: Abstract We present a complete solution for the visual navigation of a small-scale, low-cost quadrocopter in unknown environments. Our approach relies solely on a monocular camera as the main sensor, and therefore does not need external tracking aids such as GPS or visual markers. Costly computations are carried out on an external laptop that communicates over wireless LAN with the quadrocopter. Our approach consists of three components: a monocular SLAM system, an extended Kalman filter for data fusion, and a PID controller. In this paper, we (1) propose a simple, yet effective method to compensate for large delays in the control loop using an accurate model of the quadrocopter’s flight dynamics, and (2) present a novel, closed-form method to estimate the scale of a monocular SLAM system from additional metric sensors. We extensively evaluated our system in terms of pose estimation accuracy, flight accuracy, and flight agility using an external motion capture system. Furthermore, we compared the convergence and accuracy of our scale estimation method for an ultrasound altimeter and an air pressure sensor with filtering-based approaches. The complete system is available as open-source in ROS. This software can be used directly with a low-cost, off-the-shelf Parrot AR.Drone quadrocopter, and hence serves as an ideal basis for follow-up research projects.


title: The Classic: The Iliac Apophysis: An Invaluable Sign in the Management of Scoliosis

abstract: This Classic article is a reprint of the original work by Joseph C. Risser, The Iliac Apophysis: An Invaluable Sign in the Management of Scoliosis. An accompanying biographical sketch of Joseph C. Risser, MD is available at DOI 10.1007/s11999-009-1095-0. The Classic Article is ©1958 by Lippincott Williams & Wilkins and is reprinted with permission from Risser JC. The iliac apophysis: an invaluable sign in the management of scoliosis. Clin Orthop Relat Res. 1958;11:111–119.


title: Islamic vs. Conventional Banking: Business Model, Efficiency and Stability

abstract: This paper discusses Islamic banking products and interprets them in the context of financial intermediation theory. Anecdotal evidence shows that many of the conventional products can be redrafted as Sharia-compliant products, so that the differences are smaller than expected. Comparing conventional and Islamic banks and controlling for other bank and country characteristics, the authors find few significant differences in business orientation, efficiency, asset quality, or stability. While Islamic banks seem more cost-effective than conventional banks in a broad cross-country sample, this finding reverses in a sample of countries with both Islamic and conventional banks. However, conventional banks that operate in countries with a higher market share of Islamic banks are more cost-effective but less stable. There is also consistent evidence of higher capitalization of Islamic banks and this capital cushion plus higher liquidity reserves explains the relatively better performance of Islamic banks during the recent crisis.


title: Automatic Content-Aware Projection for 360° Videos

abstract: To watch 360° videos on normal 2D displays, we need to project the selected part of the 360° image onto the 2D display plane. In this paper, we propose a fully-automated framework for generating content-aware 2D normal-view perspective videos from 360° videos. Especially, we focus on the projection step preserving important image contents and reducing image distortion. Basically, our projection method is based on Pannini projection model. At first, the salient contents such as linear structures and salient regions in the image are preserved by optimizing the single Panini projection model. Then, the multiple Panini projection models at salient regions are interpolated to suppress image distortion globally. Finally, the temporal consistency for image projection is enforced for producing temporally stable normal-view videos. Our proposed projection method does not require any user-interaction and is much faster than previous content-preserving methods. It can be applied to not only images but also videos taking the temporal consistency of projection into account. Experiments on various 360° videos show the superiority of the proposed projection method quantitatively and qualitatively.


title: A VHDL Forth Core for FPGAs

abstract: Abstract The Forth programming language is typically implemented to run on some particular microprocessor. Several Forth engines have been designed that execute Forth instructions directly, typically in a single clock cycle. With the advent of high density FPGAs it has become feasible to implement a high-performance Forth core in an FPGA. This paper describes the design of a Forth core using VHDL that has been implemented on a Xilinx Spartan II FPGA. Examples are presented of high-level Forth programs that are compiled to VHDL code that implements a ROM embedded in the FPGA. The use of a Forth core in an FPGA allows for rapid prototyping of digital systems. Experiments show that an identical Forth program for the Sieve of Eratosthenes executes nearly 30 times faster on the FPGA Forth core than on a 68HC12 microcontroller at the same clock speed. This same program executes over six times faster on the FPGA Forth core than an equivalent compiled C program run on the same 68HC12. The Forth core is available as an EDIF file at www.tigs.com/fc16 , which can be included in a VHDL project and uses approximately 30% of a Spartan II FPGA.


title: Knowledge sharing in open source software communities: motivations and management

abstract: Purpose – This paper aims to add to the understanding of knowledge sharing in online communities through an investigation of the relationship between individual participant’s motivations and management in open source software (OSS) communities. Drawing on a review of literature concerning knowledge sharing in organisations, the factors that motivate participants to share their knowledge in OSS communities, and the management of such communities, it is hypothesised that the quality of management influences the extent to which the motivations of members actually result in knowledge sharing. Design/methodology/approach – To test the hypothesis, quantitative data were collected through an online questionnaire survey of OSS web developers with the aim of gathering respondents’ opinions concerning knowledge sharing, motivations to share knowledge and satisfaction with the management of OSS projects. Factor analysis, descriptive analysis, correlation analysis and regression analysis were used to explore the surv...


title: Combining Multiple Dynamic Models and Deep Learning Architectures for Tracking the Left Ventricle Endocardium in Ultrasound Data

abstract: We present a new statistical pattern recognition approach for the problem of left ventricle endocardium tracking in ultrasound data. The problem is formulated as a sequential importance resampling algorithm such that the expected segmentation of the current time step is estimated based on the appearance, shape, and motion models that take into account all previous and current images and previous segmentation contours produced by the method. The new appearance and shape models decouple the affine and nonrigid segmentations of the left ventricle to reduce the running time complexity. The proposed motion model combines the systole and diastole motion patterns and an observation distribution built by a deep neural network. The functionality of our approach is evaluated using a dataset of diseased cases containing 16 sequences and another dataset of normal cases comprised of four sequences, where both sets present long axis views of the left ventricle. Using a training set comprised of diseased and healthy cases, we show that our approach produces more accurate results than current state-of-the-art endocardium tracking methods in two test sequences from healthy subjects. Using three test sequences containing different types of cardiopathies, we show that our method correlates well with interuser statistics produced by four cardiologists.


title: A re v iew of process fault detection and diagnosis Part III : Process history based methods

abstract: In this final part, we discuss fault diagnosis methods that are based on historic process knowledge. We also compare and evaluate the various methodologies reviewed in this series in terms of the set of desirable characteristics we proposed in Part I. This comparative study reveals the relative strengths and weaknesses of the different approaches. One realizes that no single method has all the desirable features one would like a diagnostic system to possess. It is our view that some of these methods can complement one another resulting in better diagnostic systems. Integrating these complementary features is one way to develop hybrid systems that could overcome the limitations of individual solution strategies. The important role of fault diagnosis in the broader context of process operations is also outlined. We also discuss the technical challenges in research and development that need to be addressed for the successful design and implementation of practical intelligent supervisory control systems for the process industries.


title: Machine Learning Aided Static Malware Analysis: A Survey and Tutorial

abstract: Malware analysis and detection techniques have been evolving during the last decade as a reflection to development of different malware techniques to evade network-based and host-based security protections. The fast growth in variety and number of malware species made it very difficult for forensics investigators to provide an on time response. Therefore, Machine Learning (ML) aided malware analysis became a necessity to automate different aspects of static and dynamic malware investigation. We believe that machine learning aided static analysis can be used as a methodological approach in technical Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware analysis that has been thoroughly studied before. In this paper, we address this research gap by conducting an in-depth survey of different machine learning methods for classification of static characteristics of 32-bit malicious Portable Executable (PE32) Windows files and develop taxonomy for better understanding of these techniques. Afterwards, we offer a tutorial on how different machine learning techniques can be utilized in extraction and analysis of a variety of static characteristic of PE binaries and evaluate accuracy and practical generalization of these techniques. Finally, the results of experimental study of all the method using common data was given to demonstrate the accuracy and complexity. This paper may serve as a stepping stone for future researchers in cross-disciplinary field of machine learning aided malware forensics.


