title: DARPP-32: A molecular switch at the nexus of reward pathway plasticity

abstract: Substance-related disorders represent a major burden to afflicted individuals, their families, and society. Repeated drug use can result in addiction, manifested as an intense desire for the drug, along with an impaired ability to temper its use, even in the face of serious consequences. Many tens of millions of people are addicted to drugs of abuse, and with costs to society in the many hundreds of billions of dollars per annum, there is little doubt that understanding the consummate processes (followed by advances in treatments) represents a major goal for medicine (1, 2). ::: ::: Although many transmitters have been implicated in the effects of the various types of drugs of abuse, dopamine and, more recently, glutamate have been most consistently associated with the reinforcing effects. Indeed, there is considerable evidence that the mesolimbic dopaminergic pathway plays a crucial role in the selection and orchestration of goal-directed behaviors, particularly those elicited by incentive stimuli. Drugs of abuse increase extracellular dopamine concentrations in limbic regions, including the nucleus accumbens (1, 2). However, a growing body of data suggests that increases in dopamine are not directly related to reward per se , but, rather, to the learned anticipation of reward and incentive salience (3). Notably, salience affects the motivation to seek the anticipated reward and facilitates conditioned learning (3). ::: ::: The key role of conditioned learning has focused attention on neural systems that attribute incentive salience, most notably a circuit that includes glutamate projections from the prefrontal cortex to the nucleus accumbens and dorsal striatum. This finding has led to the hypothesis that key biochemical events underlying drug-induced changes in neural plasticity should occur specifically in neurons inervated by both dopamine and glutamate inputs. ::: ::: In this issue of PNAS, Valjent et al. (4) provide an important link, implicating dopamine, cAMP-regulated phosphoprotein …


title: Radiative transfer corrections for accurate spectroscopic measurements of volcanic gas emissions

abstract: There is widespread use of passive remote sensing techniques to quantify trace gas column densities in volcanic plumes utilizing scattered sunlight as a light source. Examples include passive DOAS, COSPEC, and the SO2 camera. In order to calculate trace gas concentrations or volcanic emission fluxes, knowledge about the optical path through the plume is necessary. In the past, a straight photon path through the plume has always been assumed although it was known that this is not always true. Here we present the results of model studies conducted specifically to quantify the effects of realistic radiative transfer in and around volcanic plumes on ground-based remote sensing measurements of SO2. The results show that measurements conducted without additional information on average photon paths can be inaccurate under certain conditions, with possible errors spanning more than an order of magnitude. Both over and underestimation of the true column density can occur. Actual errors depend on parameters such as distance between instrument and plume, plume SO2 concentration, plume aerosol load, as well as aerosol conditions in the ambient atmosphere. As an example, a measurement conducted with an SO2 camera is discussed, the results of which can only be correctly interpreted if realistic radiative transfer is considered. Finally, a method is presented which for the first time allows the retrieval of actual average photon paths in spectroscopic (i.e. DOAS) measurements of adequate resolution. By allowing for a wavelength dependent column density during the evaluation of DOAS measurements, we show how radiative transfer effects can be corrected using information inherently available in the measured spectra, thus greatly enhancing the accuracy of DOAS measurements of volcanic emissions.


title: Computing Length-Preserved Free Boundary for Quasi-Developable Mesh Segmentation

abstract: Stretch-free surface flattening has been requested by a variety of applications. At present, the most difficult problem is how to segment a given model into nearly developable atlases so that a nearly stretch-free flattening can be computed. The criterion for segmentation is needed to evaluate the possibility of flattening a given surface patch, which should be fast computed. In this paper, we present a method to compute the length-preserved free boundary (LPFB) of a mesh patch which speeds up the mesh parameterization. The distortion on parameterization can then be employed as the criterion in a trial-and-error algorithm for segmenting a given model into nearly developable atlases. The computation of LPFB is formulated as a numerical optimization problem in the angle space, where we are trying to optimize the angle excesses on the boundary while preserving the constraints derived from the closed-path theorem and the length preservation.


title: Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

abstract: In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM.


title: Semantic, Hierarchical, Online Clustering of Web Search Results

abstract: We propose a Semantic, Hierarchical, Online Clustering (SHOC) approach to automatically organizing Web search results into groups. SHOC combines the power of two novel techniques, key phrase discovery and orthogonal clustering; to generate clusters which are both reasonable and readable. Moreover; SHOC can work for multiple languages: not only English but also oriental languages like Chinese. The main contribution of this paper includes the following. (1) The benefits of using key phrases as Web document features are discussed. A key phrase discovery algorithm based on suffix array is presented. This algorithm is highly effective and efficient no matter how large the language's alphabet is. (2) The concept of orthogonal clustering is proposed for general clustering problems. The reason why matrix Singular Value Decomposition (SVD) can provide solution to orthogonal clustering is strictly proved. The orthogonal clustering has a solid mathematics foundation and many advantages over traditional heuristic clustering algorithms.


title: SAR Minimum-Entropy Autofocus Using an Adaptive-Order Polynomial Model

abstract: A new algorithm is presented for autofocus in synthetic aperture radar imaging. Entropy is used to measure the focus quality of the image, and better focus corresponds to smaller entropy. The phase response of the focus filter is modeled as a specially designed polynomial, and the coefficients of this polynomial are adjusted in sequence to minimize the entropy of the image. Because the order of this polynomial is adaptive, this algorithm applies more widely than the minimum-entropy algorithms with a fixed-order polynomial model


title: Real-time detection of traffic from Twitter stream analysis

abstract: Internet sites are source of info for event detection, with specific mention of the road traffic activity blockage and accidents or earth-quack sensing system. In this paper, we present a real-time monitoring system intended for traffic occasion detection coming from Twitter stream analysis. The system fetches tweets coming from Twitter as per a several search criteria; methods tweets, by applying textual content mining methods; last but not least works the classification of twitter posts. The goal is to assign suitable class packaging to every tweet, because related with an activity of traffic event or perhaps not. The traffic recognition system or framework was utilized for real- time monitoring of various areas of the street network, taking into account detection of traffic occasions just almost in actual time, regularly before on-line traffic news sites. All of us employed the support vector machine like a classification unit, furthermore, we accomplished a great accuracy value of ninety five. 75% by attempting a binary classification issue. All of us were also capable to discriminate if traffic is triggered by an external celebration or not, by resolving a multiclass classification issue and obtaining accuracy worth of 88. 89%. Key Words: Twitter, Social media; Traffic detection; Text mining; Privacy; Service Oriented Architecture (SOA), machine learning, Twitter stream analysis, Traffic event detection, tweet classification, text mining, social sensing. Keywords: Twitter-Traffic-Status Language: en


title: INFLUENCE PROCESSES FOR INFORMATION TECHNOLOGY ACCEPTANCE : AN ELABORATION LIKELIHOOD MODEL

abstract: This study examines how processes of external influence shape information technology acceptance among potential users, how such influence effects vary across a user population, and whether these effects are persistent over time. Drawing on the elaboration-likelihood model (ELM), we compared two alternative influence processes, the central and peripheral routes, in motivating IT acceptance. These processes were respectively operationalized using the argument quality and source credibility constructs, and linked to perceived usefulness and attitude, the core perceptual drivers of IT acceptance. We further examined how these influence processes were moderated by users' IT expertise and perceived job relevance and the temporal stability of such influence effects. Nine hypotheses thus developed were empirically validated using a field survey of document management system acceptance at an eastern European governmental agency. This study contributes to the IT acceptance literature by introducing ELM as a referent theory for acceptance research, by elaborating alternative modes of influence, and by specifying factors moderating their effects. For practitioners, this study introduces influence processes as policy tools that managers can employ to motivate IT acceptance within their organizations, benchmarks alternative influence strategies, and demonstrates the need for customizing influence strategies to the specific needs of a user population.


title: On the activity detection with incomplete acceleration data using iterative KNN classifier

abstract: In real time continuous activity recognition systems, utilization of a data segmentation stage increases the dependency of success ratio on the size of activity set as well as activity type, duration and sensor sampling rate. In this study, we analyzed if iterative K-Nearest Neighbour based knowledge discovery performed on acceleration data can substitute for the segmentation stage to reduce these dependencies for hand oriented activities. To this end, we compared peak frequency and wavelet entropy feature extraction schemes for the recognition of open-pill-box, put-pill-in-mouth, drink and put-glass-back actions which constitute as a whole “medication intake” activity. We evaluated the performance of these schemes on incomplete data, resulting from the iterative process. According to our findings, peak frequency outperforms wavelet entropy in terms of Intra-Class-Correlation (ICC) metric which is an indication for better adaptation to variation in activity type with incompleteness mitigation problem. Our work points out that a more proficient iterative classification algorithm is required for attaining higher adaptability to the diversification of actions that stem from the incompleteness problem.


