title: Understanding RFID Technology within a Business Intelligence Framework

abstract: Radio Frequency Identification (RFID) technology provides new and exciting opportunities for increasing organiza- tional, financial, and operational performance. With its focus on organizational efficiency and effectiveness, RFID technology is superior to barcodes in its ability to provide source automation features that increase the speed and volume of data collection for analysis. Today, applications that employ RFID are growing rapidly and this technology is in a continuous state of evolution and growth. As it continues to progress, RFID provides us with new opportunities to use business intelligence (BI) to monitor organizational operations and learn more about markets, as well as consumer attitudes, behaviors, and product preferences. This technology can even be used to prevent potentially faulty or spoiled products from ending up in the hands of consumers. However, RFID offers significant challenges to organizations that attempt to employ this technology. Most significantly, there exists the potential for RFID to overwhelm data collection and BI analytic efforts if organizations fail to effectively address RFID data integration issues. To this end, the purpose of this article is to explicate the dynamic technology of RFID and how it is being used today. Additionally, this article will provide insights into how RFID technology is evolving and how this technology relates to BI and issues related to data integration. This knowledge has never been more essential. While IT academic research into RFID development and issues has declined in recent years, RFID continues to be a vital area of exploration, especially as it relates to BI in the 21st century.


title: Multi-resolution on compressed sets of clauses

abstract: The paper presents a system based on new operators for handling sets of propositional clauses represented by means of ZBDDs (zero-suppressed binary decision diagrams). The high compression power of such data structures allows efficient encodings of structured instances. A specialized operator for the distribution of sets of clauses is introduced and used for performing multi-resolution on clause sets. Cut eliminations between sets of clauses of exponential size may then be performed using polynomial size data structures. The ZREs system, a new implementation of the Davis-Putnam procedure (M. Davis and H. Putnam, 1960), solves two hard problems for resolution, that are currently out of the scope of the best SAT provers.


title: Comparison and Evaluation of Code Clone Detection Techniques and Tools: A Qualitative Approach

abstract: Over the last decade many techniques and tools for software clone detection have been proposed. In this paper, we provide a qualitative comparison and evaluation of the current state-of-the-art in clone detection techniques and tools, and organize the large amount of information into a coherent conceptual framework. We begin with background concepts, a generic clone detection process and an overall taxonomy of current techniques and tools. We then classify, compare and evaluate the techniques and tools in two different dimensions. First, we classify and compare approaches based on a number of facets, each of which has a set of (possibly overlapping) attributes. Second, we qualitatively evaluate the classified techniques and tools with respect to a taxonomy of editing scenarios designed to model the creation of Type-1, Type-2, Type-3 and Type-4 clones. Finally, we provide examples of how one might use the results of this study to choose the most appropriate clone detection tool or technique in the context of a particular set of goals and constraints. The primary contributions of this paper are: (1) a schema for classifying clone detection techniques and tools and a classification of current clone detectors based on this schema, and (2) a taxonomy of editing scenarios that produce different clone types and a qualitative evaluation of current clone detectors based on this taxonomy.


title: A Tutorial on Independent Component Analysis

abstract: Independent component analysis (ICA) has become a standard data analysis technique applied to an array of problems in signal processing and machine learning. This tutorial provides an introduction to ICA based on linear algebra formulating an intuition for ICA from first principles. The goal of this tutorial is to provide a solid foundation on this advanced topic so that one might learn the motivation behind ICA, learn why and when to apply this technique and in the process gain an introduction to this exciting field of active research.


title: Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models

abstract: Variations in writing styles are commonly used to adapt the content to a specific context, audience, or purpose. However, applying stylistic variations is still by and large a manual process, and there have been little efforts towards automating it. In this paper we explore automated methods to transform text from modern English to Shakespearean English using an end to end trainable neural model with pointers to enable copy action. To tackle limited amount of parallel data, we pre-train embeddings of words by leveraging external dictionaries mapping Shakespearean words to modern English words as well as additional text. Our methods are able to get a BLEU score of 31+, an improvement of ~6 points above the strongest baseline. We publicly release our code to foster further research in this area.


title: A TM0 surface wave launcher by microstrip and substrate integrated waveguide technology

abstract: In this summary article, a simple surface wave launcher (SWL) in substrate integrated waveguide (SIW) technology is proposed. The launcher utilizes a conventional microstrip to SIW transition with the motivation of transferring power into the dominant TM 0 surface wave (SW) mode of a grounded dielectric slab (GDS). Moreover, by appropriate design of the SIW section and by applying field matching techniques using sub-wavelength microstrip patches while also conveniently separating some of the via conducting posts for one of the SIW side-walls, power can leak into the GDS with minimal losses for the generation of a bound and guided TM SW with a uniform phase front. The design approach is confirmed by numerical calculations and full-wave simulations for two feed systems which operate at 15 GHz, with and without the matching section. Our new approach for simple, microstrip-fed SW launching at microwave frequencies can also be useful for other SW-based guides, novel low-cost transitions, and new compact dividing/combing circuits for antenna feed systems as well as other SW structures which employ a common substrate.


title: Pipelining Localized Semantic Features for Fine-Grained Action Recognition

abstract: In fine-grained action (object manipulation) recognition, it is important to encode object semantic (contextual) information, i.e., which object is being manipulated and how it is being operated. However, previous methods for action recognition often represent the semantic information in a global and coarse way and therefore cannot cope with fine-grained actions. In this work, we propose a representation and classification pipeline which seamlessly incorporates localized semantic information into every processing step for fine-grained action recognition. In the feature extraction stage, we explore the geometric information between local motion features and the surrounding objects. In the feature encoding stage, we develop a semantic-grouped locality-constrained linear coding (SG-LLC) method that captures the joint distributions between motion and object-in-use information. Finally, we propose a semantic-aware multiple kernel learning framework (SA-MKL) by utilizing the empirical joint distribution between action and object type for more discriminative action classification. Extensive experiments are performed on the large-scale and difficult fine-grained MPII cooking action dataset. The results show that by effectively accumulating localized semantic information into the action representation and classification pipeline, we significantly improve the fine-grained action classification performance over the existing methods.


title: Network-level reorganisation of functional connectivity following arm amputation

abstract: One of the most striking demonstrations of plasticity in the adult human brain follows peripheral injury, such as amputation. In the primary sensorimotor cortex, arm amputation results in massive local remapping of the missing hands' cortical territory. However, little is known about the consequences of sensorimotor deprivation on global brain organisation. Here, we used resting-state fMRI to identify large-scale reorganisation beyond the primary sensorimotor cortex in arm amputees, compared with two-handed controls. Specifically, we characterised changes in functional connectivity between the cortical territory of the missing hand in the primary sensorimotor cortex ('missing hand cortex') and two networks of interest: the sensorimotor network, which is typically strongly associated with the hand cortex, and the default mode network (DMN), which is normally dissociated from it. Functional connectivity values between the missing hand cortex and the sensorimotor network were reduced in amputees, and connectivity was weaker in individuals amputated for longer periods. Lower levels of functional coupling between the missing hand cortex and the sensorimotor network were also associated with emerged coupling of this cortex with the DMN. Our results demonstrate that plasticity following arm amputation is not restricted to local remapping occurring within the sensorimotor homunculus of the missing hand but rather produces a cascade of cortical reorganisation at a network-level scale. These findings may provide a new framework for understanding how local deprivation following amputation could elicit complex perceptual experiences of phantom sensations, such as phantom pain.


title: Sensor Placement in Municipal Water Networks

abstract: We present a model for optimizing the placement of sensors in municipal water networks to detect maliciously injected contaminants. An optimal sensor configuration minimizes the expected fraction of the population at risk. We formulate this problem as a mixed-integer program, which can be solved with generally available solvers. We find optimal sensor placements for three test networks with synthetic risk and population data. Our experiments illustrate that this formulation can be solved relatively quickly and that the predicted sensor configuration is relatively insensitive to uncertainties in the data used for prediction.


title: A Parameter-Free Spatio-Temporal Pattern Mining Model to Catalog Global Ocean Dynamics

abstract: As spatio-temporal data have become ubiquitous, an increasing challenge facing computer scientists is that of identifying discrete patterns in continuous spatio-temporal fields. In this paper, we introduce a parameter-free pattern mining application that is able to identify dynamic anomalies in ocean data, known as ocean eddies. Despite ocean eddy monitoring being an active field of research, we provide one of the first quantitative analyses of the performance of the most used monitoring algorithms. We present an incomplete information validation technique, that uses the performance of two methods to construct an imperfect ground truth to test the significance of patterns discovered as well as the relative performance of pattern mining algorithms. These methods, in addition to the validation schemes discussed provide researchers new directions in analyzing large unlabeled climate datasets.


