title: Feasibility Analysis for Estimation of Blood Pressure and Heart Rate using A Smart Eye Wear

abstract: The major issue with hypertension is the fact that it does not have any symptoms also measurements are highly variable in nature. However, high blood pressure has severe effects on a person's health. This poses a requirement for continuous blood pressure monitoring and analysis of people suffering from chronic condition. In this paper we propose PPG based continuous blood pressure and heart rate monitoring system in form factor of a Smart Eye wear. We do a feasibility analysis of the idea and show that it is possible to determine blood pressure and heart rate by sensing PPG from the side of head region (temple) where a spectacle frame would fit. The advantage of Smart Eye Wear over other wearable device is that it provides better contact, minimal motion artifact and maintains uniform pressure without causing any trouble to user.


title: Learning-Based Uplink Interference Management in 4G LTE Cellular Systems

abstract: LTE's uplink (UL) efficiency critically depends on how the interference across different cells is controlled. The unique characteristics of LTE's modulation and UL resource assignment poses considerable challenges in achieving this goal because most LTE deployments have 1:1 frequency reuse, and the uplink interference can vary considerably across successive time-slots. In this paper, we propose LeAP, a measurement data-driven machine learning paradigm for power control to manage uplink interference in LTE. The data-driven approach has the inherent advantage that the solution adapts based on network traffic, propagation, and network topology, which is increasingly heterogeneous with multiple cell-overlays. LeAP system design consists of the following components: 1) design of user equipment (UE) measurement statistics that are succinct, yet expressive enough to capture the network dynamics, and 2) design of two learning-based algorithms that use the reported measurements to set the power control parameters and optimize the network performance. LeAP is standards-compliant and can be implemented in a centralized self-organized networking (SON) server resource (cloud). We perform extensive evaluations using radio network plans from a real LTE network operational in a major metro area in the US. Our results show that, compared to existing approaches, LeAP provides $4.9\times$ gain in the 20th percentile of user data rate, $3.25\times$ gain in median data rate.


title: Creating a Similarity Graph from WordNet

abstract: The paper addresses the problem of modeling the relationship between the words in the English language using a similarity graph. The mathematical model stores data about the strength of the relationship between words expressed as a decimal number. Both structured data from WordNet, such as that the word "canine" is a hypernym (i.e., kind of) of the word "dog", and textual descriptions, such as that the definition of the word "dog" is: "a member of the genus Canis that has been domesticated by man since prehistoric times", are used in creating the graph. The quality of the graph data is validated by comparing the similarity of pairs of words using our software that uses the graph with results of studies that are performed with human subjects. To the best of our knowledge, our software produces better correlation with the results of both the Miller and Charles study and the WordSimilarity-353 study than any other published research.


Bio-medical ( EMG ) Signal Analysis and Feature Extraction Using Wavelet Transform


Distilling Knowledge from Ensembles of Neural Networks for Speech Recognition


title: Stock market forecast using bio-inspired computing

abstract: Stock Markets represent a complex, nonlinear, ever-changing, trading systems where traders (investors) are competing for profit. Stock market forecasting have attracted some special attention in the last years. However, the irregular nature and the complex behavior of stock data transform forecasting into a very hard task, and forecasting stock index has proven to be difficult. Recently, Bio-Inspired Algorithms has emerged extremely useful for forecasting dynamical systems e.g., stock market. Though a lot of research has already been done on various stock markets, relatively less attention is given for Dhaka Stock Exchange (DSE), Bangladesh in comparison to them. This work presents a stock market forecasting system based on hybridized Bio-Inspired Algorithms for DSE. Experimental result shows that the proposed system is very useful for forecasting stock market. Another contribution of this paper is to present a review of literature application of Bio-Inspired Algorithms for stock market forecasting.


title: Ultra-Wideband Quasi-Yagi Antenna Using Dual-Resonant Driver and Integrated Balun of Stepped Impedance Coupled Structure

abstract: A quasi-Yagi antenna that has an ultra-wideband performance is presented. To enable that performance, the antenna utilizes a dual-resonant driver and a balun formed using a stepped-impedance coupled structure. The driver is designed to be dual-resonant by loading it with an inductor in the form of short section of narrow microstrip line at a certain position. The balun includes a T-junction of microstrip lines and two pairs of stepped-impedance coupled lines. The simulated and measured performance of the integrated antenna indicate less than -10 dB reflection coefficient, 3.6-4.5 dBi gain, 13-17 dB front-to-back ratio, less than -19 dB cross-polarization and more than 90% efficiency across more than 75% fractional bandwidth centered at 7.5 GHz.


title: Ball impact localization on table tennis rackets using piezo-electric sensors

abstract: In this paper, we present an approach for a ball impact localization on table tennis rackets using piezo-electric sensors. Three sensors were used as vibration sensors at the outer edge of the racket. We analyzed ball impact vibration appearances and measured time differences of arriving wave fronts for each sensor pair. A study was conducted comprising six different racket-rubber combinations. Firstly, we calculated a time difference distribution model based on training sets of impact data constructed by a grid of measuring points. Secondly, this model was used to estimate the impact position of test data sets using three different methods. Best performance for each racket-rubber combination was achieved with a linear regression based method yielding a RMSE in x-direction (transversal racket direction) of 22.1 mm and a RMSE of 19.8 mm in y-direction (longitudinal racket direction). The measuring system is small enough to be completely invisibly integrated into table tennis rackets. This contribution is the first attempt to address a ball impact localization system for table tennis rackets that has the potential to be used in real-world training exercises and competitions to assist table tennis players during matches and to support the athletes' training progress.


title: 3D printed soft actuators for a legged robot capable of navigating unstructured terrain

abstract: Soft robots have recently demonstrated impressive abilities to adapt to objects and their environment with limited sensing and actuation. However, mobile soft robots are typically fabricated using laborious molding processes that result in limited actuated degrees of freedom and hence limited locomotion capabilities. In this paper, we present a 3D printed robot with bellowed soft legs capable of rotation about two axes. This allows our robot to navigate rough terrain that previously posed a significant challenge to soft robots. We present models and FEM simulations for the soft leg modules and predict the robot locomotion capabilities. We use finite element analysis to simulate the actuation characteristics of these modules. We then compared the analytical and computational results to experimental results with a tethered prototype. The experimental soft robot is capable of lifting its legs 5.3 cm off the ground and is able to walk at speeds up to 20 mm/s (0.13 bl/s). This work represents a practical approach to the design and fabrication of functional mobile soft robots.


Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information


title: Prediction of Employee Turnover in Organizations using Machine Learning Algorithms

abstract: Employee turnover has been identified as a key issue for organizations because of its adverse impact on work place productivity and long term growth strategies. To solve this problem, organizations use machine learning techniques to predict employee turnover. Accurate predictions enable organizations to take action for retention or succession planning of employees. However, the data for this modeling problem comes from HR Information Systems (HRIS); these are typically under-funded compared to the Information Systems of other domains in the organization which are directly related to its priorities. This leads to the prevalence of noise in the data that renders predictive models prone to over-fitting and hence inaccurate. This is the key challenge that is the focus of this paper, and one that has not been addressed historically. The novel contribution of this paper is to explore the application of Extreme Gradient Boosting (XGBoost) technique which is more robust because of its regularization formulation. Data from the HRIS of a global retailer is used to compare XGBoost against six historically used supervised classifiers and demonstrate its significantly higher accuracy for predicting employee turnover.


title: A Portable 3-D Imaging FMCW MIMO Radar Demonstrator With a $24\times 24$ Antenna Array for Medium-Range Applications

abstract: Multiple-input multiple-output (MIMO) radars have been shown to improve target detection for surveillance applications thanks to their proven high-performance properties. In this paper, the design, implementation, and results of a complete 3-D imaging frequency-modulated continuous-wave MIMO radar demonstrator are presented. The radar sensor working frequency range spans between 16 and 17 GHz, and the proposed solution is based on a 24-transmitter and 24-receiver MIMO radar architecture, implemented by time-division multiplexing of the transmit signals. A modular approach based on conventional low-cost printed circuit boards is used for the transmit and receive systems. Using digital beamforming algorithms and radar processing techniques on the received signals, a high-resolution 3-D sensing of the range, azimuth, and elevation can be calculated. With the current antenna configuration, an angular resolution of 2.9° can be reached. Furthermore, by taking advantage of the 1-GHz bandwidth of the system, a range resolution of 0.5 m is achieved. The radio-frequency front-end, digital system and radar signal processing units are here presented. The medium-range surveillance potential and the high-resolution capabilities of the MIMO radar are proved with results in the form of radar images captured from the field measurements.


title: A polyphenolic pigment from black tea

abstract: A yellow polyphenolic compound has been isolated from black tea (fermented leaves of Camellia sinensis) and its structure characterized using a number of spectroscopic techniques. This compound, theacitrin A, represents a new class of polyphenolic pigments in black tea.


title: Robust sparse coding for face recognition

abstract: Recently the sparse representation (or coding) based classification (SRC) has been successfully used in face recognition. In SRC, the testing image is represented as a sparse linear combination of the training samples, and the representation fidelity is measured by the l 2 -norm or l 1 -norm of coding residual. Such a sparse coding model actually assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be accurate enough to describe the coding errors in practice. In this paper, we propose a new scheme, namely the robust sparse coding (RSC), by modeling the sparse coding as a sparsity-constrained robust regression problem. The RSC seeks for the MLE (maximum likelihood estimation) solution of the sparse coding problem, and it is much more robust to outliers (e.g., occlusions, corruptions, etc.) than SRC. An efficient iteratively reweighted sparse coding algorithm is proposed to solve the RSC model. Extensive experiments on representative face databases demonstrate that the RSC scheme is much more effective than state-of-the-art methods in dealing with face occlusion, corruption, lighting and expression changes, etc.


