title: Optic disc localization in retinal images using histogram matching

abstract: In this article, we propose a new method for localizing optic disc in retinal images. Localizing the optic disc and its center is the first step of most vessel segmentation, disease diagnostic, and retinal recognition algorithms. We use optic disc of the first four retinal images in DRIVE dataset to extract the histograms of each color component. Then, we calculate the average of histograms for each color as template for localizing the center of optic disc. The DRIVE, STARE, and a local dataset including 273 retinal images are used to evaluate the proposed algorithm. The success rate was 100, 91.36, and 98.9%, respectively.


title: Internal Covariate Shift Reduction in Encoder-Decoder Convolutional Neural Networks

abstract: Internal covariant shift in deep neural networks affects the learning and convergence speed in ConvNets. Batch normalization was recently proposed to reduce the distribution of each layer's input to accelerate the training process. It also reduces overfitting and eliminates the need for using dropout in the fully connected layers, or RELU activation. Batch normalization, in its essence, seeks stable distribution of activation values throughout training, and normalizes the inputs of nonlinear data. In order to determine the usefulness of batch normalization in neural networks that don't use fully connected layers we evaluated the performance of an encoder-decoder ConvNet with and without using batch normalization. We found that batch normalization increased the learning performance by 18% but also increased the training time in each epoch (iteration) by 26%. The code for this work and the datasets are provided in a github repository.


title: Classroom Re-design to Facilitate Student Learning: A Case Study of Changes to a University Classroom

abstract: This case study examines the physical aspects of a particular university classroom, and what affect specific changes to the classroom had on the perceptions of students, instructors and observers regarding the room as an effective learning space. We compare survey and focus group data collected from students taking courses in the classroom prior to changes to the physical environment with comparable data from students taking courses in the same classroom after specific changes had been made. Immediately following changes to the classroom, notable increases were observed in reported perceptions of student satisfaction with the physical environment, including perceptions of the classroom as a more effective and engaging learning space. Similar perceptions of improvement as a teaching-learning space were reported by instructors and observers. However, subsequent follow-up data collection and analyses suggested little if any sustained increase in perceptions of efficacy of the room as a learning space; indeed, most reported variables returned to baseline levels. The implications of these findings and their relevance to classroom design nevertheless may provide insight regarding the manner in which physical space might support or even enhance teaching and learning.


title: Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks

abstract: This paper presents a convolutional neural network (CNN) based approach for fault diagnosis of rotating machinery. The proposed approach incorporates sensor fusion by taking advantage of the CNN structure to achieve higher and more robust diagnosis accuracy. Both temporal and spatial information of the raw data from multiple sensors is considered during the training process of the CNN. Representative features can be extracted automatically from the raw signals. It avoids manual feature extraction or selection, which relies heavily on prior knowledge of specific machinery and fault types. The effectiveness of the developed method is evaluated by using datasets from two types of typical rotating machinery, roller bearings, and gearboxes. Compared with traditional approaches using manual feature extraction, the results show the superior diagnosis performance of the proposed method. The present approach can be extended to fault diagnosis of other machinery with various types of sensors due to its end to end feature learning capability.


title: Adding regular expressions to graph reachability and pattern queries

abstract: It is increasingly common to find graphs in which edges are of different types, indicating a variety of relationships. For such graphs we propose a class of reachability queries and a class of graph patterns, in which an edge is specified with a regular expression of a certain form, expressing the connectivity of a data graph via edges of various types. In addition, we define graph pattern matching based on a revised notion of graph simulation. On graphs in emerging applications such as social networks, we show that these queries are capable of finding more sensible information than their traditional counterparts. Better still, their increased expressive power does not come with extra complexity. Indeed, (1) we investigate their containment and minimization problems, and show that these fundamental problems are in quadratic time for reachability queries and are in cubic time for pattern queries. (2) We develop an algorithm for answering reachability queries, in quadratic time as for their traditional counterpart. (3) We provide two cubic-time algorithms for evaluating graph pattern queries, as opposed to the NP-completeness of graph pattern matching via subgraph isomorphism. (4) The effectiveness and efficiency of these algorithms are experimentally verified using real-life data and synthetic data.


title: A new view of language acquisition

abstract: At the forefront of debates on language are new data demonstrating infants' early acquisition of information about their native language. The data show that infants perceptually “map” critical aspects of ambient language in the first year of life before they can speak. Statistical properties of speech are picked up through exposure to ambient language. Moreover, linguistic experience alters infants' perception of speech, warping perception in the service of language. Infants' strategies are unexpected and unpredicted by historical views. A new theoretical position has emerged, and six postulates of this position are described.


title: Alumina ceramic slot antenna for UHF RFID tag embedded in vehicles

abstract: To address the problem of metallic interferences in vehicular RFID (Radio-frequency identification) applications, this paper proposes a novel alumina ceramic slot tag antenna (ACSTA). The proposed antenna consists of a grounded thin alumina ceramic substrate and a slotted top layer rectangular patch with all four shorting edges. This structure can be equivalent to a horizontal magnetic current flow above a large ground plane, so an excellent radiation performance is achieved according to the electromagnetic image theory. Meanwhile, due to the existence of the ground plane, the influences of the interferential metallic objects behind the ground to the tag antenna impedance can be maximally reduced. This ACSTA combined with a commercial chip is fabricated and measured, and the thickness of the novel tag is only 1mm. Simulation and measurement results show that the proposed antenna has an excellent performance, and the operating frequency band of the ACSTA covers U.S. legal UHF RFID (902MHz-928MHz) band, and so it is a quite suitable candidate for vehicular RFID applications.


title: A vision-based perceptual learning system for autonomous mobile robot

abstract: Autonomous robots are intelligent machines capable of performing tasks in the real world without explicit human control for extended periods of time. A high degree of autonomy is particularly desirable in fields where robots can replace human workers, such as state-of-the-practice video surveillance system and space exploration. However, not having human’s sophisticated sensing and control system, two broad open problems in autonomous robot systems are the perceptual discrepancy problem, that is, there is no guarantee that the robot sensing system can recognize or detect objects defined by a human designer, and the autonomous control problem, that is, how the robots can operate in unstructured environments without continuous human guidance. As a result, autonomous robot systems should have their own ways to acquire percepts and control by learning. ::: In this work, a computer vision system is used for visual percept acquisition and a working memory toolkit is used for robot autonomous control. Natural images contain statistical regularities which can set objects apart from each other and from random noise. For an object to be recognized in a given image, it is often necessary to segment the image into nonoverlapping but meaningful regions whose union is the entire image. Therefore, a biologically based percept acquisition system is developed to build an efficient low-level abstraction of real-world data into percepts. Perception in animals is strongly related to the type of behavior they perform. Learning plays a major part in this process. To solve how the robots can learn to autonomously control their behavior based on percepts they’ve acquired, the computer vision system is integrated with a software package called the Working Memory Toolkit (WMtk) for decision making and learning. The WMtk was developed by Joshua L. Phillips & David C. Noelle based on a neuron computational model of primate working memory system. The success of the whole system is demonstrated by its application to a navigation task.


title: Matching robot appearance and behavior to tasks to improve human-robot cooperation

abstract: A robot's appearance and behavior provide cues to the robot's abilities and propensities. We hypothesize that an appropriate match between a robot's social cues and its task improve the people's acceptance of and cooperation with the robot. In an experiment, people systematically preferred robots for jobs when the robot's humanlikeness matched the sociability required in those jobs. In two other experiments, people complied more with a robot whose demeanor matched the seriousness of the task.


title: GAVS+: an open platform for the research of algorithmic game solving

abstract: This paper presents a major revision of the tool GAVS. First, the number of supported games has been greatly extended and now encompasses in addition many classes important for the design and analysis of programs, e.g., it now allows to explore concurrent / probabilistic / distributed games, and games played on pushdown graphs. Second, among newly introduced utility functions, GAVS+ includes features such that the user can now process and synthesize planning (game) problems described in the established STRIPS/PDDL language by introducing a slight extension which allows to specify a second player. This allows researchers in verification to profit from the rich collection of examples coming from the AI community.


title: The Investigation on Using Unity3D Game Engine in Urban Design Study

abstract: Developing a virtual 3D environment by using game engine is a strategy to incorporate various multimedia data into one platform. The characteristic of game engine that is preinstalled with interactive and navigation tools allows users to explore and engage with the game objects. However, most CAD and GIS applications are not equipped with 3D tools and navigation systems intended to the user experience. In particular, 3D game engines provide standard 3D navigation tools as well as any programmable view to create engaging navigation thorough the virtual environment. By using a game engine, it is possible to create other interaction such as object manipulation, non playing character (NPC) interaction with player and/or environment. We conducted analysis on previous game engines and experiment on urban design project with Unity3D game engine for visualization and interactivity. At the end, we present the advantages and limitations using game technology as visual representation tool for architecture and urban design studies.


title: One-shot deep neural network for pose and illumination normalization face recognition

abstract: Pose and illumination are considered as two main challenges that face recognition system encounters. In this paper, we consider face recognition problem across pose and illumination variations, given small amount of training samples and single sample per gallery (a.k.a., one shot classification). We combine the strength of 3D models in generating multiviews and various illumination samples and the ability of deep learning in learning non-linear transformation, which is very suitable for pose and illumination normalization, by using a multi-task deep neural network. By the pose and illumination augmentation strategy, we train a pose and illumination normalization neural network with much less training data compared to other methods. Experiments on MultiPIE database achieve competitive recognition results, demonstrating the effectiveness of proposed method.