title: Overview and evaluation of the JPEG XT HDR image compression standard

abstract: Standards play an important role in providing a common set of specifications and allowing inter-operability between devices and systems. Until recently, no standard for High Dynamic Range (HDR) image coding had been adopted by the market, and HDR imaging relies on proprietary and vendor specific formats which are unsuitable for storage or exchange of such images. To resolve this situation, the JPEG Committee is developing a new coding standard called JPEG XT that is backwards compatible to the popular JPEG compression, allowing it to be implemented using standard 8-bit JPEG coding hardware or software. In this paper, we present design principles and technical details of JPEG XT. It is based on a two-layers design, a base layer containing a Low Dynamic Range (LDR) image accessible to legacy implementations, and an extension layer providing the full dynamic range. The paper introduces three of currently defined profiles in JPEG XT, each constraining the common decoder architecture to a subset of allowable configurations. We assess the coding efficiency of each profile extensively through subjective assessments, using 24 naive subjects to evaluate 20 images, and objective evaluations, using 106 images with five different tone-mapping operators and at 100 different bit rates. The objective results (based on benchmarking with subjective scores) demonstrate that JPEG XT can encode HDR images at bit rates varying from 1.1 to 1.9 bit/pixel for estimated mean opinion score (MOS) values above 4.5 out of 5, which is considered as fully transparent in many applications. This corresponds to 23-times bitstream reduction compared to lossless OpenEXR PIZ compression.


title: Optimization of PID Parameter for Position Control of DC-Motor using Multi-Objective Genetic Algorithm

abstract: The ambition of this paper is to design a position controller of a DC motor by selection of a PID parameter using genetic algorithm. The Proportional plus Integral plus Derivative (PID), controllers are most widely used in control theory as well as industrial plants due to their ease of execution and robustness performance. The aspiration of this deed representation capable and apace tuning approach using Genetic Algorithm (GA) to obtain the optimized criterion of the PID controller so as to appropriate the essential appearance designation of the technique below consideration. This scheme is a simulation and experimental analysis into the development of PID controller using MATLAB/SIMULINK software. There are several techniques which are used for tuning of PID controller to control the speed control of DC motor. Tuning of PID parameters is considerable because these parameters have a admirable effect on the stability and performance of the control system. Using genetic algorithms to perform the tuning of the controller results in the optimum controller being appraise for the system every time.


title: Spatial memory deficits in patients with lesions to the right hippocampus and to the right parahippocampal cortex

abstract: Abstract Spatial memory tasks, performance of which is known to be sensitive to hippocampal lesions in the rat, or to medial temporal lesions in the human, were administered in order to investigate the effects of selective damage to medial temporal lobe structures of the human brain. The patients had undergone thermo-coagulation with a single electrode along the amygdalo-hippocampal axis in an attempt to alleviate their epilepsy. With this surgical technique, lesions to single medial temporal lobe structures can be carried out. The locations of the lesions were assessed by means of digital high-resolution magnetic resonance imaging and software allowing a 3-D reconstruction of the brain. A break in the collateral sulcus, dividing it into the anterior collateral sulcus and the posterior collateral sulcus is reported. This division may correspond to the end of the entorhinal\perirhinal cortex and the start of the parahippocampal cortex. The results confirmed the role of the right hippocampus in visuo–spatial memory tasks (object location, Rey–Osterrieth Figure with and without delay) and the left for verbal memory tasks (Rey Auditory Verbal Learning Task with delay). However, patients with lesions either to the right or to the left hippocampus were unimpaired on several memory tasks, including a spatial one, with a 30 min delay, designed to be analogous to the Morris water maze. Patients with lesions to the right parahippocampal cortex were impaired on this task with a 30 min delay, suggesting that the parahippocampal cortex itself may play an important role in spatial memory.


title: Rhythms for Cognition: Communication through Coherence

abstract: I propose that synchronization affects communication between neuronal groups. Gamma-band (30-90 Hz) synchronization modulates excitation rapidly enough that it escapes the following inhibition and activates postsynaptic neurons effectively. Synchronization also ensures that a presynaptic activation pattern arrives at postsynaptic neurons in a temporally coordinated manner. At a postsynaptic neuron, multiple presynaptic groups converge, e.g., representing different stimuli. If a stimulus is selected by attention, its neuronal representation shows stronger and higher-frequency gamma-band synchronization. Thereby, the attended stimulus representation selectively entrains postsynaptic neurons. The entrainment creates sequences of short excitation and longer inhibition that are coordinated between pre- and postsynaptic groups to transmit the attended representation and shut out competing inputs. The predominantly bottom-up-directed gamma-band influences are controlled by predominantly top-down-directed alpha-beta-band (8-20 Hz) influences. Attention itself samples stimuli at a 7-8 Hz theta rhythm. Thus, several rhythms and their interplay render neuronal communication effective, precise, and selective.


title: Effect of Training Surface on Agility and Passing Skills of Prepubescent Female Volleyball Players

abstract: The purpose of the present study was to investigate the effect of different training surfaces (hard or sand surface) on agility and passing skills of prepubescent female volleyball players. 45 prep ...


title: Not so QUIC: A Performance Study of DASH over QUIC

abstract: Despite known QoE shortcomings, Dynamic Adaptive Streaming over HTTP (DASH) has been tied with TCP for many years now. The advent of HTTP/2 powered by transport protocols such as QUIC provides an excellent opportunity to revisit adaptive bitrate streaming with respect to QoE. QUIC promises improved congestion control, zero-RTT connection establishment and multiplexing logical streams. In this work, we adapt state-of-the-art DASH players with buffer-based and hybrid (rate/buffer-based) quality adaptation logic to use QUIC. Our main focus lies in contrasting the QoE performance of DASH algorithms running on top of QUIC versus TCP in various environments. Interestingly, we find through testbed and Internet measurements that QUIC does not provide a boost to current DASH algorithms but instead a degradation in the chosen quality bitrates.


title: Design of a low-cost, underwater acoustic modem for short-range sensor networks

abstract: A fundamental impediment to the use of dense underwater sensor networks is an inexpensive acoustic modem. Commercial underwater modems that do exist were designed for sparse, long range, applications rather than for small, dense, sensor nets. Thus, we are building an underwater acoustic modem starting with the most critical component from a cost perspective - the transducer. The design substitutes a commercial transducer with a homemade transducer using cheap piezo-ceramic material and builds the rest of the modem's components around the properties of the transducer to extract as much performance as possible. This paper presents the design considerations, implementation details, and initial experimental results of our modem.


To Kill a Centrifuge A Technical Analysis of What Stuxnet ’ s Creators Tried to Achieve


title: Underwater image restoration based on contrast enhancement

abstract: Low contrast and color distortion are two major problems suffered by underwater images. In this work, a novel and systematic enhancement method based on contrast enhancement is proposed to restore the degraded underwater images. The proposed method mainly contains three steps. First, the quadtree subdivision strategy is adopted to estimate the waterlight. Then we formulate a cost function and minimize it so as to find the optimal transmission map which is able to maximize the image contrast. Once the waterlight and transmission map are obtained, based on the underwater image degradation model, the backscattering effect and color distortion caused by the light attenuation along the underwater object-camera path can be corrected. Finally, to further compensate for the color loss along the surface-object propagation path, a simple but effective color correction method is applied. Experimental results demonstrate that our proposed method can achieve comparable or even better results than some state-of-the-art approaches.


title: Identify and Classify Critical Success Factor of Agile Software Development Methodology Using Mind Map

abstract: Selecting the right method, right personnel and right practices, and applying them adequately, determine the success of software development. In this paper, a qualitative study is carried out among the critical factors of success from previous studies. The factors of success match with their relative principles to illustrate the most valuable factor for agile approach success, this paper also prove that the twelve principles poorly identified for few factors resulting from qualitative and quantitative past studies. Dimensions and Factors are presented using Critical success Dimensions and Factors Mind Map Model.


title: A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques

abstract: Over recent decades, fine-scale land use and land cover classification in open-pit mine areas (LCCMA) has become very important for understanding the influence of mining activities on the regional geo-environment, and for environmental impact assessment procedure. This research reviews advances in fine-scale LCCMA from the following aspects. Firstly, it analyzes and proposes classification thematic resolution for LCCMA. Secondly, remote sensing data sources, features, feature selection methods, and classification algorithms for LCCMA are summarized. Thirdly, three major factors that affect LCCMA are discussed: significant three-dimensional terrain features, strong LCCMA feature variability, and homogeneity of spectral-spatial features. Correspondingly, three key scientific issues that limit the accuracy of LCCMA are presented. Finally, several future research directions are discussed: (1) unitization of new sensors, particularly those with stereo survey ability; (2) procurement of sensitive features by new sensors and combinations of sensitive features using novel feature selection methods; (3) development of robust and self-adjusted classification algorithms, such as ensemble learning and deep learning for LCCMA; and (4) application of fine-scale mining information for regularity and management of mines.


title: Conceptual Design of a New Huge Deployable Antenna Structure for Space Application

abstract: We present here a conceptual design of a new huge deployable antenna structure for space application. The antenna is constructed with a folded hoop and six folded ribs to form a rigid backbone. Through the ribs, there are several inflatable membrane tori. Among the tori, there are flexible cables to act as ribs. The rigid backbone, the inflated tori and the flexible cables construct the desired paraboloidal shape of the bound membrane surface. Such a novel model incorporates the advantages of both mesh antennas and inflatable antennas. The surface density is much lower than available deployable mesh and inflatable antennas. Through deploying simulations of the rigid structure and inflating simulations of the flexible structure, such a new conceptual design is predicted to be feasible for a huge deployable antenna with fairly high package ratio and fairly low surface density.