title: How emotions affect eating: A five-way model

abstract: Abstract Despite the importance of affective processes in eating behaviour, it remains difficult to predict how emotions affect eating. Emphasizing individual differences, previous research did not pay full attention to the twofold variability of emotion-induced changes of eating (variability across both individuals and emotions). By contrast, the present paper takes into account both individual characteristics and emotion features, and specifies five classes of emotion-induced changes of eating: (1) emotional control of food choice, (2) emotional suppression of food intake, (3) impairment of cognitive eating controls, (4) eating to regulate emotions, and (5) emotion-congruent modulation of eating. These classes are distinguished by antecedent conditions, eating responses and mediating mechanisms. They point to basic functional principles underlying the relations between emotions and biologically based motives: interference, concomitance and regulation. Thus, emotion-induced changes of eating can be a result of interference of eating by emotions, a by-product of emotions, and a consequence of regulatory processes (i.e., emotions may regulate eating, and eating may regulate emotions).


title: U-Air: when urban air quality inference meets big data

abstract: Information about urban air quality, e.g., the concentration of PM2.5, is of great importance to protect human health and control air pollution. While there are limited air-quality-monitor-stations in a city, air quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, traffic volume, and land uses. In this paper, we infer the real-time and fine-grained air quality information throughout a city, based on the (historical and real-time) air quality data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs). We propose a semi-supervised learning approach based on a co-training framework that consists of two separated classifiers. One is a spatial classifier based on an artificial neural network (ANN), which takes spatially-related features (e.g., the density of POIs and length of highways) as input to model the spatial correlation between air qualities of different locations. The other is a temporal classifier based on a linear-chain conditional random field (CRF), involving temporally-related features (e.g., traffic and meteorology) to model the temporal dependency of air quality in a location. We evaluated our approach with extensive experiments based on five real data sources obtained in Beijing and Shanghai. The results show the advantages of our method over four categories of baselines, including linear/Gaussian interpolations, classical dispersion models, well-known classification models like decision tree and CRF, and ANN.


title: Survey of Security and Privacy Issues of Internet of Things

abstract: This paper is a general survey of all the security issues existing in the Internet of Things (IoT) along with an analysis of the privacy issues that an end-user may face as a consequence of the spread of IoT. The majority of the survey is focused on the security loopholes arising out of the information exchange technologies used in Internet of Things. No countermeasure to the security drawbacks has been analyzed in the paper.


title: Design of miniaturized wireless power receivers for mm-sized implants

abstract: Advances in free-floating miniature medical implants promise to offer greater effectiveness, safety, endurance, and robustness than today's prevailing medical implants. Wireless power transfer (WPT) is key to miniaturized implants by eliminating the need for bulky batteries. This paper reviews design strategies for WPT with mm-sized implants focusing on resonant electromagnetic and ultrasonic transmission. While ultrasonic WPT offers shorter wavelengths for sub-mm implants, electromagnetic WPT above 100 MHz offers superior power transfer and conversion efficiency owing to better impedance matching through inhomogeneous tissue. Electromagnetic WPT also allows for fully integrating the entire wireless power receiver system with an on-chip coil. Attaining high power transfer efficiency requires careful design of the integrated coil geometry for high quality factor as well as loop-free power and signal distribution routing to avoid eddy currents. Regulating rectifiers have improved power and voltage conversion efficiency by combining the two RF to DC conversion steps into a single process. Example designs of regulating rectifiers for fully integrated wireless power receivers are presented.


title: Codex: a dual screen tablet computer

abstract: The Codex is a dual-screen tablet computer, about the size of a 4"x 6 day planner, with a self-supporting binding and embedded sensors. The device can be oriented in a variety of postures to support different nuances of individual work, ambient display, or collaboration with another user. In the context of a pen-operated note taking application, we demonstrate interaction techniques that support a fluid division of labor for tasks and information across the two displays while minimizing disruption to the primary experience of authoring notes.


title: A Survey of Heuristics for the Weighted Matching Problem

abstract: This survey paper reviews results on heuristics for two weighted matching problems: matchings where the vertices are points in the plane and weights are Euclidean distances, and the assignment problem. Several heuristics are described in detail and results are given for worst-case ratio bounds, absolute bounds, and expected bounds. Applications to practical problems and some mathematical complements are also included.


title: Design of Low-Cost Chipless System Using Printable Chipless Tag With Electromagnetic Code

abstract: In this letter, a printable chipless tag with electromagnetic code using split ring resonators is proposed. A 4 b chipless tag that can be applied to paper/plastic-based items such as ID cards, tickets, banknotes and security documents is designed. The chipless tag generates distinct electromagnetic characteristics by various combinations of a split ring resonator. Furthermore, a reader system is proposed to digitize electromagnetic characteristics and convert chipless tag to electromagnetic code.


title: Working-Memory Capacity and the Control of Attention: The Contributions of Goal Neglect, Response Competition, and Task Set to Stroop Interference

abstract: Individual differences in working-memory (WM) capacity predicted performance on the Stroop task in 5 experiments, indicating the importance of executive control and goal maintenance to selective attention. When the Stroop task encouraged goal neglect by including large numbers of congruent trials (RED presented in red), low WM individuals committed more errors than did high WM individuals on the rare incongruent trials (BLUE in red) that required maintaining access to the ?ignore-the-word? goal for accurate responding. In contrast, in tasks with no or few congruent trials, or in high-congruency tasks that followed low-congruency tasks, WM predicted response-time interference. WM was related to latency, not accuracy, in contexts that reinforced the task goal and so minimized the difficulty of actively maintaining it. The data and a literature review suggest that Stroop interference is jointly determined by 2 mechanisms, goal maintenance and competition resolution, and that the dominance of each depends on WM capacity, as well as the task set induced by current and previous contexts.


title: Single-stage repair for rectovestibular fistula without opening the fourchette.

abstract: BACKGROUND ::: Anorectal malformations are one of the most common congenital defects. This study is conducted to demonstrate new technique for treatment of rectovaginal fistula without disturbing the fourchette through posterior sagittal approach. ::: ::: ::: METHOD ::: All the patients of rectovestibular fistula admitted after the neonatal age were treated with posterior sagittal anorectoplasty without opening the fourchette. The results were evaluated for cosmetic appearance and anal continence. ::: ::: ::: RESULT ::: A total of 40 patients were included in our study. All patients were more than 1 month old. Operative time ranges from 70 to 150 minutes. The cosmetic appearance was good. Anal continence was good in 72% cases and fair in 20% cases. Fifteen percent of patients had minimal constipation and 7.5% patients had mucosal prolapse. ::: ::: ::: CONCLUSION ::: Single-stage repair for vestibular anus through posterior sagittal anorectoplasty without opening fourchette has a good cosmetic appearance and good anal continence.


title: PCN: Point Completion Network

abstract: Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications. In this work, we propose Point Completion Network (PCN), a novel learning-based approach for shape completion. Unlike existing shape completion methods, PCN directly operates on raw point clouds without any structural assumption (e.g. symmetry) or annotation (e.g. semantic class) about the underlying shape. It features a decoder design that enables the generation of fine-grained completions while maintaining a small number of parameters. Our experiments show that PCN produces dense, complete point clouds with realistic structures in the missing regions on inputs with various levels of incompleteness and noise, including cars from LiDAR scans in the KITTI dataset.


title: Overview of Beyond-CMOS Devices and a Uniform Methodology for Their Benchmarking

abstract: Multiple logic devices are presently under study within the Nanoelectronic Research Initiative (NRI) to carry the development of integrated circuits beyond the CMOS roadmap. Structure and operational principles of these devices are described. Theories used for benchmarking these devices are overviewed, and a general methodology is described for consistent estimates of the circuit area, switching time and energy. The results of the comparison of the NRI logic devices using these benchmarks are presented.


title: Trustworthiness of Grounded Theory Methodology Research in Information Systems

abstract: Grounded Theory Methodology (GTM) is being used increasingly in the Information Systems (IS) discipline. However, some consumers of IS literature are skeptical of the findings in studies using this method. In this paper, we provide some steps that can be taken by researchers to improve the credibility of their work. This can be accomplished through increased trustworthiness of their research. The quality of quantitative research can be evaluated by examining the internal validity, external validity, reliability; similarly the trustworthiness of GTM studies can be evaluated by examining the credibility, transferability, dependability and confirmability of the study. We provide specific steps that can be taken to accomplish trustworthiness of GTM research and from IS literature; we summarize some of the GTM research that has applied these steps.


title: Augmented Reality Using Projective Invariant Patterns

abstract: This paper presents an algorithm for using projective invariant patterns in augmented reality applications. It is actually an adaptation of a previous algorithm for an optical tracking device, that works with infrared illumination and filtering. The present algorithm removes the necessity of working in a controlled environment, which would be inadequate for augmented reality applications. In order to compensate the excess of image noise caused by the absence of the infrared system, the proposed algorithm includes a fast binary decision tree in the process flow. We show that the algorithm achieves real time rates.


title: Symmetric stereo matching for occlusion handling

abstract: In this paper, we propose a symmetric stereo model to handle occlusion in dense two-frame stereo. Our occlusion reasoning is directly based on the visibility constraint that is more general than both ordering and uniqueness constraints used in previous work. The visibility constraint requires occlusion in one image and disparity in the other to be consistent. We embed the visibility constraint within an energy minimization framework, resulting in a symmetric stereo model that treats left and right images equally. An iterative optimization algorithm is used to approximate the minimum of the energy using belief propagation. Our stereo model can also incorporate segmentation as a soft constraint. Experimental results on the Middlebury stereo images show that our algorithm is state-of-the-art.