title: Multichannel Decoded Local Binary Patterns for Content-Based Image Retrieval

abstract: Local binary pattern (LBP) is widely adopted for efficient image feature description and simplicity. To describe the color images, it is required to combine the LBPs from each channel of the image. The traditional way of binary combination is to simply concatenate the LBPs from each channel, but it increases the dimensionality of the pattern. In order to cope with this problem, this paper proposes a novel method for image description with multichannel decoded LBPs. We introduce adder- and decoder-based two schemas for the combination of the LBPs from more than one channel. Image retrieval experiments are performed to observe the effectiveness of the proposed approaches and compared with the existing ways of multichannel techniques. The experiments are performed over 12 benchmark natural scene and color texture image databases, such as Corel-1k, MIT-VisTex, USPTex, Colored Brodatz, and so on. It is observed that the introduced multichannel adder- and decoder-based LBPs significantly improve the retrieval performance over each database and outperform the other multichannel-based approaches in terms of the average retrieval precision and average retrieval rate.


title: RACED: an adaptive middleware for complex event detection

abstract: While several event notification systems are built around a publish-subscribe communication infrastructure, the latter only supports detection of simple events. Complex events, involving several, related events, cannot be detected. To overcome this limitation, we designed RACED, an adaptive middleware, which extends the content-based publish-subscribe paradigm to provide a complex event detection service for large scale scenarios. In this paper we describe its main aspects: the event definition language; the protocol enabling efficient and distributed detection of complex events through a network of service brokers; the mechanism that enables RACED to dynamically adapt to network traffic. A preliminary evaluation shows the benefits of RACED w.r.t. more traditional publish-subscribe infrastructures.


Body shape by 3-D photonic scanning in Thai and UK adults: comparison of national sizing surveys


title: Random dopant fluctuation in gate-all-around nanowire FET

abstract: The random dopant fluctuation (RDF) induced threshold voltage variation are compared between a junctionless and an inversion mode gate-all-around (GAA) silicon nanowire FET. We found that the RDF induced variation of junctionless GAA nanowire FET is larger and more sensitive than that of the inversion mode GAA nanowire FET, and it is contributed by the higher doping concentration in the nanowire of the junctionless device. The impact of RDF on the Id-Vg of the FETs found in this work also suggest appropriate operating conditions for the FETs in order to reduce the impact of RDF.


title: Efficient Feature Extraction, Encoding, and Classification for Action Recognition

abstract: Local video features provide state-of-the-art performance for action recognition. While the accuracy of action recognition has been continuously improved over the recent years, the low speed of feature extraction and subsequent recognition prevents current methods from scaling up to real-size problems. We address this issue and first develop highly efficient video features using motion information in video compression. We next explore feature encoding by Fisher vectors and demonstrate accurate action recognition using fast linear classifiers. Our method improves the speed of video feature extraction, feature encoding and action classification by two orders of magnitude at the cost of minor reduction in recognition accuracy. We validate our approach and compare it to the state of the art on four recent action recognition datasets.


title: GENIES : a natural-language processing system for the extraction of molecular pathways from journal articles

abstract: Systems that extract structured information from natural language passages have been highly successful in specialized domains. The time is opportune for developing analogous applications for molecular biology and genomics. We present a system, GENIES, that extracts and structures information about cellular pathways from the biological literature in accordance with a knowledge model that we developed earlier. We implemented GENIES by modifying an existing medical natural language processing system, MedLEE, and performed a preliminary evaluation study. Our results demonstrate the value of the underlying techniques for the purpose of acquiring valuable knowledge from biological


title: Non-Foster Impedance Matching of Electrically-Small Antennas

abstract: Electrically-small antennas present high-Q impedances characterized by large reactances and small radiation resistances. For such antennas, the effectiveness of passive matching is severely limited by gain-bandwidth theory, which predicts narrow bandwidths and/or poor gain. With receivers, the inability to resolve this impedance mismatch results in poor signal-to-noise (S/N) ratio, as compared to using a full-size antenna. With transmitters, the consequence is poor power efficiency. However, in many applications full-size antennas are impractical, and a means is required to effectively match their electrically-small counterparts. This paper presents the technique of non-Foster impedance matching, which employs active networks of negative inductors and capacitors to bypass the restrictions of gain-bandwidth theory. We first review the origins and development of non-Foster impedance matching, and then present experimental results for the non-Foster impedance matching of electrically-small dipoles and monopoles. For receivers, our best measurements on the antenna range demonstrate up to 20 dB improvement in S/N over 20-120 MHz; for transmitters, we show a power efficiency improvement which exceeds a factor of two over an 5% bandwidth about 20 MHz with an average signal power of 1 W to the radiation resistance.


Build-to-order supply chain management : a literature review and framework for development


title: Top-down induction of decision trees classifiers - a survey

abstract: Decision trees are considered to be one of the most popular approaches for representing classifiers. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining considered the issue of growing a decision tree from available data. This paper presents an updated survey of current methods for constructing decision tree classifiers in a top-down manner. The paper suggests a unified algorithmic framework for presenting these algorithms and describes the various splitting criteria and pruning methodologies.


title: Holographic Whisper: Rendering Audible Sound Spots in Three-dimensional Space by Focusing Ultrasonic Waves

abstract: We propose a novel method of spatial audio rendering using ultrasound. An ultrasonic phased array generates one or more focal points in air, and they act as point sources of audible sound when the ultrasound waves are modulated. Our sound-point loudspeaker has two major advantages over conventional ultrasound-based sound-beam (superdirectional) loudspeakers. The higher audience selectivity means that our sound-point loudspeaker can deliver sound to the ears of the target person, whereas a sound-beam loudspeaker delivers sound to not only the target person but also other persons standing in the same direction. The other advantage is lower exposure to ultrasound; while an audible sound beam travels along an ultrasonic beam in a soundbeam loudspeaker, audible sound can be heard along the direction perpendicular to the ultrasonic beam in our soundpoint loudspeaker. This paper reports the principles of our sound-point loudspeaker, prototype construction, evaluation, and applications.


title: An Accurate Power Control Strategy for Power-Electronics-Interfaced Distributed Generation Units Operating in a Low-Voltage Multibus Microgrid

abstract: In this paper, a power control strategy is proposed for a low-voltage microgrid, where the mainly resistive line impedance, the unequal impedance among distributed generation (DG) units, and the microgrid load locations make the conventional frequency and voltage droop method unpractical. The proposed power control strategy contains a virtual inductor at the interfacing inverter output and an accurate power control and sharing algorithm with consideration of both impedance voltage drop effect and DG local load effect. Specifically, the virtual inductance can effectively prevent the coupling between the real and reactive powers by introducing a predominantly inductive impedance even in a low-voltage network with resistive line impedances. On the other hand, based on the predominantly inductive impedance, the proposed accurate reactive power sharing algorithm functions by estimating the impedance voltage drops and significantly improves the reactive power control and sharing accuracy. Finally, considering the different locations of loads in a multibus microgrid, the reactive power control accuracy is further improved by employing an online estimated reactive power offset to compensate the effects of DG local load power demands. The proposed power control strategy has been tested in simulation and experimentally on a low-voltage microgrid prototype.