title: Brain activity on a hypersphere

abstract: Current advances in neurosciences deal with the functional architecture of the central nervous system, paving the way for holistic theories that improve our understanding of brain activity. From topology, a strong concept comes into play in the understanding of brain signals, namely, continuous mapping of the signals onto a hyper-sphere, a 4-dimensional space equipped with a doughnut-like shape that is not detected by observers living in a 3-dimensional world. We evaluate the features of the imperceptible 4th dimension based on resting state functional magnetic resonance imaging series. In particular, we looked for simultaneous activation of antipodal signals on the surface of a cortical hyper-sphere. In this article, we demonstrate that spontaneous brain activity displays the typical features which reveal the existence of a functional hyper-sphere. The suggestion here is that the brain is embedded in a hyper-sphere, which helps solve long-standing mysteries concerning our psychological activities such mind-wandering and memory retrieval or the ability to connect, pasts and future events.


title: Bias in algorithm portfolio performance evaluation

abstract: A Virtual Best Solver (VBS) is a hypothetical algorithm that selects the best solver from a given portfolio of alternatives on a per-instance basis. The VBS idealizes performance when all solvers in a portfolio are run in parallel, and also gives a valuable bound on the performance of portfolio-based algorithm selectors. Typically, VBS performance is measured by running every solver in a portfolio once on a given instance and reporting the best performance over all solvers. Here, we argue that doing so results in a flawed measure that is biased to reporting better performance when a randomized solver is present in an algorithm portfolio. Specifically, this flawed notion of VBS tends to show performance better than that achievable by a perfect selector that for each given instance runs the solver with the best expected running time. We report results from an empirical study using solvers and instances submitted to several SAT competitions, in which we observe significant bias on many random instances and some combinatorial instances. We also show that the bias increases with the number of randomized solvers and decreases as we average solver performance over many independent runs per instance. We propose an alternative VBS performance measure by (1) empirically obtaining the solver with best expected performance for each instance and (2) taking bootstrap samples for this solver on every instance, to obtain a confidence interval on VBS performance. Our findings shed new light on widely studied algorithm selection benchmarks and help explain performance gaps observed between VBS and state-of-the-art algorithm selection approaches.


title: A Nanostructured Microfluidic Artificial Olfaction for Organic Vapors Recognition

abstract: Selective and sensitive detection of volatile organic compounds (VOCs) is of great importance in applications involving monitoring of hazardous chemicals or non-invasive diagnosis. Here, polymethyl methacrylate nanoparticles with acetone recognition sites are synthesized and integrated into a 3D-printed microfluidic platform to enhance the selectivity of the device. The proposed microfluidic-based olfaction system includes two parylene C-coated microchannels, with or without polymer nanoparticles. The two channels are exposed to 200, 400, 800, 2000, and 4000 ppm of VOCs (methanol, ethanol, acetone, acetonitrile, butanone, and toluene), and sensor responses are compared using a 2D feature extraction method. Compared to current microfluidic-based olfaction systems, responses observed between coated and uncoated channels showed an increased recognition capability among VOCs (especially with respect to acetone), indicating the potential of this approach to increase and fine-tune the selectivity of microfluidic gas sensors.


title: An Improved Grid Current and DC Capacitor Voltage Balancing Method for Three-Terminal Hybrid AC/DC Microgrid

abstract: In this paper, a three-terminal ac/dc hybrid microgrid with two dc terminals and one ac terminal is proposed. The proposed system consists of cascaded H-bridge (CHB) converters based ac grid interface and two dual active bridge (DAB) converters based dc subgrid interface that connects two isolated dc buses. In order to reduce the number of power conversion stages and power devices, the DAB converters are directly connected to CHB dc rails according to the system operation requirement. To overcome the imbalanced grid currents and dc rail voltages issues caused by this modified system configuration with only two power conversion stages, an improved method is proposed through the zero-sequence voltage injection in the CHB converters. In addition, to avoid the conflicts between zero-sequence voltage injection and the voltage/current regulation of the system, the impacts of the control parameters to the system stability and dynamic response are investigated. Evaluation results from both three-terminal and five-terminal hybrid ac/dc microgrids show that the generalized effectiveness of the proposed three-phase ac current and dc rail voltage balancing method.


title: Multistory Floor Plan Generation and Room Labeling of Building Interiors from Laser Range Data

abstract: Automatic generation of building floor plans is useful in many emerging applications, including indoor navigation, augmented and virtual reality, as well as building energy simulation software. These applications require watertight models with limited complexity. In this paper, we present an approach that produces 2.5D extruded watertight models of building interiors from either 2D particle filter grid maps or full 3D point-clouds captured by mobile mapping systems. Our approach is to triangulate a 2D sampling of wall positions and separate these triangles into interior and exterior sets. We partition the interior volume of the building model by rooms, then simplify the model to reduce noise. Such labels are useful for building energy simulations involving thermal models, as well as for ensuring geometric accuracy of the resulting 3D model. We experimentally verify the performance of our proposed approach on a wide variety of buildings. Our approach is efficient enough to be used in real-time in conjunction with Simultaneous Localization and Mapping (SLAM) applications.


Fundamentals of Artificial Neural Networks [Book Reviews]


title: Cognitive PHY and MAC layers for dynamic spectrum access and sharing of TV bands

abstract: Research in the physical (PHY) and medium access control (MAC) layers for dynamic spectrum access (DSA) and dynamic spectrum sharing (DSS) is still at its infancy. Aspects such as spectrum sensing, coexistence, measurement and spectrum management, network reliability and QoS support in face of the need to avoid causing harmful interference into incumbents, to name a few, are key to the success of future cognitive radio (CR) systems and have received little attention so far. In addition, it is critical to understand the interplay of these various cognitive radio concepts and how they impact the overall network performance. In this paper we address these questions by presenting the design and performance evaluation of a CR-based PHY and MAC for DSA and DSS of vacant television (TV) channels. This air interface described here forms the baseline of the current IEEE 802.22 draft standard, and features a number of key PHY and MAC CR-based components for use by license-exempt devices in the spectrum that is currently allocated primarily to TV services. Through simulations and prototyping, we analyze the performance of this first CR-based wireless network with respect to spectrum sensing, system capacity, QoS support, coexistence, and network reliability.


title: Active Learning Methods for Remote Sensing Image Classification

abstract: In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.


title: Ensemble of local and global information for finger – knuckle-print recognition

abstract: Biometrics authentication is an effective method for automatically recognizing a person's identity. Recently, it has been found that the finger-knuckle-print (FKP), which refers to the inherent skin patterns of the outer surface around the phalangeal joint of one's finger, has high capability to discriminate different individuals, making it an emerging biometric identifier. In this paper, based on the results of psychophysics and neurophysiology studies that both local and global information is crucial for the image perception, we present an effective FKP recognition scheme by extracting and assembling local and global features of FKP images. Specifically, the orientation information extracted by the Gabor filters is coded as the local feature. By increasing the scale of Gabor filters to infinite, actually we can get the Fourier transform of the image, and hence the Fourier transform coefficients of the image can be taken as the global features. Such kinds of local and global features are naturally linked via the framework of time-frequency analysis. The proposed scheme exploits both local and global information for the FKP verification, where global information is also utilized to refine the alignment of FKP images in matching. The final matching distance of two FKPs is a weighted average of local and global matching distances. The experimental results conducted on our FKP database demonstrate that the proposed local-global information combination scheme could significantly improve the recognition accuracy obtained by either local or global information and lead to promising performance of an FKP-based personal authentication system.


title: Learning based digital matting

abstract: We cast some new insights into solving the digital matting problem by treating it as a semi-supervised learning task in machine learning. A local learning based approach and a global learning based approach are then produced, to fit better the scribble based matting and the trimap based matting, respectively. Our approaches are easy to implement because only some simple matrix operations are needed. They are also extremely accurate because they can efficiently handle the nonlinear local color distributions by incorporating the kernel trick, that are beyond the ability of many previous works. Our approaches can outperform many recent matting methods, as shown by the theoretical analysis and comprehensive experiments. The new insights may also inspire several more works.


title: Multi Activity Recognition Based on Bodymodel-Derived Primitives

abstract: We propose a novel model-based approach to activity recognition using high-level primitives that are derived from a human body model estimated from sensor data. Using short but fixed positions of the hands and turning points of hand movements, a continuous data stream is segmented in short segments of interest. Within these segments, joint boosting enables the automatic discovery of important and distinctive features ranging from motion over posture to location. To demonstrate the feasibility of our approach we present the user-dependent and across-user results of a study with 8 participants. The specific scenario that we study is composed of 20 activities in quality inspection of a car production process.