title: DeepWarp: DNN-based Nonlinear Deformation

abstract: DeepWarp is an efficient and highly re-usable deep neural network (DNN) based nonlinear deformable simulation framework. Unlike other deep learning applications such as image recognition, where different inputs have a uniform and consistent format (e.g. an array of all the pixels in an image), the input for deformable simulation is quite variable, high-dimensional, and parametrization-unfriendly. Consequently, even though DNN is known for its rich expressivity of nonlinear functions, directly using DNN to reconstruct the force-displacement relation for general deformable simulation is nearly impossible. DeepWarp obviates this difficulty by partially restoring the force-displacement relation via warping the nodal displacement simulated using a simplistic constitutive model -- the linear elasticity. In other words, DeepWarp yields an incremental displacement fix based on a simplified (therefore incorrect) simulation result other than returning the unknown displacement directly. We contrive a compact yet effective feature vector including geodesic, potential and digression to sort training pairs of per-node linear and nonlinear displacement. DeepWarp is robust under different model shapes and tessellations. With the assistance of deformation substructuring, one DNN training is able to handle a wide range of 3D models of various geometries including most examples shown in the paper. Thanks to the linear elasticity and its constant system matrix, the underlying simulator only needs to perform one pre-factorized matrix solve at each time step, and DeepWarp is able to simulate large models in real time.


title: A New Method for Automatic Sleep Stage Classification

abstract: Traditionally, automatic sleep stage classification is quite a challenging task because of the difficulty in translating open-textured standards to mathematical models and the limitations of handcrafted features. In this paper, a new system for automatic sleep stage classification is presented. Compared with existing sleep stage methods, our method can capture the sleep information hidden inside electroencephalography (EEG) signals and automatically extract features from raw data. To translate open sleep stage standards into machine rules recognized by computers, a new model named fast discriminative complex-valued convolutional neural network (FDCCNN) is proposed to extract features from raw EEG data and classify sleep stages. The new model combines complex-valued backpropagation and the Fisher criterion. It can learn discriminative features and overcome the negative effect of imbalance dataset. More importantly, the orthogonal decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron are proven. A speed-up algorithm is proposed to reduce computational workload and yield improvements of over an order of magnitude compared to the normal convolution algorithm. The classification performances of handcrafted features and different convolutional neural networks are compared with that of the FDCCNN. The total accuracy and kappa coefficient of the proposed method are 92% and 0.84, respectively. Experiment results demonstrated that the performance of our system is comparable to those of human experts.


Feature-Rich Twitter Named Entity Recognition and Classification


title: Capacitive Sensor Dedicated to Pedestrians Protection

abstract: In the case of an unavoidable frontal collision between a vehicle and a pedestrian, the activation of a protection system can reduce the severity of injuries caused by the collision. However, to fulfill this objective, it is necessary to determine when the shock will occur. To overcome this problem, we place inside the front bumper a metallic sheet that emits an electric field. This field is oriented toward the front of the vehicle by means of various guard screens. The intrusion into the emitted electric field induces a capacitive coupling with the sensor. Which will enable the electronic device to detect the presence of the pedestrian at a distance of 1 m. To avoid slow drifts in time, the change in capacity is measured every millisecond. The last hundred acquisitions are considered as a history. This history is compared with the signature of a pedestrian who was established in the laboratory. When the pedestrian is about a thirty centimeters of the vehicle and the shock is inevitable, the protection systems are activated. When the vehicle is at very low speed, this device can be used as parking assistance.


title: Inter-satellite optical wireless communication system design using diversity techniques

abstract: Superior multimedia facility to consumers at anytime and at anyplace is the requirement for next generation mobile satellite communication. Forming a network between the satellites along with various earth stations is a promising solution to achieve this goal. In this paper, a high speed inter-satellite optical wireless communication system is designed with space and polarization diversity techniques. The system is designed, simulated and analyzed for a link distance of 6,000 km with achievable bit-rate of 7.63 Gbps. The projected system provides an improvement of data rate of 1.35 times to that of the previously proposed work. The performance parameters of the system are Q-factor, eye opening, bit error rate etc. The purported setup will be very much practicable for present and forthcoming generation low earth orbit inter-satellite communication applications.


Feature Extraction based Face Recognition, Gender and Age Classification


title: The Talent Development Environment in a Norwegian top-level football club

abstract: Abstract Because international elite football is concerned with talent identification, this has generated a great deal of knowledge about what it takes to become a professional footballer. This research has however primarily focused on individual development and how external factors affect athletic performance. In this article we sought to describe the characteristics of the developmental environment in the youth department of the Norwegian elite club Rosenborg BK. Our case study, using a holistic and ecological development perspective as a framework, we followed the club’s under-19 team for a total of 21 days, during 3 separate weeks in the 2014 season. The results showed that Rosenborg seems to offer an environment focusing on aspects highlighted by the ecological model athletic talent development environment (ATDE). The club seems to focus on giving players tools and resources both on and off the field, using a holistic and systematic methodology. Yet it appears that the club has a weak relationship between the youth department and its own senior team, even indicating a competitive relationship, also found in other elite academies. Furthermore, it may seem that the importance of developing players with local ties is downgraded because of the club’s need for short-term success.


title: Real time personalized search on social networks

abstract: Internet users are shifting from searching on traditional media to social network platforms (SNPs) to retrieve up-to-date and valuable information. SNPs have two unique characteristics: frequent content update and small world phenomenon. However, existing works are not able to support these two features simultaneously. To address this problem, we develop a general framework to enable real time personalized top-k query. Our framework is based on a general ranking function that incorporates time freshness, social relevance and textual similarity. To ensure efficient update and query processing, there are two key challenges. The first is to design an index structure that is update-friendly while supporting instant query processing. The second is to efficiently compute the social relevance in a complex graph. To address these challenges, we first design a novel 3D cube inverted index to support efficient pruning on the three dimensions simultaneously. Then we devise a cube based threshold algorithm to retrieve the top-k results, and propose several pruning techniques to optimize the social distance computation, whose cost dominates the query processing. Furthermore, we optimize the 3D index via a hierarchical partition method to enhance our pruning on the social dimension. Extensive experimental results on two real world large datasets demonstrate the efficiency and the robustness of our proposed solution.


title: Beyond Pixels : Exploiting Camera Metadata for Photo Classification

abstract: Semantic scene classification based only on low-level vision cues has had limited success on unconstrained image sets. On the other hand, camera metadata related to capture conditions provide cues independent of the captured scene content that can be used to improve classification performance. We consider three problems, indoor-outdoor classification, sunset detection, and manmade-natural classification. Analysis of camera metadata statistics for images of each class revealed that metadata fields, such as exposure time, flash fired, and subject distance, are most discriminative for each problem. A Bayesian network is employed to fuse content-based and metadata cues in the probability domain and degrades gracefully even when specific metadata inputs are missing (a practical concern). Finally, we provide extensive experimental results on the three problems using content-based and metadata cues to demonstrate the efficacy of the proposed integrated scene classification scheme.


title: EBIC: an evolutionary‐based parallel biclustering algorithm for pattern discovery

abstract: In this paper a novel biclustering algorithm based on artificial intelligence (AI) is introduced. The method called EBIC aims to detect biologically meaningful, order-preserving patterns in complex data. The proposed algorithm is probably the first one capable of discovering with accuracy exceeding 50% multiple complex patterns in real gene expression datasets. It is also one of the very few biclustering methods designed for parallel environments with multiple graphics processing units (GPUs). We demonstrate that EBIC outperforms state-of-the-art biclustering methods, in terms of recovery and relevance, on both synthetic and genetic datasets. EBIC also yields results over 12 times faster than the most accurate reference algorithms. The proposed algorithm is anticipated to be added to the repertoire of unsupervised machine learning algorithms for the analysis of datasets, including those from large-scale genomic studies.


title: Deep feature for text-dependent speaker verification

abstract: Abstract Recently deep learning has been successfully used in speech recognition, however it has not been carefully explored and widely accepted for speaker verification. To incorporate deep learning into speaker verification, this paper proposes novel approaches of extracting and using features from deep learning models for text-dependent speaker verification. In contrast to the traditional short-term spectral feature, such as MFCC or PLP, in this paper, outputs from hidden layer of various deep models are employed as deep features for text-dependent speaker verification. Fours types of deep models are investigated: deep Restricted Boltzmann Machines, speech-discriminant Deep Neural Network (DNN), speaker-discriminant DNN, and multi-task joint-learned DNN. Once deep features are extracted, they may be used within either the GMM-UBM framework or the identity vector (i-vector) framework. Joint linear discriminant analysis and probabilistic linear discriminant analysis are proposed as effective back-end classifiers for identity vector based deep features. These approaches were evaluated on the RSR2015 data corpus. Experiments showed that deep feature based methods can obtain significant performance improvements compared to the traditional baselines, no matter if they are directly applied in the GMM-UBM system or utilized as identity vectors. The EER of the best system using the proposed identity vector is 0.10%, only one fifteenth of that in the GMM-UBM baseline.


