title: Mean-normalized stochastic gradient for large-scale deep learning

abstract: Deep neural networks are typically optimized with stochastic gradient descent (SGD). In this work, we propose a novel second-order stochastic optimization algorithm. The algorithm is based on analytic results showing that a non-zero mean of features is harmful for the optimization. We prove convergence of our algorithm in a convex setting. In our experiments we show that our proposed algorithm converges faster than SGD. Further, in contrast to earlier work, our algorithm allows for training models with a factorized structure from scratch. We found this structure to be very useful not only because it accelerates training and decoding, but also because it is a very effective means against overfitting. Combining our proposed optimization algorithm with this model structure, model size can be reduced by a factor of eight and still improvements in recognition error rate are obtained. Additional gains are obtained by improving the Newbob learning rate strategy.


Application on Design of Multiuser Non-Cooperative Communication Systems via Game Theory


title: DynGEM: Deep Embedding Method for Dynamic Graphs

abstract: Embedding large graphs in low dimensional spaces has recently attracted significant interest due to its wide applications such as graph visualization, link prediction and node classification. Existing methods focus on computing the embedding for static graphs. However, many graphs in practical applications are dynamic and evolve constantly over time. Naively applying existing embedding algorithms to each snapshot of dynamic graphs independently usually leads to unsatisfactory performance in terms of stability, flexibility and efficiency. In this work, we present an efficient algorithm DynGEM based on recent advances in deep autoencoders for graph embeddings, to address this problem. The major advantages of DynGEM include: (1) the embedding is stable over time, (2) it can handle growing dynamic graphs, and (3) it has better running time than using static embedding methods on each snapshot of a dynamic graph. We test DynGEM on a variety of tasks including graph visualization, graph reconstruction, link prediction and anomaly detection (on both synthetic and real datasets). Experimental results demonstrate the superior stability and scalability of our approach.


title: Towards optimal shallow ANN for recognizing isolated handwritten Bengali numerals

abstract: This work attempts to find the most optimal setting for shallow artificial neural network (ANN) for Bengali digit dataset. Recognition of handwritten Bengali numerals has recently gained much interest among researchers due to significant performance gain found in the recognition of English numerals using artificial neural network. In this work, a new dataset of 70,000 samples were created first by taking handwriting of 1750 persons where 982 persons were male and the rests were female. These individual image samples are then converted to grayscale, normalized, inverted and pickled to complete the data preprocessing step. Later this dataset was recognized using several artificial neural network settings where the most optimal setting being found to be one hidden layer with rectified linear unit activation and one output layer with softmax activation. In this study, we have given enough evidence to support our choice of these activations. We proposed the optimal number of neurons that can be used in the hidden layer is 700; also the optimal batch size is 200. The maximum accuracy on the test set has been found to be 96.05% which is a good result due to the complexity of the recognition task.


title: LetterMoose: A Handwriting Tutor Robot

abstract: We present a simple robotic tutor designed to help raise handwriting competency in school-aged children. "LetterMoose" shows the steps in how a letter is formed by writing on regular piece of paper. The child is invited to imitate LetterMoose and to scan its own letters using LetterMoose in order to get evaluative feedback (both qualitative and quantitative). We propose that LetterMoose might be particularly useful for helping children with autism attain handwriting competency, as children in this group are more likely to suffer from writing difficulties and may uniquely benefit from interacting with robot technology.


title: Towards Privacy-Preserving Content-Based Image Retrieval in Cloud Computing

abstract: Content-based image retrieval (CBIR) applications have been rapidly developed along with the increase in the quantity, availability and importance of images in our daily life. However, the wide deployment of CBIR scheme has been limited by its the severe computation and storage requirement. In this paper, we propose a privacy-preserving content-based image retrieval scheme, which allows the data owner to outsource the image database and CBIR service to the cloud, without revealing the actual content of the database to the cloud server. Local features are utilized to represent the images, and earth mover's distance (EMD) is employed to evaluate the similarity of images. The EMD computation is essentially a linear programming (LP) problem. The proposed scheme transforms the EMD problem in such a way that the cloud server can solve it without learning the sensitive information. In addition, local sensitive hash (LSH) is utilized to improve the search efficiency. The security analysis and experiments show the security and efficiency of the proposed scheme.


title: Detection of mobile applications leaking sensitive data

abstract: Smart phones are the most popular and used devices by people. They host numerous types of data which can be classified as public and private. It is a new phenomenon to develop some malicious applications to exfiltrate personal data from smart phones. Hence personal data on these devices such as short messages, contacts, photos, videos, GPS locations, etc. require a specialized security mechanism which protects them from being leaked by malicious applications. The main problem is the disclosure of sensitive information when mobile applications try to access them via android permissions. This situation tells us that these mobile applications will probably leak the sensitive data. Therefore, some solutions especially considering data leakage on smart phones are required. This paper has two goals which are detecting the applications leak sensitive data and analyzing benign applications whether they have malicious intents or not. In this study, a sensitive data leakage prevention system for Android systems was proposed. J48 classification algorithm was used to detect mobile applications leaking sensitive data. In addition, trusted mobile applications downloaded from Google Play Store were evaluated whether they have any similarity with malicious mobile applications or not by clustering with K-Means clustering algorithm. The result has shown that the proposed system can detect malign and benign applications in 98.6% accuracy.


title: Discovering Design Principles for Health Behavioral Change Support Systems: A Text Mining Approach

abstract: Behavioral Change Support Systems (BCSSs) aim to change users’ behavior and lifestyle. These systems have been gaining popularity with the proliferation of wearable devices and recent advances in mobile technologies. In this article, we extend the existing literature by discovering design principles for health BCSSs based on a systematic analysis of users’ feedback. Using mobile diabetes applications as an example of Health BCSSs, we use topic modeling to discover design principles from online user reviews. We demonstrate the importance of the design principles through analyzing their existence in users’ complaints. Overall, the results highlight the necessity of going beyond the techno-centric approach used in current practice and incorporating the social and organizational features into persuasive systems design, as well as integrating with medical devices and other systems in their usage context.