title: Adversarial learning

abstract: Many classification tasks, such as spam filtering, intrusion detection, and terrorism detection, are complicated by an adversary who wishes to avoid detection. Previous work on adversarial classification has made the unrealistic assumption that the attacker has perfect knowledge of the classifier [2]. In this paper, we introduce the adversarial classifier reverse engineering (ACRE) learning problem, the task of learning sufficient information about a classifier to construct adversarial attacks. We present efficient algorithms for reverse engineering linear classifiers with either continuous or Boolean features and demonstrate their effectiveness using real data from the domain of spam filtering.


title: Cognitive Database: A Step towards Endowing Relational Databases with Artificial Intelligence Capabilities

abstract: We propose Cognitive Databases, an approach for transparently enabling Artificial Intelligence (AI) capabilities in relational databases. A novel aspect of our design is to first view the structured data source as meaningful unstructured text, and then use the text to build an unsupervised neural network model using a Natural Language Processing (NLP) technique called word embedding. This model captures the hidden inter-/intra-column relationships between database tokens of different types. For each database token, the model includes a vector that encodes contextual semantic relationships. We seamlessly integrate the word embedding model into existing SQL query infrastructure and use it to enable a new class of SQL-based analytics queries called cognitive intelligence (CI) queries. CI queries use the model vectors to enable complex queries such as semantic matching, inductive reasoning queries such as analogies, predictive queries using entities not present in a database, and, more generally, using knowledge from external sources. We demonstrate unique capabilities of Cognitive Databases using an Apache Spark based prototype to execute inductive reasoning CI queries over a multi-modal database containing text and images. We believe our first-of-a-kind system exemplifies using AI functionality to endow relational databases with capabilities that were previously very hard to realize in practice.


title: Assessing therapeutic effectiveness of scalp treatments for dandruff and seborrheic dermatitis, part 1: a reliable and relevant method based on the adherent scalp flaking score (ASFS)

abstract: Background: Dandruff and seborrheic dermatitis (D/SD) are common and troublesome scalp conditions with the primary signs and symptoms being presence of skin flakes, pruritus, a tight feeling, and sometimes erythema. Aim: To demonstrate the reliability and relevance of a clinical model for the assessment of therapeutic efficacy of a treatment using the Adherent Scalp Flaking Score (ASFS) method to quantitate the flaking severity. Methods: Six randomized, double-blind, parallel design studies were conducted in either North America or Asia with subjects suffering from dandruff using the ASFS grading method before and after a 3-week test product treatment period. Results: Treatment with a commercial potentiated 1% zinc pyrithione (ZPT) shampoo resulted in statistically significant (p < 0.0001) improvements in total ASFS compared with the placebo cosmetic shampoo. Results were consistent across all studies, geographies, and product usage protocols (controlled on-site versus home use conditions), and were assoc...


title: Online Supermarkets : Emerging Strategies And Business Models In The UK

abstract: The Internet has facilitated the emergence of new strategies and business models in several industries. In the UK, significant changes are happening in supermarket retailing with the introduction of online shopping, especially in terms of channel development and coordination, business scope redefinition, the development of fulfilment centre model and core processes, new ways of customer value creation, and online partnerships. In fact the role of online supermarket itself has undergone some significant changes in the last few years. Based on recent empirical evidence gathered in the UK, this paper will illustrate current developments in the strategies and business models of online supermarket retailing. The main evidence has been collected through an online survey of 6 online supermarkets and in-depth case studies of two leading players. Some of the tendencies are comparable to what happened in retail banking with the introduction of Internet banking, but other tendencies are unique to the supermarket retailing industry. This is a rapidly evolving area and further studies are clearly needed.


title: Nanowire Solar Cells

abstract: The nanowire geometry provides potential advantages over planar waferbased or thin-film solar cells in every step of the photoconversion process. These advantages include reduced reflection, extreme light trapping, improved band gap tuning, facile strain relaxation, and increased defect tolerance. These benefits are not expected to increase the maximum efficiency above standard limits; instead, they reduce the quantity and quality of material necessary to approach those limits, allowing for substantial cost reductions. Additionally, nanowires provide opportunities to fabricate complex single-crystalline semiconductor devices directly on low-cost substrates and electrodes such as aluminum foil, stainless steel, and conductive glass, addressing another major cost in current photovoltaic technology. This review describes nanowire solar cell synthesis and fabrication, important characterization techniques unique to nanowire systems, and advantages of the nanowire geometry.


title: Detecting Phishing Websites using Automation of Human Behavior

abstract: In this paper, we propose a technique to detect phishing attacks based on behavior of human when exposed to fake website. Some online users submit fake credentials to the login page before submitting their actual credentials. He/She observes the login status of the resulting page to check whether the website is fake or legitimate. We automate the same behavior with our application (FeedPhish) which feeds fake values into login page. If the web page logs in successfully, it is classified as phishing otherwise it undergoes further heuristic filtering. If the suspicious site passes through all heuristic filters then the website is classified as a legitimate site. As per the experimentation results, our application has achieved a true positive rate of 97.61%, true negative rate of 94.37% and overall accuracy of 96.38%. Our application neither demands third party services nor prior knowledge like web history, whitelist or blacklist of URLS. It is able to detect not only zero-day phishing attacks but also detects phishing sites which are hosted on compromised domains.


title: Optical and Electronic Packaging Processes for Silicon Photonic Systems

abstract: Fiber optic interconnection processes and hybrid integration of electronic devices for high speed Si photonic systems are presented. Thermal effects arising from these hybrid integration processes are also investigated. An overview of ePIXfab which offers affordable access to an advanced Si photonic foundry service is also presented. This includes the presentation of fundamental photonic packaging design rules which can greatly reduce the time and cost associated with the development of complex Si photonic devices.


title: Digital beamforming and MIMO SAR: Review and new concepts

abstract: Spaceborne Synthetic Aperture Radar (SAR) is a unique tool for large scale Earth observation, but the current generation of SAR sensors suffers from some fundamental limitations with regard to their imaging and mapping capabilities. To overcome these limitations, several new instrument architectures and SAR imaging modes have been suggested that employ digital beamforming (DBF), multiple aperture data recording and MIMO SAR techniques. This paper provides a critical review of these techniques and makes some suggestions for further improvements. Special attention is given to advanced DBF and MIMO SAR concepts for ultra-wide swath imaging.


title: Immunology: Another manifestation of GOD

abstract: In studies of the evolution of the adaptive immune system, the lamprey has been an unlikely centre of attention. These studies now provide evidence of a fascinating variation on how such a system can operate.


title: Maltparser: A language-independent system for data-driven dependency parsing

abstract: Parsing unrestricted text is useful for many language technology applications but requires parsing methods that are both robust and efficient. MaltParser is a language-independent system for data-driven dependency parsing that can be used to induce a parser for a new language from a treebank sample in a simple yet flexible manner. Experimental evaluation confirms that MaltParser can achieve robust, efficient and accurate parsing for a wide range of languages without language-specific enhancements and with rather limited amounts of training data.


title: Delays and interruptions : A self-perpetuating paradox of communication technology use

abstract: In contemporary knowledge work organizations, work is often accomplished through communication. Consequently, communication disruptions often translate into work disruptions. In this paper, we identify two types of communication disruptions with implications for the relative organization of work: delays and interruptions. Communication delays contribute to work disorganization when a worker is unable to move forward with a task due to insufficient information, while interruptions derail the flow of activities directed toward the accomplishment of a task. Communication technologies are often designed with the intention of improving work organization by reducing communication delays (first-order effect), but the use of these technologies may, in practice, inadvertently contribute to an increase in work interruptions (second-order effect). We illustrate these first and second-order impacts of communication media use in a descriptive model. Then, using this model as our point of departure, we draw on prior research on personal control, relationships, and organizational culture to offer testable propositions regarding likely worker responses (third-order effect) to either communication delays or interruptions with further implications for the organization of work. Our argument suggests that communication technology use may not result in either more or less organized work overall but, rather, may simply shift the locus of control over the flow of work.


title: A COMPACT, PLANAR PLATE-TYPE ANTENNA FOR 2.4/5.2/5.8-GHz TRI-BAND WLAN OPERATION

abstract: A novel, tri-band, planar plate-type antenna made of a compact metal plate for wireless local area network (WLAN) applications in the 2.4GHz (2400{2484MHz), 5.2GHz (5150{ 5350MHz), and 5.8GHz (5725{5825MHz) bands is presented. The antenna was designed in a way that the operating principle includes dipole and loop resonant modes to cover the 2.4/5.2 and 5.8GHz bands, respectively. The antenna comprises a larger radiating arm and a smaller loop radiating arm, which are connected to each other at the signal ground point. The antenna can easily be fed by using a 50› mini-coaxial cable and shows good radiation performance. Details of the design are described and discussed in the article.