title: Evolution of Communication Technologies for Smart Grid applications

abstract: The idea of Smart Grid has started to evolve more rapidly with the enhancement in Communication Technologies. Two way communication is a key aspect in realizing Smart Grids and is easily possible with the help of modern day advancements in both wired and wireless communication technologies. This paper discusses some of the major communication technologies which include IEEE specified ZigBee, WiMAX and Wireless LAN (Wi-Fi) technologies, GSM 3G/4G Cellular, DASH 7 and PLC (Power Line Communications), with special focus on their applications in Smart Grids. The Smart Grid environments and domains such as Home Area Automation, Substation Automation, Automated Metering Infrastructure, Vehicle-to-Grid Communications, etc. are considered as priority areas for developing smarter grids. The advancements, challenges and the opportunities present in these priority areas are discussed in this paper.


title: Bayesian Classifiers Programmed in SQL

abstract: The Bayesian classifier is a fundamental classification technique. In this work, we focus on programming Bayesian classifiers in SQL. We introduce two classifiers: naive Bayes and a classifier based on class decomposition using K-means clustering. We consider two complementary tasks: model computation and scoring a data set. We study several layouts for tables and several indexing alternatives. We analyze how to transform equations into efficient SQL queries and introduce several query optimizations. We conduct experiments with real and synthetic data sets to evaluate classification accuracy, query optimizations, and scalability. Our Bayesian classifier is more accurate than naive Bayes and decision trees. Distance computation is significantly accelerated with horizontal layout for tables, denormalization, and pivoting. We also compare naive Bayes implementations in SQL and C++: SQL is about four times slower. Our Bayesian classifier in SQL achieves high classification accuracy, can efficiently analyze large data sets, and has linear scalability.


title: Conformal Printing of Electrically Small Antennas on Three-Dimensional Surfaces

abstract: J. J. Adams ,[+] Prof. J. T. Bernhard Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana, IL 61801, USA E-mail: jbernhar@illinois.edu Dr. E. B. Duoss ,[+,++] T. F. Malkowski ,[+] Dr. B. Y. Ahn , Prof. J. A. Lewis Department of Materials Science and Engineering University of Illinois at Urbana-Champaign Urbana, IL 61801 USA E-mail: jalewis@illinois.edu Dr. M. J. Motala , Prof. R. G. Nuzzo Department of Chemistry University of Illinois at Urbana-Champaign Urbana, Illinois 61801, USA [+] These authors contributed equally to this work. [++] Presently at Lawrence Livermore National Laboratory, Center for Microand NanoTechnology, Livermore, CA 94550 USA


title: Design and Development of a Class EF$_2$ Inverter and Rectifier for Multimegahertz Wireless Power Transfer Systems

abstract: This paper presents the design and implementation of a Class EF $_2$ inverter and Class EF $_2$ rectifier for two -W wireless power transfer (WPT) systems, one operating at 6.78 MHz and the other at 27.12 MHz. It will be shown that the Class EF $_2$ circuits can be designed to have beneficial features for WPT applications such as reduced second-harmonic component and lower total harmonic distortion, higher power-output capability, reduction in magnetic core requirements and operation at higher frequencies in rectification compared to other circuit topologies. A model will first be presented to analyze the circuits and to derive values of its components to achieve optimum switching operation. Additional analysis regarding harmonic content, magnetic core requirements and open-circuit protection will also be performed. The design and implementation process of the two Class-EF $_2$ -based WPT systems will be discussed and compared to an equivalent Class-E-based WPT system. Experimental results will be provided to confirm validity of the analysis. A dc–dc efficiency of 75% was achieved with Class-EF $_2$ -based systems.


title: The effect of twitter-mediated activities on learning outcome and student engagement: A case study

abstract: The study reported in this paper, examines the effects of a rigorously designed introduction of Twitter in the educational process. In specific, it examines the relation between the students use of Twitter in the context of well-organized educational activities and their grades in an academic course. The results demonstrated a significant correlation between the use of Twitter by the students and their performance in the course. Moreover, it emerged that their participation on Twitter, could improve the students academic engagement and their sense of belonging to the academic community. Finally, possible effects of Twitter s use on students perceived internet self-efficacy were also examined. The findings and their implications of the study are discussed in detail.


title: Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network

abstract: Intrusion detection for computer network systems is becoming one of the most critical tasks for network administrators today. It has an important role for organizations, governments and our society due to the valuable resources hosted on computer networks. Traditional misuse detection strategies are unable to detect new and unknown intrusion types. In contrast anomaly detection in network security aims to distinguish between illegal or malicious events and normal behavior of network systems. Anomaly detection can be considered as a classification problem where it builds models of normal network behavior, which it uses to detect new patterns that significantly deviate from the model. Most of the current research on anomaly detection is based on the learning of normal and anomaly behaviors. They have no memory that is they do not take into account previous events classify new ones. In this paper, we propose a real time collective anomaly detection model based on neural network learning. Normally a Long Short-Term Memory Recurrent Neural Network (LSTM RNN) is trained only on normal data and it is capable of predicting several time steps ahead of an input. In our approach, a LSTM RNN is trained with normal time series data before performing a live prediction for each time step. Instead of considering each time step separately, the observation of prediction errors from a certain number of time steps is now proposed as a new idea for detecting collective anomalies. The prediction errors from a number of the latest time steps above a threshold will indicate a collective anomaly. The model is built on a time series version of the KDD 1999 dataset. The experiments demonstrate that it is possible to offer reliable and efficient collective anomaly detection.


title: Demographic differences in how students navigate through MOOCs

abstract: The current generation of Massive Open Online Courses (MOOCs) attract a diverse student audience from all age groups and over 196 countries around the world. Researchers, educators, and the general public have recently become interested in how the learning experience in MOOCs differs from that in traditional courses. A major component of the learning experience is how students navigate through course content. This paper presents an empirical study of how students navigate through MOOCs, and is, to our knowledge, the first to investigate how navigation strategies differ by demographics such as age and country of origin. We performed data analysis on the activities of 140,546 students in four edX MOOCs and found that certificate earners skip on average 22% of the course content, that they frequently employ non-linear navigation by jumping backward to earlier lecture sequences, and that older students and those from countries with lower student-teacher ratios are more comprehensive and non-linear when navigating through the course. From these findings, we suggest design recommendations such as for MOOC platforms to develop more detailed forms of certification that incentivize students to deeply engage with the content rather than just doing the minimum necessary to earn a passing grade. Finally, to enable other researchers to reproduce and build upon our findings, we have made our data set and analysis scripts publicly available.


title: Using time topic modeling for semantics-based dynamic research interest finding

abstract: Researchers interests finding has been an active area of investigation for different recommendation tasks. Previous approaches for finding researchers interests exploit writing styles and links connectivity by considering time of documents, while semantics-based intrinsic structure of words is ignored. Consequently, a topic model named Author-Topic model is proposed, which exploits semantics-based intrinsic structure of words present between the authors of research papers. It ignores simultaneous modeling of time factor which results in exchangeability of topics problem, which is, important factor to deal with when finding dynamic research interests. For example, in many real world applications, like finding reviewers for papers and finding taggers in the social tagging systems one needs to consider different time periods. In this paper, we present time topic modeling approach named Temporal-Author-Topic (TAT) which can simultaneously model text, researchers and time of research papers to overcome the exchangeability of topic problem. The mixture distribution over topics is influenced by both co-occurrences of words and timestamps of the research papers. Consequently, topics occurrence and their related researchers change over time, while the meaning of particular topic almost remains unchanged. Proposed approach is used to discover topically related researchers for different time periods. We also show how their interests and relationships change over a time period. Empirical results on large research papers corpus show the effectiveness of our proposed approach and dominance over Author-Topic (AT) model, by handling the exchangeability of topics problem, which enables it to obtain similar meaning of a particular topic overtime.


title: Measuring the efficiency of cloud computing for e-learning systems

abstract: As with rapid growth of the cloud computing architecture usage, more and more industries move their focus from investing into processing power to renting processing power from a specialized vendor. Education field is no different. E-learning systems usually require many hardware and software resources. There are numerous educational institutions that cannot afford such investments, and cloud computing is the best solution for them. The implementation of a cloud computing e-learning system has its peculiarities and needs a specific approach. This paper measures the positive impact of using cloud computing architectures upon e-learning solutions development. We advance a set of cloud computing efficiency metrics for enhanced e-learning implementation process control. Also, the long term overall efficiency of the cloud computing usage in the field of e-learning system is evaluated.


title: Review Battery Management Systems in Electric and Hybrid Vehicles

abstract: The battery management system (BMS) is a critical component of electric and hybrid electric vehicles. The purpose of the BMS is to guarantee safe and reliable battery operation. To maintain the safety and reliability of the battery, state monitoring and evaluation, charge control, and cell balancing are functionalities that have been implemented in BMS. As an electrochemical product, a battery acts differently under different operational and environmental conditions. The uncertainty of a battery’s performance poses a challenge to the implementation of these functions. This paper addresses concerns for current BMSs. State evaluation of a battery, including state of charge, state of health, and state of life, is a critical task for a BMS. Through reviewing the latest methodologies for the state evaluation of batteries, the future challenges for BMSs are presented and possible solutions are proposed as well.