HBGG: a Hierarchical Bayesian Geographical Model for Group Recommendation


title: D Nearest Neighbor Data-Driven Geometric Scene Understanding Using 3 D Models

abstract: In this paper, we describe a data-driven approach to leverage repositories of 3D models for scene understanding. Our ability to relate what we see in an image to a large collection of 3D models allows us to transfer information from these models, creating a rich understanding of the scene. We develop a framework for auto-calibrating a camera, rendering 3D models from the viewpoint an image was taken, and computing a similarity measure between each 3D model and an input image. We demonstrate this data-driven approach in the context of geometry estimation and show the ability to find the identities, poses and styles of objects in a scene. The true benefit of 3DNN compared to a traditional 2D nearest-neighbor approach is that by generalizing across viewpoints, we free ourselves from the need to have training examples captured from all possible viewpoints. Thus, we are able to achieve comparable results using orders of magnitude less data, and recognize objects from never-before-seen viewpoints. In this work, we describe the 3DNN algorithm and rigorously evaluate its performance for the tasks of geometry estimation and object detection/segmentation, as well as two novel applications: affordance estimation and photorealistic object insertion.


title: Metamorphic Testing of RESTful Web APIs

abstract: Web Application Programming Interfaces (APIs) allow systems to interact with each other over the network. Modern Web APIs often adhere to the REST architectural style, being referred to as RESTful Web APIs. RESTful Web APIs are decomposed into multiple resources (e.g., a video in the YouTube API) that clients can manipulate through HTTP interactions. Testing Web APIs is critical but challenging due to the difficulty to assess the correctness of API responses, i.e., the oracle problem. Metamorphic testing alleviates the oracle problem by exploiting relations (so-called metamorphic relations) among multiple executions of the program under test. In this paper, we present a metamorphic testing approach for the detection of faults in RESTful Web APIs. We first propose six abstract relations that capture the shape of many of the metamorphic relations found in RESTful Web APIs, we call these Metamorphic Relation Output Patterns (MROPs). Each MROP can then be instantiated into one or more concrete metamorphic relations. The approach was evaluated using both automatically seeded and real faults in six subject Web APIs. Among other results, we identified 60 metamorphic relations (instances of the proposed MROPs) in the Web APIs of Spotify and YouTube. Each metamorphic relation was implemented using both random and manual test data, running over 4.7K automated tests. As a result, 11 issues were detected (3 in Spotify and 8 in YouTube), 10 of them confirmed by the API developers or reproduced by other users, supporting the effectiveness of the approach.


title: Model evolution by run-time parameter adaptation

abstract: Models can help software engineers to reason about design-time decisions before implementing a system. This paper focuses on models that deal with non-functional properties, such as reliability and performance. To build such models, one must rely on numerical estimates of various parameters provided by domain experts or extracted by other similar systems. Unfortunately, estimates are seldom correct. In addition, in dynamic environments, the value of parameters may change over time. We discuss an approach that addresses these issues by keeping models alive at run time and feeding a Bayesian estimator with data collected from the running system, which produces updated parameters. The updated model provides an increasingly better representation of the system. By analyzing the updated model at run time, it is possible to detect or predict if a desired property is, or will be, violated by the running implementation. Requirement violations may trigger automatic reconfigurations or recovery actions aimed at guaranteeing the desired goals. We illustrate a working framework supporting our methodology and apply it to an example in which a Web service orchestrated composition is modeled through a Discrete Time Markov Chain. Numerical simulations show the effectiveness of the approach.


title: Predicting employee expertise for talent management in the enterprise

abstract: Strategic planning and talent management in large enterprises composed of knowledge workers requires complete, accurate, and up-to-date representation of the expertise of employees in a form that integrates with business processes. Like other similar organizations operating in dynamic environments, the IBM Corporation strives to maintain such current and correct information, specifically assessments of employees against job roles and skill sets from its expertise taxonomy. In this work, we deploy an analytics-driven solution that infers the expertise of employees through the mining of enterprise and social data that is not specifically generated and collected for expertise inference. We consider job role and specialty prediction and pose them as supervised classification problems. We evaluate a large number of feature sets, predictive models and postprocessing algorithms, and choose a combination for deployment. This expertise analytics system has been deployed for key employee population segments, yielding large reductions in manual effort and the ability to continually and consistently serve up-to-date and accurate data for several business functions. This expertise management system is in the process of being deployed throughout the corporation.


title: Data mining techniques in Indian healthcare: A short review

abstract: Healthcare is a fast-growing field in developed and developing countries alike. Indian healthcare has also witnessed rapid growth in the recent past. The data generated is of very high volume and exhibit wide diversity, and hence data mining opportunities available in various areas of health care industry are immense. In this paper, a brief review of data mining applications in healthcare has been presented. The key differentiating factors of the paper is its focus on India and a patient lifecycle oriented view of the problems. As found by the study, most of the researches have been focused on predicting a disease which is a part of ongoing care management. This paper presents many uncharted areas of future research in this domain especially in Indian context.


title: Neurophysiological Correlates of Musical Giftedness in Autism Spectrum Disorders

abstract: Despite their social deficits and behavioral abnormalities, individuals with autism may frequently display unexpected and unusual areas of interest and giftedness in music. In the present work, we discuss the natural predisposition to music shown in autism using a neurophysiological framework, exploring the possible correlations between the musical experience and the connectivity alterations of the autistic brain. After a brief description of the complex cerebral organization in autism spectrum disorder (ASD), the article focuses on the microcircuit alterations, in particular on the excess synaptic excitation and minicolumnopathy, which are strongly linked to the peculiar attention in detecting, processing, and focusing on detail, widely described by the weak central coherence theory. Superior pitch discrimination, labeling, and memory, demonstrated in ASD, even seem to derive from these complex mental processes. The connectivity alterations seem to be also present in the cerebellum, where they damage the timekeeper function and produce severe dysfunctions in rhythmicity and synchronization of thoughts and emotions. We propose that music might restore the natural rhythmicity in the cerebellum of patients with ASD and that this could explain the natural predisposition to music seen in this condition. Finally, we focus on the usefulness of music for rehabilitative purposes.


A Recommender System using Principal Component Analysis


title: Robust and Fine-grained Prosody Control of End-to-end Speech Synthesis

abstract: We propose prosody embeddings for emotional and expressive speech synthesis networks. The proposed methods introduce temporal structures in the embedding networks, thus enabling fine-grained control of the speaking style of the synthesized speech. The temporal structures can be designed either on the speech side or the text side, leading to different control resolutions in time. The prosody embedding networks are plugged into end-to-end speech synthesis networks and trained without any other supervision except for the target speech for synthesizing. It is demonstrated that the prosody embedding networks learned to extract prosodic features. By adjusting the learned prosody features, we could change the pitch and amplitude of the synthesized speech both at the frame level and the phoneme level. We also introduce the temporal normalization of prosody embeddings, which shows better robustness against speaker perturbations during prosody transfer tasks.