Technical Report on Autonomous Mobile Robot navigation


title: A Byzantine Attack Defender in Cognitive Radio Networks: The Conditional Frequency Check

abstract: Security concerns are raised for collaborative spectrum sensing due to its vulnerabilities to the potential attacks from malicious secondary users. Most existing malicious user detection methods are reputation-based, which become incapable when the malicious users dominate the network. On the other hand, although Markovian models characterize the spectrum state behavior more precisely, there is a scarcity of malicious user detection methods which fully explore this feature. In this paper, a new malicious user detection method using two proposed conditional frequency check (CFC) statistics is developed under the Markovian model for the spectrum state. With the assistance of one trusted user, the proposed method can achieve high malicious user detection accuracy (≥ 95%) for arbitrary percentage of malicious users that may even be equipped with more advanced sensing devices, and can thus improve the collaborative spectrum sensing performance significantly. Simulation results are provided to verify the theoretical analysis and effectiveness of the proposed method.


title: Probabilistic structural analysis of aerospace components using NESSUS

abstract: Probabilistic structural analysis of a space shuttle min engine (SSME) turbopump blade is con;tucted using the ccmputer code NESSUS (Numerical Ekaluation of Stochastic Structures Under Stress). The goal of the analysis is to derive probabilistic characteristics of blade response given probabilistic descriptions of uncertainties in blade gec~netry, material properties, and temperature and pressure distributions. Probability densities are derived for critical blade responses. Risk assessment and failure life analysis is conducted asmmbg different failure models.


title: ScaffCC: a framework for compilation and analysis of quantum computing programs

abstract: Quantum computing is a promising technology for high-performance computation, but requires mature toolflows that can map large-scale quantum programs onto targeted hardware. In this paper, we present a scalable compiler for large-scale quantum applications, and show the opportunities for reducing compilation and analysis time, as well as output code size. We discuss the similarities and differences between compiling for a quantum computer as opposed to a classical computer, and present a state-of-the-art approach for compilation of classical circuits into quantum circuits. Our work also highlights the importance of high-level quantum compilation for logical circuit translation, quantitative analysis of algorithms, and optimization of circuit lengths.


title: Efficient Privacy-Aware Authentication Scheme for Mobile Cloud Computing Services

abstract: With the exponential increase of the mobile devices and the fast development of cloud computing, a new computing paradigm called mobile cloud computing (MCC) is put forward to solve the limitation of the mobile device's storage, communication, and computation. Through mobile devices, users can enjoy various cloud computing services during their mobility. However, it is difficult to ensure security and protect privacy due to the openness of wireless communication in the new computing paradigm. Recently, Tsai and Lo proposed a privacy-aware authentication (PAA) scheme to solve the identification problem in MCC services and proved that their scheme was able to resist many kinds of existing attacks. Unfortunately, we found that Tsai and Lo's scheme cannot resist the service provider impersonation attack, i.e., an adversary can impersonate the service provider to the user. Also, the adversary can extract the user's real identity during executing the service provider impersonation attack. To address the above problems, in this paper, we construct a new PAA scheme for MCC services by using an identity-based signature scheme. Security analysis shows that the proposed PAA scheme is able to address the serious security problems existing in Tsai and Lo's scheme and can meet security requirements for MCC services. The performance evaluation shows that the proposed PAA scheme has less computation and communication costs compared with Tsai and Lo's PAA scheme.


Casino Gambling in America and Its Economic Impacts


title: Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans

abstract: Abstract Background Manual lesion delineation by an expert is the standard for lesion identification in MRI scans, but it is time-consuming and can introduce subjective bias. Alternative methods often require multi-modal MRI data, user interaction, scans from a control population, and/or arbitrary statistical thresholding. New method We present an approach for automatically identifying stroke lesions in individual T1-weighted MRI scans using naive Bayes classification. Probabilistic tissue segmentation and image algebra were used to create feature maps encoding information about missing and abnormal tissue. Leave-one-case-out training and cross-validation was used to obtain out-of-sample predictions for each of 30 cases with left hemisphere stroke lesions. Results Our method correctly predicted lesion locations for 30/30 un-trained cases. Post-processing with smoothing (8 mm FWHM) and cluster-extent thresholding (100 voxels) was found to improve performance. Comparison with existing method Quantitative evaluations of post-processed out-of-sample predictions on 30 cases revealed high spatial overlap (mean Dice similarity coefficient = 0.66) and volume agreement (mean percent volume difference = 28.91; Pearson's r = 0.97) with manual lesion delineations. Conclusions Our automated approach agrees with manual tracing. It provides an alternative to automated methods that require multi-modal MRI data, additional control scans, or user interaction to achieve optimal performance. Our fully trained classifier has applications in neuroimaging and clinical contexts.


Information Bottleneck Co-clustering


title: A Framework for Task-specific Short Document Expansion

abstract: Collections that contain a large number of short texts are becoming increasingly common (eg., tweets, reviews, etc). Analytical tasks (such as classification, clustering, etc.) involving short texts could be challenging due to the lack of context and owing to their sparseness. An often encountered problem is low accuracy on the task. A standard technique used in the handling of short texts is expanding them before subjecting them to the task. However, existing works on short text expansion suffer from certain limitations: (i) they depend on domain knowledge to expand the text; (ii) they employ task-specific heuristics; and (iii) the expansion procedure is tightly coupled to the task. This makes it hard to adapt a procedure, designed for one task, into another. We present an expansion technique -- TIDE (Task-specIfic short Document Expansion) -- that can be applied on several Machine Learning, NLP and Information Retrieval tasks on short texts (such as short text classification, clustering, entity disambiguation, and the like) without using task specific heuristics and domain-specific knowledge for expansion. At the same time, our technique is capable of learning to expand short texts in a task-specific way. That is, the same technique that is applied to expand a short text in two different tasks is able to learn to produce different expansions depending upon what expansion benefits the task's performance. To speed up the learning process, we also introduce a technique called block learning. Our experiments with classification and clustering tasks show that our framework improves upon several baselines according to the standard evaluation metrics which includes the accuracy and normalized mutual information (NMI).


title: Enhancing quantitative intra-day stock return prediction by integrating both market news and stock prices information

abstract: The interaction between stock price process and market news has been widely analyzed by investors on different markets. Previous works, however, focus either on market news purely as exogenous factors that tend to lead price process or on the analysis of how past stock price process can affect future stock returns. To take a step forward, we quantitatively integrate information from both market news and stock prices in order to improve the accuracy of prediction on stock future price return in an intra-day trading context. In this paper, we present the design and architecture of our approach for market information fusion. By means of multiple kernel learning, the hidden information behind the two sources is effectively extracted, and more importantly, seamlessly integrated rather than simply combined by a single kernel approach. Experiments on comprehensive comparisons between our approach and three baseline methods (which use only one type of information, or naively combine the two sources) have been conducted on the intra-day tick-by-tick data of the Hong Kong Stock Exchange and market news archives of the same period. It has been shown that for both cross-validation and independent testing, our approach is able to achieve the best results.


title: Using Prefix-Trees for Efficiently Computing Set Joins

abstract: Joins on set-valued attributes (set joins) have numerous database applications. In this paper we propose PRETTI (PREfix Tree based seT joIn) – a suite of set join algorithms for containment, overlap and equality join predicates. Our algorithms use prefix trees and inverted indices. These structures are constructed on-the-fly if they are not already precomputed. This feature makes our algorithms usable for relations without indices and when joining intermediate results during join queries with more than two relations. Another feature of our algorithms is that results are output continuously during their execution and not just at the end. Experiments on real life datasets show that the total execution time of our algorithms is significantly less than that of previous approaches, even when the indices required by our algorithms are not precomputed.