title: Large Scale Real-time Ridesharing with Service Guarantee on Road Networks

abstract: Urban traffic gridlock is a familiar scene. At the same time, the mean occupancy rate of personal vehicle trips in the United States is only 1.6 persons per vehicle mile. Ridesharing has the potential to solve many environmental, congestion, pollution, and energy problems. In this paper, we introduce the problem of large scale real-time ridesharing with service guarantee on road networks. Trip requests are dynamically matched to vehicles while trip waiting and service time constraints are satisfied. We first propose two scheduling algorithms: a branch-and-bound algorithm and an integer programing algorithm. However, these algorithms do not adapt well to the dynamic nature of the ridesharing problem. Thus, we propose kinetic tree algorithms which are better suited to efficient scheduling of dynamic requests and adjust routes on-the-fly. We perform experiments on a large Shanghai taxi dataset. Results show that the kinetic tree algorithms outperform other algorithms significantly.


title: Weakly supervised object detection using pseudo-strong labels

abstract: Object detection is an import task of computer vision.A variety of methods have been proposed,but methods using the weak labels still do not have a satisfactory result.In this paper,we propose a new framework that using the weakly supervised method's output as the pseudo-strong labels to train a strongly supervised model.One weakly supervised method is treated as black-box to generate class-specific bounding boxes on train dataset.A de-noise method is then applied to the noisy bounding boxes.Then the de-noised pseudo-strong labels are used to train a strongly object detection network.The whole framework is still weakly supervised because the entire process only uses the image-level labels.The experiment results on PASCAL VOC 2007 prove the validity of our framework, and we get result 43.4% on mean average precision compared to 39.5% of the previous best result and 34.5% of the initial method,respectively.And this frame work is simple and distinct,and is promising to be applied to other method easily.


title: Combining Long Short-Term Memory and Dynamic Bayesian Networks for Incremental Emotion-Sensitive Artificial Listening

abstract: The automatic estimation of human affect from the speech signal is an important step towards making virtual agents more natural and human-like. In this paper, we present a novel technique for incremental recognition of the user's emotional state as it is applied in a sensitive artificial listener (SAL) system designed for socially competent human-machine communication. Our method is capable of using acoustic, linguistic, as well as long-range contextual information in order to continuously predict the current quadrant in a two-dimensional emotional space spanned by the dimensions valence and activation. The main system components are a hierarchical dynamic Bayesian network (DBN) for detecting linguistic keyword features and long short-term memory (LSTM) recurrent neural networks which model phoneme context and emotional history to predict the affective state of the user. Experimental evaluations on the SAL corpus of non-prototypical real-life emotional speech data consider a number of variants of our recognition framework: continuous emotion estimation from low-level feature frames is evaluated as a new alternative to the common approach of computing statistical functionals of given speech turns. Further performance gains are achieved by discriminatively training LSTM networks and by using bidirectional context information, leading to a quadrant prediction F1-measure of up to 51.3 %, which is only 7.6 % below the average inter-labeler consistency.


title: Dynamic Integration of Background Knowledge in Neural NLU Systems

abstract: Common-sense or background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, the requisite background knowledge is indirectly acquired from static corpora. We develop a new reading architecture for the dynamic integration of explicit background knowledge in NLU models. A new task-agnostic reading module provides refined word representations to a task-specific NLU architecture by processing background knowledge in the form of free-text statements, together with the task-specific inputs. Strong performance on the tasks of document question answering (DQA) and recognizing textual entailment (RTE) demonstrate the effectiveness and flexibility of our approach. Analysis shows that our models learn to exploit knowledge selectively and in a semantically appropriate way.


title: Tempo Detection of Urban Music Using Tatum Grid Non Negative Matrix Factorization

abstract: High tempo detection accuracies have been reported for the analysis of percussive, constant-tempo, Western music audio signals. As a consequence, active research in the tempo detection domain has been shifted to yet open tasks like tempo analysis of non-percussive, expressive, or non-western music. Also, tempo detection is included in a large range of music-related software. In DJ software, features like beat-synching or tempo-synchronized sound effects are widely accepted in the DJ community, and their users rely on correct tempo hypothesis as their basis. In this paper, we are evaluating both academic and commercial tempo detection systems on a typical dataset of an urban club music DJ. Based on this evaluation, we identify octave errors as a problem that has not yet been solved. Further, an approach based on non-negative matrix factorization is presented. In its current state it can compete with the state of the art. It further provides a foundation to tackle the octave error issue in future research.


title: A New Pairwise Ensemble Approach

abstract: Text classification, whether by topic or genre, is an important task that contributes to text extraction, retrieval, summarization and question answering. In this paper we present a new pairwise ensemble approach, which uses pairwise Support Vector Machine (SVM) classifiers as base classifiers and "input-dependent latent variable" method for model combination. This new approach better captures the characteristics of genre classification, including its heterogeneous nature. Our experiments on two multi-genre collections and one topic-based classification datasets show that the pairwise ensemble method outperforms both boosting, which has been demonstrated as a powerful ensemble approach, and Error-Correcting Output Codes (ECOC), which applies pairwise-like classifiers for multiclass classification problems.


A Novel AC–DC Step-Up Converter For Energy Harvesting


title: Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators

abstract: We provide finite-sample analysis of a general framework for using k-nearest neighbor statistics to estimate functionals of a nonparametric continuous probability density, including entropies and divergences. Rather than plugging a consistent density estimate (which requires $k \to \infty$ as the sample size $n \to \infty$) into the functional of interest, the estimators we consider fix k and perform a bias correction. This is more efficient computationally, and, as we show in certain cases, statistically, leading to faster convergence rates. Our framework unifies several previous estimators, for most of which ours are the first finite sample guarantees.


A Universal Measure of Intelligence for Artificial Agents


title: Real-Time Wireless Sensor Network for Landslide Detection

abstract: Wireless sensor networks are one of the emerging areas which have equipped scientists with the capability of developing real-time monitoring systems. This paper discusses the development of a wireless sensor network(WSN) to detect landslides, which includes the design, development and implementation of a WSN for real time monitoring, the development of the algorithms needed that will enable efficient data collection and data aggregation, and the network requirements of the deployed landslide detection system. The actual deployment of the testbed is in the Idukki district of the Southern state of Kerala, India, a region known for its heavy rainfall, steep slopes, and frequent landslides.


title: A Ka-band digitally-controlled phase shifter with sub-degree phase precision

abstract: We present a passive digital-to-phase converter with sub-degree phase precision for phased array frontends. The phase tuning approach is based on manipulating the electromagnetic properties of an artificially constructed transmission line. By simultaneously controlling dispersion, characteristic impedance, and loss across the structure, the phase shifter minimizes phase imprecisions while ensuring a flat amplitude response across different phase settings. The chip prototype is fabricated in a 130nm SiGe BiCMOS process, occupies an area of 0.18mm2, and consumes no power. The insertion loss is −9.3 dB ± 0.25 dB at 28 GHz. The phase control operates with 4.75 degree steps while maintaining an RMS phase error of 0.6 degrees across multiple chips and temperatures, demonstrating the best phase and amplitude accuracy when compared to state-of-the-art integrated microwave and mm-wave phase shifters.


title: /spl Sigma/High-frequency LC VCO design using capacitive degeneration

abstract: In this paper, we evaluate the high-frequency performance limitations of traditional LC voltage-controlled oscillators (VCOs) that use a cross-coupled negative resistance cell and propose a new topology that overcomes these limitations. The proposed cell is based on a capacitively emitter degenerated topology which uses a cross-coupled MOS pair as the degeneration cell. The cross-coupled MOS pair contributes additional conductance and results in a higher maximum attainable oscillation frequency and better negative resistance characteristics as compared to the other topologies at high frequencies. These properties combined with its small effective capacitance enable low-power low-noise high-frequency VCO implementations. The proposed topology is demonstrated through a 20-GHz fully integrated LC VCO implemented in the IBM SiGe 0.25-μm BiCMOS process. A comparison of its figure of merit with previously reported 20-GHz VCOs shows the effectiveness of the proposed topology.