Large scale laboratory rearing of black flies.


Why did California Electricity Crisis Occur? A Numerical Analysis Using Multi-Agent Intelligent Simulator


title: Exploring end-user smartphone security awareness within a South African context

abstract: International research has shown that users are complacent when it comes to smartphone security behaviour. This is contradictory, as users perceive data stored on the ‘smart’ devices to be private and worth protecting. Traditionally less attention is paid to human factors compared to technical security controls (such as firewalls and antivirus), but there is a crucial need to analyse human aspects as technology alone cannot deliver complete security solutions. Increasing a user's knowledge can improve compliance with good security practices, but for trainers and educators to create meaningful security awareness materials they must have a thorough understanding of users' existing behaviours, misconceptions and general attitude towards smartphone security.


title: The conformation of α-lactalbumin as a function of pH, heat treatment and adsorption at hydrophobic surfaces studied by FTIR

abstract: Abstract The secondary structure of α-lactalbumin (α-la) from bovine milk has been studied using Fourier Transform Infra Red (FTIR)spectroscopy. It was found that the secondary structure of α-la, after having being heated above its denaturation temperature, was permanently altered even if the temperature was brought back to room temperature. The reorganized structure still contained a large proportion of the helical structure but it differed from the native structure. The reorganized structure was similar for samples heated at 70 °C and 80 °C at either pH 6 or 7 and at 90 °C at pH 7 for 2 min, but after heating at 90 gC at pH 6,the reorganized structure was less well-defined, possibly because of alterations to the disulfide bonds. When α-la was adsorbed to an oil-water interface, the 3 10 -helix denatured first, giving an increase in unfolded structure. Additional β-sheet was formed at the expense of the helical structure. The two different absorption bands for the α-helix at 1650 and 1657 cm −1 became even more distinctive after the adsorption on the interface. The structure of adsorbed α-la depended on the concentration of the protein rather than the pH and the age of the emulsion. At higher protein concentration, more β-sheet structure was formed and the adsorption for 3 10 -helix was shifted from 1639 to 1641 cm −1 , below the frequency of unordered structure. At 1% α-la, the 3 10 -helical structure became unordered structure (1643 cm −1 ) and there was less β-sheet structure


title: Towards Directly Modeling Raw Speech Signal for Speaker Verification Using CNNS

abstract: Speaker verification systems traditionally extract and model cepstral features or filter bank energies from the speech signal. In this paper, inspired by the success of neural network-based approaches to model directly raw speech signal for applications such as speech recognition, emotion recognition and anti-spoofing, we propose a speaker verification approach where speaker discriminative information is directly learned from the speech signal by: (a) first training a CNN-based speaker identification system that takes as input raw speech signal and learns to classify on speakers (unknown to the speaker verification system); and then (b) building a speaker detector for each speaker in the speaker verification system by replacing the output layer of the speaker identification system by two outputs (genuine, impostor), and adapting the system in a discriminative manner with enrollment speech of the speaker and impostor speech data. Our investigations on the Voxforge database shows that this approach can yield systems competitive to state-of-the-art systems. An analysis of the filters in the first convolution layer shows that the filters give emphasis to information in low frequency regions (below 1000 Hz) and implicitly learn to model fundamental frequency information in the speech signal for speaker discrimination.


title: Ultrasonication-assisted extraction of a phytomelatonin-rich, erucic acid-lean nutraceutical supplement from mustard seeds: an antioxidant synergy in the extract by reductionism

abstract: Phytomelatonin-rich (194.02 ± 2.45–205.80 ± 1.67 ng/g of dry mustard seeds) and erucic acid-lean (below 2%) extracts from an oilseed crop, (yellow and black mustard seeds) have been successfully obtained by ultrasonication-assisted-extraction in ethanol–water. Fourier-transform infrared spectroscopy and liquid chromatography-mass spectrum analyses have confirmed the presence of phytomelatonin along with tocopherol, ascorbic acid, limonene and linalool in the extract. Field emission scanning electron micrographs confirmed the cavitational effects of sonication on mustard seed matrices. Electron paramagnetic resonance spectroscopy established the strong antioxidant activities (72.25–75.49%) of the extracts foregoing erroneous spectrophotometric result of pan assay interference compounds. A synergistic effect value of 1.13 (greater than unity) confirmed synergistic co-existence of the antioxidants in the extract. This study interestingly revealed that an antioxidant synergy could be obtained by classical reductionism. Acute oral toxicity of the extracts were found to be greater than 5000 mg/kg body weight of rats. The extracts are perfectly safe to be utilized as antioxidative food supplements.


title: The Use of Wearable Microsensors to Quantify Sport-Specific Movements

abstract: BackgroundMicrotechnology has allowed sport scientists to understand the locomotor demands of various sports. While wearable global positioning technology has been used to quantify the locomotor demands of sporting activities, microsensors (i.e. accelerometers, gyroscopes and magnetometers) embedded within the units also have the capability to detect sport-specific movements.ObjectiveThe objective of this study was to determine the extent to which microsensors (also referred to as inertial measurement units and microelectromechanical sensors) have been utilised in quantifying sport-specific movements.MethodsA systematic review of the use of microsensors and associated terms to evaluate sport-specific movements was conducted; permutations of the terms used included alternate names of the various technologies used, their applications and different applied environments. Studies for this review were published between 2008 and 2014 and were identified through a systematic search of six electronic databases: Academic Search Complete, CINAHL, PsycINFO, PubMed, SPORTDiscus, and Web of Science. Articles were required to have used athlete-mounted sensors to detect sport-specific movements (e.g. rugby union tackle) rather than sensors mounted to equipment and monitoring generic movement patterns.ResultsA total of 2395 studies were initially retrieved from the six databases and 737 results were removed as they were duplicates, review articles or conference abstracts. After screening titles and abstracts of the remaining papers, the full text of 47 papers was reviewed, resulting in the inclusion of 28 articles that met the set criteria around the application of microsensors for detecting sport-specific movements. Eight articles addressed the use of microsensors within individual sports, team sports provided seven results, water sports provided eight articles, and five articles addressed the use of microsensors in snow sports. All articles provided evidence of the ability of microsensors to detect sport-specific movements. Results demonstrated varying purposes for the use of microsensors, encompassing the detection of movement and movement frequency, the identification of movement errors and the assessment of forces during collisions.ConclusionThis systematic review has highlighted the use of microsensors to detect sport-specific movements across a wide range of individual and team sports. The ability of microsensors to capture sport-specific movements emphasises the capability of this technology to provide further detail on athlete demands and performance. However, there was mixed evidence on the ability of microsensors to quantify some movements (e.g. tackling within rugby union, rugby league and Australian rules football). Given these contrasting results, further research is required to validate the ability of wearable microsensors containing accelerometers, gyroscopes and magnetometers to detect tackles in collision sports, as well as other contact events such as the ruck, maul and scrum in rugby union.