title: Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions

abstract: Physical activity has a positive impact on people's well-being, and it may also decrease the occurrence of chronic diseases. Activity recognition with wearable sensors can provide feedback to the user about his/her lifestyle regarding physical activity and sports, and thus, promote a more active lifestyle. So far, activity recognition has mostly been studied in supervised laboratory settings. The aim of this study was to examine how well the daily activities and sports performed by the subjects in unsupervised settings can be recognized compared to supervised settings. The activities were recognized by using a hybrid classifier combining a tree structure containing a priori knowledge and artificial neural networks, and also by using three reference classifiers. Activity data were collected for 68 h from 12 subjects, out of which the activity was supervised for 21 h and unsupervised for 47 h. Activities were recognized based on signal features from 3-D accelerometers on hip and wrist and GPS information. The activities included lying down, sitting and standing, walking, running, cycling with an exercise bike, rowing with a rowing machine, playing football, Nordic walking, and cycling with a regular bike. The total accuracy of the activity recognition using both supervised and unsupervised data was 89% that was only 1% unit lower than the accuracy of activity recognition using only supervised data. However, the accuracy decreased by 17% unit when only supervised data were used for training and only unsupervised data for validation, which emphasizes the need for out-of-laboratory data in the development of activity-recognition systems. The results support a vision of recognizing a wider spectrum, and more complex activities in real life settings.


title: Design and DfT of a high-speed area-efficient embedded asynchronous FIFO

abstract: Embedded First-In First-Out (FIFO) memories are increasingly used in many IC designs. We have created a new full-custom embedded ripple-through FIFO module with asynchronous read and write clocks. The implementation is based on a micropipeline architecture and is at least a factor two smaller than SRAM-based and standard-cell-based counterparts. This paper gives an overview of the most important design features of the new FIFO module and describes its test and design-for-test approach.


title: Single-Photon Avalanche Diodes in a 0.16 μm BCD Technology With Sharp Timing Response and Red-Enhanced Sensitivity

abstract: CMOS single-photon avalanche diodes (SPADs) have recently become an emerging imaging technology for applications requiring high sensitivity and high frame-rate in the visible and near-infrared range. However, a higher photon detection efficiency (PDE), particularly in the 700–950 nm range, is highly desirable for many growing markets, such as eye-safe three-dimensional imaging (LIDAR). In this paper, we report the design and characterization of SPADs fabricated in a 0.16 μ m BCD (Bipolar-CMOS-DMOS) technology. The overall detection performance is among the best reported in the literature: 1) PDE of 60% at 500 nm wavelength and still 12% at 800 nm; 2) very low dark count rate of μ m2 (in counts per second per unit area); 3) < 1% afterpulsing probability with 50 ns dead-time; and 4) temporal response with 30 ps full width at half-maximum and less than 50 ps diffusion tail time constant.


title: An Efficient Method for Bayesian Network Parameter Learning from Incomplete Data

abstract: We propose an efficient method for estimating the parameters of a Bayesian network, from incomplete datasets, i.e., datasets containing variables with missing values. In contrast to textbook approaches such as EM and the gradient method, our approach is non-iterative, yields closed form parameter estimates, and eliminates the need for inference in a Bayesian network. Our approach is capable of producing consistent parameter estimates for missing data problems that are MCAR, MAR, and in some cases, MNAR. Empirically, our approach is orders of magnitude faster than EM. When data is scarce, we learn parameters of comparable quality to EM. Given sufficient data, we can learn parameters that are orders of magnitude closer to the true parameters.


title: Incorporating Modification Requests in Updating Consistent Knowledge Bases

abstract: An important problem that arises when updating a knowledge base is related to integrity constraints maintenance. That is, given a consistent knowledge base and an update request, to obtain all possible ways of updating the base facts such that the request is satisfied and consistency is maintained. In this paper we present a new method for providing automatic support in these situations. Our method takes into account information about the keys of the predicates and it is able to handle updates which consist of a set of insertions, deletions and/or modifications of base and derived predicates.


title: Biomedical Applications of Untethered Mobile Milli/Microrobots

abstract: Untethered robots miniaturized to the length scale of millimeter and below attract growing attention for the prospect of transforming many aspects of health care and bioengineering. As the robot size goes down to the order of a single cell, previously inaccessible body sites would become available for high-resolution in situ and in vivo manipulations. This unprecedented direct access would enable an extensive range of minimally invasive medical operations. Here, we provide a comprehensive review of the current advances in biome dical untethered mobile milli/microrobots. We put a special emphasis on the potential impacts of biomedical microrobots in the near future. Finally, we discuss the existing challenges and emerging concepts associated with designing such a miniaturized robot for operation inside a biological environment for biomedical applications.


title: A design-based research case study documenting a constructivist ID process and instructional solution for a cross-cultural workforce

abstract: As the need for instructing a globalized workforce increases, instructional designers must embrace the constraints and the opportunities these projects provide in order to move the field of cross-cultural instructional design (ID) forward. Cross-cultural projects offer multiple avenues for growth in ID practice, overcoming cultural barriers, and a venue to apply and test contemporary models, methodologies, and theories in ID. This design-based research case study employed a rapid prototyping methodology and the constructivist ID model, Layers of Negotiation, to collect critical cultural information for the design of this cross-cultural instruction. Instructional strategies deemed effective for instructing an unskilled workforce in Dubai, United Arab Emirates, included job aids, situated learning and apprenticeship principles consisting of modeling, coaching and scaffolding. Use of the constructivist ID model and instructional solution for a cross-cultural workforce for The Dubai Mall are presented. Evaluation results indicate the success of the instructional strategies varied dependent upon worker culture, and the sequential nature of the instructional strategies.


Beam forming network technologies and related manufacturing processes


title: Control of self-organizing nonlinear systems

abstract: Controlling chimera patterns in networks: Interplay of structure, noise, and delay.- Emergence of clusters of locked and whirling oscillators in the Kuramoto model with Inertia.- Control of synchronization in delay-coupled networks.- Controlling oscillations in nonlinear systems with delayed output feedback.- Global effects of delayed feedback control applied to the Lorenz system.- Spatio-temporal patterns: observation, control, and design.- On the interplay of noise and delay in coupled oscillators.- Noisy dynamical systems with time delay: some basic analytical perturbation schemes with applications.- Study on critical conditions and transient behaviors in noise-induced bifurcation.- Analytical, optimal and sparse optimal control of the FitzHugh-Nagumo and Schlogl model.- Recent advances in reaction-diffusion equations with non-ideal relays.- Deriving effective models for multiscale systems via evolutionary Gamma convergence.- Moment closure - A brief review.- Feedback control in quantum transport.- Controlling the stability of steady states in continuous variable quantum systems.- Quantum signatures of chimera states.- Controlled switching between time-periodic square-waves in photonic devices.- Exploiting multistabilities to achieve stable chimera states in all-to-all coupled laser networks.- Feedback control of colloidal transport.- Swarming of self-propelled particles on the surface of a dewetting liquid film.- Time-delayed feedback control of spatio-temporal self-organized patterns in dissipative systems.- Control of epidemics on hospital network.- Intrinsic control mechanisms of neuronal network dynamics.- Oscillations and intrinsic uctuations in evolutionary dynamics: how payoffs, dynamics and population sizes control the stability, and implementation of global feedback.


title: Carolina voices : two hundred years of student experiences

abstract: In celebration of the University of South Carolina's bicentennial, this remarkable volume reveals the personal stories of Carolina students over the past two hundred years. Told in their own words, these writings - from antebellum manuscripts to e-mails - capture the color and spirit of the times, revealing attitudes and opinions, issues and passions. From those first young boys chopping wood to warm their rooms, to today's young men and women surrounded by the latest technology, the voices of Carolina's students testify to the transforming power of the college experience. To gather this collection of texts from 1801 to the present, the editors searched family papers, collections of letters, university archives, autobiographies and histories, journals and diaries, student newspapers and literary magazines, and oral histories. In this volume students share their own versions of the University of South Carolina story - explaining their activities, voicing their opinions, cheering their teams, and sometimes writing home from far away. Some of their concerns have changed; twenty-first century students worry more about parking tickets and phone bills than the price of fuel or the threat of smallpox. Throughout the years, however, each generation weaves tales of living conditions and classroom practices within the context of larger historical events. In their letters and diaries, poems and memoirs, editorials and e-mails, students illustrate a colorful picture of campus history and evoke the important events of their time. Their words provide intimate illustrations of life on and around the campus as they reveal the rich history of the University of South Carolina.


title: A connectedness constraint for learning sparse graphs

abstract: Graphs are naturally sparse objects that are used to study many problems involving networks, for example, distributed learning and graph signal processing. In some cases, the graph is not given, but must be learned from the problem and available data. Often it is desirable to learn sparse graphs. However, making a graph highly sparse can split the graph into several disconnected components, leading to several separate networks. The main difficulty is that connectedness is often treated as a combinatorial property, making it hard to enforce in e.g. convex optimization problems. In this article, we show how connectedness of undirected graphs can be formulated as an analytical property and can be enforced as a convex constraint. We especially show how the constraint relates to the distributed consensus problem and graph Laplacian learning. Using simulated and real data, we perform experiments to learn sparse and connected graphs from data.


title: The phrasal lexicon

abstract: Theoretical linguists have in recent years concentrated their attention on the productive aspect of language, wherein utterances are formed combinatorically from units the size of words or smaller. This paper will focus on the contrary aspect of language, wherein utterances are formed by repetition, modification, and concatenation of previously-known phrases consisting of more than one word. I suspect that we speak mostly by stitching together swatches of text that we have heard before; productive processes have the secondary role of adapting the old phrases to the new situation. The advantage of this point of view is that it has the potential to account for the observed linguistic behavior of native speakers, rather than discounting their actual behavior as irrelevant to their language. In particular, this point of view allows us to concede that most utterances are produced in stereotyped social situations, where the communicative and ritualistic functions of language demand not novelty, but rather an appropriate combination of formulas, cliches, idioms, allusions, slogans, and so forth. Language must have originated in such constrained social contexts, and they are still the predominant arena for language production. Therefore an understanding of the use of phrases is basic to the understanding of language as a whole.You are currently reading a much-abridged version of a paper that will be published elsewhere later.