Issues for DSM-V: Internet Addiction


title: An Overview of the SWI-Prolog Programming Environment

abstract: The Prolog programmer’s needs have always been the focus for guiding the development of the SWI-Prolog system. This article accompanies an invited talk about how the SWI-Prolog environment helps the Prolog programmer solve common problems. It describes the central parts of the graphical development environment as well as the command line tools which we see as vital to the success of the system. We hope this comprehensive overview of particularly useful features will both inspire other Prolog developers, and help SWI-Prolog users to make more productive use of the system.


title: ISTI@TREC Microblog Track 2011: Exploring the Use of Hashtag Segmentation and Text Quality Ranking

abstract: In the rst year of the TREC Micro Blog track, our participation has focused on building from scratch an IR system based on the Whoosh IR library. Though the design of our system (CipCipPy) is pretty standard it includes three ad-hoc solutions for the track: (i) a dedicated indexing function for hashtags that automatically recognizes the distinct words composing an hashtag, (ii) expansion of tweets based on the title of any referred Web page, and (iii) a tweet ranking function that ranks tweets in results by their content quality, which is compared against a reference corpus of Reuters news. In this preliminary paper we describe all the components of our system, and the ecacy scored by our runs. The CipCipPy system is available under a GPL license.


title: BayDNN: Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network

abstract: Friendship is the cornerstone to build a social network. In online social networks, statistics show that the leading reason for user to create a new friendship is due to recommendation. Thus the accuracy of recommendation matters. In this paper, we propose a Bayesian Personalized Ranking Deep Neural Network (BayDNN) model for friend recommendation in social networks. With BayDNN, we achieve significant improvement on two public datasets: Epinions and Slashdot. For example, on Epinions dataset, BayDNN significantly outperforms the state-of-the-art algorithms, with a 5% improvement on NDCG over the best baseline. The advantages of the proposed BayDNN mainly come from its underlying convolutional neural network (CNN), which offers a mechanism to extract latent deep structural feature representations of the complicated network data, and a novel Bayesian personalized ranking idea, which precisely captures the users' personal bias based on the extracted deep features. To get good parameter estimation for the neural network, we present a fine-tuned pre-training strategy for the proposed BayDNN model based on Poisson and Bernoulli probabilistic models.


title: Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

abstract: Motivation: Biomedical named entity recognition (BioNER) is the most fundamental task in biomedical text mining. State-of-the-art BioNER systems often require handcrafted features specifically designed for each type of biomedical entities. This feature generation process requires intensive labors from biomedical and linguistic experts, and makes it difficult to adapt these systems to new biomedical entity types. Although recent studies explored using neural network models for BioNER to free experts from manual feature generation, these models still require substantial human efforts to annotate massive training data. ::: Results: We propose a multi-task learning framework for BioNER that is based on neural network models to save human efforts. We build a global model by collectively training multiple models that share parameters, each model capturing the characteristics of a different biomedical entity type. In experiments on five BioNER benchmark datasets covering four major biomedical entity types, our model outperforms state-of-the-art systems and other neural network models by a large margin, even when only limited training data are available. Further analysis shows that the large performance gains come from sharing character- and word-level information between different biomedical entities. The approach creates new opportunities for text-mining approaches to help biomedical scientists better exploit knowledge in biomedical literature.


title: A robust preprocessing algorithm for iris segmentation from low contrast eye images

abstract: Iris recognition systems offer highly accurate personal identification both on small and very-large scale systems needed in government, forensic and commercial applications. The automatic segmentation of a noise-free iris region is imperative for optimal performance of the system. However, image characteristics such as brightness and contrast, the differing levels of pigmentation, occlusion by eyelashes and/or eyelids, coupled with varying sensor and environmental conditions, makes iris segmentation a huge and difficult task. This paper proposes an image pre-processing algorithm for robust iris segmentation of low contrast images, aimed at reducing mis-localization errors of basic curve-fitting algorithms. Similar to face detection, the algorithm performs iris detection with a k-NN classifier trained with features extracted by a rotation-invariant texture descriptor based on the co-occurrence of local binary patterns. The integration of the proposed algorithm into an existing open-source iris segmentation module offered a 40% improvement in execution time; a segmentation accuracy of 92% was also recorded over 1,898 low contrast eye images acquired from African subjects. The low contrast eye images were acquired to support diversity in iris recognition.


title: Self-localization and navigation of holonomic mobile robot using omni-directional wheel odometry

abstract: This paper proposes a simple but effective self-localization and navigation algorithm for the omni-directional mobile robot equipped with three driving and three odometry wheels. Not only can this technique locates the robot's position, but it can also align its heading with respect to the selected target while navigating along the pre-defined trajectory. With a certain configuration of odometry omni-directional wheels, rotational readings from three encoders attached to these odometry wheels provide linear and angular velocities of the mobile robot. Integrating these velocities would result in position and heading of the mobile robot. As a result, the mobile robot becomes a complete holonomic system. Furthermore, navigation algorithm is illustrated for two cases namely point-to-point and multi-point schemes. Three experimental results are conducted to verify the effectiveness of this algorithm.


title: CGRA express: accelerating execution using dynamic operation fusion

abstract: Coarse-grained reconfigurable architectures (CGRAs) present an appealing hardware platform by providing programmability with the potential for high computation throughput, scalability, low cost, and energy efficiency. CGRAs have been effectively used for innermost loops that contain an abundant of instruction-level parallelism. Conversely, non-loop and outer-loop code are latency constrained and do not offer significant amounts of instruction-level parallelism. In these situations, CGRAs are ineffective as the majority of the resources remain idle. In this paper, dynamic operation fusion is introduced to enable CGRAs to effectively accelerate latency-constrained code regions. Dynamic operation fusion is enabled through the combination of a small bypass network added between function units in a conventional CGRA and a sub-cycle modulo scheduler to automatically identify opportunities for fusion. Results show that dynamic operation fusion reduced total application run-time by up to 17% on a 4x4 CGRA.


title: A 28GHz CMOS Phase Shifter Supporting 11.2Gb/s in 256QAM with an RMS Gain Error of 0.13dB for 5G Mobile Network

abstract: In this paper, a 28GHz CMOS vector summing phase shifter designed for the 5G mobile network is introduced. With the proposed gain-error compensation technique, the measured gain variation due to phase tuning within 27.4 ~28.6GHz is less than 0.47dB together with an RMS gain error of 0.13dB. The measured RMS phase error is less than 0.54°. Gain-invariant, high-accuracy phase control is realized by this work. In the measurement, the proposed phase shifter supports a maximum data rate of 11.2Gb/s in 256QAM. The measured power consumption is 25.2mW.


Apparel and Textiles Education: A Case for Rural-Urban Interface


title: An instance level analysis of data complexity

abstract: Most data complexity studies have focused on characterizing the complexity of the entire data set and do not provide information about individual instances. Knowing which instances are misclassified and understanding why they are misclassified and how they contribute to data set complexity can improve the learning process and could guide the future development of learning algorithms and data analysis methods. The goal of this paper is to better understand the data used in machine learning problems by identifying and analyzing the instances that are frequently misclassified by learning algorithms that have shown utility to date and are commonly used in practice. We identify instances that are hard to classify correctly (instance hardness) by classifying over 190,000 instances from 64 data sets with 9 learning algorithms. We then use a set of hardness measures to understand why some instances are harder to classify correctly than others. We find that class overlap is a principal contributor to instance hardness. We seek to integrate this information into the training process to alleviate the effects of class overlap and present ways that instance hardness can be used to improve learning.


title: Mechanical design of robot lower body based on four-bar linkage structure for energy efficient bipedal walking

abstract: Since the Fukushima nuclear plant disaster in Japan 2011, a lot of robot has been developed on behalf of human in the disaster. But they are not capable of coping with the disaster yet. The main reasons are the lack of bipedal walking technology on uneven terrain and short operating time due to low energy efficiency during walking. In the case of the existing bipedal walking robot which is driven by actuators in each joint, energy efficiency is poor because moment of inertia and mass are high due to heavy actuators located far from the rotational axis. To solve this problem, this paper suggests the six degree-of-freedom robot leg using the four-bar linkage mechanism with high rigidity. The actuator of the knee joint and ankle joint are positioned on the hip to reduce the leg mass and moment of inertia. Our aim is bipedal walking robot lower body design to increase the energy efficiency and walk on highspeed. To do so, we designed the robot lower body that has optimal dimension to endure external forces with minimum mass. And we simulated the result of each joint trajectory, torque and energy efficiency based on the designed model to generate a walking pattern. We will produce the actual robot lower body in detail based on the derived data, and compare to the existing robots' performance.


title: Exploration of Large Networks with Covariates via Fast and Universal Latent Space Model Fitting

abstract: Latent space models are effective tools for statistical modeling and exploration of network data. These models can effectively model real world network characteristics such as degree heterogeneity, transitivity, homophily, etc. Due to their close connection to generalized linear models, it is also natural to incorporate covariate information in them. The current paper presents two universal fitting algorithms for networks with edge covariates: one based on nuclear norm penalization and the other based on projected gradient descent. Both algorithms are motivated by maximizing likelihood for a special class of inner-product models while working simultaneously for a wide range of different latent space models, such as distance models, which allow latent vectors to affect edge formation in flexible ways. These fitting methods, especially the one based on projected gradient descent, are fast and scalable to large networks. We obtain their rates of convergence for both inner-product models and beyond. The effectiveness of the modeling approach and fitting algorithms is demonstrated on five real world network datasets for different statistical tasks, including community detection with and without edge covariates, and network assisted learning.


title: Neuromorphic Computing Based on Emerging Memory Technologies

abstract: In this paper, we review some of the novel emerging memory technologies and how they can enable energy-efficient implementation of large neuromorphic computing systems. We will highlight some of the key aspects of biological computation that are being mimicked in these novel nanoscale devices, and discuss various strategies employed to implement them efficiently. Though large scale learning systems have not been implemented using these devices yet, we will discuss the ideal specifications and metrics to be satisfied by these devices based on theoretical estimations and simulations. We also outline the emerging trends and challenges in the path towards successful implementations of large learning systems that could be ubiquitously deployed for a wide variety of cognitive computing tasks.