title: An over-67-GHz-bandwidth 2 Vppd linear differential amplifier with gain control in 0.25-µm InP DHBT technology

abstract: In this report, a differential linear amplifier for high-symbol-rate multilevel transmission is presented. We designed and fabricated the amplifier IC by using newly developed in-house 0.25-µm InP DHBT technology, which yields peak ƒt and ƒmax of over 400 GHz. This amplifier consists of lumped variable-gain amplifier and distributed output buffer for achieving large gain control range and ultra-broadband performance. The −3 dB bandwidth and the differential gain are over 67 GHz and 10.7 dB, respectively. Output return loss is better than −10 dB up to 63 GHz. In addition to the ultra-broadband characteristics, nearly 10-dB variable gain range are obtained. The amplifier provides clear output waveform at a symbol rates up to 100 Gbaud with NRZ signal, and up to 56 Gbaud with PAM4 signal respectively.


title: Probabilistic Error Analysis of Approximate Recursive Multipliers

abstract: Approximate multipliers are gaining importance in energy-efficient computing and require careful error analysis. In this paper, we present the error probability analysis for recursive approximate multipliers with approximate partial products. Since these multipliers are constructed from smaller approximate multiplier building blocks, we propose to derive the error probability in an arbitrary bit-width multiplier from the probabilistic model of the basic building block and the probability distributions of inputs. The analysis is based on common features of recursive multipliers identified by carefully studying the behavioral model of state-of-the-art designs. By building further upon the analysis, Probability Mass Function (PMF) of error is computed by individually considering all possible error cases and their inter-dependencies. We further discuss the generalizations for approximate adder trees, signed multipliers, squarers and constant multipliers. The proposed analysis is validated by applying it to several state-of-the-art approximate multipliers and comparing with corresponding simulation results. The results show that the proposed analysis serves as an effective tool for predicting, evaluating and comparing the accuracy of various multipliers. Results show that for the majority of the recursive multipliers, we get accurate error performance evaluation. We also predict the multipliers’ performance in an image processing application to demonstrate its practical significance.


title: Dataset linking in a multilingual linked open data context

abstract: Although, the syntactical and structural heterogeneities among inter-language linked open data (LOD) data sources bring many challenges, entity co-reference resolution in a multilingual linked open data (MLOD) setting is not well studied. In this research, a three phase approach is proposed. First, statistical relational learning (SRL) with factorization of three way tensor is used to compute structural similarity between entities. Second, textual data from the Web of documents is associated in order to increase our knowledge of entities. Through a latent Dirichlet allocation (LDA), entities' textual data is projected into a cross-lingual topic space. This cross-lingual topic space is used to find textual similarities between entities. Third, a belief aggregation strategy is used to combine the structural and textual similarity results into a global similarity score. We have shown by experiments that our algorithm out-performs state of the art approaches based on tensor decomposition for the task of entity co-reference resolution in a MLOD setting.


title: Quantifying the User Experience: Practical Statistics for User Research

abstract: You're being asked to quantify usability improvements with statistics. But even with a background in statistics, you are hesitant to statistically analyze the data, as you may be unsure about which statistical tests to use and have trouble defending the use of the small test sample sizes associated with usability studies. ::: The book is about providing a practical guide on how to use statistics to solve common quantitative problems arising in user research. It addresses common questions?you face every day?such as: Is the current product more usable than our competition? Can we be sure at least 70% of users can complete the task on the 1st attempt? How long will it take users to purchase products on the website??This book shows you which test to use, and how provide a foundation for both the statistical theory and best practices in applying them. The authors draw on decades of statistical literature from Human Factors, Industrial Engineering and Psychology, as well as their own published research to provide the best solutions. They provide both concrete solutions (excel formula, links to their own web-calculators) along with an engaging discussion about the statistical reasons for why the tests work, and how to effectively communicate the results. ::: ::: ::: ::: Provides practical guidance on solving usability testing problems with statistics for any project, including those using Six Sigma practices ::: Show practitioners which test to use, why they work, best practices in application, along with easy-to-use excel formulas and web-calculators for analyzing data ::: Recommends ways for practitioners to communicate results to stakeholders in plain English ::: ::: Table of Contents ::: ::: ::: Dedication Acknowledgements About the Authors Chapter 1: Introduction and How to Use This Book Chapter 2: Quantifying User Research Chapter 3: How Precise Are Our Estimates? Confidence Intervals Chapter 4: Did We Meet or Exceed Our Goal? Chapter 5: Is There a Statistical Difference between Designs? Chapter 6: What Sample Sizes Do We Need? Part 1: Summative Studies Chapter 7: What Sample Sizes Do We Need? Part 2: Formative Studies Chapter 8: Standard Usability Questionnaires Chapter 9: Six Enduring Controversies in Measurement and Statistics Chapter 10: Wrapping Up Appendix Index


title: Time Symmetry and the Many‐Worlds Interpretation

abstract: An attempt to solve the collapse problem in the framework of a time-symmetric quantum formalism is reviewed. Although the proposal does not look very attractive, its concept - a world defined by two quantum states, one evolving forwards and one evolving backwards in time - is found to be useful in modifying the many-worlds picture of Everett’s theory.