Age of Onset of Mental Disorders


title: Natural Neural Networks

abstract: We introduce Natural Neural Networks, a novel family of algorithms that speed up convergence by adapting their internal representation during training to improve conditioning of the Fisher matrix. In particular, we show a specific example that employs a simple and efficient reparametrization of the neural network weights by implicitly whitening the representation obtained at each layer, while preserving the feed-forward computation of the network. Such networks can be trained efficiently via the proposed Projected Natural Gradient Descent algorithm (PRONG), which amortizes the cost of these reparametrizations over many parameter updates and is closely related to the Mirror Descent online learning algorithm. We highlight the benefits of our method on both unsupervised and supervised learning tasks, and showcase its scalability by training on the large-scale ImageNet Challenge dataset.


title: The Epsilon Transformation Language

abstract: Support for automated model transformation is essential for realizing a Model Driven Development (MDD) process. However, model transformation is only one of the many tools in a model engineering toolkit. To apply MDD in the large, automated support for a number of additional tasks such as model comparison, merging, validation and model-to-text transformation, is essential. While a number of successful model transformation languages have been currently proposed, the majority of them have been developed in isolation and as a result, they face consistency and integration difficulties with languages that support other model management tasks. We present the Epsilon Transformation Language (ETL), a hybrid model transformation language that has been developed atop the infrastructure provided by the Epsilon model management platform. By building atop Epsilon, ETL is seamlessly integrated with a number of other task-specific languages to help to realize composite model management workflows.


Business Analytics: Radical Shift or Incremental Change?


title: UAV-Enabled Wireless Power Transfer: Trajectory Design and Energy Optimization

abstract: This paper studies a new unmanned aerial vehicle (UAV)-enabled wireless power transfer system, where a UAV-mounted mobile energy transmitter is dispatched to deliver wireless energy to a set of energy receivers (ERs) at known locations on the ground. We investigate how the UAV should optimally exploit its mobility via trajectory design to maximize the amount of energy transferred to all ERs during a finite charging period. First, we consider the maximization of the sum energy received by all ERs by optimizing the UAV’s trajectory subject to its maximum speed constraint. Although this problem is non-convex, we obtain its optimal solution, which shows that the UAV should hover at one single fixed location during the whole charging period. However, the sum-energy maximization incurs a “near-far” fairness issue, where the received energy by the ERs varies significantly with their distances to the UAV’s optimal hovering location. To overcome this issue, we consider a different problem to maximize the minimum received energy among all ERs, which, however, is more challenging to solve than the sum-energy maximization. To tackle this problem, we first consider an ideal case by ignoring the UAV’s maximum speed constraint, and show that the relaxed problem can be optimally solved via the Lagrange dual method. The obtained trajectory solution implies that the UAV should hover over a set of fixed locations with optimal hovering time allocations among them. Then, for the general case with the UAV’s maximum speed constraint considered, we propose a new successive hover-and-fly trajectory motivated by the optimal trajectory in the ideal case and obtain efficient trajectory designs by applying the successive convex programing optimization technique. Finally, numerical results are provided to evaluate the performance of the proposed designs under different setups, as compared with benchmark schemes.


title: Millimeter Wave MIMO With Lens Antenna Array: A New Path Division Multiplexing Paradigm

abstract: Millimeter wave (mmWave) communication is a promising technology for future wireless systems, while one practical challenge is to achieve its large-antenna gains with only limited radio frequency (RF) chains for cost-effective implementation. To this end, we study in this paper a new lens antenna array enabled mmWave multiple-input multiple-output (MIMO) communication system. We first show that the array response of lens antenna arrays follows a “sinc” function, where the antenna element with the peak response is determined by the angle of arrival (AoA)/departure (AoD) of the received/transmitted signal. By exploiting this unique property along with the multi-path sparsity of mmWave channels, we propose a novel low-cost and capacity-achieving spatial multiplexing scheme for both narrow-band and wide-band mmWave communications, termed path division multiplexing (PDM) , where parallel data streams are transmitted over different propagation paths with simple per-path processing. We further propose a simple path grouping technique with group-based small-scale MIMO processing to effectively mitigate the inter-stream interference due to similar AoAs/AoDs. Numerical results are provided to compare the performance of the proposed mmWave lens MIMO against the conventional MIMO with uniform planar arrays (UPAs) and hybrid analog/digital processing. It is shown that the proposed design achieves significant throughput gains as well as complexity and cost reductions, thus leading to a promising new paradigm for mmWave MIMO communications.


title: Technology Corner Virtual Crime Scene Reconstruction: The Basics of 3D Modeling

abstract: Digital crime scenes take place in the context of physical crime scenes. Virtual crime scene reconstruction is an activity where investigators create a 3-dimensional (3-D) model of an actual crime scene for the purpose of determining the events that lead to the crime. While virtual crime scene reconstruction is currently used for analyzing physical scenes, it can also help investigators visualize and explore ways digital media could have been used to perpetrate a crime. In this technology corner we explore one of the technologies underlying virtual crime scene reconstruction: 3-D modeling. Â


title: Entrepreneurship in the large corporation: a longitudinal study of how established firms create breakthrough inventions

abstract: We present a model that explains how established firms create breakthrough inventions. We identify three organizational pathologies that inhibit breakthrough inventions: the familiarity trap – favoring the familiar; the maturity trap – favoring the mature; and the propinquity trap – favoring search for solutions near to existing solutions. We argue that by experimenting with novel (i.e., technologies in which the firm lacks prior experience), emerging (technologies that are recent or newly developed in the industry), and pioneering (technologies that do not build on any existing technologies) technologies firms can overcome these traps and create breakthrough inventions. Empirical evidence from the chemicals industry supports our model. Copyright © 2001 John Wiley & Sons, Ltd.


Effective Use Of Concurrent Engineering Tools In Engineering Education


title: Three-phase resonant inverter for wireless power transfer

abstract: A three-phase resonant inverter for wireless power transfer applications is presented in this paper. A phase-frequency hybrid control strategy is proposed as an alternative to the traditional frequency and phase control methods. An experimental prototype of the three-phase resonant inverter was built and connected with wireless power link and rectifier. The hybrid phase-frequency control strategy was implemented and compared with frequency control at the output power up to 10 kW. The experimental results show that the proposed inverter can operate within the desired frequency range with high efficiency while regulating output from zero to maximum.


title: A Single-Actuator Gripper with a Working Mode Switching Mechanism for Grasping and Rolling Manipulation

abstract: This paper presents a three-fingered gripper with a working mode switching mechanism for both grasping and rolling manipulation tasks, actuated by only one motor. A newly proposed Continuum Differential Mechanism (CDM) was utilized with an underactuated finger design to generate adaptive grasps. A switching mechanism was designed to change the gripper's working mode between grasping and rolling manipulation under the actuation of the same motor. Besides being expected to grasp various objects, the gripper can roll grasped objects with its two fingers, utilizing the CDM's elasticity, in the rolling manipulation mode. Experimental results verified that the gripper can adaptively grasp various objects in the grasping mode and can also perform in-hand rolling tasks using two of its fingers in the manipulation mode, demonstrating the effectiveness of the proposed idea.


title: A Memory-Network Based Solution for Multivariate Time-Series Forecasting

abstract: Multivariate time series forecasting is extensively studied throughout the years with ubiquitous applications in areas such as finance, traffic, environment, etc. Still, concerns have been raised on traditional methods for incapable of modeling complex patterns or dependencies lying in real word data. To address such concerns, various deep learning models, mainly Recurrent Neural Network (RNN) based methods, are proposed. Nevertheless, capturing extremely long-term patterns while effectively incorporating information from other variables remains a challenge for time-series forecasting. Furthermore, lack-of-explainability remains one serious drawback for deep neural network models. Inspired by Memory Network proposed for solving the question-answering task, we propose a deep learning based model named Memory Time-series network (MTNet) for time series forecasting. MTNet consists of a large memory component, three separate encoders, and an autoregressive component to train jointly. Additionally, the attention mechanism designed enable MTNet to be highly interpretable. We can easily tell which part of the historic data is referenced the most.


Breaking CAPTCHAs on the Dark Web 11 February , 2018


title: Improved Feynman propagators on a grid and non-adiabatic corrections within the path integral framework

abstract: Abstract The idea of using a good representation as the zeroth order description of a problem, which is widely used in perturbation theory and in basis set calculations, is extended to the path integral formulation of quantum mechanics by constructing improved Feynman propagators. The best zeroth order propagators cannot be expressed in closed form in general, and are therefore constructed numerically and stored on one-dimensional grids. Use of improved propagators in discretized path integral calculations involves a trivial modification of Monte Carlo techniques and leads to convergence with fewer time slices. Application of a quasi-adiabatic propagator to a system coupled to a harmonic bath leads to a low-dimensional path integral with a non-local influence functional which incorporates the non-adiabatic corrections to the Born—Oppenheimer approximation and which (with parameters typical of chemical processes) can be evaluated by quadrature, providing an accurate method for investigating the real time quantum dynamics of system—bath Hamiltonians.