Self-induced chatter vibration of lathe tools


title: Effective Removal of Artifacts from Views Synthesized using Depth Image Based Rendering

abstract: Depth Image Based Rendering (DIBR), as a free- viewpoint synthesis technique, enables interactive selection of the view for watching. However, many rendering methods based on DIBR usually bring contour, crack and disocclusion artifacts. To address these problems, we propose effective methods to remove these artifacts. Firstly, before warping, the reference depth maps and color images are analyzed to find the regions causing contour artifacts. A combination of depth map edge detection and color consistency correction is applied to the analysis. By omitting warping the found regions, the synthesized views contain no contour artifacts and edges of foreground objects are well preserved. Secondly, cracks are filled using surrounding non- crack pixels rather than filtering for local consistency and smoothness. Thirdly, we apply a method based on texture extrapolation with depth information to inpaint the disocclusions. For two well-known sequences, 'Ballet' and Breakdancers', we obtain large Peak Signal to Noise Ratio (PSNR) gains in comparison to state-of-the-art techniques. In addition, the proposed method also obtains good results in Structural Similarity Index Measurement (SSIM) and visual quality.


title: Deep Object-Centric Policies for Autonomous Driving

abstract: While learning visuomotor skills in an end-to-end manner is appealing, deep neural networks are often uninterpretable and fail in surprising ways. For robotics tasks, such as autonomous driving, models that explicitly represent objects may be more robust to new scenes and provide intuitive visualizations. We describe a taxonomy of “object-centric” models which leverage both object instances and end-to-end learning. In the Grand Theft Auto V simulator, we show that object-centric models outperform object-agnostic methods in scenes with other vehicles and pedestrians, even with an imperfect detector. We also demonstrate that our architectures perform well on real-world environments by evaluating on the Berkeley DeepDrive Video dataset, where an object-centric model outperforms object-agnostic models in the low-data regimes.


title: How much is enough? choosing ε for differential privacy

abstract: Differential privacy is a recent notion, and while it is nice conceptually it has been difficult to apply in practice. The parameters of differential privacy have an intuitive theoretical interpretation, but the implications and impacts on the risk of disclosure in practice have not yet been studied, and choosing appropriate values for them is non-trivial. Although the privacy parameter e in differential privacy is used to quantify the privacy risk posed by releasing statistics computed on sensitive data, e is not an absolute measure of privacy but rather a relative measure. In effect, even for the same value of e, the privacy guarantees enforced by differential privacy are different based on the domain of attribute in question and the query supported. We consider the probability of identifying any particular individual as being in the database, and demonstrate the challenge of setting the proper value of e given the goal of protecting individuals in the database with some fixed probability.


Incorporation of immunostaining data in anatomic pathology reports.


title: Using geolocated Twitter data to monitor the prevalence of healthy and unhealthy food references across the US

abstract: Abstract Mining the social media outlet Twitter for geolocated messages provides a rich database of information on people's thoughts and sentiments about myriad topics, like public health. Examining this spatial data has been particularly useful to researchers interested in monitoring and mapping disease outbreaks, like influenza. However, very little has been done to utilize this massive resource to examine other public health issues. This paper uses an advanced data-mining framework with a novel use of social media data retrieval and sentiment analysis to understand how geolocated tweets can be used to explore the prevalence of healthy and unhealthy food across the contiguous United States. Additionally, tweets are associated with spatial data provided by the US Department of Agriculture (USDA) of low-income, low-access census tracts (e.g. food deserts), to examine whether tweets about unhealthy foods are more common in these disadvantaged areas. Results show that these disadvantaged census tracts tend to have both a lower proportion of tweets about healthy foods with a positive sentiment, and a higher proportion of unhealthy tweets in general. These findings substantiate the methods used by the USDA to identify regions that are at risk of having low access to healthy foods.


title: Kafka versus RabbitMQ

abstract: Publish/subscribe is a distributed interaction paradigm well adapted to the deployment of scalable and loosely coupled systems. ::: Apache Kafka and RabbitMQ are two popular open-source and commercially-supported pub/sub systems that have been around for almost a decade and have seen wide adoption. Given the popularity of these two systems and the fact that both are branded as pub/sub systems, two frequently asked questions in the relevant online forums are: how do they compare against each other and which one to use? ::: In this paper, we frame the arguments in a holistic approach by establishing a common comparison framework based on the core functionalities of pub/sub systems. Using this framework, we then venture into a qualitative and quantitative (i.e. empirical) comparison of the common features of the two systems. Additionally, we also highlight the distinct features that each of these systems has. After enumerating a set of use cases that are best suited for RabbitMQ or Kafka, we try to guide the reader through a determination table to choose the best architecture given his/her particular set of requirements.


title: Transfer Learning for Named-Entity Recognition with Neural Networks

abstract: Recent approaches based on artificial neural networks (ANNs) have shown promising results for named-entity recognition (NER). In order to achieve high performances, ANNs need to be trained on a large labeled dataset. However, labels might be difficult to obtain for the dataset on which the user wants to perform NER: label scarcity is particularly pronounced for patient note de-identification, which is an instance of NER. In this work, we analyze to what extent transfer learning may address this issue. In particular, we demonstrate that transferring an ANN model trained on a large labeled dataset to another dataset with a limited number of labels improves upon the state-of-the-art results on two different datasets for patient note de-identification.


title: Medical Datamining with a New Algorithm for Feature Selection and Naive Bayesian Classifier

abstract: A pivot construction for connecting a pair of relatively movable members that include a stepped diameter pin one end of which is fastened to one of the members and the other end of which is received in an end cap member that serves to preload a tapered roller bearing assembly mounted on the other of the members.


title: Drone-based reconstruction for 3D geospatial data processing

abstract: Urban air quality affects health and well-being of more than half of the world's population. Measurement, modeling and real time action on pollution sources can alleviate their impact. A preliminary study for drone-based image acquisition and processing steps required for 3D reconstruction of potential pollution sources is presented. The 3D surface terrain models combined with sensor data are inputs into air pollution models for visualization, understanding and potential mitigation of methane plumes. Scaling these technologies across large areas requires the integration of big geospatial data with modeling, machine learning, and image processing.


title: PETCHA: a programming exercises teaching assistant

abstract: This paper presents a tool called Petcha that acts as an automated Teaching Assistant in computer programming courses. The ultimate objective of Petcha is to increase the number of programming exercises effectively solved by students. Petcha meets this objective by helping both teachers to author programming exercises and students to solve them. It also coordinates a network of heterogeneous systems, integrating automatic program evaluators, learning management systems, learning object repositories and integrated programming environments. This paper presents the concept and the design of Petcha and sets this tool in a service oriented architecture for managing learning processes based on the automatic evaluation of programming exercises. The paper presents also a case study that validates the use of Petcha and of the proposed architecture.


title: Density-Based Data Streams Clustering over Sliding Windows

abstract: Data stream clustering is an important task in data stream mining. In this paper, we propose SDStream, a new method for performing density-based data streams clustering over sliding windows. SDStream adopts CluStream clustering framework. In the online component, the potential core-micro-cluster and outlier micro-cluster structures are introduced to maintain the potential clusters and outliers. They are stored in the form of Exponential Histogram of Cluster Feature (EHCF) in main memory and are maintained by the maintenance of EHCFs. Outdated micro-clusters which need to be deleted are found by the value of t in Temporal Cluster Feature (TCF). In the offline component, the final clusters of arbitrary shape are generated according to all the potential core-micro-clusters maintained online by DBSCAN algorithm. Experimental results show that SDStream which can generate clusters of arbitrary shape has a much higher clustering quality than CluStream which generates spherical clusters.