title: GraphP: Reducing Communication for PIM-Based Graph Processing with Efficient Data Partition

abstract: Processing-In-Memory (PIM) is an effective technique that reduces data movements by integrating processing units within memory. The recent advance of “big data” and 3D stacking technology make PIM a practical and viable solution for the modern data processing workloads. It is exemplified by the recent research interests on PIM-based acceleration. Among them, TESSERACT is a PIM-enabled parallel graph processing architecture based on Micron’s Hybrid Memory Cube (HMC), one of the most prominent 3D-stacked memory technologies. It implements a Pregel-like vertex-centric programming model, so that users could develop programs in the familiar interface while taking advantage of PIM. Despite the orders of magnitude speedup compared to DRAM-based systems, TESSERACT generates excessive crosscube communications through SerDes links, whose bandwidth is much less than the aggregated local bandwidth of HMCs. Our investigation indicates that this is because of the restricted data organization required by the vertex programming model. In this paper, we argue that a PIM-based graph processing system should take data organization as a first-order design consideration. Following this principle, we propose GraphP, a novel HMC-based software/hardware co-designed graph processing system that drastically reduces communication and energy consumption compared to TESSERACT. GraphP features three key techniques. 1) “Source-cut” partitioning, which fundamentally changes the cross-cube communication from one remote put per cross-cube edge to one update per replica. 2) “Two-phase Vertex Program”, a programming model designed for the “source-cut” partitioning with two operations: GenUpdate and ApplyUpdate. 3) Hierarchical communication and overlapping, which further improves performance with unique opportunities offered by the proposed partitioning and programming model. We evaluate GraphP using a cycle accurate simulator with 5 real-world graphs and 4 algorithms. The results show that it provides on average 1.7 speedup and 89% energy saving compared to TESSERACT.


title: Protecting Sensory Data against Sensitive Inferences

abstract: There is growing concern about how personal data are used when users grant applications direct access to the sensors of their mobile devices. In fact, high resolution temporal data generated by motion sensors reflect directly the activities of a user and indirectly physical and demographic attributes. In this paper, we propose a feature learning architecture for mobile devices that provides flexible and negotiable privacy-preserving sensor data transmission by appropriately transforming raw sensor data. The objective is to move from the current binary setting of granting or not permission to an application, toward a model that allows users to grant each application permission over a limited range of inferences according to the provided services. The internal structure of each component of the proposed architecture can be flexibly changed and the trade-off between privacy and utility can be negotiated between the constraints of the user and the underlying application. We validated the proposed architecture in an activity recognition application using two real-world datasets, with the objective of recognizing an activity without disclosing gender as an example of private information. Results show that the proposed framework maintains the usefulness of the transformed data for activity recognition, with an average loss of only around three percentage points, while reducing the possibility of gender classification to around 50%, the target random guess, from more than 90% when using raw sensor data. We also present and distribute MotionSense, a new dataset for activity and attribute recognition collected from motion sensors.


title: Collaborative Metric Learning Recommendation System: Application to Theatrical Movie Releases

abstract: Product recommendation systems are important for major movie studios during the movie greenlight process and as part of machine learning personalization pipelines. Collaborative Filtering (CF) models have proved to be effective at powering recommender systems for online streaming services with explicit customer feedback data. CF models do not perform well in scenarios in which feedback data is not available, in cold start situations like new product launches, and situations with markedly different customer tiers (e.g., high frequency customers vs. casual customers). Generative natural language models that create useful theme-based representations of an underlying corpus of documents can be used to represent new product descriptions, like new movie plots. When combined with CF, they have shown to increase the performance in cold start situations. Outside of those cases though in which explicit customer feedback is available, recommender engines must rely on binary purchase data, which materially degrades performance. Fortunately, purchase data can be combined with product descriptions to generate meaningful representations of products and customer trajectories in a convenient product space in which proximity represents similarity. Learning to measure the distance between points in this space can be accomplished with a deep neural network that trains on customer histories and on dense vectorizations of product descriptions. We developed a system based on Collaborative (Deep) Metric Learning (CML) to predict the purchase probabilities of new theatrical releases. We trained and evaluated the model using a large dataset of customer histories, and tested the model for a set of movies that were released outside of the training window. Initial experiments show gains relative to models that do not train on collaborative preferences.


Limited attention and status quo bias


title: [Penetration of benzoyl peroxide in the skin].

abstract: Benzoyl peroxide preparations have proven to be effective agents in the treatment of acne. In comparison to the numerous clinical communications, very few reports exist concerning the pharmacokinetics of benzoyl peroxide. The benzoyl peroxide content of abraded horny layers and its metabolite benzoic acid were investigated by high-pressure liquid chromatography after application (1-2 min) of emulsion containing benzoyl peroxide. It was found that benzoyl peroxide penetrates the stratum corneum very quickly where it is rapidly degraded to benzoic acid. No benzoyl peroxide depot in the stratum corneum could be demonstrated.


title: Non-Invasive Brain-to-Brain Interface (BBI): Establishing Functional Links between Two Brains

abstract: Transcranial focused ultrasound (FUS) is capable of modulating the neural activity of specific brain regions, with a potential role as a non-invasive computer-to-brain interface (CBI). In conjunction with the use of brain-to-computer interface (BCI) techniques that translate brain function to generate computer commands, we investigated the feasibility of using the FUS-based CBI to non-invasively establish a functional link between the brains of different species (i.e. human and Sprague-Dawley rat), thus creating a brain-to-brain interface (BBI). The implementation was aimed to non-invasively translate the human volunteer’s intention to stimulate a rat’s brain motor area that is responsible for the tail movement. The volunteer initiated the intention by looking at a strobe light flicker on a computer display, and the degree of synchronization in the electroencephalographic steady-state-visual-evoked-potentials (SSVEP) with respect to the strobe frequency was analyzed using a computer. Increased signal amplitude in the SSVEP, indicating the volunteer’s intention, triggered the delivery of a burst-mode FUS (350 kHz ultrasound frequency, tone burst duration of 0.5 ms, pulse repetition frequency of 1 kHz, given for 300 msec duration) to excite the motor area of an anesthetized rat transcranially. The successful excitation subsequently elicited the tail movement, which was detected by a motion sensor. The interface was achieved at 94.0±3.0% accuracy, with a time delay of 1.59±1.07 sec from the thought-initiation to the creation of the tail movement. Our results demonstrate the feasibility of a computer-mediated BBI that links central neural functions between two biological entities, which may confer unexplored opportunities in the study of neuroscience with potential implications for therapeutic applications.


title: Effects of Yoga on Mental and Physical Health: A Short Summary of Reviews

abstract: This report summarizes the current evidence on the effects of yoga interventions on various components of mental and physical health, by focussing on the evidence described in review articles. Collectively, these reviews suggest a number of areas where yoga may well be beneficial, but more research is required for virtually all of them to firmly establish such benefits. The heterogeneity among interventions and conditions studied has hampered the use of meta-analysis as an appropriate tool for summarizing the current literature. Nevertheless, there are some meta-analyses which indicate beneficial effects of yoga interventions, and there are several randomized clinical trials (RCT's) of relatively high quality indicating beneficial effects of yoga for pain-associated disability and mental health. Yoga may well be effective as a supportive adjunct to mitigate some medical conditions, but not yet a proven stand-alone, curative treatment. Larger-scale and more rigorous research with higher methodological quality and adequate control interventions is highly encouraged because yoga may have potential to be implemented as a beneficial supportive/adjunct treatment that is relatively cost-effective, may be practiced at least in part as a self-care behavioral treatment, provides a life-long behavioural skill, enhances self-efficacy and self-confidence and is often associated with additional positive side effects.