Body Area Networks (BANs) – An Overview with Smart Sensors based Telemedical Monitoring System


title: Increased cortical porosity and reduced cortical thickness of the proximal femur are associated with nonvertebral fracture independent of Fracture Risk Assessment Tool and Garvan estimates in postmenopausal women

abstract: The Fracture Risk Assessment Tool (FRAX) and Garvan Calculator have improved the individual prediction of fracture risk. However, additional bone measurements that might enhance the predictive ability of these tools are the subject of research. There is increasing interest in cortical parameters, especially cortical porosity. Neither FRAX nor Garvan include measurements of cortical architecture, important for bone strength, and providing independent information beyond the conventional approaches. We tested the hypothesis that cortical parameters are associated with fracture risk, independent of FRAX and Garvan estimates. This nested case-control study included 211 postmenopausal women aged 54–94 years with nonvertebral fractures, and 232 controls from the Tromso Study in Norway. We assessed FRAX and Garvan 10-year risk estimates for fragility fracture, and quantified femoral subtrochanteric cortical porosity, thickness, and area from computed tomography images using StrAx1.0 software. Per standard deviation higher cortical porosity, thinner cortices, and smaller cortical area, the odds ratio (95% confidence interval) for fracture was 1.71 (1.38–2.11), 1.79 (1.44–2.23), and 1.52 (1.19–1.95), respectively. Cortical porosity and thickness, but not area, remained associated with fracture when adjusted for FRAX and Garvan estimates. Adding cortical porosity and thickness to FRAX or Garvan resulted in greater area under the receiver operating characteristic curves. When using cortical porosity (>80th percentile) or cortical thickness ( 20%), 45.5% and 42.7% of fracture cases were identified, respectively. Using the same cutoffs for cortical porosity or thickness combined with Garvan (threshold >25%), 51.2% and 48.3% were identified, respectively. Specificity for all combinations ranged from 81.0–83.6%. Measurement of cortical porosity or thickness identified 20.4% and 17.5% additional fracture cases that, were unidentified using FRAX alone, and 16.6% and 13.7% fracture cases unidentified using Garvan alone. In conclusion, cortical parameters may help to improve identification of women at risk for fracture.


title: Poselets: Body part detectors trained using 3D human pose annotations

abstract: We address the classic problems of detection, segmentation and pose estimation of people in images with a novel definition of a part, a poselet. We postulate two criteria (1) It should be easy to find a poselet given an input image (2) it should be easy to localize the 3D configuration of the person conditioned on the detection of a poselet. To permit this we have built a new dataset, H3D, of annotations of humans in 2D photographs with 3D joint information, inferred using anthropometric constraints. This enables us to implement a data-driven search procedure for finding poselets that are tightly clustered in both 3D joint configuration space as well as 2D image appearance. The algorithm discovers poselets that correspond to frontal and profile faces, pedestrians, head and shoulder views, among others. Each poselet provides examples for training a linear SVM classifier which can then be run over the image in a multiscale scanning mode. The outputs of these poselet detectors can be thought of as an intermediate layer of nodes, on top of which one can run a second layer of classification or regression. We show how this permits detection and localization of torsos or keypoints such as left shoulder, nose, etc. Experimental results show that we obtain state of the art performance on people detection in the PASCAL VOC 2007 challenge, among other datasets. We are making publicly available both the H3D dataset as well as the poselet parameters for use by other researchers.


title: New techniques for automated architectural reconstruction from photographs

abstract: We investigate a strategy for reconstructing of buildings from multiple (uncalibrated) images. In a similar manner to the Facade approach we first generate a coarse piecewise planar model of the principal scene planes and their delineations, and then use these facets to guide the search for indentations and protrusions such as windows and doors. However, unlike the Facade approach which involves manual selection and alignment of the geometric primitives, the strategy here is fully automatic.There are several points of novelty: first we demonstrate that the use of quite generic models together with particular scene constraints (the availability of several principal directions) is sufficiently powerful to enable successful reconstruction of the targeted scenes. Second, we develop and refine a technique for piecewise planar model fitting involving sweeping polygonal primitives, and assess the performance of this technique. Third, lines at infinity are constructed from image correspondences and used to sweep planes in the principal directions.The strategy is illustrated on several image triplets of College buildings. It is demonstrated that convincing texture mapped models are generated which include the main walls and roofs, together with inset windows and also protruding (dormer) roof windows.


title: A Random Walk Through Eigenspace

abstract: SUMMARY It has been over a decade since the “Eigenfaces” approach to automatic face recognition, and other appearancebased methods, made an impression on the computer vision research community and helped spur interest in vision systems being used to support biometrics and human-computer interface. In this paper I give a personal viewof the original motivation for the work, some of the strengths and limitation of the approach, and progress in the years since. Appearance-based approaches to recognition complement feature- or shape-based approaches, and a practical face recognition system should have elements of both. Eigenfaces is not a general approach to recognition, but rather one tool out of many to be applied and evaluated in the appropriate context.


title: An ancient anion‐binding structural module in RNA and DNA helicases

abstract: RNA and DNA helicases manipulate or translocate along single strands of nucleic acids by grasping them using a conserved structural motif. We have examined the available crystal structures of helicases of the two principal superfamilies, SF1 and SF2, and observed that the most conserved interactions with the nucleic acid occur between the phosphosugar backbone of a trinucleotide and the three strand-helix loops within a (β-strand/α-helix)3 structural module. At the first and third loops is a conserved hydrogen-bonded feature called a thr-motif, often seen at α-helical N-termini, with the threonine as the N-cap residue. These loops can be aligned with few insertions or deletions, and their main chain atoms are structurally congruent amongst the family members and between the two modules found as tandem pairs in all SF1 and SF2 proteins. The other highly conserved interactions with nucleic acid involve mainchain NH groups, often at the helical N-termini, interacting with phosphate groups. We comment on how the sequence motifs that are commonly used to identify helicases map to locations on the module and discuss the implications of the conserved orientation of nucleic acid on the surface of the module for directional stepping along DNA or RNA. Proteins 2010. © 2010 Wiley-Liss, Inc.


title: Credit Card Fraud Detection: A Hybrid Approach Using Fuzzy Clustering & Neural Network

abstract: Due to the rapid progress of the e-commerce and online banking, use of credit cards has increased considerably leading to a large number of fraud incidents. In this paper, we have proposed a novel approach towards credit card fraud detection in which the fraud detection is done in three phases. The first phase does the initial user authentication and verification of card details. If the check is successfully cleared, then the transaction is passed to the next phase where fuzzy c-means clustering algorithm is applied to find out the normal usage patterns of credit card users based on their past activity. A suspicion score is calculated according to the extent of deviation from the normal patterns and thereby the transaction is classified as legitimate or suspicious or fraudulent. Once a transaction is found to be suspicious, neural network based learning mechanism is applied to determine whether it was actually a fraudulent activity or an occasional deviation by a genuine user. Extensive experimentation with stochastic models shows that the combined use of clustering technique along with learning helps in detecting fraudulent activities effectively while minimizing the generation of false alarms.


title: Design and Analysis of Permanent Magnet Linear Synchronous Motor with Special Pole Shape

abstract: Permanent magnet linear synchronous motors (PMLSMs), which have great advantages, such as simple structure, high positioning accuracy, good performance, high thrust force density, high dynamic response, are widely used in high precision direct drive field. As a research focus and difficulty, the force ripper and dynamic characteristic of PMLSM is important for its performance. In this paper, the present research status and characteristics of PMLSM is discussed firstly. Then a 16-pole 15-slot PMLSM is built used finite element method (FEM). Based on the finite element model, the thrust force ripple and minimization technologies, the power factor and efficiency of PMLSM with different load, were solved and analyzed, and an optimized model was got. The analysis result of thrust and magnetic flux density shows that through the new geometry of primary tooth shape, named optimized model, can improve the average thrust and reduce thrust fluctuations. The optimized model also has higher the power factor and efficiency. The back EMF of original and optimized model are also compared and discussed.


title: A Scale-Out Blockchain for Value Transfer with Spontaneous Sharding

abstract: Blockchain technology, sometimes known by its applications like cryptocurrencies, suffers from the scalability problem mainly due to the unideal throughput of Byzantine fault tolerance consensus algorithms. Recently, many blockchains have been proposed to achieve scale-out throughput, i.e., the throughput of the system grows with the number of nodes. In this paper, we propose a novel scale-out blockchain system for the most considered type of ledgers, we call Value Transferring Ledgers, in which a transaction is a transfer of positive value from one node to another. In our system, nodes commonly agree on a main chain and individually generate their own chains. We propose a locally executable validation scheme with uncompromised validity and scalable throughput. Furthermore, a smart transacting algorithm is introduced so that the system is spontaneously sharded for individual transactions and achieves scale-out throughput.