title: Methodologic Innovation in Creating Clinical Practice Guidelines: Insights From the 2018 Society of Critical Care Medicine Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption Guideline Effort

abstract: OBJECTIVES ::: To describe novel guideline development strategies created and implemented as part of the Society of Critical Care Medicine's 2018 clinical practice guidelines for pain, agitation (sedation), delirium, immobility (rehabilitation/mobility), and sleep (disruption) in critically ill adults. ::: ::: ::: DESIGN ::: We involved critical illness survivors from start to finish, used and expanded upon Grading of Recommendations, Assessment, Development and Evaluation methodology for making recommendations, identified evidence gaps, and developed communication strategies to mitigate challenges. ::: ::: ::: SETTING/SUBJECTS ::: Thirty-two experts from five countries, across five topic-specific sections; four methodologists, two medical librarians, four critical illness survivors, and two Society of Critical Care Medicine support staff. ::: ::: ::: INTERVENTIONS ::: Unique approaches included the following: 1) critical illness survivor involvement to help ensure patient-centered questions and recommendations; 2) qualitative and semiquantitative approaches for developing descriptive statements; 3) operationalizing a three-step approach to generating final recommendations; and 4) systematic identification of evidence gaps. ::: ::: ::: MEASUREMENTS AND MAIN RESULTS ::: Critical illness survivors contributed to prioritizing topics, questions, and outcomes, evidence interpretation, recommendation formulation, and article review to ensure that their values and preferences were considered in the guidelines. Qualitative and semiquantitative approaches supported formulating descriptive statements using comprehensive literature reviews, summaries, and large-group discussion. Experts (including the methodologists and guideline chairs) developed and refined guideline recommendations through monthly topic-specific section conference calls. Recommendations were precirculated to all members, presented to, and vetted by, most members at a live meeting. Final electronic voting provided links to all forest plots, evidence summaries, and "evidence to decision" frameworks. Written comments during voting captured dissenting views and were integrated into evidence to decision frameworks and the guideline article. Evidence gaps, reflecting clinical uncertainty in the literature, were identified during the evidence to decision process, live meeting, and voting and formally incorporated into all written recommendation rationales. Frequent scheduled "check-ins" mitigated communication gaps. ::: ::: ::: CONCLUSIONS ::: Our multifaceted, interdisciplinary approach and novel methodologic strategies can help inform the development of future critical care clinical practice guidelines.


title: LSTM-Based Zero-Velocity Detection for Robust Inertial Navigation

abstract: We present a method to improve the accuracy of a zero-velocity-aided inertial navigation system (INS) by replacing the standard zero-velocity detector with a long short-term memory (LSTM) neural network. While existing threshold-based zero-velocity detectors are not robust to varying motion types, our learned model accurately detects stationary periods of the inertial measurement unit (IMU) despite changes in the motion of the user. Upon detection, zero-velocity pseudo-measurements are fused with a dead reckoning motion model in an extended Kalman filter (EKF). We demonstrate that our LSTM-based zero-velocity detector, used within a zero-velocity-aided INS, improves zero-velocity detection during human localization tasks. Consequently, localization accuracy is also improved. ::: Our system is evaluated on more than 7.5 km of indoor pedestrian locomotion data, acquired from five different subjects. We show that 3D positioning error is reduced by over 34% compared to existing fixed-threshold zero-velocity detectors for walking, running, and stair climbing motions. Additionally, we demonstrate how our learned zero-velocity detector operates effectively during crawling and ladder climbing. Our system is calibration-free (no careful threshold-tuning is required) and operates consistently with differing users, IMU placements, and shoe types, while being compatible with any generic zero-velocity-aided INS.


title: A world's first product of three-dimensional vertical NAND Flash memory and beyond

abstract: In this work, we present a 3D 128Gb 2bit/cell vertical-AND (V-NAND) Flash product. The use of barrier-engineered materials and gate all-around structure in the 3D V-NAND cell exhibits advantages over 1xnm planar NAND, such as small Vth shift due to small cell coupling and narrow natural Vth distribution. Also, a negative counter-pulse scheme realizes a tightly programmed cell distribution. In order to reduce the effect of a large WL coupling, a glitch-canceling discharge scheme and a pre-offset control scheme is implemented. Furthermore, an external high-voltage supply scheme along with the proper protection scheme for a high-voltage failure is used to achieve low power consumption. The chip accomplishes 50MB/s write throughput with 3K endurance for typical embedded applications. Also, extended endurance of 35K is achieved with 36MB/s of write throughput for data center and enterprise SSD applications. And 2nd generation of 3D V-NAND opens up a whole new world at SSD endurance, density and battery life for portables.


title: Transfer of Learning After Updating Training Mediated by the Striatum

abstract: Process-specific training can improve performance on untrained tasks, but the magnitude of gain is variable and often there is no transfer at all. We demonstrate transfer to a 3-back test of working memory after 5 weeks of training in updating. The transfer effect was based on a joint training-related activity increase for the criterion (letter memory) and transfer tasks in a striatal region that also was recruited pretraining. No transfer was observed to a task that did not engage updating and striatal regions, and age-related striatal changes imposed constraints on transfer. These findings indicate that transfer can occur if the criterion and transfer tasks engage specific overlapping processing components and brain regions.


title: Detecting Jihadist Messages on Twitter

abstract: Jihadist groups such as ISIS are spreading online propaganda using various forms of social media such as Twitter and YouTube. One of the most common approaches to stop these groups is to suspend accounts that spread propaganda when they are discovered. This approach requires that human analysts manually read and analyze an enormous amount of information on social media. In this work we make a first attempt to automatically detect messages released by jihadist groups on Twitter. We use a machine learning approach that classifies a tweet as containing material that is supporting jihadists groups or not. Even tough our results are preliminary and more tests needs to be carried out we believe that results indicate that an automated approach to aid analysts in their work with detecting radical content on social media is a promising way forward. It should be noted that an automatic approach to detect radical content should only be used as a support tool for human analysts in their work.


title: Mastering Sketching: Adversarial Augmentation for Structured Prediction

abstract: We present an integral framework for training sketch simplification networks that convert challenging rough sketches into clean line drawings. Our approach augments a simplification network with a discriminator network, training both networks jointly so that the discriminator network discerns whether a line drawing is a real training data or the output of the simplification network, which in turn tries to fool it. This approach has two major advantages. First, because the discriminator network learns the structure in line drawings, it encourages the output sketches of the simplification network to be more similar in appearance to the training sketches. Second, we can also train the simplification network with additional unsupervised data, using the discriminator network as a substitute teacher. Thus, by adding only rough sketches without simplified line drawings, or only line drawings without the original rough sketches, we can improve the quality of the sketch simplification. We show how our framework can be used to train models that significantly outperform the state of the art in the sketch simplification task, despite using the same architecture for inference. We additionally present an approach to optimize for a single image, which improves accuracy at the cost of additional computation time. Finally, we show that, using the same framework, it is possible to train the network to perform the inverse problem, i.e., convert simple line sketches into pencil drawings, which is not possible using the standard mean squared error loss. We validate our framework with two user tests, where our approach is preferred to the state of the art in sketch simplification 92.3% of the time and obtains 1.2 more points on a scale of 1 to 5.


title: What’s the Use of Culture? Cinematographers and the Culture Film in Japan in the Early 1940s

abstract: In the early 1940s Japan, cinematographers and critics feverishly discussed the notions of immediacy and authorship in relation to documentary practices. The status of cinematographers as the authors of the images that they shot was particularly questioned in those conversations due to the mechanical nature of the motion picture camera. This article mainly focuses on the discussions in the journal Eiga Gijutsu (Film Technology) in 1941–1942 over the notion of culture, and examines how cinematographers imagined their new roles in documentary practices in the cinema.