title: Mind, Brain, and Education in Socioeconomic Context

abstract: Cognitive neuroscience offers an important perspective on the persistence of poverty by illuminating the ways in which the experience of growing up poor reduces people’s ability to escape poverty. Neuroscience research on the effects of early experience on animal brain development suggests how childhood poverty might constrain human brain development. Specifically, the reduced opportunities for stimulating experience and increased stress of poverty would be expected to exert a negative influence on neurocognitive development. Without good neurocognitive development, intellectual and educational attainments are limited, which in turn limits upward socioeconomic mobility. The research summarized in this chapter by Farah and others is aimed at understanding the ways in which childhood poverty, including experiences prior to school entry, affects cognitive development. A better understanding of the ways in which childhood experience and classroom instruction shape brain function will suggest new ways of preventing and remediating some of the disadvantages suffered by poor children.


title: Exploring user sociocentric and egocentric behaviors in online and detected social networks

abstract: Social ties are increasingly constructed and maintained by online social networking tools. These online tools provide an alternative to the more traditional physical meeting approach of establishing and maintaining social relationships. This paper explores the similarities and the differences between the Bluetooth-based detected social network and the Facebook social network, for a particular set of users. Specifically, we explore and compare the egocentric and the sociocentric behaviors of nodes, highlighting the structural similarities between the two types of networks and the differences in how individuals take part in detected social network and online social network. Performing a sociocentric network analysis, our results indicate that betweenness centrality has a relatively high correlation, while the other users' centrality measures in the online social network and the detected social network vary considerably. Moreover, our results shows that the community structures of the two networks differ. The egocentric analysis further confirms that the detected and the online social networks have different structural characteristics.


Automated Data Analysis in Eddy Current Inspection of Steam Generator Tubes


title: Data Mining Instant Messaging Communications to Perform Author Identification for Cybercrime Investigations

abstract: Instant messaging is a form of computer-mediated communication (CMC) with unique characteristics that reflect a realistic presentation of an author’s online stylistic characteristics. Instant messaging communications use virtual identities, which hinder social accountability and facilitate IM-related cybercrimes. Criminals often use virtual identities to hide their true identity and may also supply false information on their virtual identities. This paper presents an IM authorship analysis framework and feature set taxonomy for use in cyber forensics and cybercrime investigations. We explore authorship identification of IM messages to discover the parameters with the highest accuracy for determining the identity of a cyber criminal.


title: Recurrent Highway Networks with Language CNN for Image Captioning.

abstract: In this paper, we propose a Recurrent Highway Network with Language CNN for image caption generation. Our network consists of three sub-networks: the deep Convolutional Neural Network for image representation, the Convolutional Neural Network for language modeling, and the Multimodal Recurrent Highway Network for sequence prediction. Our proposed model can naturally exploit the hierarchical and temporal structure of history words, which are critical for image caption generation. The effectiveness of our model is validated on two datasets MS COCO and Flickr30K. Our extensive experiment results show that our method is competitive with the state-of-the-art methods.


title: Sterile inflammation: sensing and reacting to damage

abstract: Over the past several decades, much has been revealed about the nature of the host innate immune response to microorganisms, with the identification of pattern recognition receptors (PRRs) and pathogen-associated molecular patterns, which are the conserved microbial motifs sensed by these receptors. It is now apparent that these same PRRs can also be activated by non-microbial signals, many of which are considered as damage-associated molecular patterns. The sterile inflammation that ensues either resolves the initial insult or leads to disease. Here, we review the triggers and receptor pathways that result in sterile inflammation and its impact on human health.


title: Attribution Required: Stack Overflow Code Snippets in GitHub Projects

abstract: Stack Overflow (SO) is the largest Q&A website for developers, providing a huge amount of copyable code snippets. Using these snippets raises various maintenance and legal issues. The SO license requires attribution, i.e., referencing the original question or answer, and requires derived work to adopt a compatible license. While there is a heated debate on SO's license model for code snippets and the required attribution, little is known about the extent to which snippets are copied from SO without proper attribution. In this paper, we present the research design and summarized results of an empirical study analyzing attributed and unattributed usages of SO code snippets in GitHub projects. On average, 3.22% of all analyzed repositories and 7.33% of the popular ones contained a reference to SO. Further, we found that developers rather refer to the whole thread on SO than to a specific answer. For Java, at least two thirds of the copied snippets were not attributed.


title: Mutations in a steroid hormone-regulated gene disrupt the metamorphosis of internal tissues in Drosophila: salivary glands, muscle, and gut

abstract: In holometabolous insects, the steroid molting hormone 20-OH-ecdysone (ecdysterone) orchestrates the diverse developmental events of metamorphosis, in large part by regulating gene expression. In Drosophila, the Broad Complex (BR-C) is one of the first loci to be induced by ecdysterone at the end of larval life, and is essential for translating the hormonal signal into the behavioral and anatomical events which herald the onset of metamorphosis. BR-C products are believed to act by binding to and modifying the transcriptional activities of other hormone-sensitive genes. In addition to abnormalities of the epidermis, BR-C mutants dying during metamorphosis manifest a syndrome of multiple internal tissue defects which represent a failure of the larval-to-adult transition. We have reported features of central nervous system metamorphosis requiring BR-C function, notably morphogenetic movements and optic lobe organization. In this paper we describe defective development of salivary glands, flight muscles, and gut in BR-C mutants, including: persistence of larval salivary glands; failure of the adult salivary glands to extend into the thorax; abnormalities of midgut transition and of proventriculus structure and location; and absence of dorsal-ventral indirect flight muscles. Some of these abnormalities represent defects in programmed cell death. Distinct patterns of phenotypes were seen in mutants of each of the three lethal complementation groups comprising the BR-C. The patterns of phenotypes suggest overlapping but distinct functions encoded by this complex locus.