title: A text representation model using Sequential Pattern-Growth method

abstract: Text representation is an essential task in transforming the input from text into features that can be later used for further Text Mining and Information Retrieval tasks. The commonly used text representation model is Bags-of-Words (BOW) and the N-gram model. Nevertheless, some known issues of these models, which are inaccurate semantic representation of text and high dimensionality of word size combination, should be investigated. A pattern-based model named Frequent Adjacent Sequential Pattern (FASP) is introduced to represent the text using a set of sequence adjacent words that are frequently used across the document collection. The purpose of this study is to discover the similarity of textual pattern between documents that can be later converted to a set of rules to describe the main news event. The FASP is based on the Pattern-Growth’s divide-and-conquer strategy where the main difference between FASP and the prior technique is in the Pattern Generation phase. This approach is tested against the BOW and N-gram text representation model using Malay and English language news dataset with different term weightings in the Vector Space Model (VSM). The findings demonstrate that the FASP model has a promising performance in finding similarities between documents with the average vector size reduction of 34% against the BOW and 77% against the N-gram model using the Malay dataset. Results using the English dataset is also consistent, indicating that the FASP approach is also language independent.


title: Onset of Entanglement and Noise Cross-Correlations in Two-Qubit System Interacting with Common Bosonic Bath

abstract: We summarize our recent results for the induced exchange interaction due to thermal bosonic environment (bath) which also generates quantum noise. Our focus here is on the onset of the interaction. We demonstrate that the induced interaction can be used to manipulate and create entanglement over time scales sufficiently large for controlling the two-qubit system for quantum computing applications, though ultimately the noise effects are dominate


title: Three-dimensional E-CSAR imaging of a T-72 tank and synthesis of its spotlight, stripmap and interferometric SAR reconstructions

abstract: This paper presents the results of three-dimensional imaging of a T-72 tank using its angular azimuthal (turntable) and linear elevation synthetic aperture data at X band. This is achieved using an accurate and computationally-efficient wavefront (Fourier-based) reconstruction algorithm for elevation and circular (E-CSAR) data. The E-CSAR 3D images are then used to synthesize 2D spotlight and stripmap slant plane SAR images of the target at a desired range and squint angle. For this purpose, a procedure is introduced that incorporates the spatially-varying azimuthal and elevation Doppler signatures of individual reflectors on the target as well as the mean range, azimuth and elevation of the flight path. Results using the E-CSAR images of the T-72 tank are provided.


title: Undercover: gene control by metabolites and metabolic enzymes

abstract: To make the appropriate developmental decisions or maintain homeostasis, cells and organisms must coordinate the expression of their genome and metabolic state. However, the molecular mechanisms that relay environmental cues such as nutrient availability to the appropriate gene expression response remain poorly understood. There is a growing awareness that central components of intermediary metabolism are cofactors or cosubstrates of chromatin-modifying enzymes. As such, their concentrations constitute a potential regulatory interface between the metabolic and chromatin states. In addition, there is increasing evidence for a direct involvement of classic metabolic enzymes in gene expression control. These dual-function proteins may provide a direct link between metabolic programing and the control of gene expression. Here, we discuss our current understanding of the molecular mechanisms connecting metabolism to gene expression and their implications for development and disease.


title: An improved ant colony optimization for vehicle routing problem

abstract: The vehicle routing problem (VRP), a well-known combinatorial optimization problem, holds a central place in logistics management. This paper proposes an improved ant colony optimization (IACO), which possesses a new strategy to update the increased pheromone, called ant-weight strategy, and a mutation operation, to solve VRP. The computational results for fourteen benchmark problems are reported and compared to those of other metaheuristic approaches.


title: Talking Face Generation by Conditional Recurrent Adversarial Network

abstract: Given an arbitrary face image and an arbitrary speech clip, the proposed work attempts to generating the talking face video with accurate lip synchronization while maintaining smooth transition of both lip and facial movement over the entire video clip. Existing works either do not consider temporal dependency on face images across different video frames thus easily yielding noticeable/abrupt facial and lip movement or are only limited to the generation of talking face video for a specific person thus lacking generalization capacity. We propose a novel conditional video generation network where the audio input is treated as a condition for the recurrent adversarial network such that temporal dependency is incorporated to realize smooth transition for the lip and facial movement. In addition, we deploy a multi-task adversarial training scheme in the context of video generation to improve both photo-realism and the accuracy for lip synchronization. Finally, based on the phoneme distribution information extracted from the audio clip, we develop a sample selection method that effectively reduces the size of the training dataset without sacrificing the quality of the generated video. Extensive experiments on both controlled and uncontrolled datasets demonstrate the superiority of the proposed approach in terms of visual quality, lip sync accuracy, and smooth transition of lip and facial movement, as compared to the state-of-the-art.


title: Enhancement of optic cup to disc ratio detection in glaucoma diagnosis

abstract: Glaucoma is a major global cause of blindness. An approach to automatically extract the main features in color fundus images is proposed in this paper. The optic cup-to-disc ratio (CDR) in retinal fundus images is one of the principle physiological characteristics in the diagnosis of glaucoma. The least square fitting algorithm aims to improve the accuracy of the boundary estimation. The technique used here is a core component of ARGALI (Automatic cup-to-disc Ratio measurement system for Glaucoma detection and AnaLysIs), a system for automated glaucoma risk assessment. The algorithm's effectiveness is demonstrated manually on segmented retina fundus images. By comparing the automatic cup height measurement to ground truth, we found that the method accurately detected neuro-retinal cup height. This work improves the efficiency of clinical interpretation of Glaucoma in fundus images of the eye. The tool utilized to accomplish the objective is MATLAB7.5


Implicitures: Cancelability and Non-detachability


title: Identifying FrameNet frames for verbs from a real-text corpus

abstract: Previous systems that automatically tag text with FrameNet labels have been trained from the FrameNet example data, as there is no FrameNet annotated corpus. The FrameNet data is systematically biased by the criteria for the examples’ selection, as annotators attempt to select simple sentences that include the target word. Instead of using the FrameNet examples, we train a maximum entropy model classifier to identify verb frames on text from the Penn Treebank. We use examples of verbs with only one entry in FrameNet as training data, and evaluate the system on human annotated text from the Wall Street Journal. We accurately identify the frame used by 76% of finite verbs. We also investigate how well the system performs on verbs it has not encountered before. This task examines the feasibility of using the system to automatically extend the coverage of FrameNet by classifying verbs with no FrameNet entries. The classifier accurately assigns a frame to 55% of instances of verbs it has not been trained on.


title: A comparative study of Hough transform methods for circle finding

abstract: Abstract A variety of circle detection methods which are based on variations of the Hough Transform are investigated. The five methods considered are the standard Hough Transform, the Fast Hough Transform of Li et al. 1 , a two stage Hough method, and two space saving approaches based on the method devised by Gerig and Klein 2 . The performance of each of the methods has been compared on synthetic imagery and real images from a metallurgical application. Figures and comments are presented concerning the accuracy, reliability, computational efficiency and storage requirements of each of the methods.


title: A Benchmark and Simulator for UAV Tracking

abstract: In this paper, we propose a new aerial video dataset and benchmark for low altitude UAV target tracking, as well as, a photo-realistic UAV simulator that can be coupled with tracking methods. Our benchmark provides the first evaluation of many state-of-the-art and popular trackers on 123 new and fully annotated HD video sequences captured from a low-altitude aerial perspective. Among the compared trackers, we determine which ones are the most suitable for UAV tracking both in terms of tracking accuracy and run-time. The simulator can be used to evaluate tracking algorithms in real-time scenarios before they are deployed on a UAV “in the field”, as well as, generate synthetic but photo-realistic tracking datasets with automatic ground truth annotations to easily extend existing real-world datasets. Both the benchmark and simulator are made publicly available to the vision community on our website to further research in the area of object tracking from UAVs. (https://ivul.kaust.edu.sa/Pages/pub-benchmark-simulator-uav.aspx.).


title: Nonlinear canonical correlation analysis by neural networks

abstract: Canonical correlation analysis (CCA) is widely used to extract the correlated patterns between two sets of variables. A nonlinear canonical correlation analysis (NLCCA) method is formulated here using three feedforward neural networks. The first network has a double-barreled architecture, and an unconventional cost function, which maximizes the correlation between the two output neurons (the canonical variates). The remaining two networks map from the canonical variates back to the original two sets of variables. Tested on data sets with correlated nonlinear structures, NLCCA showed that the underlying nonlinear structures could be retrieved accurately under moderately noisy conditions. After a mode had been retrieved, NLCCA was applied to the residual to successfully retrieve the next mode. When tested for prediction skills, the NLCCA outperformed the CCA when the two sets of variables contained correlated nonlinear structures.


title: Context-Aware Bandits

abstract: We propose an efficient Context-Aware clustering of Bandits (CAB) algorithm, which can capture collaborative effects. CAB can be easily deployed in a real-world recommendation system, where multi-armed bandits have been shown to perform well in particular with respect to the cold-start problem. CAB utilizes a context-aware clustering augmented by exploration-exploitation strategies. CAB dynamically clusters the users based on the content universe under consideration. We give a theoretical analysis in the standard stochastic multi-armed bandits setting. We show the efficiency of our approach on production and real-world datasets, demonstrate the scalability, and, more importantly, the significant increased prediction performance against several state-of-the-art methods.