International Journal of Advanced Robotic Systems Mechanical Design of a Hybrid Leg Exoskeleton to Augment Load-Carrying for Walking Regular Paper


title: Gas Turbine Performance.

abstract: Industrial gas turbines show performance characteristics that distinctly depend on ambient and operating conditions. They are not only influenced by site elevation, ambient temperature, and relative humidity, but also by the speed of the driven equipment, the fuel, and the load conditions. Proper application of gas turbines requires consideration of these factors. This tutorial explains these characteristics based on the performance of the engine compressor, the combustor and the turbine section, and certain control strategies. It introduces fundamental concepts that help to understand the flow of energy between the components. Additionally, methods are introduced that allow the use of data for trending and comparison purposes. The impact of component degradation on individual component performance, as well as overall engine performance is discussed, together with strategies to reduce the impact of degradation.


title: Unsupervised Video Summarization Based on Consistent Clip Generation

abstract: It becomes increasingly convenient for people to shoot, store and share videos of their daily life on social networks, which makes it increasingly difficult to find desired video content from the massive video data. Therefore, it is necessary to develop automatic video summarization methods. Previous methods focus on category-specific videos and build various complex models, and recent deep learning approaches need a large amount of annotated data to train the network. This paper proposes a new unsupervised video summarization method, which selects a group of highlight clips with self-consistency. Specifically, we propose a consistent clip generation method, i.e. the cutting-merging-adjusting scheme, by exploring the clip similarity and the local similarity. The consistent clips are obtained by merging similar clips iteratively and adjusting the boundaries of each consistent clip to remove the inconsistency of the boundaries between clips and logical events. Then, we estimate the importance score of each consistent clip by computing the interestingness score of its frames, based on which we select the top important clips to generate a video summary. Experimental results show that our method is able to generate high-quality summaries which are closer to human perception, compared to several existing methods.


title: SLAYER: Spike Layer Error Reassignment in Time

abstract: Configuring deep Spiking Neural Networks (SNNs) is an exciting research avenue for low power spike event based computation. However, the spike generation function is non-differentiable and therefore not directly compatible with the standard error backpropagation algorithm. In this paper, we introduce a new general backpropagation mechanism for learning synaptic weights and axonal delays which overcomes the problem of non-differentiability of the spike function and uses a temporal credit assignment policy for backpropagating error to preceding layers. We describe and release a GPU accelerated software implementation of our method which allows training both fully connected and convolutional neural network (CNN) architectures. Using our software, we compare our method against existing SNN based learning approaches and standard ANN to SNN conversion techniques and show that our method achieves state of the art performance for an SNN on the MNIST, NMNIST, DVS Gesture, and TIDIGITS datasets.


title: LIMERIC: A Linear Adaptive Message Rate Algorithm for DSRC Congestion Control

abstract: Wireless vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) communication holds great promise for significantly reducing the human and financial costs of vehicle collisions. A common characteristic of this communication is the broadcast of a device's core state information at regular intervals (e.g., vehicle speed and location or traffic signal state and timing). Unless controlled, the aggregate of these broadcasts will congest the channel under dense traffic scenarios, reducing the effectiveness of collision avoidance applications that use transmitted information. Active congestion control using distributed techniques is a topic of great interest for establishing the scalability of this technology. This paper defines a new adaptive congestion control algorithm that can be applied to the message rate of devices in this vehicular environment. While other published approaches rely on binary control, the LInear MEssage Rate Integrated Control (LIMERIC) algorithm takes advantage of full-precision control inputs that are available on the wireless channel. The result is provable convergence to fair and efficient channel utilization in the deterministic environment, under simple criteria for setting adaptive parameters. This “perfect” convergence avoids the limit cycle behavior that is inherent to binary control. We also discuss several practical aspects associated with implementing LIMERIC, including guidelines for the choice of system parameters to obtain desired utilization outcomes, a gain saturation technique that maintains robust convergence under all conditions, convergence with asynchronous updates, and using channel load to determine the aggregate message rate that is observable at a receiver. This paper also extends the convergence analysis for two important cases, i.e., measurement noise in the input signal and delay in the update process. This paper illustrates key analytical results using MATLAB numerical results and employs standard NS-2 simulations to demonstrate the performance of LIMERIC in several high-density scenarios.


title: Gamma-aminobutyric Acid (GABA) Production by Eight Common Bacterial Pathogens

abstract: Gamma-aminobutyric acid (GABA) is a potent amino acid neurotransmitter that suppresses normal neuronal activity in the central nervous system. Recently it has been suggested that GABA may play an important role in the pathogenesis of hepatic encephalopathy. In the present study GABA production by 8 common bacterial pathogens was measured during midlog, stationary and mid-death phases of growth. All bacteria produced some GABA (range: 160–50 250 pmole/ml) with the majority of GABA production occurring during the mid-death phase of growth. These results suggest that the depressed levels of consciousness seen in patients with overwhelming sepsis or advanced liver disease and extraintestinal infection may in part be secondary to increased bacterial GABA production.


title: A 0.5-28GB/S Wireline Tranceiver with 15-Tap DFE and Fast-Locking Digital CDR in 7NM FinFET

abstract: A 28Gb/s NRZ wireline transceiver is implemented in 7nm FinFET. A transformer-based LC-PLL sends a single-phase differential clock to the voltage-mode transmitter and the receiver. Local multi-phase clocks are generated in each TX/RX lane to support digital phase interpolation. The receiver equalization consists of a single-stage CTLE that performs both high-frequency peaking and long-tail cancellation, a two-stage programmable gain amplifier, and a 15-tap DFE. The digital CDR achieves <14ns lock time in a synchronous burst-mode operation. At 28Gb/s, the transceiver achieves <1E-15 BER over 30dB channel while consuming 283mW.


title: Automatically profiling the author of an anonymous text

abstract: A syringe infusion or other patient health care fluid delivery arrangement or system having a substantially constant force syringe holder/driver for a fluid-dispensing syringe to which an on-off valve and effectively bendably flexible tube connecting set are connected for delivery of a health care fluid to a patient delivery site at a preselected preset rate which is precision self-metered and determined by the fluid pressure and viscosity and the resistence to flow of fluid from the syringe and through the tube connecting set. The tube connecting set may be precision made to enable enhancement of predictability of fluid flow rate therethrough, and the various sets may be individually provided with indicia indicative of flow rate for given fluid and fluid pressure conditions. A single responsible health care professional may fill a syringe with a prescribed dosage and concentration of fluid to be administered to a patient and may preselect the delivery rate, by selecting and assembling a given syringe holder/driver, fluid-filled syringe and tube connecting set. The syringe holder/driver is powered by a pair of Neg'ator constant force springs which roll off and onto ball-bearing-mounted rotatable drums carried by a syringe plunger pusher or driver which is centered and guided in its longitudinal movement by the opposed Neg'ator springs and the syringe plunger, which springs are laterally stabilized by nonsliding strip-laying stabilizing retentive engagement with, and strip-pickup removal from engagement with, the syringe holder/driver cover.


title: Answering Table Queries on the Web using Column Keywords

abstract: We present the design of a structured search engine which returns a multi-column table in response to a query consisting of keywords describing each of its columns. We answer such queries by exploiting the millions of tables on the Web because these are much richer sources of structured knowledge than free-format text. However, a corpus of tables harvested from arbitrary HTML web pages presents huge challenges of diversity and redundancy not seen in centrally edited knowledge bases. We concentrate on one concrete task in this paper. Given a set of Web tables T1,..., Tn, and a query Q with q sets of keywords Q1,..., Qq, decide for each Ti if it is relevant to Q and if so, identify the mapping between the columns of Ti and query columns. We represent this task as a graphical model that jointly maps all tables by incorporating diverse sources of clues spanning matches in different parts of the table, corpus-wide co-occurrence statistics, and content overlap across table columns. We define a novel query segmentation model for matching keywords to table columns, and a robust mechanism of exploiting content overlap across table columns. We design efficient inference algorithms based on bipartite matching and constrained graph cuts to solve the joint labeling task. Experiments on a workload of 59 queries over a 25 million web table corpus shows significant boost in accuracy over baseline IR methods.


title: GK-Tail+ An Efficient Approach to Learn Software Models

abstract: Inferring models of program behavior from execution samples can provide useful information about a system, also in the increasingly common case of systems that evolve and adapt in their lifetime, and without requiring large developers’ effort. Techniques for learning models of program behavior from execution traces shall address conflicting challenges of recall, specificity and performance: They shall generate models that comprehensively represent the system behavior (recall) while limiting the amount of illegal behaviors that may be erroneously accepted by the model (specificity), and should infer the models within a reasonable time budget to process industrial scale systems (performance). In our early work, we designed GK-tail, an approach that can infer guarded finite state machines that model the behavior of object-oriented programs in terms of sequences of method calls and constraints on the parameter values. GK-tail addresses well two of the three main challenges, since it infers guarded finite state machines with a high level of recall and specificity, but presents severe limitations in terms of performance that reduce its scalability. In this paper, we present GK-tail+, a new approach to infer guarded finite state machines from execution traces of object-oriented programs. GK-tail+ proposes a new set of inference criteria that represent the core element of the inference process: It largely reduces the inference time of GK-tail while producing guarded finite state machines with a comparable level of recall and specificity. Thus, GK-tail+ advances the preliminary results of GK-tail by addressing all the three main challenges of learning models of program behavior from execution traces.