title: Active Appearance Models

abstract: We demonstrate a novel method of interpreting images using an Active Appearance Model (AAM). An AAM contains a statistical model of the shape and grey-level appearance of the object of interest which can generalise to almost any valid example. During a training phase we learn the relationship between model parameter displacements and the residual errors induced between a training image and a synthesised model example. To match to an image we measure the current residuals and use the model to predict changes to the current parameters, leading to a better fit. A good overall match is obtained in a few iterations, even from poor starting estimates. We describe the technique in detail and give results of quantitative performance tests. We anticipate that the AAM algorithm will be an important method for locating deformable objects in many applications.


title: Robustness of Offline Signature Verification Based on Gray Level Features

abstract: Several papers have recently appeared in the literature which propose pseudo-dynamic features for automatic static handwritten signature verification based on the use of gray level values from signature stroke pixels. Good results have been obtained using rotation invariant uniform local binary patterns LBP8,1riu2 plus LBP16,2riu2 and statistical measures from gray level co-occurrence matrices (GLCM) with MCYT and GPDS offline signature corpuses. In these studies the corpuses contain signatures written on a uniform white “nondistorting” background, however the gray level distribution of signature strokes changes when it is written on a complex background, such as a check or an invoice. The aim of this paper is to measure gray level features robustness when it is distorted by a complex background and also to propose more stable features. A set of different checks and invoices with varying background complexity is blended with the MCYT and GPDS signatures. The blending model is based on multiplication. The signature models are trained with genuine signatures on white background and tested with other genuine and forgeries mixed with different backgrounds. Results show that a basic version of local binary patterns (LBP) or local derivative and directional patterns are more robust than rotation invariant uniform LBP or GLCM features to the gray level distortion when using a support vector machine with histogram oriented kernels as a classifier.


title: System design of a low noise, low offset instrumentation amplifier with chopper stabilization

abstract: A low noise, low offset instrumentation amplifier with chopper stabilization is presented. The equivalent input noise and offset is reduced using the technique of chopper stabilization with a chopping frequency of 100 kHz. The channel charge injection is cancelled by the symmetrical circuit topology. With the wide swing bias circuit design, the amplifier achieves a very high power-supply-rejection ratio (PSRR), and it is operated at a single supply voltage of 5 V. The instrumentation amplifier is simulated using TSMC 0.35-mum CMOS technology. It achieves an input-referred noise voltage of 345 nV / radicHz @ about 1 kHz, which is not very good for muV level signal processing because of the large thermal noise parameter of the process model.


title: A Hybrid Approach for the Dynamic Vehicle Routing Problem with Time Windows

abstract: The topic of this paper is a Genetic Algorithm solution to the Vehicle Routing Problem with Time Windows, a variant of one of the most common problems in contemporary operations research. The paper will introduce the problem starting with more general Traveling Salesman and Vehicle Routing problems and present some of the prevailing strategies for solving them, focusing on Genetic Algorithms. At the end, it will summarize the Genetic Algorithm solution proposed by K.Q. Zhu which was used in the programming part of the project.


title: Transcranial Pulsed Ultrasound Stimulates Intact Brain Circuits

abstract: Summary Electromagnetic-based methods of stimulating brain activity require invasive procedures or have other limitations. Deep-brain stimulation requires surgically implanted electrodes. Transcranial magnetic stimulation does not require surgery, but suffers from low spatial resolution. Optogenetic-based approaches have unrivaled spatial precision, but require genetic manipulation. In search of a potential solution to these limitations, we began investigating the influence of transcranial pulsed ultrasound on neuronal activity in the intact mouse brain. In motor cortex, ultrasound-stimulated neuronal activity was sufficient to evoke motor behaviors. Deeper in subcortical circuits, we used targeted transcranial ultrasound to stimulate neuronal activity and synchronous oscillations in the intact hippocampus. We found that ultrasound triggers TTX-sensitive neuronal activity in the absence of a rise in brain temperature (


title: End-To-End Memory Networks

abstract: We introduce a neural network with a recurrent attention model over a possibly large external memory. The architecture is a form of Memory Network (Weston et al., 2015) but unlike the model in that work, it is trained end-to-end, and hence requires significantly less supervision during training, making it more generally applicable in realistic settings. It can also be seen as an extension of RNNsearch to the case where multiple computational steps (hops) are performed per output symbol. The flexibility of the model allows us to apply it to tasks as diverse as (synthetic) question answering and to language modeling. For the former our approach is competitive with Memory Networks, but with less supervision. For the latter, on the Penn TreeBank and Text8 datasets our approach demonstrates comparable performance to RNNs and LSTMs. In both cases we show that the key concept of multiple computational hops yields improved results.


Nine Terminology Extraction Tools : Are they useful for translators ?


title: Key Abstractions for IoT-Oriented Software Engineering

abstract: Despite the progress in Internet of Things (IoT) research, a general software engineering approach for systematic development of IoT systems and applications is still missing. A synthesis of the state of the art in the area can help frame the key abstractions related to such development. Such a framework could be the basis for guidelines for IoT-oriented software engineering.


Autonomous Driving Goes Downtown


title: A semantic-based question answering system for indonesian translation of Quran

abstract: This paper presents a work in developing a semantic-based question answering system (QAS) for Indonesian Translation of Quran (ITQ). This research is motivated by the lacks of previous built QAS that caused by a keyword-based retrieval. Instead of keeping the retrieval method, we shifted to a semantic approach where the retrieval process is done by using a semantic similarity measurement. In doing so, we built an ontology of ITQ to get the concepts as well as verses where they appear in. We applied three factoid question types on the QAS that including Who, Where, and When. Furthermore, a weighted vector for each concept that belongs to respective expected answering type (also called as named entity group) i.e. Person, Location, and Time is generated in order to feed semantic interpreter on user question. From 222 concepts defined from the ontology, we clustered them into 77, 24, and 6 concepts for Person, Location, and Time respectively. Since we found there are some characteristics of texts in ITQ, we developed our own modules to deal with including generate the inverted index and named entity recognition. Answer extraction is conducted by applying some features extraction in order to score the answer candidates. Evaluation of the system is designed by providing two data set of question and answer where the first one is purposed to measure the effectiveness of semantic approach comparing with keyword-based retrieval and the last one aims to know system performance in regard the appearance of concepts in ITQ.