Transient acantholytic dermatosis associated with B symptoms of follicular lymphoma.


title: Improved Shortest Path Maps with GPU Shaders

abstract: We present in this paper several improvements for computing shortest path maps using OpenGL shaders. The approach explores GPU rasterization as a way to propagate optimal costs on a polygonal 2D environment, producing shortest path maps which can efficiently be queried at run-time. Our improved method relies on Compute Shaders for improved performance, does not require any CPU pre-computation, and handles shortest path maps both with source points and with line segment sources. The produced path maps partition the input environment into regions sharing a same parent point along the shortest path to the closest source point or segment source. Our method produces paths with global optimality, a characteristic which has been mostly neglected in animated virtual environments. The proposed approach is particularly suitable for the animation of multiple agents moving toward the entrances or exits of a virtual environment, a situation which is efficiently represented with the proposed path maps.


title: Mitochondria and Cancer

abstract: Mitochondria are bioenergetic, biosynthetic, and signaling organelles that are integral in stress sensing to allow for cellular adaptation to the environment. Therefore, it is not surprising that mitochondria are important mediators of tumorigenesis, as this process requires flexibility to adapt to cellular and environmental alterations in addition to cancer treatments. Multiple aspects of mitochondrial biology beyond bioenergetics support transformation, including mitochondrial biogenesis and turnover, fission and fusion dynamics, cell death susceptibility, oxidative stress regulation, metabolism, and signaling. Thus, understanding mechanisms of mitochondrial function during tumorigenesis will be critical for the next generation of cancer therapeutics.


title: Mining Compatible/Incompatible Entities from Question and Answering via Yes/No Answer Classification using Distant Label Expansion

abstract: Product Community Question Answering (PCQA) provides useful information about products and their features (aspects) that may not be well addressed by product descriptions and reviews. We observe that a product's compatibility issues with other products are frequently discussed in PCQA and such issues are more frequently addressed in accessories, i.e., via a yes/no question "Does this mouse work with windows 10?". In this paper, we address the problem of extracting compatible and incompatible products from yes/no questions in PCQA. This problem can naturally have a two-stage framework: first, we perform Complementary Entity (product) Recognition (CER) on yes/no questions; second, we identify the polarities of yes/no answers to assign the complementary entities a compatibility label (compatible, incompatible or unknown). We leverage an existing unsupervised method for the first stage and a 3-class classifier by combining a distant PU-learning method (learning from positive and unlabeled examples) together with a binary classifier for the second stage. The benefit of using distant PU-learning is that it can help to expand more implicit yes/no answers without using any human annotated data. We conduct experiments on 4 products to show that the proposed method is effective.


title: Building Caring Healthcare Systems in the Internet of Things

abstract: The nature of healthcare and the computational and physical technologies and constraints present a number of challenges to systems designers and implementers. In spite of the challenges, there is a significant market for systems and products to support caregivers in their tasks as the number of people needing assistance grows substantially. In this paper, we present a structured approach for describing Internet of Things (IoT) for healthcare systems. We illustrate the approach for three use cases and discuss relevant quality issues that arise, in particular, the need to consider caring as a requirement.


title: MINIX 3: a highly reliable, self-repairing operating system

abstract: Different kinds of people use computers now than several decades ago, but operating systems have not fully kept pace with this change. It is true that we have point-and-click GUIs now instead of command line interfaces, but the expectation of the average user is different from what it used to be, because the user is different. Thirty or 40 years ago, when operating systems began to solidify into their current form, almost all computer users were programmers, scientists, engineers, or similar professionals doing heavy-duty computation, and they cared a great deal about speed. Few teenagers and even fewer grandmothers spent hours a day behind their terminal. Early users expected the computer to crash often; reboots came as naturally as waiting for the neighborhood TV repairman to come replace the picture tube on their home TVs. All that has changed and operating systems need to change with the times.


title: Handover performance in LTE-A HetNets through inter-site distance differentiation

abstract: Mobility Management in future cellular networks is becoming more challenging due to transition from macro only to multi-tier deployments. In this framework, the massive use of small cells rendered traditional Handover algorithms inappropriate to deal effectively with frequent Handovers, especially for fast users, in dense urban scenarios. Studies in this area focus mainly on the adjustment of the Hysteresis Margin and on the Time-to-Trigger (TTT) selection in line with the Self-Organized Networks (SON), concept. In that sense, the ability of each node to adapt its parameters to the actual scenario is considered vital for the smooth operation of the network. This work contributes to the latter by analyzing the dependence of the Handover performance on the inter-site distance between the macro cell and the small cell. Specifically, the most common KPIs (i.e. Handover, Ping Pong and Radio Link Failure probabilities) are analyzed for different inter-site distances and TTT values to provide solid basis for the TTT selection.


title: Chinese Law and the International Protection of Religious Freedom

abstract: This article gives an overview of the international law of religious freedom and also Chinese laws regarding the practice of religion. It then compares Chinese laws with the international standards in three particular areas: the definition of religion or belief (using the case study of Falun Gong); the scope of protected manifestations of religion or belief (using the case study of politico-religious activity in Tibet); and the role of the state in controlling religious groups and practices. It concludes that, while China certainly deals more harshly with religious groups than most states, many of the problems that it faces are also faced by other states and are areas where it is difficult to find clear guidance in international law. The exception to this is the high level of state control exercised over official religions and the discouragement of unofficial religions.


title: Content vs. context for sentiment analysis: a comparative analysis over microblogs

abstract: Microblog content poses serious challenges to the applicability of traditional sentiment analysis and classification methods, due to its inherent characteristics. To tackle them, we introduce a method that relies on two orthogonal, but complementary sources of evidence: content-based features captured by n-gram graphs and context-based ones captured by polarity ratio. Both are language-neutral and noise-tolerant, guaranteeing high effectiveness and robustness in the settings we are considering. To ensure our approach can be integrated into practical applications with large volumes of data, we also aim at enhancing its time efficiency: we propose alternative sets of features with low extraction cost, explore dimensionality reduction and discretization techniques and experiment with multiple classification algorithms. We then evaluate our methods over a large, real-world data set extracted from Twitter, with the outcomes indicating significant improvements over the traditional techniques.


title: Test report prioritization to assist crowdsourced testing

abstract: In crowdsourced testing, users can be incentivized to perform testing tasks and report their results, and because crowdsourced workers are often paid per task, there is a financial incentive to complete tasks quickly rather than well. These reports of the crowdsourced testing tasks are called "test reports" and are composed of simple natural language and screenshots. Back at the software-development organization, developers must manually inspect the test reports to judge their value for revealing faults. Due to the nature of crowdsourced work, the number of test reports are often difficult to comprehensively inspect and process. In order to help with this daunting task, we created the first technique of its kind, to the best of our knowledge, to prioritize test reports for manual inspection. Our technique utilizes two key strategies: (1) a diversity strategy to help developers inspect a wide variety of test reports and to avoid duplicates and wasted effort on falsely classified faulty behavior, and (2) a risk strategy to help developers identify test reports that may be more likely to be fault-revealing based on past observations. Together, these strategies form our DivRisk strategy to prioritize test reports in crowd- sourced testing. Three industrial projects have been used to evaluate the effectiveness of test report prioritization methods. The results of the empirical study show that: (1) DivRisk can significantly outperform random prioritization; (2) DivRisk can approximate the best theoretical result for a real-world industrial mobile application. In addition, we provide some practical guidelines of test report prioritization for crowdsourced testing based on the empirical study and our experiences.


title: A Game Theoretic Approach to Energy Trading in Multi-Microgrid Systems

abstract: Microgrid (MG) cooperation, which allows information and energy exchange among multiple MGs, can improve the reliability of MGs and thus becomes a promising technique for multi-microgrid (MMG) systems. Since MG is generally selfish in nature, how to motivate MGs to participate in MG cooperation becomes an important issue. To do this, a Stackelberg game theoretic framework to MG cooperation, called the MG energy trading game (MGETG), is proposed. In the MGETG, an MMG center as the leader motivates and regulates the MG cooperation by setting the energy trading prices. Then, MGs as the followers determine their energy trading strategies so as to maximize their utilities. Nikaido-Isoda relaxation algorithm is applied to find the Nash equilibrium of MGs actions and a pricing algorithm is devised to find the energy trading price based on the golden section search. Our simulation results show that the proposed algorithms can improve the renewable utilization by 25.9% and reduce the total operation cost by 35.5% through MG energy trading.


title: Results from a Study for Teaching Human Body Systems to Primary School Students Using Tablets

abstract: The paper presents the results from a study which examined whether tablets together with a mobile application with augmented reality features can help students to better understand the functions of the respiratory and the circulatory system. The target group was 75 sixth-grade primary school students, divided into three groups. The first group was taught conventionally; students studied using a printed handbook. In the second, a constructivist teaching model was used, but the instruction was not technologically enhanced. The third group of students used tablets and an application, and the teaching was based on a slightly modified version of Bybee's 5Es model. All three groups of students worked in pairs, they were taught the same learning material, and the teacher acted as a facilitator of the process. Data were collected by means of a questionnaire and evaluation sheets. Results indicated that students in the third group outperformed students in the other two groups. The results can be attributed to students' enjoyment, motivation, and positive attitude towards the use of tablets as well as to the teaching method. The study's implications are also discussed.