Levels of Processing : A Framework for Memory Research 1


NEUROPSYCHOLOGY AND THE NEUROPHYSIOLOGY OF PERCEPTUAL MICROGENESIS


Towards a big data reference architecture


title: EncFS goes multi-user: Adding access control to an encrypted file system

abstract: Among the different existing cryptographic file systems, EncFS has a unique feature that makes it attractive for backup setups involving untrusted (cloud) storage. It is a file-based overlay file system in normal operation (i.e., it maintains a directory hierarchy by storing encrypted representations of files and folders in a specific source folder), but its reverse mode allows to reverse this process: Users can mount deterministic, encrypted views of their local, unencrypted files on the fly, allowing synchronization to untrusted storage using standard tools like rsync without having to store encrypted representations on the local hard drive. So far, EncFS is a single-user solution: All files of a folder are encrypted using the same, static key; file access rights are passed through to the encrypted representation, but not otherwise considered. In this paper, we work out how multi-user support can be integrated into EncFS and its reverse mode in particular. We present an extension that a) stores individual files' owner/group information and permissions in a confidential and authenticated manner, and b) cryptographically enforces thereby specified read rights. For this, we introduce user-specific keys and an appropriate, automatic key management. Given a user's key and a complete encrypted source directory, the extension allows access to exactly those files the user is authorized for according to the corresponding owner/group/permissions information. Just like EncFS, our extension depends only on symmetric cryptographic primitives.


title: Improving test case generation for web applications using automated interface discovery

abstract: With the growing complexity of web applications, identifying web interfaces that can be used for testing such applications has become increasingly challenging. Many techniques that work effectively when applied to simple web applications are insufficient when used on modern, dynamic web applications, and may ultimately result in inadequate testing of the applications' functionality. To address this issue, we present a technique for automatically discovering web application interfaces based on a novel static analysis algorithm. We also report the results of an empirical evaluation in which we compare our technique against a traditional approach. The results of the comparison show that our technique can (1) discover a higher number of interfaces and (2) help generate test inputs that achieve higher coverage.


title: A Practical Guide to Factorial Validity Using PLS-Graph: Tutorial and Annotated Example

abstract: This tutorial explains in detail what factorial validity is and how to run its various aspects in PLS. The tutorial is written as a teaching aid for doctoral seminars that may cover PLS and for researchers interested in learning PLS. An annotated example with data is provided as an additional tool to assist the reader in reconstructing the detailed example.


title: RESEARCH COMMUNICATIONS RESEARCH COMMUNICATIONS Protected area needs in a changing climate

abstract: Range shifts due to climate change may cause species to move out of protected areas. Climate change could therefore result in species range dynamics that reduce the relevance of current fixed protected areas in future conservation strategies. Here, we apply species distribution modeling and conservation planning tools in three regions (Mexico, the Cape Floristic Region of South Africa, and Western Europe) to examine the need for additional protected areas in light of anticipated species range shifts caused by climate change. We set species representation targets and assessed the area required to meet those targets in the present and in the future, under a moderate climate change scenario. Our findings indicate that protected areas can be an important conservation strategy in such a scenario, and that early action may be both more effective and less costly than inaction or delayed action. According to our projections, costs may vary among regions and none of the three areas studied will fully meet all conservation targets, even under a moderate climate change scenario. This suggests that limiting climate change is an essential complement to adding protected areas for conservation of biodiversity.


title: Microalgae community structure analysis based on 18S rDNA amplification from DNA extracted directly from soil as a potential soil bioindicator

abstract: Soil algae are photosynthetically active microorganisms showing changeable community structure, depending on the soil type, the agricultural practices and the application of pesticides. To characterise algal community structure, molecular approaches complementary to classical microbiological approaches based on the isolation and the culture of soil algae are required. Our study describes a polymerase chain reaction (PCR) approach targeting algal 18S rDNA sequences of desoxyribonucleic acid (DNA) samples extracted either from unialgal eukaryotic microalgae culture, complex assemblages of microalgae populations or natural soil communities. Our first results showed that microalgae rDNA can be amplified by PCR from soil DNA samples. They also indicated difficulties extracting DNA from diatoms directly from soils, probably because of the presence of robust silicate valves. An 18S rDNA library has been established and preliminary phylogenetic analysis showed the feasibility of applying molecular methods to studying edaphic algae community structure. This is promising for soil algae ecology and for developing soil biological indicators.


title: Analysis of Facial Skin Thickness: Defining the Relative Thickness Index

abstract: Background:The determination of human skin thickness has been achieved through various methods, both in vivo and in vitro. Ultrasound and histometric analyses have been the most commonly used. However, absolute values of epidermal and dermal thicknesses have demonstrated variability among the differ


title: Polymerization Methods of Systems-of-Systems Integration Effect Based on Choquet Integral

abstract: Systems-of-systems integration is an important aspect of systems-of-systems and it's engineering research. It is comprehensive expression of overall emergent property and overall optimization property of systems-of-systems. Integration effect is measurement of systems-of-systems integration process. Systems-of-systems integration activities have strong randomness, which decides that systems-of-systems integration effect polymerization has strong non-linear additivity, making evaluation polymerization of systems-of-systems integration effect difficult. Systems-of-systems integration activity characteristics are analyzed from information integration, function integration, business process integration in the Paper firstly to establish evaluation index system of systems-of-systems integration effect; secondly, analytic hierarchy process method is adopted to establish weight vector among evaluation index system level of integration effect; then, basic set-valued random variable not only can describe random nature of development of things, but also can describe uncertainty of development state of things, and Choquet integral is a non-additive and non-linear integral based on fuzzy measurement, polymerization methods of systems-of-systems integration effect based on Choquet integral are proposed. Finally, cases are adopted to illustrate effectiveness of methods in the Paper.


title: Learning to detect loop closure from range data

abstract: Despite significant developments in the Simultaneous Localisation and Mapping (SLAM) problem, loop closure detection is still challenging in large scale unstructured environments. Current solutions rely on heuristics that lack generalisation properties, in particular when range sensors are the only source of information about the robot's surrounding environment. This paper presents a machine learning approach for the loop closure detection problem using range sensors. A binary classifier based on boosting is used to detect loop closures. The algorithm performs robustly, even under potential occlusions and significant changes in rotation and translation. We developed a number of features, extracted from range data, that are invariant to rotation. Additionally, we present a general framework for scan-matching SLAM in outdoor environments. Experimental results in large scale urban environments show the robustness of the approach, with a detection rate of 85% and a false alarm rate of only 1%. The proposed algorithm can be computed in real-time and achieves competitive performance with no manual specification of thresholds given the features.


title: Design of a Planar Slotted Waveguide Array Antenna for X-band Radar Applications

abstract: A planar slotted waveguide array antenna has been designed at 9.37 GHz for X-band radar applications. The antenna consists of multiple branchline waveguides with broadwall radiating shunt slots and a main waveguide to feed the branch waveguides through a series of inclined coupling slots. The antenna feed point is located at the center of the main waveguide. Element weights in the array have been calculated bysampling a continuous circular Taylor aperture distribution at the 25 dB sidelobe level in both the E and Hplanes. A commercially available electromagnetic (EM) simulation tool has been used to characterize the individual isolated slot and that data hassubsequently been used to design the planar array. The array is finally analyzed in a CST Microwave studio and the measured and simulated results have been found to be in good agreement.


title: FPGA based wireless sensor node with customizable event-driven architecture

abstract: This article presents the design and implementation of modular customizable event-driven architecture with parallel execution capability for the first time with wireless sensor nodes using stand alone FPGA. This customizable event-driven architecture is based on modular generic event dispatchers and autonomous event handlers, which will help WSN application developers to quickly develop their applications by adding the required number of event dispatchers and event handlers as per the need of a WSN application. This architecture can handle multiple events in parallel, including high priority ones. Additionally, it provides non-preemptive operation which removes the timing uncertainty and overhead involved with interrupt-driven processor-based sensor node implementation, which is required in real-time wireless sensor networks (WSNs). Thus, higher computation power of FPGAs combined with the non-preemptive modular event-driven architecture with parallel execution capability enables a variety of new WSN applications and facilitates rapid prototyping of WSN applications. In this article, the performance of FPGA-based sensor device is compared with general purpose processor-based implementations of sensor devices. Results show that our FPGA-based implementation provides significant improvement in system efficiency measured in terms of clock cycle counts required for typical sensor network tasks such as packet transmission, relay and reception.


title: Real-time automatic tag recommendation

abstract: Tags are user-generated labels for entities. Existing research on tag recommendation either focuses on improving its accuracy or on automating the process, while ignoring the efficiency issue. We propose a highly-automated novel framework for real-time tag recommendation. The tagged training documents are treated as triplets of (words, docs, tags), and represented in two bipartite graphs, which are partitioned into clusters by Spectral Recursive Embedding (SRE). Tags in each topical cluster are ranked by our novel ranking algorithm. A two-way Poisson Mixture Model (PMM) is proposed to model the document distribution into mixture components within each cluster and aggregate words into word clusters simultaneously. A new document is classified by the mixture model based on its posterior probabilities so that tags are recommended according to their ranks. Experiments on large-scale tagging datasets of scientific documents (CiteULike) and web pages del.icio.us) indicate that our framework is capable of making tag recommendation efficiently and effectively. The average tagging time for testing a document is around 1 second, with over 88% test documents correctly labeled with the top nine tags we suggested.


title: BitFunnel: Revisiting Signatures for Search

abstract: Since the mid-90s there has been a widely-held belief that signature files are inferior to inverted files for text indexing. In recent years the Bing search engine has developed and deployed an index based on bit-sliced signatures. This index, known as BitFunnel, replaced an existing production system based on an inverted index. The driving factor behind the shift away from the inverted index was operational cost savings. This paper describes algorithmic innovations and changes in the cloud computing landscape that led us to reconsider and eventually field a technology that was once considered unusable. The BitFunnel algorithm directly addresses four fundamental limitations in bit-sliced block signatures. At the same time, our mapping of the algorithm onto a cluster offers opportunities to avoid other costs associated with signatures. We show these innovations yield a significant efficiency gain versus classic bit-sliced signatures and then compare BitFunnel with Partitioned Elias-Fano Indexes, MG4J, and Lucene.