title: Insights into native epitopes of proliferating cell nuclear antigen using recombinant DNA protein products

abstract: A cDNA clone encoding full-length human proliferating cell nuclear antigen (PCNA) was used to generate a panel of in vitro translated labeled protein products with COOH-terminal deletions and to construct a set of fusion proteins with COOH- and NH2-terminal deletions. A rabbit antiserum raised against an NH2-terminal peptide, a well-characterized murine monoclonal antibody (mAb), and 14 human lupus sera with autoantibody to PCNA were analyzed for their reactivity with the constructs using both immunoprecipitation and immunoblotting techniques. The rabbit antiserum reacted in immunoprecipitation and immunoblotting with constructs containing the appropriate NH2-terminal sequence and mAb reacted with a sequence from the midregion of PCNA. These experimentally induced antibodies also reacted with 15-mer synthetic peptides in enzyme-linked immunosorbent assay (ELISA). In contrast, none of the lupus sera reacted with synthetic peptides in ELISA. 9 of the 14 lupus sera also failed to react in Western immunoblotting with any recombinant fusion protein, although they all immunoprecipitated in vitro translated full-length protein. Four of the nine had variable patterns of immunoprecipitation with shorter constructs. The remaining five lupus sera were able to immunoprecipitate translation products as well as Western blot recombinant fusion proteins. From analysis of the patterns of reactivity of human lupus sera, it was deduced that the apparent heterogeneity of human autoantibodies to PCNA could be explained by immune response to highly conformational epitopes. These observations demonstrate that there might be special features in "native" epitopes of intranuclear antigens that are recognized by autoantibodies, and that these special features of native epitopes might not be present in prepared antigen used for experimental immunization. These features may be related to protein folding or to association of the antigen with other intranuclear proteins or nucleic acids, as might occur with antigens that are components of subcellular particles.


title: QUORUM SENSING: Cell-to-Cell Communication in Bacteria

abstract: Bacteria communicate with one another using chemical signal molecules. As in higher organisms, the information supplied by these molecules is critical for synchronizing the activities of large groups of cells. In bacteria, chemical communication involves producing, releasing, detecting, and responding to small hormone-like molecules termed autoinducers . This process, termed quorum sensing, allows bacteria to monitor the environment for other bacteria and to alter behavior on a population-wide scale in response to changes in the number and/or species present in a community. Most quorum-sensing-controlled processes are unproductive when undertaken by an individual bacterium acting alone but become beneficial when carried out simultaneously by a large number of cells. Thus, quorum sensing confuses the distinction between prokaryotes and eukaryotes because it enables bacteria to act as multicellular organisms. This review focuses on the architectures of bacterial chemical communication networks; how chemical information is integrated, processed, and transduced to control gene expression; how intra- and interspecies cell-cell communication is accomplished; and the intriguing possibility of prokaryote-eukaryote cross-communication.


title: Context- ware collaborative topic regression with social matrix factorization for recommender systems

abstract: Online social networking sites have become popular platforms on which users can link with each other and share information, not only basic rating information but also information such as contexts, social relationships, and item contents. However, as far as we know, no existing works systematically combine diverse types of information to build more accurate recommender systems. In this paper, we propose a novel context-aware hierarchical Bayesian method. First, we propose the use of spectral clustering for user-item subgrouping, so that users and items in similar contexts are grouped. We then propose a novel hierarchical Bayesian model that can make predictions for each user-item subgroup, our model incorporate not only topic modeling to mine item content but also social matrix factorization to handle ratings and social relationships. Experiments on an Epinions dataset show that our method significantly improves recommendation performance compared with six categories of state-of-the-art recommendation methods in terms of both prediction accuracy and recall. We have also conducted experiments to study the extent to which ratings, contexts, social relationships, and item contents contribute to recommendation performance in terms of prediction accuracy and recall.


title: A nonlocal-means approach to exemplar-based inpainting

abstract: This paper introduces a novel approach to the problem of image inpainting through the use of nonlocal-means. In traditional inpainting techniques, only local information around the target regions are used to fill in the missing information, which is insufficient in many cases. More recent inpainting techniques based on the concept of exemplar-based synthesis utilize nonlocal information but in a very limited way. In the proposed algorithm, we use nonlocal image information from multiple samples within the image. The contribution of each sample to the reconstruction of a target pixel is determined using an weighted similarity function and aggregated to form the missing information. Experimental results show that the proposed method yields quantitative and qualitative improvements compared to the current exemplar-based approach. The proposed approach can also be integrated into existing exemplar-based inpainting techniques to provide improved visual quality.


title: Learning with Memory Embeddings

abstract: Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a tensor representation whose entries are predicted by models using latent representations of generalized entities. Latent variable models are well suited to deal with the high dimensionality and sparsity of typical knowledge graphs. In recent publications the embedding models were extended to also consider time evolutions, time patterns and subsymbolic representations. In this paper we map embedding models, which were developed purely as solutions to technical problems for modelling temporal knowledge graphs, to various cognitive memory functions, in particular to semantic and concept memory, episodic memory, sensory memory, short-term memory, and working memory. We discuss learning, query answering, the path from sensory input to semantic decoding, and the relationship between episodic memory and semantic memory. We introduce a number of hypotheses on human memory that can be derived from the developed mathematical models.


title: Radar Cross-Section Analysis of Backscattering RFID Tags

abstract: In this letter, we present a graphical method based on Green's analysis of scattering antennas to optimize the load impedances for achieving maximum modulated radar cross-section (RCS) of backscattering radio-frequency identification (RFID) tags. A planar tag antenna specifically designed for working in the ultra-high-frequency band is used to experimentally validate the method. The measured RCS of the loaded tag antenna shows acceptable agreement with the theory and simulated results. This method is especially valuable to the design of semipassive RFID tags when the communication range is solely determined by the backscattering modulation efficiency of tags.


title: Control of Functional Connectivity in Cerebral Cortex by Basal Ganglia Mediated Synchronization

abstract: Since the earliest electroencephalography experiments, large scale oscillations have been observed in the mammalian brain. In more recent studies they have been identified not only in the cerebral cortex and thalamus, but pervasively in the healthy basal ganglia. While correlations with stimuli, behavior, and mental states have long been recognized, the precise function of these oscillations has often been mysterious, particularly in the basal ganglia. In this paper, I propose the basal ganglia mediated synchronization model to help explain many of these correlated oscillatory phenomena, relating stimulus-response and reinforcement mechanisms to associative synchrony mechanisms. In this model, patterns of activity in cortex stimulate striatal responses whose spike timing precisely reflects that prevailing in the input pattern. These responses are then recirculated in closed and open loops, chiefly via the thalamus, to the feedback-recipient layers of cortex, where they selectively establish and reinforce effective connections by controlling spike-timing-dependent gain. Corticostriatal and striatonigral conduction delays are critical to this mechanism, and evidence suggests that these delays are unusually long, and unusually varied, in arrangements that might facilitate learning of useful time alignments. Structural arrangements in the basal ganglia show further specialization for this role, with convergence in the inputs from cortex, and divergence in many of the return paths to cortex, that systematically reflect corticocortical anatomical connectivity. The basal ganglia also target the dopaminergic, cholinergic, and serotonergic centers of the brainstem and basal forebrain, and the reticular nucleus of the thalamus, structures broadly implicated in the modulation of oscillatory network activity and expressions of plasticity. The basal ganglia, by learning to coordinate [...]


A Rare Case of Male‐to‐Eunuch Gender Dysphoria


title: Miniaturized Dual-Band Antenna for Implantable Wireless Communications

abstract: A miniaturized dual-band antenna is presented for implantable wireless communications. By introducing an offset at the feedpoint, dual-band performance of a dipole antenna is realized, operating at both Medical Implant Communications Service (MICS) 402-405 MHz and Industrial, Scientific, and Medical (ISM) 2.40-2.48 GHz bands. Parylene-C of thickness 20 μm is taken as a biocompatible insulating layer, and a rather compact size of 67.8 mm ::: 3 ::: (10.02 mm × 10.02 mm × 0.675 mm) is obtained. In order to achieve good impedance matching at the feedpoint, an inductive loop is loaded at one of the dipole arms. The measured |S ::: 11 ::: | in skin-mimicking gel shows that a bandwidth of 47.5% and 31.6% can be achieved at the MICS and ISM bands, respectively.


title: Phishing counter measures and their effectiveness – literature review

abstract: Purpose – Phishing is essentially a social engineering crime on the Web, whose rampant occurrences and technique advancements are posing big challenges for researchers in both academia and the industry. The purpose of this study is to examine the available phishing literatures and phishing countermeasures, to determine how research has evolved and advanced in terms of quantity, content and publication outlets. In addition to that, this paper aims to identify the important trends in phishing and its countermeasures and provides a view of the research gap that is still prevailing in this field of study.Design/methodology/approach – This paper is a comprehensive literature review prepared after analysing 16 doctoral theses and 358 papers in this field of research. The papers were analyzed based on their research focus, empirical basis on phishing and proposed countermeasures.Findings – The findings reveal that the current anti‐phishing approaches that have seen significant deployments over the internet can b...