title: Active noise cancellation with TMS320C5402 DSP starter kit

abstract: The paper describes the use TMS320C5402 DSP for single channel active noise cancellation (ANC) in duct system. The canceller uses a feedback control topology and is designed to cancel narrowband periodic tones. The signal is processed with well-known filtered-X least mean square (filtered-X LMS) Algorithm in the digital signal processing. The paper describes the hardware and use chip support libraries for data streaming. The FXLMS algorithm is written in assembly language callable from C main program. The results obtained are compatible to the expected result in the literature available. The paper highlights the features of cancellation and analyzes its performance at different gain and frequency.


title: Data Mining in Higher Education

abstract: Higher Education targets to develop complex theoretical, abstract and analytical reasoning capabilities in the alumni. This objective can be accomplished addressing four major steps: Theoretical foundation, practice, communication and assessment (Petry, 2002). Theoretical background and practical exercise comprise the basic knowledge building process at initial stages. Assessment guides the alumni through higher complexity studies permitting the student to identify the weak points in the knowledge building where further theory study and/or practice is required. Theoretical foundation and problem-solving practice are well known aspects in Higher Education. High-quality materials in printed and electronic format, sometimes including multimedia –audio, video or computer graphics–, sometimes delivered through the Internet are readily available today. Teaching-aids as computer-feed overhead projectors or electronic blackboards in the classroom are common place and facilitate the knowledgebuilding process. Moreover, computers in the classroom are a powerful tool linking theory and problem-solving practice in engineering studies (Beyerlein et al., 1993). On the other hand, the assessment process has been evolving slowly in the last decades. Pen-and-paper examination techniques have been translated to the computer-enabled classroom as software applications that present an exam in the screen and record the student answers. This process can be seen as an external assessment targeting to evaluate the skills of the student in order to give a pass/fail on a given subject. The external evaluation can be useful for the student in order to know the skill level, but usually fails short when the student wants to now “what’s wrong”, i.e. to know not only what question was missed but also what knowledge areas the student is finding difficulties. Weak areas identification cannot be done from a single question or set of questions. It requires the assessment process, examination or similar, to be considered as a whole. Student attention time or time spent thinking on a specific question –relative to the other question, as some students think faster than othersclearly indicates the areas where difficulties hide, by example. The pattern followed when answering the exam questions, by example, is another useful indicator. The student will try to answer first the questions he feels more comfortable with. Dubitation on the answer –change the answer several times– is another useful parameter. All these parameters and many others can be compiled and processed by the unprecedented analytic processing capabilities of modern data mining techniques. Collaborative assessment appears as a the natural evolution from the individual learning to the collaborative learning, which was proposed as a suitable technique to speed-up development of analytical reasoning as different approaches are continuously suggested


title: Single-Channel Multi-Speaker Separation using Deep Clustering

abstract: Deep clustering is a recently introduced deep learning architecture that uses discriminatively trained embeddings as the basis for clustering. It was recently applied to spectrogram segmentation, resulting in impressive results on speaker-independent multi-speaker separation. In this paper we extend the baseline system with an end-to-end signal approximation objective that greatly improves performance on a challenging speech separation. We first significantly improve upon the baseline system performance by incorporating better regularization, larger temporal context, and a deeper architecture, culminating in an overall improvement in signal to distortion ratio (SDR) of 10.3 dB compared to the baseline of 6.0 dB for two-speaker separation, as well as a 7.1 dB SDR improvement for three-speaker separation. We then extend the model to incorporate an enhancement layer to refine the signal estimates, and perform end-to-end training through both the clustering and enhancement stages to maximize signal fidelity. We evaluate the results using automatic speech recognition. The new signal approximation objective, combined with end-to-end training, produces unprecedented performance, reducing the word error rate (WER) from 89.1% down to 30.8%. This represents a major advancement towards solving the cocktail party problem.


title: Planar Probe Coaxial-Waveguide Power Combiner/Divider

abstract: A novel axially symmetric oversized coaxial waveguide power combiner/divider, which utilizes a planar probe array to transform multiway input to a central oversized coaxial waveguide, is presented in this paper. The use of a coaxial waveguide allows standard transverse electromagnetic design theory to be used, and the characteristics of the coaxial taper transition and the planar probe array are analyzed in detail. A design procedure is also established for the structure. A ten-way planar probe coaxial-waveguide power combiner/divider over the Ku-band is designed and measured. The measured results show a reasonable agreement with the simulated results. The measured 15-dB return-loss bandwidth is approximately 4.3 GHz, and a maximum amplitude imbalance of plusmn 1 dB and a phase imbalance of plusmn5deg are observed in the 11.5-16-GHz band. Furthermore, the isolation between the peripheral ports is also discussed.


Communication Robots for Elementary Schools


title: Co-FAIS: Cooperative fuzzy artificial immune system for detecting intrusion in wireless sensor networks

abstract: Abstract Due to the distributed nature of Denial-of-Service attacks, it is tremendously challenging to identify such malicious behavior using traditional intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a bio-inspired method is introduced, namely the cooperative-based fuzzy artificial immune system (Co-FAIS). It is a modular-based defense strategy derived from the danger theory of the human immune system. The agents synchronize and work with one another to calculate the abnormality of sensor behavior in terms of context antigen value (CAV) or attackers and update the fuzzy activation threshold for security response. In such a multi-node circumstance, the sniffer module adapts to the sink node to audit data by analyzing the packet components and sending the log file to the next layer. The fuzzy misuse detector module (FMDM) integrates with a danger detector module to identify the sources of danger signals. The infected sources are transmitted to the fuzzy Q-learning vaccination modules (FQVM) in order for particular, required action to enhance system abilities. The Cooperative Decision Making Modules (Co-DMM) incorporates danger detector module with the fuzzy Q-learning vaccination module to produce optimum defense strategies. To evaluate the performance of the proposed model, the Low Energy Adaptive Clustering Hierarchy (LEACH) was simulated using a network simulator. The model was subsequently compared against other existing soft computing methods, such as fuzzy logic controller (FLC), artificial immune system (AIS), and fuzzy Q-learning (FQL), in terms of detection accuracy, counter-defense, network lifetime and energy consumption, to demonstrate its efficiency and viability. The proposed method improves detection accuracy and successful defense rate performance against attacks compared to conventional empirical methods.


title: Application of Microsoft Kinect Sensor for Tracking Construction Workers

abstract: The image processing based human recognition is yet a challenging task because of series of complications such as variations in pose, lighting conditions and complexity of background in the tracking environment. This study introduces a novel methodology to track construction workers using image processing techniques and depth information generated from the Microsoft Kinect sensor. Kinect is a new game controller technology introduced by Microsoft in November 2010. This automated real-time worker tracking system provides an opportunity to track the construction worker location and their movements in a specified indoor work area. The research study proposes a properly color coded “construction hardhat” as a key tracking object which can be used to differentiate site personnel (worker, supervisor, engineer, etc.). The proposed method detects construction workers in three major stages which includes human recognition, hardhat recognition and 3D localization. The human recognition is done by analysing human body parts. 3D positions of body joints are accurately predicted from a single depth image. The construction hardhat detection is based on characteristics of the hardhat such as unique shape and color. Template based template matching is used as the pattern recognition technique.


title: Evolving spiking neural networks for audiovisual information processing

abstract: This paper presents a new modular and integrative sensory information system inspired by the way the brain performs information processing, in particular, pattern recognition. Spiking neural networks are used to model human-like visual and auditory pathways. This bimodal system is trained to perform the specific task of person authentication. The two unimodal systems are individually tuned and trained to recognize faces and speech signals from spoken utterances, respectively. New learning procedures are designed to operate in an online evolvable and adaptive way. Several ways of modelling sensory integration using spiking neural network architectures are suggested and evaluated in computer experiments.


title: Decision Directed Estimation of Threshold Voltage Distribution in NAND Flash Memory

abstract: High-density NAND flash memory suffers from the data retention problem because even small charge leakage incurs a large threshold voltage shift as the transistor size shrinks. In this paper, we develop a decision directed estimation (DDE) algorithm to know the effects of charge leakage in NAND flash memory using the error pattern of the accessed data. While the conventional sensing directed estimation (SDE) method demands extra memory sensing to know the signal distribution, the proposed DDE algorithm induces no sensing overheads. The proposed algorithm approximates the threshold voltage distribution as a Gaussian mixture and also assumes that the increase of standard deviation is linearly proportional to the amount of mean shift. Since the threshold voltage distribution in real devices is not a perfect Gaussian mixture, we conduct error analysis of the proposed algorithm to examine the sources of estimation errors and quantify their impact on the estimation accuracy. We also present a combined estimation scheme that employs the DDE and the SDE algorithms to minimize the number of memory sensing operations while maintaining high estimation accuracy. The developed algorithm is applied to both simulated and real NAND flash memory, and the estimation errors are measured.


title: SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News

abstract: Horizon 2020 ICT Program Project SSIX: Social ::: Sentiment analysis financial IndeXes, has received ::: funding from the European Union’s Horizon 2020 ::: Research and Innovation Program ICT 2014 - Information ::: and Communications Technologies under ::: grant agreement No. 645425


title: Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles

abstract: Reviews of products or services on Internet marketplace websites contain a rich amount of information. Users often wish to survey reviews or review snippets from the perspective of a certain aspect, which has resulted in a large body of work on aspect identification and extraction from such corpora. In this work, we evaluate a newly-proposed neural model for aspect extraction on two practical tasks. The first is to extract canonical sentences of various aspects from reviews, and is judged by human evaluators against alternatives. A $k$-means baseline does remarkably well in this setting. The second experiment focuses on the suitability of the recovered aspect distributions to represent users by the reviews they have written. Through a set of review reranking experiments, we find that aspect-based profiles can largely capture notions of user preferences, by showing that divergent users generate markedly different review rankings.