title: An Interactionist Perspective on the Socioeconomic Context of Human Development

abstract: This article addresses the relationship between socioeconomic status (SES), family processes, and human development. The topic is framed as part of the general issue of health disparities, which involves the oft-observed positive relationship between SES and the cognitive, social, emotional, and physical well-being of adults and children. A review of recent research and theory identifies three general theoretical approaches that provide possible explanations for the association between SES and individual development: the social causation, social selection, and interactionist perspectives. Empirical evidence demonstrates support for the social causation view that SES affects families and the development of children in terms of both family stress processes (the family stress model) and family investments in children (the family investment model). However, there also is empirical support for the social selection argument that individual characteristics lead to differences in SES. Especially important, recent research is consistent with an interactionist approach, which proposes a dynamic relationship between SES and developmental change over time. Drawing on the combined set of research findings, the article concludes with the description of an interactionist model that serves as a heuristic for future studies of the links among SES, parenting behaviors, and child development.


title: Use of Web 2.0 technologies in K-12 and higher education: The search for evidence-based practice

abstract: Evidence-based practice in education entails making pedagogical decisions that are informed by relevant empirical research evidence. The main purpose of this paper is to discuss evidence-based pedagogical approaches related to the use of Web 2.0 technologies in both K-12 and higher education settings. The use of such evidence-based practice would be useful to educators interested in fostering student learning through Web 2.0 tools. A comprehensive literature search across the Academic Search Premier, Education Research Complete, ERIC, and PsycINFO databases was conducted. Empirical studies were included for review if they specifically examined the impact of Web 2.0 technologies on student learning. Articles that merely described anecdotal studies such as student perception or feeling toward learning using Web 2.0, or studies that relied on student self-report data such as student questionnaire survey and interview were excluded. Overall, the results of our review suggested that actual evidence regarding the impact of Web 2.0 technologies on student learning is as yet fairly weak. Nevertheless, the use of Web 2.0 technologies appears to have a general positive impact on student learning. None of the studies reported a detrimental or inferior effect on learning. The positive effects are not necessarily attributed to the technologies per se but to how the technologies are used, and how one conceptualizes learning. It may be tentatively concluded that a dialogic, constructionist, or co-constructive pedagogy supported by activities such as Socratic questioning, peer review and self-reflection appeared to increase student achievement in blog-, wiki-, and 3-D immersive virtual world environments, while a transmissive pedagogy supported by review activities appeared to enhance student learning using podcast.


title: The influence of business strategy on project portfolio management and its success — A conceptual framework

abstract: Firms are facing more difficulties with the implementation of strategies than with its formulation. Therefore, this paper examines the linkage between business strategy, project portfolio management, and business success to close the gap between strategy formulation and implementation. Earlier research has found some supporting evidence of a positive relationship between isolated concepts, but so far there is no coherent and integral framework covering the whole cycle from strategy to success. Therefore, the existing research on project portfolio management is extended by the concept of strategic orientation. Based on a literature review, a comprehensive conceptual model considering strategic orientation, project portfolio structuring, project portfolio success, and business success is developed. This model can be used for future empirical research on the influence of strategy on project portfolio management and its success. Furthermore, it can easily be extended e.g. by contextual factors. © 2010 Elsevier Ltd. and IPMA. All rights reserved.


title: Instagram and WhatsApp in Health and Healthcare: An Overview

abstract: Instagram and WhatsApp are two social media and networking services introduced in 2010. They are currently subsidiaries of Facebook, Inc., California, USA. Using evidence from the published literature and case reports indexed in PubMed and other sources, we present an overview of the various applications of Instagram and WhatsApp in health and healthcare. We also briefly describe the main issues surrounding the uses of these two apps in health and medicine.


title: Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement

abstract: Textual similarity measurement is a challenging problem, as it requires understanding the semantics of input sentences. Most previous neural network models use coarse-grained sentence modeling, which has difficulty capturing fine-grained word-level information for semantic comparisons. As an alternative, we propose to explicitly model pairwise word interactions and present a novel similarity focus mechanism to identify important correspondences for better similarity measurement. Our ideas are implemented in a novel neural network architecture that demonstrates state-ofthe-art accuracy on three SemEval tasks and two answer selection tasks.


title: Joint Gap Detection and Inpainting of Line Drawings

abstract: We propose a novel data-driven approach for automatically detecting and completing gaps in line drawings with a Convolutional Neural Network. In the case of existing inpainting approaches for natural images, masks indicating the missing regions are generally required as input. Here, we show that line drawings have enough structures that can be learned by the CNN to allow automatic detection and completion of the gaps without any such input. Thus, our method can find the gaps in line drawings and complete them without user interaction. Furthermore, the completion realistically conserves thickness and curvature of the line segments. All the necessary heuristics for such realistic line completion are learned naturally from a dataset of line drawings, where various patterns of line completion are generated on the fly as training pairs to improve the model generalization. We evaluate our method qualitatively on a diverse set of challenging line drawings and also provide quantitative results with a user study, where it significantly outperforms the state of the art.


title: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics

abstract: This paper describes an architecture that combines the complementary strengths of probabilistic graphical models and declarative programming to enable robots to represent and reason with qualitative and quantitative descriptions of uncertainty and domain knowledge. The architecture comprises three components: a controller, a logician and an executor. The logician uses a coarse-resolution, qualitative model of the world and logical reasoning to plan a sequence of abstract actions for an assigned goal. For each abstract action to be executed, the executor uses probabilistic information at finer granularity and probabilistic algorithms to execute a sequence of concrete actions, and to report observations of the environment. The controller dispatches goals and relevant information to the logician and executor, collects and processes information from these components, refines the coarse-resolution description to obtain the probabilistic information at finer granularity for each abstract action, and computes the policy for executing the abstract action using probabilistic algorithms. The architecture is evaluated in simulation and on a mobile robot moving objects in an indoor domain, to show that it supports reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.


title: Broadband antenna array aperture made of tightly couple printed dipoles

abstract: This study reports a novel tight-coupled dipole array antenna to operate with up to six octave bandwidth and 60 degrees scanning. The array was designed through full-wave EM simulations by employing the current sheet array radiator concept that was advanced by a novel integrated feed network. Several prototypes of planar and conformal arrays across 0.3–20 GHz have been fabricated and tested with good agreement observed between all predicted and measured terminal and radiation features. The exemplified arrays have been designed for 1.2–6 GHz with relative radiator height 0.12 of maximum operational wavelength.


title: Mapping perceived socio-emotive quality of small-group functioning

abstract: This chapter demonstrates the influence of the socio-emotional quality of small-group functioning in a collaborative learning setting. It reports a case study from a sophomores’ class at a Belgian university. The subjects were 142 undergraduates sub-divided into 12 project-groups of 12 students each. Following a description of the collaborative learning setting, a longitudinal survey study focusing upon the evolution of the learners’ perception of their own group’s socio-emotional functioning is presented. The aims of the study were to map group member’s perception of the socio-emotive quality of their own group functioning and to examine if and how problems in groups of learners can be detected as soon as possible. Having demonstrated that dysfunctionalities within groups can be detected rather early, the authors hope that corrective interventions can be implemented when they can still have an effect.


title: Li-Fi Based Automatic Traffic Signal Control for Emergency Vehicles

abstract: India is one of the fastest developing country in the world. One of the disadvantages of this unprecedented growth is the ineffective traffic management. Light Fidelity (Li-Fi) is a form of bi-directional Visible Light Communication (VLC) in which light is modulated at speeds greater than a human eye can follow. Li-Fi can be used to implement Vehicle to Vehicle (V2V) communication as it has many advantages over other communication protocols. One of the main advantages of Li-Fi is that it provides connectivity within a very large area with more security and higher data rates. Emergency vehicles such as Ambulances, Firefighting trucks, Police vehicles etc. can propagate faster through traffic-dense roads using Li-Fi based V2V communication system. In this paper, a V2V communication system is proposed in which the headlight and tail light of a vehicle are made as Li-Fi transmitter and Li-Fi receiver respectively. Using the proposed system, when a traffic signal receives an alert regarding an emergency vehicle, the traffic signal would immediately turn green. Thus, decreasing the waiting time of emergency vehicles in traffic dense lanes. The proposed system was designed, implemented and tested for its operation.


title: Deep-Learning Systems for Domain Adaptation in Computer Vision: Learning Transferable Feature Representations

abstract: Domain adaptation algorithms address the issue of transferring learning across computational models to adapt them to data from different distributions. In recent years, research in domain adaptation has been making great progress owing to the advancements in deep learning. Deep neural networks have demonstrated unrivaled success across multiple computer vision applications, including transfer learning and domain adaptation. This article outlines the latest research in domain adaptation using deep neural networks. It begins with an introduction to the concept of knowledge transfer in machine learning and the different paradigms of transfer learning. It provides a brief survey of nondeep-learning techniques and organizes the rapidly growing research in domain adaptation based on deep learning. It also highlights some drawbacks with the current state of research in this area and offers directions for future research.