title: Scalable Logging through Emerging Non-Volatile Memory

abstract: Emerging byte-addressable, non-volatile memory (NVM) is fundamentally changing the design principle of transaction logging. It potentially invalidates the need for flush-before-commit as log records are persistent immediately upon write. Distributed logging---a once prohibitive technique for single node systems in the DRAM era---becomes a promising solution to easing the logging bottleneck because of the non-volatility and high performance of NVM. ::: ::: In this paper, we advocate NVM and distributed logging on multicore and multi-socket hardware. We identify the challenges brought by distributed logging and discuss solutions. To protect committed work in NVM-based systems, we propose passive group commit, a lightweight, practical approach that leverages existing hardware and group commit. We expect that durable processor cache is the ultimate solution to protecting committed work and building reliable, scalable NVM-based systems in general. We evaluate distributed logging with logging-intensive workloads and show that distributed logging can achieve as much as ~3x speedup over centralized logging in a modern DBMS and that passive group commit only induces minuscule overhead.


title: A task‐focused literature recommender system for digital libraries

abstract: – The scientific literature has played an important role in the dissemination of new knowledge throughout the past century. However, the increasing numbers of scientific articles being published in recent years has intensified the perception of information overload for users attempting to find relevant scientific information. The purpose of this paper is to describe a task‐focused strategy that employs the task profiles of users to make recommendations in a digital library., – This paper combines information retrieval, common citation analysis, and coauthor relationship analysis techniques with a citation network analysis technique – the CiteRank algorithm – to find relevant and high‐quality articles. In total, nine variations of the proposed approach were tested using articles downloaded from the CiteSeerX system and usage logs collected from the authors' experimental server., – The results from the authors' experimental evaluations demonstrate that the proposed Content‐citation approach outperforms the Relevance‐CiteRank, Relevance‐citation count, and Relevance‐only approaches., – This paper describes an original study that has produced a novel way to combine information retrieval, common citation analysis, and coauthor relationship analysis techniques to find relevant and high‐quality articles for recommendation in a digital library.


title: Diversified Top-k Subgraph Querying in a Large Graph

abstract: Subgraph querying in a large data graph is interesting for different applications. A recent study shows that top-k diversified results are useful since the number of matching subgraphs can be very large. In this work, we study the problem of top-k diversified subgraph querying that asks for a set of up to k subgraphs isomorphic to a given query graph, and that covers the largest number of vertices. We propose a novel level-based algorithm for this problem which supports early termination and has a theoretical approximation guarantee. From experiments, most of our results on real datasets used in previous works are near optimal with a query time within 10ms on a commodity machine.


title: Social network visualization: can we go beyond the graph

abstract: Visualizations have played an important role in generating new insights in social network analysis. We suggest that such visualizations can be of interest not only to analysts and researchers but also to the people whose data is being analyzed. In this paper we briefly talk about two visualizations of email that we developed to give people a better sense of their email archives and social networks. One visualization shows a traditional network graph with email contacts as nodes. The second visualization depicts the temporal rhythms of interactions in dyadic relationships between ego and individual contacts. While observing and interviewing users of these systems, it became clear that, when used in tandem, these visualizations complemented and clarified each other’s depiction of a person’s social network. Based on our experience with these two systems, we propose that visualizations of social networks that are aimed at end users ought to go beyond the graph paradigm. We posit that basic cartographic principles – such as adaptive zooming and multiple viewing modes – provide system designers with useful visual solutions to the depiction


title: Blurring human–machine distinctions: Anthropomorphic appearance in social robots as a threat to human distinctiveness

abstract: The present research aims at gaining a better insight on the psychological barriers to the introduction of social robots in society at large. Based on social psychological research on intergroup distinctiveness, we suggested that concerns toward this technology are related to how we define and defend our human identity. A threat to distinctiveness hypothesis was advanced. We predicted that too much perceived similarity between social robots and humans triggers concerns about the negative impact of this technology on humans, as a group, and their identity more generally because similarity blurs category boundaries, undermining human uniqueness. Focusing on the appearance of robots, in two studies we tested the validity of this hypothesis. In both studies, participants were presented with pictures of three types of robots that differed in their anthropomorphic appearance varying from no resemblance to humans (mechanical robots), to some body shape resemblance (biped humanoids) to a perfect copy of human body (androids). Androids raised the highest concerns for the potential damage to humans, followed by humanoids and then mechanical robots. In Study 1, we further demonstrated that robot anthropomorphic appearance (and not the attribution of mind and human nature) was responsible for the perceived damage that the robot could cause. In Study 2, we gained a clearer insight in the processes underlying this effect by showing that androids were also judged as most threatening to the human–robot distinction and that this perception was responsible for the higher perceived damage to humans. Implications of these findings for social robotics are discussed.


title: Enhanced word clustering for hierarchical text classification

abstract: In this paper we propose a new information-theoretic divisive algorithm for word clustering applied to text classification. In previous work, such "distributional clustering" of features has been found to achieve improvements over feature selection in terms of classification accuracy, especially at lower number of features [2, 28]. However the existing clustering techniques are agglomerative in nature and result in (i) sub-optimal word clusters and (ii) high computational cost. In order to explicitly capture the optimality of word clusters in an information theoretic framework, we first derive a global criterion for feature clustering. We then present a fast, divisive algorithm that monotonically decreases this objective function value, thus converging to a local minimum. We show that our algorithm minimizes the "within-cluster Jensen-Shannon divergence" while simultaneously maximizing the "between-cluster Jensen-Shannon divergence". In comparison to the previously proposed agglomerative strategies our divisive algorithm achieves higher classification accuracy especially at lower number of features. We further show that feature clustering is an effective technique for building smaller class models in hierarchical classification. We present detailed experimental results using Naive Bayes and Support Vector Machines on the 20 Newsgroups data set and a 3-level hierarchy of HTML documents collected from Dmoz Open Directory.


title: Cumulative and Career-Stage Citation Impact of Social-Personality Psychology Programs and Their Members

abstract: Number of citations and the h-index are popular metrics for indexing scientific impact. These, and other existing metrics, are strongly related to scientists’ seniority. This article introduces complementary indicators that are unrelated to the number of years since PhD. To illustrate cumulative and career-stage approaches for assessing the scientific impact across a discipline, citations for 611 scientists from 97 U.S. and Canadian social psychology programs are amassed and analyzed. Results provide benchmarks for evaluating impact across the career span in psychology and other disciplines with similar citation patterns. Career-stage indicators provide a very different perspective on individual and program impact than cumulative impact, and may predict emerging scientists and programs. Comparing social groups, Whites and men had higher impact than non-Whites and women, respectively. However, average differences in career stage accounted for most of the difference for both groups.


Places to Hide : Terrain , Ethnicity , and Civil War


title: A method for positioning electrodes during surface EMG recordings in lower limb muscles

abstract: Purpose: The aim of this work is to provide information about the degree of inter-subject uniformity of location of innervation zone (IZ) in 13 superficial muscles of the lower limb. The availability of such information will allow researchers to standardize and optimize their electrode positioning procedure and to obtain accurate and repeatable estimates of surface electromyography (sEMG) signal amplitude, spectral variables and muscle fiber conduction velocity. Methods: Surface EMG signals from gluteus maximus, gluteus medius, tensor faciae latae, biceps femoris, semitendinosus, vastus medialis obliquus, vastus lateralis, rectus femoris, tibialis anterior, peroneus longus, soleus, gastrocnemius medialis and lateralis muscles of ten healthy male subjects aged between 25 and 34 years (average=29.2 years, S.D.=2.5 years) were recorded to assess individual IZ location and signal quality. Results: Tensor faciae latae, biceps femoris, semitendinosus, vastus lateralis, gastrocnemius medialis and lateralis showed a high level of both signal quality and IZ location uniformity. In contrast, rectus femoris, gluteus medius and peroneus longus were found to show poor results for both indexes. Gluteus maximus, vastus medialis obliquus and tibialis anterior were found to show high signal quality but low IZ location uniformity. Finally, soleus muscle was found to show low signal quality but high IZ location uniformity. Conclusions: This study identifies optimal electrode sites for muscles in the lower extremity by providing a standard landmarking technique for the localization of the IZ of each muscle so that surface EMG electrodes can be properly positioned between the IZ and a tendon.


title: DATA WAREHOUSING, DATA MINING, OLAP AND OLTP TECHNOLOGIES ARE ESSENTIAL ELEMENTS TO SUPPORT DECISION-MAKING PROCESS IN INDUSTRIES

abstract: This paper provides an overview of Data warehousing, Data Mining, OLAP, OLTP technologies, exploring the features, applications and the architecture of Data Warehousing. The data warehouse supports on-line analytical processing (OLAP), the functional and performance requirements of which are quite different from those of the on-line transaction processing (OLTP) applications traditionally supported by the operational databases. Data warehouses provide on-line analytical processing (OLAP) tools for the interactive analysis of multidimensional data of varied granularities, which facilitates effective data mining. Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. OLTP is customer-oriented and is used for transaction and query processing by clerks, clients and information technology professionals. An OLAP system is market-oriented and is used for data analysis by knowledge workers, including managers, executives and analysts. Data warehousing and OLAP have emerged as leading technologies that facilitate data storage, organization and then, significant retrieval. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications.