title: SaC2Vec: Information Network Representation with Structure and Content

abstract: Network representation learning (also known as information network embedding) has been the central piece of research in social and information network analysis for the last couple of years. An information network can be viewed as a linked structure of a set of entities. A set of linked web pages and documents, a set of users in a social network are common examples of information network. Network embedding learns low dimensional representations of the nodes, which can further be used for downstream network mining applications such as community detection or node clustering. Information network representation techniques traditionally use only the link structure of the network. But in real world networks, nodes come with additional content such as textual descriptions or associated images. This content is semantically correlated with the network structure and hence using the content along with the topological structure of the network can facilitate the overall network representation. In this paper, we propose Sac2Vec, a network representation technique that exploits both the structure and content. We convert the network into a multi-layered graph and use random walk and language modeling technique to generate the embedding of the nodes. Our approach is simple and computationally fast, yet able to use the content as a complement to structure and vice-versa. We also generalize the approach for networks having multiple types of content in each node. Experimental evaluations on four real world publicly available datasets show the merit of our approach compared to state-of-the-art algorithms in the domain.


title: QR code security using proxy re-encryption

abstract: As the details that is being encoded only readable with machines, humans cannot compare the malicious data or malicious free data ie, a valid data. QR code is a machine readable encoded form used for data storage in a limited amount of space in an image. Sending of QR code images from one host to another may cause malicious attacks and data may lose in the process of communication. Proxy Re-Encryption system helps to avoid these malicious attacks. Re-Encryption helps to provide secure and flexible condition to store and share data with other hosts. Security of proxy Re-Encryption system confirms that the sender and the receiver does know about the data. QR code security using Proxy Re-Encryption method provide efficient and secure data communication between the sender and receiver hosts in the communication system.


title: Classification of Packed Executables for Accurate Computer Virus Detection

abstract: Executable packing is the most common technique used by computer virus writers to obfuscate malicious code and evade detection by anti-virus software. Universal unpackers have been proposed that can detect and extract encrypted code from packed executables, therefore potentially revealing hidden viruses that can then be detected by traditional signature-based anti-virus software. However, universal unpackers are computationally expensive and scanning large collections of executables looking for virus infections may take several hours or even days. In this paper we apply pattern recognition techniques for fast detection of packed executables. The objective is to efficiently and accurately distinguish between packed and non-packed executables, so that only executables detected as packed will be sent to an universal unpacker, thus saving a significant amount of processing time. We show that our system achieves very high detection accuracy of packed executables with a low average processing time.


title: MtNet: A Multi-Task Neural Network for Dynamic Malware Classification

abstract: In this paper, we propose a new multi-task, deep learning architecture for malware classification for the binary i.e. malware versus benign malware classification task. All models are trained with data extracted from dynamic analysis of malicious and benign files. For the first time, we see improvements using multiple layers in a deep neural network architecture for malware classification. The system is trained on 4.5 million files and tested on a holdout test set of 2 million files which is the largest study to date. To achieve a binary classification error rate of 0.358i¾?%, the objective functions for the binary classification task and malware family classification task are combined in the multi-task architecture. In addition, we propose a standard i.e. non multi-task malware family classification architecture which also achieves a malware family classification error rate of 2.94i¾?%.


title: Low-rank matrix completion using alternating minimization

abstract: Alternating minimization represents a widely applicable and empirically successful approach for finding low-rank matrices that best fit the given data. For example, for the problem of low-rank matrix completion, this method is believed to be one of the most accurate and efficient, and formed a major component of the winning entry in the Netflix Challenge. In the alternating minimization approach, the low-rank target matrix is written in a bi-linear form, i.e. $X = UV^\dag$; the algorithm then alternates between finding the best $U$ and the best $V$. Typically, each alternating step in isolation is convex and tractable. However the overall problem becomes non-convex and there has been almost no theoretical understanding of when this approach yields a good result. In this paper we present first theoretical analysis of the performance of alternating minimization for matrix completion, and the related problem of matrix sensing. For both these problems, celebrated recent results have shown that they become well-posed and tractable once certain (now standard) conditions are imposed on the problem. We show that alternating minimization also succeeds under similar conditions. Moreover, compared to existing results, our paper shows that alternating minimization guarantees faster (in particular, geometric) convergence to the true matrix, while allowing a simpler analysis.


title: Live-streaming changes the (video) game

abstract: Video games are inherently an active medium, without interaction a video game is benign. Yet there is a growing community of video game spectating that exists on the Internet, at events across the world and, in part, as traditional television broadcasts. In this paper we look at the different communities that have grown around video game spectating, the incentives of all stakeholders and the technologies involved. An interesting part of this phenomenon is its relation to the malleability of activity and passivity; video games are traditionally active but spectatorship brings an element of passivity, whereas television is traditionally passive but interactive television brings an element of activity. We explore this phenomenon based on selected examples and stimulate a discussion around how such understanding from the video game field could be interesting for interactive television.


title: Speech translation for low-resource languages: The case of Pashto,” in Eurospeech

abstract: We present a number of challenges and solutions that have arisen in the development of a speech translation system for American English and Pashto, highlighting those specific to a very low resource language. In particular, we address issues posed by Pashto in the areas of written representation, corpus creation, speech recognition, speech synthesis, and grammar development for translation.


title: The expanding scope of antimicrobial peptide structures and their modes of action

abstract: Antimicrobial peptides (AMPs) are an integral part of the innate immune system that protect a host from invading pathogenic bacteria. To help overcome the problem of antimicrobial resistance, cationic AMPs are currently being considered as potential alternatives for antibiotics. Although extremely variable in length, amino acid composition and secondary structure, all peptides can adopt a distinct membrane-bound amphipathic conformation. Recent studies demonstrate that they achieve their antimicrobial activity by disrupting various key cellular processes. Some peptides can even use multiple mechanisms. Moreover, several intact proteins or protein fragments are now being shown to have inherent antimicrobial activity. A better understanding of the structure–activity relationships of AMPs is required to facilitate the rational design of novel antimicrobial agents.


title: Evaluation of Power System Transient Stability a nd Definition of the Basic Criterion

abstract: Power system stability is defined as an ability of the power system to reestablish the initial steady state or come into the new steady state after any variation of the system's operation value or after systems breakdown. The stability and reliability of the electric power system is highly actual topic nowadays, especially in the light of recent accidents like splitting of UCTE system and north! American blackouts. This paper deals with the potential of the evaluation in term of transient stability of the electric power system within the defense plan and the definition of the basic criterion for the transient stability - Critical Clearing Time (CCT). Keywords—critical clearing time (CCT), defense plan, power system (PS), synchronous generator, transient stability, three!phase fault.


A Study To Improve Organizational Citizenship Behaviors


title: Learning and Inference with Constraints

abstract: Probabilistic modeling has been a dominant approach in Machine Learning research. As the field evolves, thc problems of interest become increasingly challenging and complex. Making complex decisions in real world problems often involves assigning values to sets of interdependent variables where the expressive dependency structure can influence, or even dictate, what assignments are possible. However, incorporating nonlocal depcndencies in a probabilistic model can lead to intractable training and inference. This paper presents Constraints Conditional Models (CCMs), a framework that augments probabilistic models with declarative constraints as a way to support decisions in an expressive output space while maintaining modularity and tractability of training. We further show that declarative constraints can be used to take advantage of unlabeled data when training the probabilistic model.


title: Voxel-Based Morphometry of the Human Brain: Methods and Applications

abstract: In recent years, a whole-brain unbiased objective technique, known as voxel-based morphometry (VBM), has been developed to characterise brain differences in vivo using structural magnetic resonance images. The present review provides a brief description of VBM and then focuses on exemplar applications in healthy and diseased subjects. The procedure involves normalising high-resolution structural magnetic resonance images to a standard template in stereotactic space. Normalised images are then segmented into gray and white matter and smoothed using an isotropic Gaussian kernel. Finally, a series of voxel-wise comparisons of gray and white matter in different groups of subjects are performed, using Random Field theory to correct for multiple comparisons. VBM has been useful in characterizing subtle changes in brain structure in a variety of diseases associated with neurological and psychiatric dysfunction. These include schizophrenia, developmental and congenital disorders, temporal lobe epilepsy and even cluster headache. In addition, VBM has been successful in identifying gross structural abnormalities, such as those observed in herpes simplex encephalitis, multiple sclerosis and Alzheimer's disease. Studies of normal subjects, on the other hand, have focussed on the impact of learning and practice on brain structure. These studies have led to the finding that environmental demands may be associated with changes in gray and white matter. For instance, it has been reported that the structure of the brain alters when human beings learn to navigate, read music, speak a second language and even perform a complex motor task such as juggling. We conclude the present review by discussing the potential limitations of the technique.