title: Fully-functional succinct trees

abstract: We propose new succinct representations of ordinal trees, which have been studied extensively. It is known that any n-node static tree can be represented in 2n + o(n) bits and a large number of operations on the tree can be supported in constant time under the word-RAM model. However existing data structures are not satisfactory in both theory and practice because (1) the lower-order term is Ω(n log log n/ log n), which cannot be neglected in practice, (2) the hidden constant is also large, (3) the data structures are complicated and difficult to implement, and (4) the techniques do not extend to dynamic trees supporting insertions and deletions of nodes. We propose a simple and flexible data structure, called the range min-max tree, that reduces the large number of relevant tree operations considered in the literature to a few primitives, which are carried out in constant time on sufficiently small trees. The result is then extended to trees of arbitrary size, achieving 2n + O(n/polylog(n)) bits of space. The redundancy is significantly lower than in any previous proposal, and the data structure is easily implemented. Furthermore, using the same framework, we derive the first fully-functional dynamic succinct trees.


title: Towards detecting target link flooding attack

abstract: A new class of target link flooding attacks (LFA) can cut off the Internet connections of a target area without being detected because they employ legitimate flows to congest selected links. Although new mechanisms for defending against LFA have been proposed, the deployment issues limit their usages since they require modifying routers. In this paper, we propose LinkScope, a novel system that employs both the end-to-end and the hop-by-hop network measurement techniques to capture abnormal path performance degradation for detecting LFA and then correlate the performance data and traceroute data to infer the target links or areas. Although the idea is simple, we tackle a number of challenging issues, such as conducting large-scale Internet measurement through noncooperative measurement, assessing the performance on asymmetric Internet paths, and detecting LFA. We have implemented LinkScope with 7174 lines of C codes and the extensive evaluation in a testbed and the Internet show that LinkScope can quickly detect LFA with high accuracy and low false positive rate.


title: Combining Recurrent and Convolutional Neural Networks for Relation Classification

abstract: This paper investigates two different neural architectures for the task of relation classification: convolutional neural networks and recurrent neural networks. For both models, we demonstrate the effect of different architectural choices. We present a new context representation for convolutional neural networks for relation classification (extended middle context). Furthermore, we propose connectionist bi-directional recurrent neural networks and introduce ranking loss for their optimization. Finally, we show that combining convolutional and recurrent neural networks using a simple voting scheme is accurate enough to improve results. Our neural models achieve state-of-the-art results on the SemEval 2010 relation classification task.


title: Error correction in a Chinese OCR test collection

abstract: This article proposes a technique for correcting Chinese OCR errors to support retrieval of scanned documents. The technique uses a completely automatic technique (no manually constructed lexicons or confusion resources) to identify both keywords and confusable terms. Improved retrieval effectiveness on a single term query experiment is demonstrated.


title: Variational Bayesian Reinforcement Learning with Regret Bounds

abstract: We consider the exploration-exploitation trade-off in reinforcement learning and we show that an agent imbued with a risk-seeking utility function is able to explore efficiently, as measured by regret. The parameter that controls how risk-seeking the agent is can be optimized exactly, or annealed according to a schedule. We call the resulting algorithm K-learning and show that the corresponding K-values are optimistic for the expected Q-values at each state-action pair. The K-values induce a natural Boltzmann exploration policy for which the `temperature' parameter is equal to the risk-seeking parameter. This policy achieves an expected regret bound of $\tilde O(L^{3/2} \sqrt{S A T})$, where $L$ is the time horizon, $S$ is the number of states, $A$ is the number of actions, and $T$ is the total number of elapsed time-steps. This bound is only a factor of $L$ larger than the established lower bound. K-learning can be interpreted as mirror descent in the policy space, and it is similar to other well-known methods in the literature, including Q-learning, soft-Q-learning, and maximum entropy policy gradient, and is closely related to optimism and count based exploration methods. K-learning is simple to implement, as it only requires adding a bonus to the reward at each state-action and then solving a Bellman equation. We conclude with a numerical example demonstrating that K-learning is competitive with other state-of-the-art algorithms in practice.


title: Apoptosis in cancer

abstract: In the last decade, basic cancer research has produced remarkable advances in our understanding of cancer biology and cancer genetics. Among the most important of these advances is the realization that apoptosis and the genes that control it have a profound effect on the malignant phenotype. For example, it is now clear that some oncogenic mutations disrupt apoptosis, leading to tumor initiation, progression or metastasis. Conversely, compelling evidence indicates that other oncogenic changes promote apoptosis, thereby producing selective pressure to override apoptosis during multistage carcinogenesis. Finally, it is now well documented that most cytotoxic anticancer agents induce apoptosis, raising the intriguing possibility that defects in apoptotic programs contribute to treatment failure. Because the same mutations that suppress apoptosis during tumor development also reduce treatment sensitivity, apoptosis provides a conceptual framework to link cancer genetics with cancer therapy. An intense research effort is uncovering the underlying mechanisms of apoptosis such that, in the next decade, one envisions that this information will produce new strategies to exploit apoptosis for therapeutic benefit.


Specific Architecture & its Speech based Implementation for Smart Home Applications


title: Feminizing Robots: User Responses to Gender Cues on Robot Body and Screen

abstract: Robots are increasingly being deployed in a number of domains typically associated with female workers, e.g., caregiving. Although past robot morphology has favored a machine-like appearance that is often perceived as masculine, monitor-based robots provide designers with enhanced opportunities to include interface cues that convey femininity. However, it is unclear whether screen-based interface cues can elicit perceptions of femininity and what effects, if any, they would have on user evaluations of a robot. To address this, we conducted an experiment examining the effect of gender cue (male vs. female) and cue location (robot body vs. robot screen) upon user evaluations. Female interface cues, especially when conveyed via the screen, elicited greater perceptions of robot femininity than male interface cues. Implications for the design of social robotics are discussed.


title: The explicit dynamic model and inertial parameters of the PUMA 560 arm

abstract: To provide COSMOS, a dynamic model based manipulator control system, with an improved dynamic model, a PUMA 560 arm was disassembled; the inertial properties of the individual links were measured; and an explicit model incorporating all of the non-zero measured parameters was derived. The explicit model of the PUMA arm has been obtained with a derivation procedure comprised of several heuristic rules for simplification. A simplified model, abbreviated from the full explicit model with a 1% significance criterion, can be evaluated with 805 calculations, one fifth the number required by the recursive Newton-Euler method. The procedure used to derive the model is laid out; the measured inertial parameters are presented, and the model is included in an appendix.


title: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring

abstract: Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.


title: Evaluation of haploidization efficiency in winter squash (Cucurbita maxima Duch.) and pumpkin (Cucurbita moschata Duch.) through anther culture

abstract: The production of large numbers of haploids is the crucial point of the dihaploidisation process. Although in vitro haploid plants were successfully produced by irradiated pollen technique in winter squash (Cucurbita maxima Duch.) and pumpkin (Cucurbita moschata Duch.), the frequency is still insufficient for using in a large-scale breeding programme. Thus, the present study was conducted to determine the efficacy of the anther culture technique on the production of in vitro haploids in the aforementioned species for which there have been no successful reports concerning by androgenesis. The anthers at uninucleate microspore stage were collected at different florescence times and then cultured on a solid MS medium supplemented by different combinations of 2,4-D (2,4-dichlorophenoxyacetic acid), BAP (6-benzylaminopurine), KN (kinetin) with the constant addition of NAA (naphthalene acetic acid) to induce callogenesis, embryogenesis and plantlet initiation. The combination of PGR, genotype and anther collection time played an important role in the androgenic response. The highest response was obtained from 57SI21 and G9 lines with the combination of 2.0 or 4.0 mg/l BAP + 0.05 mg/l NAA (E6 medium) at the first anther collection time. Plantlets were regenerated and rooted on MS medium supplemented by 0.01 mg/l IAA. In total, 74 plants were recovered and propagated with micro-cuttings. The ploidy analyses revealed that 35 plants (47.3 %) were haploid (n = 20), and the others (52.7 %) were diploid (2n = 40).