title: Bringing the cloud to the edge

abstract: Edge services become increasingly important as the Internet transforms into an Internet of Things (IoT). Edge services require bounded latency, bandwidth reduction between the edge and the core, service resiliency with graceful degradation, and access to resources visible only inside the NATed and secured edge networks. While the data center based cloud excels at providing general purpose computation/storage at scale, it is not suitable for edge services. We present a new model for cloud computing, which we call the Edge Cloud, that addresses edge computing specific issues by augmenting the traditional data center cloud model with service nodes placed at the network edges. We describe the architecture of the Edge Cloud and its implementation as an overlay hybrid cloud using the industry standard OpenStack cloud management framework. We demonstrate the advantages garnered by two new classes of applications enabled by the Edge Cloud - a highly accurate indoor localization that saves on latency, and a scalable and resilient video monitoring that saves on bandwidth.


title: A grounded theory of information sharing behavior in a personal learning space

abstract: This paper presents a grounded theory of information sharing behavior of the users of a personal learning space. A personal learning space is an environment consisted of weblog, ePortfolio, and social networking functionality. It is primarily used within education as a tool to enhance learning, but is also used as a knowledge management tool and to develop communities of practice. Our results identify privacy as a main concern for users of a personal learning space and illustrate challenges users face in ensuring privacy of their information and strategies they employ to achieve the desired level of privacy. We then identify factors that affect users' decisions regarding disclosure of their personal artifacts to various people and groups in a personal learning space. The three main themes as emerged in our study include current stage in the information life cycle, the nature of trust between the owner and the receiver of information, and the dynamics of the group or community within which the information is being shared. Together, these themes portrayed a clearer picture of users' perspective on the privacy of their information in a personal learning space. The findings offer some ideas about how to create privacy management mechanisms for personal learning spaces that are based on users' mental model of information privacy. Practical implications of the results are also discussed.


title: Personalized PageRank Estimation and Search: A Bidirectional Approach

abstract: We present new algorithms for Personalized PageRank estimation and Personalized PageRank search. First, for the problem of estimating Personalized PageRank (PPR) from a source distribution to a target node, we present a new bidirectional estimator with simple yet strong guarantees on correctness and performance, and 3x to 8x speedup over existing estimators in experiments on a diverse set of networks. Moreover, it has a clean algebraic structure which enables it to be used as a primitive for the Personalized PageRank Search problem: Given a network like Facebook, a query like "people named John," and a searching user, return the top nodes in the network ranked by PPR from the perspective of the searching user. Previous solutions either score all nodes or score candidate nodes one at a time, which is prohibitively slow for large candidate sets. We develop a new algorithm based on our bidirectional PPR estimator which identifies the most relevant results by sampling candidates based on their PPR; this is the first solution to PPR search that can find the best results without iterating through the set of all candidate results. Finally, by combining PPR sampling with sequential PPR estimation and Monte Carlo, we develop practical algorithms for PPR search, and we show via experiments that our algorithms are efficient on networks with billions of edges.


title: A 10-year experience in nasal reconstruction with the three-stage forehead flap.

abstract: Because of its ideal color and texture, forehead skin is acknowledged as the best donor site with which to resurface the nose. However, all forehead flaps, regardless of their vascular pedicles, are thicker than normal nasal skin. Stiff and flat, they do not easily mold from a two-dimensional to a three-dimensional shape. Traditionally, the forehead is transferred in two stages. At the first stage, frontalis muscle and subcutaneous tissue are excised distally and the partially thinned flap is inset into the recipient site. At a second stage, 3 weeks later, the pedicle is divided. However, such soft-tissue thinning is limited, incomplete, and piecemeal. Flap necrosis and contour irregularities are especially common in smokers and in major nasal reconstructions. To overcome these problems, the technique of forchead flap transfer was modified. An extra operation was added between transfer and division. At the first stage, a full-thickness forehead flap is elevated with all its layers and is transposed without thinning except for the columellar inset. Primary cartilage grafts are placed if vascularized intranasal lining is present or restored. Importantly, at the first stage, skin grafts or a folded forehead flap can be used effectively for lining. A full-thickness skin graft will reliably survive when placed on a highly vascular bed. A full-thickness forehead flap can be folded to replace missing cover skin, with a distal extension, in continuity, to supply lining. At the second stage, 3 weeks later during an intermediate operation, the full-thickness forehead flap, now healed to its recipient bed, is physiologically delayed. Forehead skin with 3 to 4 mm of subcutaneous fat (nasal skin thickness) is elevated in the unscarred subcutaneous plane over the entire nasal inset, except for the columella. Skin grafts or folded flaps integrate into adjacent normal lining and can be completely separated from the overlying cover from which they were initially vascularized. If used, a folded forehead flap is incised free along the rim, completely separating the proximal cover flap from the distal lining extension. The underlying subcutaneous tissue, frontalis muscle, and any previously positioned cartilage grafts are now widely exposed, and excess soft tissue can be excised to carve an ideal subunit, rigid subsurface architecture. Previous primary cartilage grafts can be repositioned, sculpted, or augmented, if required. Delayed primary cartilage grafts can be placed to support lining created from a skin graft or a folded flap. The forehead cover skin (thin, supple, and conforming) is then replaced on the underlying rigid, recontoured, three-dimensional recipient bed. The pedicle is not transected. At a third stage, 3 weeks later (6 weeks after the initial transfer), the pedicle is divided. Over 10 years in 90 nasal reconstructions for partial and full-thickness defects, the three-stage forehead flap technique with an intermediate operation was used with primary and delayed primary grafts, and with intranasal lining flaps (n = 15), skin grafts (n = 11), folded forehead flaps (n = 3), turnover flaps (n = 5), prefabricated flaps (n = 4), and free flaps for lining (n = 2). Necrosis of the forchead flap did not occur. Late revisions were not required or were minor in partial defects. In full-thickness defects, a major revision and more than two minor revisions were performed in less than 5 percent of patients.


title: Long-Term Follow-Up of Adults with Gender Identity Disorder

abstract: The aim of this study was to re-examine individuals with gender identity disorder after as long a period of time as possible. To meet the inclusion criterion, the legal recognition of participants’ gender change via a legal name change had to date back at least 10 years. The sample comprised 71 participants (35 MtF and 36 FtM). The follow-up period was 10–24 years with a mean of 13.8 years (SD = 2.78). Instruments included a combination of qualitative and quantitative methods: Clinical interviews were conducted with the participants, and they completed a follow-up questionnaire as well as several standardized questionnaires they had already filled in when they first made contact with the clinic. Positive and desired changes were determined by all of the instruments: Participants reported high degrees of well-being and a good social integration. Very few participants were unemployed, most of them had a steady relationship, and they were also satisfied with their relationships with family and friends. Their overall evaluation of the treatment process for sex reassignment and its effectiveness in reducing gender dysphoria was positive. Regarding the results of the standardized questionnaires, participants showed significantly fewer psychological problems and interpersonal difficulties as well as a strongly increased life satisfaction at follow-up than at the time of the initial consultation. Despite these positive results, the treatment of transsexualism is far from being perfect.


title: Conquering the City: Understanding perceptions of Mobility and Human Territoriality in Location-based Mobile Games

abstract: With the increasing popularity of mobile video games, game designers and developers are starting to integrate geolocation into video games. Popular location-based games such as Ingress or Pokémon Go have millions of users, yet little is known about how the use of such games influences the nature of a user’s interaction with other users and their physical surroundings. To investigate how location-based games are integrated into a player’s daily life, how they influence a player’s mobility through the city, their perception of places and the role of human territoriality in this context, we have developed a location-based mobile multiplayer game called CityConqueror. In this paper, we present CityConqueror and the results of a study, which has focused on participants playing the game over a period of two weeks. The findings show that location-based games can be designed to give the player the illusion of playing in the context of the “real” world rather than a virtual or hybrid game reality. Our findings also suggest that location-based games can have a strong influence on a player’s mobility and perception of urban space and that human territoriality can be expressed through location-based games. Based on our findings we propose a series of design implications for the design of mobile location-based games.


title: A Domain Oriented LDA Model for Mining Product Defects from Online Customer Reviews

abstract: Online reviews provide important demand-side ::: knowledge for product manufacturers to improve ::: product quality. However, discovering and quantifying potential products’ defects from large amounts of online reviews is a nontrivial task. In this paper, we propose a Latent Product Defect Mining model that identifies critical product defects. We define domain-oriented key attributes, such as components and keywords used to describe a defect, and build a novel LDA model to identify and acquire integral information about product defects. We conduct comprehensive evaluations including ::: quantitative and qualitative evaluations to ensure the ::: quality of discovered information. Experimental results show that the proposed model outperforms the standard LDA model, and could find more valuable information. Our research contributes to the extant product quality analytics literature and has significant managerial implications for researchers, policy makers, customers, and practitioners.


Is NDVI a predictor of C. tarsalis in Eastern Montana


title: Incorporating Rich Features into Deep Knowledge Tracing

abstract: Knowledge Tracing aims to model student knowledge by predicting the correctness of each next item as students work through an assignment. Through recent developments in deep learning, Deep Knowledge Tracing (DKT) was explored as a method to improve upon traditional methods. Thus far, the DKT model has only considered the knowledge components and correctness as input, neglecting the other important features collected by computer-based learning platforms. This paper seeks to further improve upon DKT by incorporating more problem-level features. With this higher dimensional input, an adaption to the original DKT model structure is also proposed to convert the input into a low dimensional feature vector. Our results show that this adapted DKT model can effectively improve accuracy.


title: Internet of Things: Ubiquitous Home Control and Monitoring System using Android based Smart Phone

abstract: Th is paper presents a low cost and flexib le home control and monitoring system using an embedded micro-web server, with IP connectivity for accessing and controlling devices and appliances remotely using Android based Smart phone app. The proposed system does not require a dedicated server PC with respect to similar systems and offers a novel communicat ion protocol to monitor and control the home environment with more than just the switching functionality. To demonstrate the feasibility and effectiveness of this system, devices such as light switches, power p lug, temperature sensor and current sensor have been integrated with the proposed home control system.