Multifocal tDCS targeting the resting state motor network increases cortical excitability beyond traditional tDCS targeting unilateral motor cortex


title: Smart Grids: A Cyber–Physical Systems Perspective

abstract: Smart grids are electric networks that employ advanced monitoring, control, and communication technologies to deliver reliable and secure energy supply, enhance operation efficiency for generators and distributors, and provide flexible choices for prosumers. Smart grids are a combination of complex physical network systems and cyber systems that face many technological challenges. In this paper, we will first present an overview of these challenges in the context of cyber–physical systems. We will then outline potential contributions that cyber–physical systems can make to smart grids, as well as the challenges that smart grids present to cyber–physical systems. Finally, implications of current technological advances to smart grids are outlined.


title: An architecture for energy management in wireless sensor networks

abstract: Sensornets are becoming more widely adopted for commercial and scientific use and, in settings where battery replacement or recharging is difficult, it is important that sensornets have long and predictable lifetimes. We thus expect energy management to play an increasingly important role in meeting user requirements. Today, system developers seek a balance between network lifetime and performance, but recent history shows that unexpected and dynamic environmental conditions often scuttle expected energy budgets. For example, many nodes in the Great Duck Island deployment were conjectured to have died prematurely because unexpected overhearing of traffic caused radios to become operational for longer than originally predicted [22]. This pattern was repeated in the Redwoods deployment, but for a supposedly different reason: some nodes seemingly died prematurely because they became disconnected from the wireless network and depleted their batteries trying to reconnect [24]. Even systems augmented with energy harvesting are still susceptible to this type of problem. In the Trio Testbed, seasonal and daily variations in solar power, the angle of inclination of the solar cell, the effect of dirt and bird droppings on the cells, and the inefficiencies in power storage and transfer resulted in node duty cycles ranging from 20% to 100% [5]. The issues with these deployments arise from mistaken assumptions, unforeseen difficulties, and unpredictable environmental dynamics. Solutions to these issues take two extreme approaches. At one extreme, some have proposed runtime adaptation to meet lifetime requirements [16] or energy availability [11, 10]. While promising, these efforts have addressed rather coarse-grained, high-level adaptation – for example, by adjusting sampling rates or varying the system-wide duty cycle – but they remain silent on prioritizing energy usage in a fine-grained and flexible manner. At the other extreme, low-level energy management mechanisms that give direct control over the hardware to multiple entities (e.g. network protocols) can be tedious to implement and difficult to debug because of the lack of any isolation. Arbitration can address the isolation problem, but it does not enable runtime adaptation to varying workloads [12]. We believe that using an energy management architecture would alleviate or even prevent these types of problems. SecEnforcement


title: Robust Single Image Super-Resolution via Deep Networks With Sparse Prior

abstract: Single image super-resolution (SR) is an ill-posed problem, which tries to recover a high-resolution image from its low-resolution observation. To regularize the solution of the problem, previous methods have focused on designing good priors for natural images, such as sparse representation, or directly learning the priors from a large data set with models, such as deep neural networks. In this paper, we argue that domain expertise from the conventional sparse coding model can be combined with the key ingredients of deep learning to achieve further improved results. We demonstrate that a sparse coding model particularly designed for SR can be incarnated as a neural network with the merit of end-to-end optimization over training data. The network has a cascaded structure, which boosts the SR performance for both fixed and incremental scaling factors. The proposed training and testing schemes can be extended for robust handling of images with additional degradation, such as noise and blurring. A subjective assessment is conducted and analyzed in order to thoroughly evaluate various SR techniques. Our proposed model is tested on a wide range of images, and it significantly outperforms the existing state-of-the-art methods for various scaling factors both quantitatively and perceptually.


title: Parallel Graph Coloring for Manycore Architectures

abstract: Graph algorithms are challenging to parallelize on manycore architectures due to complex data dependencies and irregular memory access. We consider the well studied problem of coloring the vertices of a graph. In many applications it is important to compute a coloring with few colors in near-lineartime. In parallel, the optimistic (speculative) coloring method by Gebremedhin and Manne is the preferred approach but it needs to be modified for manycore architectures. We discuss a range of implementation issues for this vertex-based optimistic approach. We also propose a novel edge-based optimistic approach that has more parallelism and is better suited to GPUs. We study the performance empirically on two architectures(Xeon Phi and GPU) and across many data sets (from finite element problems to social networks). Our implementation uses the Kokkos library, so it is portable across platforms. We show that on GPUs, we significantly reduce the number of colors (geometric mean 4X, but up to 48X) as compared to the widely used cuSPARSE library. In addition, our edge-based algorithm is 1.5 times faster on average than cuSPARSE, where it hasspeedups up to 139X on a circuit problem. We also show the effect of the coloring on a conjugate gradient solver using multi-colored Symmetric Gauss-Seidel method as preconditioner, the higher coloring quality found by the proposed methods reduces the overall solve time up to 33% compared to cuSPARSE.


title: Hand-based interaction for object manipulation with augmented reality glasses

abstract: An increase number of gesture sensors and AR(augmented reality) displays are emerging these years. There are more and more study working on spatial interaction. In this work, we focus on evaluating the feasibility of hand-based interaction designed for AR glasses to do basic manipulation on 3D virtual objects in AR applications with current interaction technology. We design a user study with 10 volunteers to discuss whether and how to use natural user interfaces like gestures, with the help of 3D menus, to finish the tasks of manipulating AR scenes. The interface including 3D menus presents the advantages of usability and merits of intuition; However, it also exposes some issues like ergonomic discomfort and limitation like imprecise input. So our results indicate the kind of interactive device and technology are valuable in natural interaction without extra knowledge, though they are limited in serious applications now.


title: A lidar-based approach for near range lane detection

abstract: This paper presents an approach for object and lane detection using only a scanning lidar sensor. The test vehicle used and the applied lane detection algorithm based on an extended Kalman-filter are described. Measurements on public roads are performed and compared to the results of an optical lane recognition as a reference.


title: DC-Link voltage ripple minimization in a modular multilevel voltage source converter for HVDC power transmission

abstract: This paper presents a method for DC-Link voltage ripple reduction for a recently proposed modular multilevel (M2C) hybrid voltage source converter intended for the HVDC market. A mode of operation is described that enables both control of the modulation index and reduction of the DC-Link voltage and current ripple. This is achieved by superimposing multiple triplen harmonic components to the rectified fundamental component synthesised by the M2C Chain-Link converter.


title: Using machine learning techniques for rising star prediction in co-author network

abstract: Online bibliographic databases are powerful resources for research in data mining and social network analysis especially co-author networks. Predicting future rising stars is to find brilliant scholars/researchers in co-author networks. In this paper, we propose a solution for rising star prediction by applying machine learning techniques. For classification task, discriminative and generative modeling techniques are considered and two algorithms are chosen for each category. The author, co-authorship and venue based information are incorporated, resulting in eleven features with their mathematical formulations. Extensive experiments are performed to analyze the impact of individual feature, category wise and their combination w.r.t classification accuracy. Then, two ranking lists for top 30 scholars are presented from predicted rising stars. In addition, this concept is demonstrated for prediction of rising stars in database domain. Data from DBLP and Arnetminer databases (1996---2000 for wide disciplines) are used for algorithms' experimental analysis.


title: Performance Evaluation of a Hybrid Cooling–lubrication System in Micro-milling of Ti6Al4V Alloy☆

abstract: Abstract In this paper, a hybrid system was designed for cooling and lubrication in micro-milling of Ti6Al4 V alloy. This hybrid system is based on mixing the oil with chilled air. The effect of the hybrid system on cutting performance is evaluated in terms of tool wear and burr formation in the machining of Ti6Al4 V alloy. For cutting tests, an ultra-fine grained, uncoated carbide micro end mill with a diameter of 508 μm and K10–K20 grades was used. The efficiency of the designed hybrid system was compared with other cooling/lubrication methods (wet, chilled air, and MQL) and dry cutting conditions. The experimental results showed that the hybrid system gave the minimum tool wear and burr size. In addition, it was found that the chilled air with a lower temperature of –30°C led to chipping type damage on the cutting edge of the micro-tool.


title: Let's play chinese characters: mobile learning approaches via culturally inspired group games

abstract: In many developing countries such as India and China, low educational levels often hinder economic empowerment. In this paper, we argue that mobile learning games can play an important role in the Chinese literacy acquisition process. We report on the unique challenges in the learning Chinese language, especially its logographic writing system. Based on an analysis of 25 traditional Chinese games currently played by children in China, we present the design and implementation of two culturally inspired mobile group learning games, Multimedia Word and Drumming Strokes. These two mobile games are designed to match Chinese children's understanding of everyday games. An informal evaluation reveals that these two games have the potential to enhance the intuitiveness and engagement of traditional games, and children may improve their knowledge of Chinese characters through group learning activities such as controversy, judgments and self-correction during the game play.


title: Automatic generation control of a two area multi-unit interconnected power system with Proportional-Integral-Derivative controller with Filter(PIDF) optimized by TLBO algorithm

abstract: This article describes the automatic generation control of a two area multi-unit interconnected power system with Proportional-Integral-Derivative controller with Filter (PIDF) tuned by teaching learning based optimization (TLBO) algorithm. Each area is equipped with three reheat thermal units with hydraulic amplifiers. The gains of the PIDF controllers are simultaneously optimized by applying a step load perturbation of 0.01 in area 1 considering integral time absolute error (ITAE) as the fitness function. The enhancement of the control behavior of the proposed optimized PIDF controller is proved in comparison with the conventional PID controller. The dynamic responses of the system is compared in terms of settling time, peak overshoots and undershoots. Further the sensitiveness and robustness of system is verified by applying disturbances in both the area simultaneously.