title: SEMAFOR 1 . 0 : A Probabilistic Frame-Semantic Parser

abstract: An elaboration on (Das et al., 2010), this report formalizes frame-semantic parsing as a structure prediction problem and describes an implemented parser that transforms an English sentence into a frame-semantic representation. SEMAFOR 1.0 finds words that evoke FrameNet frames, selects frames for them, and locates the arguments for each frame. The system uses two feature-based, discriminative probabilistic (log-linear) models, one with latent variables to permit disambiguation of new predicate words. The parser is demonstrated to significantly outperform previously published results and is released for public use.


title: Predicting grammatical classes from phonological cues: An empirical test

abstract: This paper investigates to what extent the grammatical class(es) of a word can be predicted on the basis of phonological and prosodic information only. We report on several experiments with an artificial learning system which has to assign English word forms to their appropriate grammatical class, using various types of phonological and prosodic information. First of all, we examine several phonological cues which were claimed by Kelly (1996) to be particularly good for distinguishing English nouns from verbs. Our results indicate that these cues are indeed partially predictive for the problem at hand and reveal that a combination of cues yields significantly better results than those obtained for each cue individually. We then show experimentally that ‘raw’ segmental information, augmented with word stress, allows the learning system to improve considerably upon those results . Secondly, we investigate several generalizations of the approach: basic segmental information also proves to be more predictive when the task is extended to encompass all open class words in English, and these findings can be replicated for a different (though related) language such as Dutch.


title: Real-time pose estimation of rigid objects using RGB-D imagery

abstract: Using full scale (480×640) RGB-D imagery, we here present an approach for tracking 6d pose of rigid objects at runtime frequency of up to 15fps. This approach is useful for robotic perception systems to efficiently track object's pose during camera movements in tabletop manipulation tasks with high detection rate and real-time performance. Specifically, appearance-based feature correspondences are used for initial object detection. We make use of Oriented Brief (ORB) feature key-points to perform fast segmentation of object candidates in the 3d point cloud. The task of 6d pose estimation is handled in the Cartesian space by finding an interest window around the segmented object and 3d geometry operations. The interest window is later used for feature extraction in the subsequent camera frames to speed up the object detection process. This also allows for an efficient pose tracking of scenes where there are significantly large false matches between feature correspondences due to scene clutter. Our approach was tested using an RGB-D dataset comprising of scenes from video sequences of tabletops with multiple objects in household environments. Experiments show that our approach is capable of performing 3d segmentation followed by 6d pose tracking at higher frame rates compared to existing techniques.


title: Accurate Continuous Sweeping Framework in Indoor Spaces With Backpack Sensor System for Applications to 3-D Mapping

abstract: In indoor environments, there exists a few distinctive indoor spaces' features (ISFs). However, up to our knowledge, there is no algorithm that fully utilizes ISF for accurate 3-D SLAM. In this letter, we suggest a sensor system that efficiently captures ISF and propose an algorithm framework that accurately estimates sensor's 3-D poses by utilizing ISF. Experiments conducted in six representative indoor spaces show that the accuracy of the proposed method is better than the previous method. Furthermore, the proposed method shows robust performances in a sense that a set of adjusted parameters of the related algorithms does not need to be recalibrated as target environment changes. We also demonstrate that the proposed method not only generates 3-D depth maps but also builds a dense 3-D RGB-D map.


title: PEV charging in the future smart grid

abstract: Plug-in electric vehicles (PEVs) represent economic and eco-friendly means of transportation. High PEV penetration levels are expected during 2020 – 2050. Consequently, a considerable portion of the total power consumption in the future smart grid will be due to the PEV charging demands. Therefore, PEV charging has drawn significant attention from both academia and industry. In this paper, we shed some light on key research issues related to PEV charging in the future smart grid. Specifically, we discuss the challenges related to charging facility planning and PEV charging coordination for both mobile and parked vehicles. In addition, we summarize our main research results related to the aforementioned topics. Finally, future research directions are provided.


title: Pervasive, Dynamic Authentication of Physical Items

abstract: The use of silicon PUF circuits


title: TESC: An approach to TExt classification using Semi-supervised Clustering

abstract: Abstract This paper proposes an approach called TESC (TExt classification using Semi-supervised Clustering) to improve text classification. The basic idea is to regard one category of texts from one or more than one components. Thus, we use clustering to identify the components in text collection. In clustering process, TESC makes use of labeled texts to capture silhouettes of text clusters and unlabeled texts to adapt its centroids. The category of each text cluster is labeled by the label of texts in it. When a new unlabeled text is incoming, we measure its similarity with the text clusters and give its label with that of the nearest text clusters. Experiments on Reuters-21578 and TanCorp V1.0 text collection demonstrate that, in text classification, TESC outperforms Support Vector Machines (SVMs) and back propagation neural network (BPNN), and produces comparable performance to naive Bayes with EM (Expectation Maximization) however with lower computation complexity.


title: A Reconfigurable Streaming Deep Convolutional Neural Network Accelerator for Internet of Things

abstract: Convolutional neural network (CNN) offers significant accuracy in image detection. To implement image detection using CNN in the internet of things (IoT) devices, a streaming hardware accelerator is proposed. The proposed accelerator optimizes the energy efficiency by avoiding unnecessary data movement. With unique filter decomposition technique, the accelerator can support arbitrary convolution window size. In addition, max pooling function can be computed in parallel with convolution by using separate pooling unit, thus achieving throughput improvement. A prototype accelerator was implemented in TSMC 65nm technology with a core size of 5mm2. The accelerator can support major CNNs and achieve 152GOPS peak throughput and 434GOPS/W energy efficiency at 350mW, making it a promising hardware accelerator for intelligent IoT devices.


title: Retargeting Technical Documentation to Augmented Reality

abstract: We present a system which automatically transfers printed technical documentation, such as handbooks, to three-dimensional Augmented Reality. Our system identifies the most frequent forms of instructions found in printed documentation, such as image sequences, explosion diagrams, textual annotations and arrows indicating motion. The analysis of the printed documentation works automatically, with minimal user input. The system only requires the documentation itself and a CAD model or 3D scan of the object described in the documentation. The output is a fully interactive Augmented Reality application, presenting the information from the printed documentation in 3D, registered to the real object.


title: Analyzing Stock Market Movements Using News, Tweets, Stock Prices and Transactions Volume Data for APPLE (AAPL), GOOGLE (GOOG) and SONY (SNE)

abstract: Goal: Today's financial markets are of complex behavior which is the result of decisions made by many traders. Goal of this research is to calculate the relationship between financial markets stock prices, volumes, counts in financial news and tweets. Method: Collect the data sets for the three companies - Apple, Google and Sony 1. Collect tweets using Twitter API written in Python and extract tweet counts only related to stocks for the above companies. 2. Collect News data counts using News API, written in Python, only related to stocks for the above companies. 3. Collect stocks data including Volume, Close Price, etc. for the above companies. Findings: We find a positive correlation between the daily number of mentions of the above companies in the Tweets, News, daily stocks close prices and daily transactions volume of a company's stock after the tweets and news are released. Our results provide measurable support for the suggestion that activities in financial markets, news and tweets are fundamentally interlinked.