Memory Network : Enabling Technology for Scalable Near-Data Computing


Control of Hybrid Dynamical Systems : An Overview of Recent Advances


title: Common genetic variants on 5p14.1 associate with autism spectrum disorders

abstract: Autism spectrum disorders (ASDs) represent a group of childhood neurodevelopmental and neuropsychiatric disorders characterized by deficits in verbal communication, impairment of social interaction, and restricted and repetitive patterns of interests and behaviour. To identify common genetic risk factors underlying ASDs, here we present the results of genome-wide association studies on a cohort of 780 families (3,101 subjects) with affected children, and a second cohort of 1,204 affected subjects and 6,491 control subjects, all of whom were of European ancestry. Six single nucleotide polymorphisms between cadherin 10 (CDH10) and cadherin 9 (CDH9)-two genes encoding neuronal cell-adhesion molecules-revealed strong association signals, with the most significant SNP being rs4307059 (P = 3.4 x 10(-8), odds ratio = 1.19). These signals were replicated in two independent cohorts, with combined P values ranging from 7.4 x 10(-8) to 2.1 x 10(-10). Our results implicate neuronal cell-adhesion molecules in the pathogenesis of ASDs, and represent, to our knowledge, the first demonstration of genome-wide significant association of common variants with susceptibility to ASDs.


title: Machine-to-Machine Communications in Cognitive Cellular Systems

abstract: Machine-to-machine (M2M) communication systems consist of massive autonomous devices which hold the ability to sense, communicate, and process information ubiquitously. The major challenges posed by M2M devices are spectrum scarcity, spectrum inefficiency, and network overload. Endowing M2M devices with cognitive radio technology can be a promising solution to these challenges. This paper presents an overview of cognitive M2M communication. We present cognitive cellular system architecture for M2M communication and discuss the possible transmission scenarios. In addition, the paper offers a spectrum sensing and resource allocation framework for cognitive M2M communication. Focus is given to lightweight selective compressive sensing and energy efficient resource allocation. Finally, the paper is supported by simulation results showing the benefits offered by cognitive cellular systems for M2M communications.


title: Scaling life-long off-policy learning

abstract: We pursue a life-long learning approach to artificial intelligence that makes extensive use of reinforcement learning algorithms. We build on our prior work with general value functions (GVFs) and the Horde architecture. GVFs have been shown able to represent a wide variety of facts about the world's dynamics that may be useful to a long-lived agent (Sutton et al. 2011). We have also previously shown scaling - that thousands of on-policy GVFs can be learned accurately in real-time on a mobile robot (Modayil, White & Sutton 2011). That work was limited in that it learned about only one policy at a time, whereas the greatest potential benefits of life-long learning come from learning about many policies in parallel, as we explore in this paper. Many new challenges arise in this off-policy learning setting. To deal with convergence and efficiency challenges, we utilize the recently introduced GTD({\lambda}) algorithm. We show that GTD({\lambda}) with tile coding can simultaneously learn hundreds of predictions for five simple target policies while following a single random behavior policy, assessing accuracy with interspersed on-policy tests. To escape the need for the tests, which preclude further scaling, we introduce and empirically vali- date two online estimators of the off-policy objective (MSPBE). Finally, we use the more efficient of the two estimators to demonstrate off-policy learning at scale - the learning of value functions for one thousand policies in real time on a physical robot. This ability constitutes a significant step towards scaling life-long off-policy learning.


title: Superconductivity in heavily boron-doped silicon carbide

abstract: The discoveries of superconductivity in heavily boron-doped diamond in 2004 and silicon in 2006 have renewed the interest in the superconducting state of semiconductors. Charge-carrier doping of wide-gap semiconductors leads to a metallic phase from which upon further doping superconductivity can emerge. Recently, we discovered superconductivity in a closely related system: heavily boron-doped silicon carbide. The sample used for that study consisted of cubic and hexagonal SiC phase fractions and hence this led to the question which of them participated in the superconductivity. Here we studied a hexagonal SiC sample, free from cubic SiC phase by means of x-ray diffraction, resistivity, and ac susceptibility.


title: Limits to community participation in the tourism development process in developing countries

abstract: Abstract This study deals with a normative concept of participatory development approach, which originates in the developed world. In particular, it analyses and explains the limitations to the participatory tourism development approach in the context of developing countries. It was found that there are operational, structural and cultural limits to community participation in the TDP in many developing countries although they do not equally exist in every tourist destination. Moreover, while these limits tend to exhibit higher intensity and greater persistence in the developing world than in the developed world, they appear to be a reflection of prevailing socio-political, economic and cultural structure in many developing countries. On the other hand, it was also found that although these limitations may vary over time according to types, scale and levels of tourism development, the market served, and cultural attributes of local communities, forms and scale of tourism developed are beyond the control of local communities. It concludes that formulating and implementing the participatory tourism development approach requires a total change in socio-political, legal, administrative and economic structure of many developing countries, for which hard political choices and logical decisions based on cumbersome social, economic and environmental trade-offs are sine qua non alongside deliberate help, collaboration and co-operation of major international donor agencies, NGOs, international tour operators and multinational companies.


title: madmom: A New Python Audio and Music Signal Processing Library

abstract: In this paper, we present madmom, an open-source audio processing and music information retrieval (MIR) library written in Python. madmom features a concise, NumPy-compatible, object oriented design with simple calling conventions and sensible default values for all parameters, which facilitates fast prototyping of MIR applications. Prototypes can be seamlessly converted into callable processing pipelines through madmom's concept of Processors, callable objects that run transparently on multiple cores. Processors can also be serialised, saved, and re-run to allow results to be easily reproduced anywhere. Apart from low-level audio processing, madmom puts emphasis on musically meaningful high-level features. Many of these incorporate machine learning techniques and madmom provides a module that implements some methods commonly used in MIR such as hidden Markov models and neural networks. Additionally, madmom comes with several state-of-the-art MIR algorithms for onset detection, beat, downbeat and meter tracking, tempo estimation, and chord recognition. These can easily be incorporated into bigger MIR systems or run as stand-alone programs.


title: Towards Real-Time Physical Human-Robot Interaction Using Skeleton Information and Hand Gestures

abstract: For successful physical human-robot interaction, the capability of a robot to understand its environment is imperative. More importantly, the robot should extract from the human operator as much information as possible. A reliable 3D skeleton extraction is essential for a robot to predict the intentions of the operator while s/he moves toward the robot or performs a meaningful gesture. For this purpose, we have integrated a time-of-flight depth camera with a state-of-the-art 2D skeleton extraction library namely Openpose, to obtain 3D skeletal joint coordinates reliably. We have also developed a robust and rotation invariant (in the coronal plane)hand gesture detector using a convolutional neural network. At run time (after having been trained)the detector does not require any pre-processing of the hand images. A complete pipeline for skeleton extraction and hand gesture recognition is developed and employed for real-time physical human-robot interaction, demonstrating the promising capability of the designed framework. This work establishes a firm basis and will be extended for the development of intelligent human intention detection in physical human-robot interaction scenarios, to efficiently recognize a variety of static as well as dynamic gestures.


title: 3-D Printed Horn Antennas and Components Performance for Space and Telecommunications

abstract: In this work, a study of the performance of several antennas and components, manufactured using an accurate stereolithography three-dimensional printer and resin for maximum accuracy and detail, is carried out. Electroless plating of the components to cover the pieces with a thick copper layer to improve their mechanical resistance is undertaken. Several SatCom horn antennas and components are designed, manufactured, and tested. A detailed study of a spline horn antenna and a filter is covered. These results are compared to the metallic lathe manufactured versions. Conclusions are derived from these tests, which are useful to improve additive manufactured components in future versions.


title: A Brief Survey on Privacy Preserving Techniques in Data Mining

abstract: Data mining is a process of extracting the required information from large datasets. Privacy preserving data mining deals with hiding a person's sensitive identity without losing the usability of data. Sensitive identities include some private information about persons, companies, and governments that have to be suppressed before it is shared or published. Thus, privacy preserving data mining has become a vital field of research. The capability of privacy preserving data mining techniques is measured by using metrics such as performance in terms of time efficiency, data utility and level of uncertainty or resistance to data mining algorithms. In this paper, various privacy preserving techniques such as Data anonymization, Data Randomization, use of cryptography are presented. General Terms: Data Mining, Privacy and Security.