title: A Dual-Band Balun Using Partially Coupled Stepped-Impedance Coupled-Line Resonators

abstract: In this paper, a recently proposed dual-band resonator structure consisting of a pair of partially coupled stepped-impedance lines is theoretically investigated. The new resonator configuration contains a number of attractive features for dual-band applications. A new type of dual-band balun that is constructed using a pair of such coupled stepped-impedance resonators is fully studied both theoretically and experimentally. Design equations for the balun are derived by an even- and odd-mode approach. A prototype of such dual-band balun operating at 900 MHz and 1.8 GHz is designed, fabricated, and measured. Good agreement between the designed and measured responses proves the concept of the dual-band balun and validates the design formulas.


title: Second language experience modulates functional brain network for the native language production in bimodal bilinguals

abstract: The functional brain network of a bilingual's first language (L1) plays a crucial role in shaping that of his or her second language (L2). However, it is less clear how L2 acquisition changes the functional network of L1 processing in bilinguals. In this study, we demonstrate that in bimodal (Chinese spoken-sign) bilinguals, the functional network supporting L1 production (spoken language) has been reorganized to accommodate the network underlying L2 production (sign language). Using functional magnetic resonance imaging (fMRI) and a picture naming task, we find greater recruitment of the right supramarginal gyrus (RSMG), the right temporal gyrus (RSTG), and the right superior occipital gyrus (RSOG) for bilingual speakers versus monolingual speakers during L1 production. In addition, our second experiment reveals that these regions reflect either automatic activation of L2 (RSOG) or extra cognitive coordination (RSMG and RSTG) between both languages during L1 production. The functional connectivity between these regions, as well as between other regions that are L1- or L2-specific, is enhanced during L1 production in bimodal bilinguals as compared to their monolingual peers. These findings suggest that L1 production in bimodal bilinguals involves an interaction between L1 and L2, supporting the claim that learning a second language does, in fact, change the functional brain network of the first language.


title: Learning both Weights and Connections for Efficient Neural Networks

abstract: Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional networks fix the architecture before training starts; as a result, training cannot improve the architecture. To address these limitations, we describe a method to reduce the storage and computation required by neural networks by an order of magnitude without affecting their accuracy by learning only the important connections. Our method prunes redundant connections using a three-step method. First, we train the network to learn which connections are important. Next, we prune the unimportant connections. Finally, we retrain the network to fine tune the weights of the remaining connections. On the ImageNet dataset, our method reduced the number of parameters of AlexNet by a factor of 9x, from 61 million to 6.7 million, without incurring accuracy loss. Similar experiments with VGG-16 found that the number of parameters can be reduced by 13x, from 138 million to 10.3 million, again with no loss of accuracy.


title: A User-Centered Segmentation Method for Complex Historical Manuscripts Based on Document Graphs

abstract: In historical manuscripts, humans can detect handwritten words, lines, and decorations with lightness even if they do not know the language or the script. Yet for automatic processing this task has proven elusive, especially in the case of handwritten documents with complex layouts, which is why semiautomatic methods that integrate the human user into the process are needed. In this paper, we introduce a user-centered segmentation method based on document graphs and scribbling interaction. The graphs capture a sparse representation of the document's structure that can then be edited by the user with a stylus on a touch-sensitive screen. We evaluate the proposed method on a newly introduced database of historical manuscripts with complex layout and demonstrate, first, that the document graphs are already close to the desired segmentation and, second, that scribbling allows a natural and efficient interaction.


title: Intelligent Hybrid Approach for Android Malware Detection based on Permissions and API Calls

abstract: Android malware is rapidly becoming a potential threat to users. The number of Android malware is growing exponentially; they become significantly sophisticated and cause potential financial and information losses for users. Hence, there is a need for effective and efficient techniques to detect the Android malware applications. This paper proposes an intelligent hybrid approach for Android malware detection using the permissions and API calls in the Android application. The proposed approach consists of two steps. The first step involves finding the most significant permissions and Application Programming Interfaces (API) calls that leads to efficient discrimination between the malware and good ware applications. For this purpose, two features selection algorithms, Information Gain (IG) and Pearson CorrCoef (PC) are employed to rank the individual permissions and API’s calls based on their importance for classification. In the second step, the proposed new hybrid approach for Android malware detection based on the combination of the Adaptive neural fuzzy Inference System (ANFIS) with the Particle Swarm Optimization (PSO), is employed to differentiate between the malware and goodware Android applications (apps). The PSO is intelligently utilized to optimize the ANFIS parameters by tuning its membership functions to generate reliable and more precise fuzzy rules for Android apps classification. Using a dataset consists of 250 goodware and 250 malware apps collected from different recourse, the conducted experiments show that the suggested method for Android malware detection is effective and achieved an accuracy of 89%.


title: Reconfigurable Binding against FPGA Replay Attacks

abstract: The FPGA replay attack, where an attacker downgrades an FPGA-based system to the previous version with known vulnerabilities, has become a serious security and privacy concern for FPGA design. Current FPGA intellectual property (IP) protection mechanisms target the protection of FPGA configuration bitstreams by watermarking or encryption or binding. However, these mechanisms fail to prevent replay attacks. In this article, based on a recently reported PUF-FSM binding method that protects the usage of configuration bitstreams, we propose to reconfigure both the physical unclonable functions (PUFs) and the locking scheme of the finite state machine (FSM) in order to defeat the replay attack. We analyze the proposed scheme and demonstrate how replay attack would fail in attacking systems protected by the reconfigurable binding method. We implement two ways to build reconfigurable PUFs and propose two practical methods to reconfigure the locking scheme. Experimental results show that the two reconfigurable PUFs can generate significantly distinct responses with average reconfigurability of more than 40p. The reconfigurable locking schemes only incur a timing overhead less than 1p.


title: Brief Papers The Compact Genetic Algorithm

abstract: Introduces the compact genetic algorithm (cGA) which represents the population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. It processes each gene independently and requires less memory than the simple GA. The development of the compact GA is guided by a proper understanding of the role of the GA's parameters and operators. The paper clearly illustrates the mapping of the simple GA's parameters into those of an equivalent compact GA. Computer simulations compare both algorithms in terms of solution quality and speed. Finally, this work raises important questions about the use of information in a genetic algorithm, and its ramifications show us a direction that can lead to the design of more efficient GAs.


title: Masked presentations of emotional facial expressions modulate amygdala activity without explicit knowledge

abstract: Functional magnetic resonance imaging (fMRI) of the human brain was used to study whether the amygdala is activated in response to emotional stimuli, even in the absence of explicit knowledge that such stimuli were presented. Pictures of human faces bearing fearful or happy expressions were presented to 10 normal, healthy subjects by using a backward masking procedure that resulted in 8 of 10 subjects reporting that they had not seen these facial expressions. The backward masking procedure consisted of 33 msec presentations of fearful or happy facial expressions, their offset coincident with the onset of 167 msec presentations of neutral facial expressions. Although subjects reported seeing only neutral faces, blood oxygen level-dependent (BOLD) fMRI signal in the amygdala was significantly higher during viewing of masked fearful faces than during the viewing of masked happy faces. This difference was composed of significant signal increases in the amygdala to masked fearful faces as well as significant signal decreases to masked happy faces, consistent with the notion that the level of amygdala activation is affected differentially by the emotional valence of external stimuli. In addition, these facial expressions activated the sublenticular substantia innominata (SI), where signal increases were observed to both fearful and happy faces—suggesting a spatial dissociation of territories that respond to emotional valence versus salience or arousal value. This study, using fMRI in conjunction with masked stimulus presentations, represents an initial step toward determining the role of the amygdala in nonconscious processing.


title: Developing a grounded theory to explain the practices of self-organizing Agile teams

abstract: Software Engineering researchers are constantly looking to improve the quantity and quality of their research findings through the use of an appropriate research methodology. Over the last decade, there has been a sustained increase in the number of researchers exploring the human and social aspects of Software Engineering, many of whom have used Grounded Theory. We have used Grounded Theory as a qualitative research method to study 40 Agile practitioners across 16 software organizations in New Zealand and India and explore how these Agile teams self-organize. We use our study to demonstrate the application of Grounded Theory to Software Engineering. In doing so, we present (a) a detailed description of the Grounded Theory methodology in general and its application in our research in particular; (b) discuss the major challenges we encountered while performing Grounded Theory's various activities and our strategies for overcoming these challenges; and (c) we present a sample of our data and results to illustrate the artifacts and outcomes of Grounded Theory research.


title: The analysis of social networks

abstract: Many questions about the social organization of medicine and health services involve interdependencies among social actors that may be depicted by networks of relationships. Social network studies have been pursued for some time in social science disciplines, where numerous descriptive methods for analyzing them have been proposed. More recently, interest in the analysis of social network data has grown among statisticians, who have developed more elaborate models and methods for fitting them to network data. This article reviews fundamentals of, and recent innovations in, social network analysis using a physician influence network as an example. After introducing forms of network data, basic network statistics, and common descriptive measures, it describes two distinct types of statistical models for network data: individual-outcome models in which networks enter the construction of explanatory variables, and relational models in which the network itself is a multivariate dependent variable. Complexities in estimating both types of models arise due to the complex correlation structures among outcome measures.