title: Dynamic parallax occlusion mapping with approximate soft shadows

abstract: This paper presents a per-pixel ray tracing algorithm with dynamic lighting of surfaces in real-time on the GPU. First, we propose a method for increased precision of the critical ray-height field intersection and adaptive height field sampling. We achieve higher quality results than the existing inverse displacement mapping algorithms. Second, soft shadows are computed by estimating light visibility for the displaced surfaces. Third, we describe an adaptive level-of-detail system which uses the information supplied by the graphics hardware during rendering to automatically manage shader complexity. This LOD scheme maintains smooth transitions between the full displacement computation and a simplified representation at a lower level of detail without visual artifacts. The algorithm performs well for animated objects and supports dynamic rendering of height fields for a variety of interesting displacement effects. The presented method is scalable for a range of consumer grade GPU products. It exhibits a low memory footprint and can be easily integrated into existing art pipelines for games and effects rendering.


title: Fact Checking: Task definition and dataset construction

abstract: In this paper we introduce the task of fact checking, i.e. the assessment of the truthfulness of a claim. The task is commonly performed manually by journalists verifying the claims made by public figures. Furthermore, ordinary citizens need to assess the truthfulness of the increasing volume of statements they consume. Thus, developing fact checking systems is likely to be of use to various members of society. We first define the task and detail the construction of a publicly available dataset using statements fact-checked by journalists available online. Then, we discuss baseline approaches for the task and the challenges that need to be addressed. Finally, we discuss how fact checking relates to mainstream natural language processing tasks and can stimulate further research.


title: Wideband circularly polarized antenna based on Vivaldi antenna structure

abstract: This letter presents a novel wideband circularly polarized (CP) antenna based on two orthogonally placed Vivaldi antennas. The two antenna elements are excited by a power divider that splits an input signal into two signals with equal magnitude and orthogonal phase. Linear tapered microstrip feeding structure is designed to realize the impedance matching. The antenna shows a −10 dB impedance bandwidth of 89%, −3 dB R bandwidth (AR<3 dB) of 91%, and the maximum realized gain is 11.6 dBi. Meanwhile, good radiation patterns are achieved with the X-polarization is 20 dB lower than Co-polarization across the operating bandwidth in the boresight direction.


title: Passive Millimeter Wave Imaging

abstract: This article introduces the concept of passive millimeter-wave (PMMW) imaging, describes the phenomenology that defines its performance, explains the technology advances that have made these systems a reality, and presents some of the missions in which these sensors can be used.


title: Coffin-Siris syndrome: phenotypic evolution of a novel SMARCA4 mutation.

abstract: Coffin-Siris Syndrome (CSS) is an intellectual disability disorder caused by mutation of components of the SWI/SNF chromatin-remodeling complex. We describe the evolution of the phenotypic features for a male patient with CSS from birth to age 7 years and 9 months and by review of reported CSS patients, we expand the phenotype to include neonatal and infantile hypertonia and upper airway obstruction. The propositus had a novel de novo heterozygous missense mutation in exon 17 of SMARCA4 (NM_001128849.1:c.2434C>T (NP_001122321.1:p.Leu812Phe)). This is the first reported mutation within motif Ia of the SMARCA4 SNF2 domain. In summary, SMARCA4-associated CSS is a pleiotropic disorder in which the pathognomic clinical features evolve and for which the few reported individuals do not demonstrate a clear genotype–phenotype correlation. © 2014 Wiley Periodicals, Inc.


title: Correlation of clinical, diagnostic and histopathological parameters in dogs with chronic lymphocytic-plasmacytic enteropathy.

abstract: OBJECTIVE ::: The objective of this study was to correlate clinical signs and diagnostic parameters with duodenal inflammatory and architectural changes in dogs with lymphocytic-plasmacytic enteropathy. ::: ::: ::: MATERIAL AND METHODS ::: In a retrospective study dogs presented between 2003 and 2014 with chronic gastrointestinal signs (duration > 3 weeks) and histologic evidence of intestinal lymphocytic-plasmacytic inflammation were evaluated. Clinical signs, serum albumin, cobalamin and folic acid concentrations were recorded and a sonographic, endoscopic, histologic and cytological inflammatory score was determined. Furthermore, the presence of lacteal dilation, villus stunting, crypt lesions, epithelial integrity and increased intraepithelial lympho cytes was evaluated. ::: ::: ::: RESULTS ::: A total of 270 dogs were retrospectively evaluated. No significant correlation was found between clinical signs and sonographic, endoscopic or duodenal inflammatory score. Dogs with histological signs of lacteal dilation (p = 0.001) and increased intraepithelial lymphocytes (p = 0.005) had significantly higher clinical scores compared to dogs without these changes. No correlation was found between clinical score and villous stunting or crypt lesions. Hypoalbuminemia and hypocobalaminemia correlated significantly with lacteal dilation (p = 0.001, p = 0.009) and increased intraepithelial lymphocytes (p = 0.036, p = 0.018). ::: ::: ::: CLINICAL SIGNIFICANCE ::: Some clinical and diagnostic parameter correlate with histopathologic features whereas others do not. Morphological features seem to be more important than the intensity of the duodenal inflammation in the assessment of the disease.


title: An Improved Supervised Learning Algorithm Using Triplet-Based Spike-Timing-Dependent Plasticity

abstract: The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit arbitrary spike trains in response to given synaptic inputs. Recent years, the supervised learning algorithms based on synaptic plasticity have developed rapidly. As one of the most efficient supervised learning algorithms, the remote supervised method (ReSuMe) uses the conventional pair-based spike-timing-dependent plasticity rule, which depends on the precise timing of presynaptic and postsynaptic spikes. In this paper, using the triplet-based spike-timing-dependent plasticity, which is a powerful synaptic plasticity rule and acts beyond the classical rule, a novel supervised learning algorithm, dubbed T-ReSuMe, is presented to improve ReSuMe’s performance. The proposed algorithm is successfully applied to various spike trains learning tasks, in which the desired spike trains are encoded by Poisson process. The experimental results show that T-ReSuMe has higher learning accuracy and fewer iteration epoches than the traditional ReSuMe, so it is effective for solving complex spatio-temporal pattern learning problems.


title: Efficient location-aware influence maximization

abstract: users in a social network to maximize the expected number of users influenced by the selected users (called influence spread), has been extensively studied, existing works neglected the fact that the location information can play an important role in influence maximization. Many real-world applications such as location-aware word-of-mouth marketing have location-aware requirement. In this paper we study the location-aware influence maximization problem. One big challenge in location-aware influence maximization is to develop an efficient scheme that offers wide influence spread. To address this challenge, we propose two greedy algorithms with 1-1/e approximation ratio. To meet the instant-speed requirement, we propose two efficient algorithms with e· (1-1/e) approximation ratio for any e ∈ (0,1]. Experimental results on real datasets show our method achieves high performance while keeping large influence spread and significantly outperforms state-of-the-art algorithms.


title: 25 Tweets to Know You: A New Model to Predict Personality with Social Media

abstract: Predicting personality is essential for social applications supporting human-centered activities, yet prior modeling methods with users written text require too much input data to be realistically used in the context of social media. In this work, we aim to drastically reduce the data requirement for personality modeling and develop a model that is applicable to most users on Twitter. Our model integrates Word Embedding features with Gaussian Processes regression. Based on the evaluation of over 1.3K users on Twitter, we find that our model achieves comparable or better accuracy than state of the art techniques with 8 times fewer data.


title: Precision and Recall for Regression

abstract: Cost sensitive prediction is a key task in many real world applications. Most existing research in this area deals with classification problems. This paper addresses a related regression problem: the prediction of rare extreme values of a continuous variable. These values are often regarded as outliers and removed from posterior analysis. However, for many applications (e.g. in finance, meteorology, biology, etc.) these are the key values that we want to accurately predict. Any learning method obtains models by optimizing some preference criteria. In this paper we propose new evaluation criteria that are more adequate for these applications. We describe a generalization for regression of the concepts of precision and recall often used in classification. Using these new evaluation metrics we are able to focus the evaluation of predictive models on the cases that really matter for these applications. Our experiments indicate the advantages of the use of these new measures when comparing predictive models in the context of our target applications.


title: Synthesizing API usage examples

abstract: Key program interfaces are sometimes documented with usage examples: concrete code snippets that characterize common use cases for a particular data type. While such documentation is known to be of great utility, it is burdensome to create and can be incomplete, out of date, or not representative of actual practice. We present an automatic technique for mining and synthesizing succinct and representative human-readable documentation of program interfaces. Our algorithm is based on a combination of path sensitive dataflow analysis, clustering, and pattern abstraction. It produces output in the form of well-typed program snippets which document initialization, method calls, assignments, looping constructs, and exception handling. In a human study involving over 150 participants, 82% of our generated examples were found to be at least as good at human-written instances and 94% were strictly preferred to state of the art code search.


title: Emotional Integration and Advertising Effectiveness: Case of Perfumes Advertising

abstract: This paper examines emotions in advertising, its effects and functioning. Using an interview-based experiment on 256 participants, we found that emotions perceived during an advertising exposure could play an important role in eliciting responses towards the ad and the brand. However, this process is true provided that consumers associate the perceived emotion to the exposed brand or its consumption experience. Furthermore, we have identified efficiency differences between magazine ads, depending on how they visually describe emotions. In particular, we study emotional integration in advertising, i.e. salience of emotions expressed towards the ad, the presence of core brand information and clarity of the advertising message about the hedonic attributes of consumption. Interestingly, the impact of the staging process of emotions is moderated by respondents-specific variables, including their need for emotion, tolerance for ambiguity, their need for structure and need for cognition.