title: A Low-Power High-Speed Comparator for Precise Applications

abstract: A low-power comparator is presented. pMOS transistors are used at the input of the preamplifier of the comparator as well as the latch stage. Both stages are controlled by a special local clock generator. At the evaluation phase, the latch is activated with a delay to achieve enough preamplification gain and avoid excess power consumption. Meanwhile, small cross-coupled transistors increase the preamplifier gain and decrease the input common mode of the latch to strongly turn on the pMOS transistors (at the latch input) and reduce the delay. Unlike the conventional comparator, the proposed structure let us set the optimum delay for preamplification and avoid excess power consumption. The speed and the power benefits of the comparator were verified using solid analytical derivations, process–VDD–temperature corners, and Monte Carlo simulations along with silicon measurements in $0.18~\mu \text{m}$ . The tests confirm that the proposed circuit reduces the power consumption by 50% and provides 30% better comparison speed at the same offset and almost the same noise budgets. Moreover, the comparator provides a rail-to-rail input $V_{\text {cm}}$ range in $f_{\text {clk}} = 500$ MHz.


title: DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training

abstract: Dynamic MR image reconstruction from incomplete k-space data has generated great research interest due to its capability in reducing scan time. Nevertheless, the reconstruction problem is still challenging due to its ill-posed nature. Most existing methods either suffer from long iterative reconstruction time or explore limited prior knowledge. This paper proposes a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training, dubbed as DIMENSION. Specifically, the DIMENSION architecture consists of a frequential prior network for updating the k-space with its network prediction and a spatial prior network for capturing image structures and details. Furthermore, a multisupervised network training technique is developed to constrain the frequency domain information and reconstruction results at different levels. The comparisons with classical k-t FOCUSS, k-t SLR, L+S and the state-of-the-art CNN-based method on in vivo datasets show our method can achieve improved reconstruction results in shorter time.


title: Combining clustering and co-training to enhance text classification using unlabelled data

abstract: In this paper, we present a new co-training strategy that makes use of unlabelled data. It trains two predictors in parallel, with each predictor labelling the unlabelled data for training the other predictor in the next round. Both predictors are support vector machines, one trained using data from the original feature space, the other trained with new features that are derived by clustering both the labelled and unlabelled data. Hence, unlike standard co-training methods, our method does not require a priori the existence of two redundant views either of which can be used for classification, nor is it dependent on the availability of two different supervised learning algorithms that complement each other.We evaluated our method with two classifiers and three text benchmarks: WebKB, Reuters newswire articles and 20 NewsGroups. Our evaluation shows that our co-training technique improves text classification accuracy especially when the number of labelled examples are very few.


title: View-invariant feature discovering for multi-camera human action recognition

abstract: Intelligent video surveillance system is built to automatically detect events of interest, especially on object tracking and behavior understanding. In this paper, we focus on the task of human action recognition under surveillance environment, specifically in a multi-camera monitoring scene. Despite many approaches have achieved success in recognizing human action from video sequences, they are designed for single view and generally not robust against viewpoint invariant. Human action recognition across different views remains challenging due to the large variations from one view to another. We present a framework to solve the problem of transferring action models learned in one view (source view) to another view (target view). First, local space-time interest point feature and global shape-flow feature are extracted as low-level feature, followed by building the hybrid Bag-of-Words model for each action sequence. The data distribution of relevant actions from source view and target view are linked via a cross-view discriminative dictionary learning method. Through the view-adaptive dictionary pair learned by the method, the data from source and target view can be respectively mapped into a common space which is view-invariant. Furthermore, We extend our framework to transfer action models from multiple views to one view when there are multiple source views available. Experiments on the IXMAS human action dataset, which contains videos captured with five viewpoints, show the efficacy of our framework.


title: Pendulum-balanced autonomous unicycle: Conceptual design and dynamics model

abstract: A new mechanism to balance an autonomous unicycle is explored which makes use of a simple pendulum. Mounted laterally on the unicycle chassis, the pendulum provides a means of controlling the unicycle balance in the lateral (left-right) direction. Longitudinal (forward-backward) balance is achieved by controlling the unicycle wheel, a mechanism exactly the same as that of wheeled inverted pendulum. In this paper, the pendulum-balancing concept is explained and the dynamics model of an autonomous unicycle balanced by such mechanism is derived by Lagrange-Euler formulation. The behavior is analyzed by dynamic simulation in MATLAB. Dynamics comparison with wheeled inverted pendulum and Acrobot is also performed.


title: A Distributed Multi Agents Based Platform for High Performance Computing Infrastructures

abstract: This work introduces a novel, modular, layered web based platform for managing machine learning experiments on grid-based High Performance Computing infrastructures. The coupling of the communication services offered by the grid, with an administration layer and conventional web server programming, via a data synchronization utility, leads to the straightforward development of a web-based user interface that allows the monitoring and managing of diverse online distributed computing applications. It also introduces an experiment generation and monitoring tool particularly suitable for investigating machine learning in game playing. The platform is demonstrated with experiments for two different games.


title: Event Detection and Analysis from Video Streams

abstract: We present a system which takes as input a video stream obtained from an airborne moving platform and produces an analysis of the behavior of the moving objects in the scene. To achieve this functionality, our system relies on two modular blocks. The first one detects and tracks moving regions in the sequence. It uses a set of features at multiple scales to stabilize the image sequence, that is, to compensate for the motion of the observer, then extracts regions with residual motion and uses an attribute graph representation to infer their trajectories. The second module takes as input these trajectories, together with user-provided information in the form of geospatial context and goal context to instantiate likely scenarios. We present details of the system, together with results on a number of real video sequences and also provide a quantitative analysis of the results.


title: Constructing Clinical Use: An Activity-Theoretical Perspective on Implementing New Technology

abstract: How do individual users survive the introduction of an innovation into complex technological and organizational contexts? An analysis is presented of the implementation of radically new technology, a neuromagnetic measuring system (MEG), to hospital environment. The analysis, in 1997, covers the attempt during its first five months to take MEG into clinical use at a hospital laboratory, and follows the central actor, a consulting physician who was hired to start the clinical service. An activity-theoretical framework is used to analyze the diffculties of the multi-organizational implementation process. The framework (1) emphasizes the importance of analyzing the perspectives of diUerent actors on implementation; (2) provides means to explore the developmentally significant contradictions in an organizational setting of an implementation and (3) discusses the user problems and user initiatives signalling a need for collective learning within implementation.


End-to-End Neural Relation Extraction with Global Optimization


title: An Incremental Algorithm for a Generalization of the Shortest-Path Problem

abstract: Thegrammar problem, a generalization of the single-source shortest-path problem introduced by D. E. Knuth (Inform. Process. Lett.6(1) (1977), 1?5) is to compute the minimum-cost derivation of a terminal string from each nonterminal of a given context-free grammar, with the cost of a derivation being suitably defined. This problem also subsumes the problem of finding optimal hyperpaths in directed hypergraphs (under varying optimization criteria) that has received attention recently. In this paper we present an incremental algorithm for a version of the grammar problem. As a special case of this algorithm we obtain an efficient incremental algorithm for the single-source shortest-path problem with positive edge lengths. The aspect of our work that distinguishes it from other work on the dynamic shortest-path problem is its ability to handle “multiple heterogeneous modifications”: between updates, the input graph is allowed to be restructured by an arbitrary mixture of edge insertions, edge deletions, and edge-length changes.


title: Interactive Rendering using the Render Cache

abstract: Interactive rendering requires rapid visual feedback. The render cache is a new method for achieving this when using high-quality pixel-oriented renderers such as ray tracing that are usually considered too slow for interactive use. The render cache provides visual feedback at a rate faster than the renderer can generate complete frames, at the cost of producing approximate images during camera and object motion. The method works both by caching previous results and reprojecting them to estimate the current image and by directing the renderer's sampling to more rapidly improve subsequent images. ::: ::: Our implementation demonstrates an interactive application working with both ray tracing and path tracing renderers in situations where they would normally be considered too expensive. Moreover we accomplish this using a software only implementation without the use of 3D graphics hardware.


title: Texture Cache Approximation on GPUs

abstract: We present texture cache approximation as a method for using existing hardware on GPUs to eliminate costly global memory accesses. We develop a technique for using a GPU’s texture fetch units to generate approximate values, and argue that this technique is applicable to a wide variety of GPU kernels. Applying texture cache approximation to an image blur kernel on an NVIDIA 780GTX, we obtain a 12% reduction in kernel execution time while only introducing 0.4% output error in the final image.


title: A survey of security issues in hardware virtualization

abstract: Virtualization is a powerful technology for increasing the efficiency of computing services; however, besides its advantages, it also raises a number of security issues. In this article, we provide a thorough survey of those security issues in hardware virtualization. We focus on potential vulnerabilities and existing attacks on various virtualization platforms, but we also briefly sketch some possible countermeasures. To the best of our knowledge, this is the first survey of security issues in hardware virtualization with this level of details. Moreover, the adversary model and the structuring of the attack vectors are original contributions, never published before.