title: Effect of bushing material on the electric field distribution of 150 kV GIS bushing

abstract: This paper deals with electric field analysis on 150 kV GIS bushing using various type of insulation material. The basic bushing insulation model is made from oil impregnated paper with permittivity equals 4. The dimension of the model of bushing before and after modification is same. Modification done by changed the insulation material bushing from oil impregnated paper with relative permittivity 4 to various material with different relative permittivity. The maximum electric field on ISO kV GIS bushing without modification is 80.54 kV/cm. The maximum electric field intensity is still below the electric field breakdown of oil impregnated paper (284 kV/cm). Changed in insulation material cause various electric field intensity because relative permittivity changed.


title: A Small Antipodal Vivaldi Antenna for Ultrawide-Band Applications

abstract: A compact antipodal Vivaldi antenna for ultrawide-band (UWB) applications is proposed. The antenna operates across the entire UWB spectrum from 3.1 to 10.6 GHz, and also has low cross-polarization levels and reasonable gain values over the same frequency band. Two different substrates, Rogers RO3006 and FR4, are considered, and results regarding return loss, far field pattern, phase response, group delay, and gain are presented.


title: Video quality measurement standards — Current status and trends

abstract: With the wide-spread use of digital video, quality considerations have become essential, and industry demand for video quality measurement standards is rising. Various organizations are working on such standards. This paper reviews some of the existing standards documents, on-going work and future trends in this space.


title: Semantic Labeling in Very High Resolution Images via a Self-Cascaded Convolutional Neural Network

abstract: Semantic labeling for very high resolution (VHR) images in urban areas, is of significant importance in a wide range of remote sensing applications. However, many confusing manmade objects and intricate fine-structured objects make it very difficult to obtain both coherent and accurate labeling results. For this challenging task, we propose a novel deep model with convolutional neural networks (CNNs), i.e., an end-to-end self-cascaded network (ScasNet). Specifically, for confusing manmade objects, ScasNet improves the labeling coherence with sequential global-to-local contexts aggregation. Technically, multi-scale contexts are captured on the output of a CNN encoder, and then they are successively aggregated in a self-cascaded manner. Meanwhile, for fine-structured objects, ScasNet boosts the labeling accuracy with a coarse-to-fine refinement strategy. It progressively refines the target objects using the low-level features learned by CNN's shallow layers. In addition, to correct the latent fitting residual caused by multi-feature fusion inside ScasNet, a dedicated residual correction scheme is proposed. It greatly improves the effectiveness of ScasNet. Extensive experimental results on three public datasets, including two challenging benchmarks, show that ScasNet achieves the state-of-the-art performance.


title: Survey of Authentication and Authorization for the Internet of Things

abstract: The Internet of Things is currently getting significant interest from the scientific community. Academia and industry are both focused on moving ahead in attempts to enhance usability, maintainability, and security through standardization and development of best practices. We focus on security because of its impact as one of the most limiting factors to wider Internet of Things adoption. Numerous research areas exist in the security domain, ranging from cryptography to network security to identity management. This paper provides a survey of existing research applicable to the Internet of Things environment at the application layer in the areas of identity management, authentication, and authorization. We survey and analyze more than 200 articles, categorize them, and present current trends in the Internet of Things security domain.


title: A Walk-based Model on Entity Graphs for Relation Extraction

abstract: We present a novel graph-based neural network model for relation extraction. Our model treats multiple pairs in a sentence simultaneously and considers interactions among them. All the entities in a sentence are placed as nodes in a fully-connected graph structure. The edges are represented with position-aware contexts around the entity pairs. In order to consider different relation paths between two entities, we construct up to l-length walks between each pair. The resulting walks are merged and iteratively used to update the edge representations into longer walks representations. We show that the model achieves performance comparable to the state-of-the-art systems on the ACE 2005 dataset without using any external tools.


Hand gesture recognition using a real-time tracking method and hidden Markov models q


title: Unsupervised Learning of Narrative Event Chains

abstract: Hand-coded scripts were used in the 1970-80s as knowledge backbones that enabled inference and other NLP tasks requiring deep semantic knowledge. We propose unsupervised induction of similar schemata called narrative event chains from raw newswire text. A narrative event chain is a partially ordered set of events related by a common protagonist. We describe a three step process to learning narrative event chains. The first uses unsupervised distributional methods to learn narrative relations between events sharing coreferring arguments. The second applies a temporal classifier to partially order the connected events. Finally, the third prunes and clusters self-contained chains from the space of events. We introduce two evaluations: the narrative cloze to evaluate event relatedness, and an order coherence task to evaluate narrative order. We show a 36% improvement over baseline for narrative prediction and 25% for temporal coherence.


title: Analysis of factors influencing the quality of intranet website based on WebQual approach case study in agency X

abstract: Improving the quality of websites, especially intranet should be performed continuously, because intranet is a medium of communication between employees in an organization. Users' perceptions on quality of intranet in agency X need to be measured to determine the level of user satisfaction. The method used in this research is survey method with WebQual approach. Sampling was performed purposively in some work units in agency X as research object. The WebQual approach consists of 4 variables: usability, Information Quality, Service Interaction and Overall Impression. The results show by ANNOVA test that the three independent variables of WebQual have significant effects on the quality of intranet website in agency X simultaneously. In individual parameter test, Usability and Service Interaction variables have significant (positive) effecton the quality of intranet, but Information Quality variable has no significant effect (negative). This research contributes for improvements website in agency X, and as suggestions at the time of re-design for improvements in certain parts (quality information), as a result of the research.


title: Reliability Comparison of a Dual Boost and a Triangular Current Mode Resonant-Transition PFC Converter Topology

abstract: In PFC rectifier systems for telecom applications, a general trend towards higher efficiency and also higher power density converter designs can be observed. Due to the passive components and the switching losses, a compromise between power density and efficiency has to be made. A possibility to overcome this limitation are soft-switched converter systems, which, however, often come along with a higher component count, a higher component stress and/or a more complex control. These issues might impair the system reliability although the lower losses might reduce the thermal stress of the components. Therefore, in this work the reliability of a relatively simple, hard switched dual boost PFC rectifier and a soft switched triangular current mode PFC is evaluated and the components influencing the reliability most are identified. The evaluation is based on the prediction models given in IEC TR 62380.


title: INCREMENTAL LEARNING OF CONVOLUTIONAL NEURAL NETWORKS

abstract: Convolutional neural networks provide robust feature extraction with ability to learn complex, highdimensional non-linear mappings from collection of examples. To accommodate new, previously unseen data, without the need of retraining the whole network architecture we introduce an algorithm for incremental learning. This algorithm was inspired by AdaBoost algorithm. It utilizes ensemble of modified convolutional neural networks as classifiers by generating multiple hypotheses. Furthermore, with this algorithm we can work with the confidence score of classification, which can play crucial importance in specific real world tasks. This approach was tested on handwritten numbers classification. The classification error achieved by this approach was highly comparable with non-incremental learning.


title: Limits and sustainable interaction design: Obsolescence in a future of collapse and resource scarcity

abstract: Electronic waste, caused by the advancements of technology and its rapidly increasing obsolescence, represents a major threat to environmental sustainability. Research in Sustainable HCI has proposed a variety of solutions to tackle this issue, but has yet to create a major impact in product design. While currently industry’s goals are opposed to research’s concepts of addressing obsolescence, a future of collapse and resource scarcity requires a revisit of those contributions: changes in society at large, such as a decrease of resource availability, different needs, requirements, and desires of the consumer, but also new directions of industry and marketing might enable researchers to bring their old concepts into practice. We take a look at a variety of obsolescence-related research in Sustainable HCI and foreshadow its potential for such a future of collapse and resource scarcity.


title: Navigating from hippocampus to parietal cortex

abstract: The navigational system of the mammalian cortex comprises a number of interacting brain regions. Grid cells in the medial entorhinal cortex and place cells in the hippocampus are thought to participate in the formation of a dynamic representation of the animal's current location, and these cells are presumably critical for storing the representation in memory. To traverse the environment, animals must be able to translate coordinate information from spatial maps in the entorhinal cortex and hippocampus into body-centered representations that can be used to direct locomotion. How this is done remains an enigma. We propose that the posterior parietal cortex is critical for this transformation.


title: Automated assessment and experiences of teaching programming

abstract: This article reports on the design, implementation, and usage of the CourseMarker (formerly known as CourseMaster) courseware Computer Based Assessment (CBA) system at the University of Nottingham. Students use CourseMarker to solve (programming) exercises and to submit their solutions. CourseMarker returns immediate results and feedback to the students. Educators author a variety of exercises that benefit the students while offering practical benefits. To date, both educators and students have been hampered by CBA software that has been constructed to assess text-based or multiple-choice answers only. Although there exist a few CBA systems with some capability to automatically assess programming coursework, none assess Java programs and none are as flexible, architecture-neutral, robust, or secure as the CourseMarker CBA system.