title: Implementing Project Management Category Process Areas of CMMI Version 1.3 Using Scrum Practices, and Assets

abstract: Software development organizations that rely on Capability Maturity Model Integration (CMMI) to assess and improve their processes have realized that agile approaches can provide improvements as well. CMMI and agile methods can work well together and exploit synergies that have the potential to improve dramatically business performance. The major question is: How to realize the integration of these two seemingly different approaches? In an earlier work, we have conducted a field study within six companies. These companies worked with agile methods for years and the Egyptian Software Engineering Competence Center (SECC), which is the regional CMMI appraisal center, assessed them. This study was mainly conducted to enhance the empirical understanding in this research field. Additionally, it showed that companies usually don’t use agile in a good way that helps in covering the CMMI specific practices. In this paper, we present a new approach for mapping between CMMI and Scrum method. This mapping has been analyzed, enhanced, and then applied to the same companies. Putting in considerations that other previous efforts have worked in the same context but for an older version of CMMI, our research is using the latest CMMI version, which is 1.3. The research shows that our mapping approach has resulted in 37% satisfaction and achieved 17% partial satisfaction for CMMI specific practices. This resembles 19.4% enhancement in the satisfaction, and 6.2% improvement in the partial satisfaction against the previous related research effort that was already not targeting the latest CMMI version.


title: Micromachined microsensors for manufacturing

abstract: The rapid development of microelectromechanical systems (MEMS) technologies in recent years has provided a high degree of spatial miniaturization and integration of electromechanical components, which enable integrated sensing and control in manufacturing. This article provides an overview of the state of the art of micromachined microsensors for the measurement of mechanical signals in the manufacturing industry for automobiles, manufacturing, medical equipment, environment, robotics, food, and other consumer products. Measurements of pressure, acceleration, and acoustic emission (AE) are the three physical quantities that are most encountered in industrial and manufacturing applications. Discussed are the major design parameters of such microsensors, such as dynamic range, sensitivity, resolution, and accuracy and provide a real-world application. Also discussed are the issues related to the spatial integration of signal processing, power supply, and wireless communication with the sensing elements, which are of direct relevance to the overall performance of the microsensors.


title: Privacy free indoor action detection system using top-view depth camera based on key-poses

abstract: In this paper, we propose an indoor action detection system which can automatically keep the log of users' activities of daily life since each activity generally consists of a number of actions. The hardware setting here adopts top-view depth cameras which makes our system less privacy sensitive and less annoying to the users, too. We regard the series of images of an action as a set of key-poses in images of the interested user which are arranged in a certain temporal order and use the latent SVM framework to jointly learn the appearance of the key-poses and the temporal locations of the key-poses. In this work, two kinds of features are proposed. The first is the histogram of depth difference value which can encode the shape of the human poses. The second is the location-signified feature which can capture the spatial relations among the person, floor, and other static objects. Moreover, we find that some incorrect detection results of certain type of action are usually associated with another certain type of action. Therefore, we design an algorithm that tries to automatically discover the action pairs which are the most difficult to be differentiable, and suppress the incorrect detection outcomes. To validate our system, experiments have been conducted, and the experimental results have shown effectiveness and robustness of our proposed method.


title: An efficient cooperative hybrid routing protocol for hybrid wireless mesh networks

abstract: Hybrid wireless mesh networks are the most generic types of wireless mesh networks. Unlike static mesh routers, which have multiple radio interfaces and almost no energy constraint, mobile mesh clients are usually equipped with a single radio interface and have energy limitations. A cooperative hybrid routing protocol (CHRP) combining advantages of proactive and reactive routing protocols by letting them work cooperatively is proposed in this paper, which can adapt to features of both routers and clients. In CHRP, in order to make a proper route selection, channel condition, interference and constrained energy of clients are considered in the node-aware routing metric. Besides, a cross-layer approach is used in CHRP. Both gateway and client oriented data flows are considered comprehensively. The simulation results using ns-3 show the advantage of the proposed CHRP in terms of average packet loss rate, average latency, average network throughput, average energy consumption of clients and the minimum residual energy of clients.


title: A dataset for developing and benchmarking active vision

abstract: We present a new public dataset with a focus on simulating robotic vision tasks in everyday indoor environments using real imagery. The dataset includes 20,000+ RGB-D images and 50,000+ 2D bounding boxes of object instances densely captured in 9 unique scenes. We train a fast object category detector for instance detection on our data. Using the dataset we show that, although increasingly accurate and fast, the state of the art for object detection is still severely impacted by object scale, occlusion, and viewing direction all of which matter for robotics applications. We next validate the dataset for simulating active vision, and use the dataset to develop and evaluate a deep-network-based system for next best move prediction for object classification using reinforcement learning. Our dataset is available for download at cs.unc.edu/∼ammirato/active_vision_dataset_website/.


title: The impact of emotional intelligence on work engagement of registered nurses: the mediating role of organisational justice

abstract: Aims and objectives ::: To explore the impact of emotional intelligence and organisational justice on work engagement in Chinese nurses and to examine the mediating role of organisational justice to provide implications for promoting clinical nurses’ work engagement. ::: ::: Background ::: The importance of work engagement on nurses’ well-being and quality of care has been well documented. Work engagement is significantly predicted by job resources. However, little research has concentrated simultaneously on the influence of both personal and organisational resources on nurses’ work engagement. ::: ::: Design ::: A descriptive, cross-sectional design was employed. ::: ::: Methods ::: A total of 511 nurses from four public hospitals were enrolled by multistage sampling. Data collection was undertaken using the Wong and Law Emotional Intelligence Scale, the Organizational Justice questionnaire and the Utrecht Work Engagement Scale-9. We analysed the data using structural equation modelling. ::: ::: Results ::: Emotional intelligence and organisational justice were significant predictors and they accounted for 44% of the variance in nurses’ work engagement. Bootstrap estimation confirmed an indirect effect of emotional intelligence on work engagement via organisational justice. ::: ::: Conclusions ::: Emotional intelligence and organisational justice positively predict work engagement and organisational justice partially mediates the relationship between emotional intelligence and work engagement. ::: ::: Relevance to clinical practice ::: Our study supports the idea that enhancing organisational justice can increase the impact of emotional intelligence. Managers should take into account the importance of emotional intelligence and perceptions of organisational justice in human resources management and apply targeted interventions to foster work engagement.


title: Multiple Scale Faster-RCNN Approach to Driver’s Cell-Phone Usage and Hands on Steering Wheel Detection

abstract: In this paper, we present an advanced deep learning based approach to automatically determine whether a driver is using a cell-phone as well as detect if his/her hands are on the steering wheel (i.e. counting the number of hands on the wheel). To robustly detect small objects such as hands, we propose Multiple Scale Faster-RCNN (MSFRCNN) approach that uses a standard Region Proposal Network (RPN) generation and incorporates feature maps from shallower convolution feature maps, i.e. conv3 and conv4, for ROI pooling. In our driver distraction detection framework, we first make use of the proposed MS-FRCNN to detect individual objects, namely, a hand, a cell-phone, and a steering wheel. Then, the geometric information is extracted to determine if a cell-phone is being used or how many hands are on the wheel. The proposed approach is demonstrated and evaluated on the Vision for Intelligent Vehicles and Applications (VIVA) Challenge database and the challenging Strategic Highway Research Program (SHRP-2) face view videos that was acquired to monitor drivers under naturalistic driving conditions. The experimental results show that our method archives better performance than Faster R-CNN on both hands on wheel detection and cell-phone usage detection while remaining at similar testing cost. Compare to the state-of-the-art cell-phone usage detection, our approach obtains higher accuracy, is less time consuming and is independent to landmarking. The groundtruth database will be publicly available.