Local-Global Vectors to Improve Unigram Terminology Extraction


title: Optimality of HLF for scheduling divide-and-conquer UET task graphs on identical parallel processors

abstract: The problem of scheduling a set of n unit execution time (UET) tasks subject to precedence constraints on m identical parallel processors is known to be NP-hard in the strong sense. However, polynomial time algorithms exist for some classes of precedence graphs. In this paper, we consider a class of divide-and-conquer graphs that naturally models the execution of the recursive control abstraction of divide-and-conquer algorithms. We prove that the Highest Level First (HLF) strategy minimizes the schedule length for this class, thus settling a conjecture of Rayward-Smith and Clark.


title: Compressing Convolutional Neural Networks

abstract: Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities of labeled data through millions of parameters. However, as model sizes increase, so do the storage and memory requirements of the classifiers. We present a novel network architecture, Frequency-Sensitive Hashed Nets (FreshNets), which exploits inherent redundancy in both convolutional layers and fully-connected layers of a deep learning model, leading to dramatic savings in memory and storage consumption. Based on the key observation that the weights of learned convolutional filters are typically smooth and low-frequency, we first convert filter weights to the frequency domain with a discrete cosine transform (DCT) and use a low-cost hash function to randomly group frequency parameters into hash buckets. All parameters assigned the same hash bucket share a single value learned with standard back-propagation. To further reduce model size we allocate fewer hash buckets to high-frequency components, which are generally less important. We evaluate FreshNets on eight data sets, and show that it leads to drastically better compressed performance than several relevant baselines.


title: Approximate Primal Solutions and Rate Analysis for Dual Subgradient Methods

abstract: In this paper, we study methods for generating approximate primal solutions as a byproduct of subgradient methods applied to the Lagrangian dual of a primal convex (possibly nondifferentiable) constrained optimization problem. Our work is motivated by constrained primal problems with a favorable dual problem structure that leads to efficient implementation of dual subgradient methods, such as the recent resource allocation problems in large-scale networks. For such problems, we propose and analyze dual subgradient methods that use averaging schemes to generate approximate primal optimal solutions. These algorithms use a constant stepsize in view of its simplicity and practical significance. We provide estimates on the primal infeasibility and primal suboptimality of the generated approximate primal solutions. These estimates are given per iteration, thus providing a basis for analyzing the trade-offs between the desired level of error and the selection of the stepsize value. Our analysis relies on the Slater condition and the inherited boundedness properties of the dual problem under this condition. It also relies on the boundedness of subgradients, which is ensured by assuming the compactness of the constraint set.


A Taxonomy and Qualitative Comparison of Program Analysis Techniques for Security Assessment of Android Apps


title: Annotated gigaword

abstract: We have created layers of annotation on the English Gigaword v.5 corpus to render it useful as a standardized corpus for knowledge extraction and distributional semantics. Most existing large-scale work is based on inconsistent corpora which often have needed to be re-annotated by research teams independently, each time introducing biases that manifest as results that are only comparable at a high level. We provide to the community a public reference set based on current state-of-the-art syntactic analysis and coreference resolution, along with an interface for programmatic access. Our goal is to enable broader involvement in large-scale knowledge-acquisition efforts by researchers that otherwise may not have had the ability to produce such a resource on their own.


title: Design of a New Type Six-Axis Force Sensor

abstract: A new type of six-axis full shearing strain force sensor has been developed. It uses strain gauge as sensing element for the detection of force. The elastic body is a thin cylinder on which four holes are opened evenly in two levels. There are six Wheatstone bridges on the beams to detect the force in six directions. The sensor was designed from the results of finite element method (FEM) simulation. As indicated by the calculations and experiments, this sensor is not only of high sensitivity and adequate rigidity in the six directions, but also of fairly small cross sensitivities and low hysteresis.


title: Pedagogy and innovation in education with digital technologies

abstract: In a process for the preparation of 2-mercaptobenzothiazole from aniline, sulfur and carbon disulfide under pressure and for separation of the 2-mercaptobenzothiazole from the crude reaction product by cooling and recycling the desirable intermediates and by-products into the reactor together with aniline, sulfur and carbon disulfide, the reaction is carried out using a residence time in the reactor of at least one hour to a time before the 2-mercaptobenzothiazole product thus-formed begins to decompose into undesirable intermediate products. A maximum hydrogen sulfide pressure is maintained on quenching to a temperature ranging between 220 and 280 DEG C. by mixing the crude reaction product with carbon disulfide at a temperature sufficient to obtain a homogeneous quenched mixture. The hydrogen sulfide is removed completely only after crystallization of the 2-mercaptobenzothiazole.


title: An overview of evolutionary algorithms: practical issues and common pitfalls

abstract: Abstract An overview of evolutionary algorithms is presented covering genetic algorithms, evolution strategies, genetic programming and evolutionary programming. The schema theorem is reviewed and critiqued. Gray codes, bit representations and real-valued representations are discussed for parameter optimization problems. Parallel Island models are also reviewed, and the evaluation of evolutionary algorithms is discussed.


title: A Deep Cascade Model for Multi-Document Reading Comprehension

abstract: A fundamental trade-off between effectiveness and efficiency needs to be balanced when designing an online question answering system. Effectiveness comes from sophisticated functions such as extractive machine reading comprehension (MRC), while efficiency is obtained from improvements in preliminary retrieval components such as candidate document selection and paragraph ranking. Given the complexity of the real-world multi-document MRC scenario, it is difficult to jointly optimize both in an end-to-end system. To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the documentlevel and paragraph-level ranking of candidate texts to more precise answer extraction with machine reading comprehension. Specifically, irrelevant documents and paragraphs are first filtered out with simple functions for efficiency consideration. Then we jointly train three modules on the remaining texts for better tracking the answer: the document extraction, the paragraph extraction and the answer extraction. Experiment results show that the proposed method outperforms the previous state-of-the-art methods on two large-scale multidocument benchmark datasets, i.e., TriviaQA and DuReader. In addition, our online system can stably serve typical scenarios with millions of daily requests in less than 50ms.


title: Compact slow-wave millimeter-wave bandpass filter (BPF) using open-loop resonator

abstract: A compact slow-wave millimeter-wave bandpass filter for wireless communication systems is developed and proposed in this paper. The filter is based on an open-loop resonator that is composed of a microstrip line with both ends loaded with folded open stubs. The folded arms of the open stubs are utilized to increase the loading capacitance to ground. In addition, the folded arms of the open stubs are also used to introduce cross couplings. The cross couplings can create transmission zeros at the edges of the desired passband. As a result, the filter can exhibit a passband with sharp rejection skirts. The filter is designed to have a fractional bandwidth of about 3.3% at a center frequency of 30.0 GHz. The filter design is realized using Rogers RT6002 substrate with a dielectric constant of 2.94 and a thickness of 0.127 mm. The filter design is successfully fabricated using the printed circuit board (PCB) technology. The fabricated filter is measured and an excellent agreement between the simulated and measured results is achieved. The fabricated filter has the advantages of compact size and excellent performance.


title: Using EELs, a practical approach to outerjoin and antijoin reordering

abstract: Outerjoins and antijoins are two important classes of joins in database systems. Reordering outerjoins and antijoins with innerjoins is challenging because not all the join orders preserve the semantics of the original query. Previous work did not consider antijoins and was restricted to a limited class of queries. We consider using a conventional bottom-up optimizer to reorder different types of joins. We propose extending each join predicate's eligibility list, which contains all the tables referenced in the predicate. An extended eligibility list (EEL) includes all the tables needed by a predicate to preserve the semantics of the original query. We describe an algorithm that can set up the EELs properly in a bottom-up traversal of the original operator tree. A conventional join optimizer is then modified to check the EELs when generating sub-plans. Our approach handles antijoin and can resolve many practical issues. It is now being implemented in an upcoming release of IBM's Universal Database Server for Unix, Windows and OS/2.


title: Digital droplets: Microsoft SkyDrive forensic data remnants

abstract: Cloud storage services such as the popular Microsoft(C) SkyDrive(C) provide both organisational and individual users a cost-effective, and in some cases free, way of accessing, storing and disseminating data. The identification of digital evidence relating to cloud storage services can, however, be a challenge in a digital forensic investigation. Using SkyDrive as a case study, we identified the types of terrestrial artefacts that are likely to remain on a client's machine (in the context of our experiments; computer hard drive and iPhone), and where the access point(s) for digital forensics examiners are, that will allow them to undertake steps to secure evidence in a timely fashion.


title: Visual to Sound: Generating Natural Sound for Videos in the Wild

abstract: As two of the five traditional human senses (sight, hearing, taste, smell, and touch), vision and sound are basic sources through which humans understand the world. Often correlated during natural events, these two modalities combine to jointly affect human perception. In this paper, we pose the task of generating sound given visual input. Such capabilities could help enable applications in virtual reality (generating sound for virtual scenes automatically) or provide additional accessibility to images or videos for people with visual impairments. As a first step in this direction, we apply learning-based methods to generate raw waveform samples given input video frames. We evaluate our models on a dataset of videos containing a variety of sounds (such as ambient sounds and sounds from people/animals). Our experiments show that the generated sounds are fairly realistic and have good temporal synchronization with the visual inputs.


title: Positivity-preserving DG and central DG methods for ideal MHD equations

abstract: Ideal MHD equations arise in many applications such as astrophysical plasmas and space physics, and they consist of a system of nonlinear hyperbolic conservation laws. The exact density @r and pressure p should be non-negative. Numerically, such positivity property is not always satisfied by approximated solutions. One can encounter this when simulating problems with low density, high Mach number, or much large magnetic energy compared with internal energy. When this occurs, numerical instability may develop and the simulation can break down. In this paper, we propose positivity-preserving discontinuous Galerkin and central discontinuous Galerkin methods for solving ideal MHD equations by following [X. Zhang, C.-W. Shu, Journal of Computational Physics 229 (2010) 8918-8934]. In one dimension, the positivity-preserving property is established for both methods under a reasonable assumption. The performance of the proposed methods, in terms of accuracy, stability and positivity-preserving property, is demonstrated through a set of one and two dimensional numerical experiments. The proposed methods formally can be of any order of accuracy.