title: Scalable RF Energy Harvesting

abstract: This paper discusses harvesting of low-power density incident plane waves for electronic devices in environments where it is difficult or impossible to change batteries and where the exact locations of the energy sources are not known. As the incident power densities vary over time and space, distributed arrays of antennas with optimized power-management circuits are introduced to increase harvested power and efficiency. Scaling in array size, power, dc load, frequency, and gain is discussed through three example arrays: a dual industrial-scientific-medical band Yagi-Uda array with a low-power startup circuit; a narrowband 1.96-GHz dual-polarized patch rectenna array with a reconfigurable dc output network designed for harvesting base-station power; and a broadband dual-polarized 2-18-GHz array with multi-tone performance. The efficiency of rectification and power management is investigated for incident power densities in the 1-100-μW/cm2 range.


title: Logical reversibility of computation

abstract: The usual general-purpose computing automaton (e.g.. a Turing machine) is logically irreversible- its transition function lacks a single-valued inverse. Here it is shown that such machines may he made logically reversible at every step, while retainillg their simplicity and their ability to do general computations. This result is of great physical interest because it makes plausible the existence of thermodynamically reversible computers which could perform useful computations at useful speed while dissipating considerably less than kT of energy per logical step. In the first stage of its computation the logically reversible automaton parallels the corresponding irreversible automaton, except that it saves all intermediate results, there by avoiding the irreversible operation of erasure. The second stage consists of printing out the desired output. The third stage then reversibly disposes of all the undesired intermediate results by retracing the steps of the first stage in backward order (a process which is only possible because the first stage has been carried out reversibly), there by restoring the machine (except for the now-written output tape) to its original condition. The final machine configuration thus contains the desired output and a reconstructed copy of the input, but no other undesired data. The foregoing results are demonstrated explicitly using a type of three-tape Turing machine. The biosynthesis of messenger RNA is discussed as a physical example of reversible computation.


title: The Use of Short Spreader Grafts in Rhinoplasty for Patients with Thick Nasal Skin

abstract: BACKGROUND ::: Thick nasal skin presents a challenge in rhinoplasty. Conventional spreader grafts in patients with thick nasal skin may result in an undesirable widening of the lower half of the nose where the nasal skin is thick. The use of short spreader grafts was evaluated in patients with thick nasal skin. ::: ::: ::: METHODS ::: For this study, 32 patients underwent closed rhinoplasty in which short spreader grafts were used. The spreader graft was 1 to 2 cm long and extended only to the transition of thin and thick nasal skin. The patients were followed for more than 1 year postoperatively. ::: ::: ::: RESULTS ::: Aesthetic improvement was observed in all the patients. Nasal airway patency was improved or maintained in 94 % of the patients. ::: ::: ::: CONCLUSIONS ::: Short spreader grafts effectively restore or maintain the internal nasal valve area after dorsal hump reduction while allowing for more aggressive narrowing of the lower part of the nose where nasal skin is thick. ::: ::: ::: LEVEL OF EVIDENCE IV ::: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .


title: Subspace clustering with automatic feature grouping

abstract: This paper proposes a subspace clustering algorithm with automatic feature grouping for clustering high-dimensional data. In this algorithm, a new component is introduced into the objective function to capture the feature groups and a new iterative process is defined to optimize the objective function so that the features of high-dimensional data are grouped automatically. Experiments on both synthetic data and real data show that the new algorithm outperforms the FG-k-means algorithm in terms of accuracy and choice of parameters. HighlightsWe study the problem of subspace clustering with feature grouping.We propose a k-means-type algorithm by incorporating feature grouping into the objective function.The algorithm is able to determine feature groups automatically.Experiments on synthetic and real data show that the algorithm performs well.


title: A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities

abstract: In this work we present A-Wristocracy, a novel framework for recognizing very fine-grained and complex inhome activities of human users (particularly elderly people) with wrist-worn device sensing. Our designed A-Wristocracy system improves upon the state-of-the-art works on in-home activity recognition using wearables. These works are mostly able to detect coarse-grained ADLs (Activities of Daily Living) but not large number of fine-grained and complex IADLs (Instrumental Activities of Daily Living). These are also not able to distinguish similar activities but with different context (such as sit on floor vs. sit on bed vs. sit on sofa). Our solution helps accurate detection of in-home ADLs/ IADLs and contextual activities, which are all critically important for remote elderly care in tracking their physical and cognitive capabilities. A-Wristocracy makes it feasible to classify large number of fine-grained and complex activities, through Deep Learning based data analytics and exploiting multi-modal sensing on wrist-worn device. It exploits minimal functionality from very light additional infrastructure (through only few Bluetooth beacons), for coarse level location context. A-Wristocracy preserves direct user privacy by excluding camera/ video imaging on wearable or infrastructure. The classification procedure consists of practical feature set extraction from multi-modal wearable sensor suites, followed by Deep Learning based supervised fine-level classification algorithm. We have collected exhaustive home-based ADLs and IADLs data from multiple users. Our designed classifier is validated to be able to recognize very fine-grained complex 22 daily activities (much larger number than 6–12 activities detected by state-of-the-art works using wearable and no camera/ video) with high average test accuracies of 90% or more for two users in two different home environments.


title: Efficient CP-ABE Attribute/Key Management for IoT Applications

abstract: Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a promising cryptographic mechanism for fine-grained access control to shared data. Attribute/Key management is a keystone issue in CP-APE because of low efficiency of existing attribute revocation techniques. Indeed, existing solutions induce great side effect after each attribute revocation. The side effect induces rekeying and/or re-assignment of attributes to all users. In this paper, we propose a solution which does not require extra entities like proxies to re-encrypt data after every access policy change. Moreover, our solution does not imply latencies following access grants and revocations. We compare our solution with the batch-based CP-ABE attribute management technique and we show that our solution outperforms existing rekeying/revocation techniques in terms of overhead.


title: Automated Tongue Feature Extraction for ZHENG Classification in Traditional Chinese Medicine

abstract: ZHENG, Traditional Chinese Medicine syndrome, is an integral and essential part of Traditional Chinese Medicine theory. It defines the theoretical abstraction of the symptom profiles of individual patients and thus, used as a guideline in disease classification in Chinese medicine. For example, patients suffering from gastritis may be classified as Cold or Hot ZHENG, whereas patients with different diseases may be classified under the same ZHENG. Tongue appearance is a valuable diagnostic tool for determining ZHENG in patients. In this paper, we explore new modalities for the clinical characterization of ZHENG using various supervised machine learning algorithms. We propose a novel-color-space-based feature set, which can be extracted from tongue images of clinical patients to build an automated ZHENG classification system. Given that Chinese medical practitioners usually observe the tongue color and coating to determine a ZHENG type and to diagnose different stomach disorders including gastritis, we propose using machine-learning techniques to establish the relationship between the tongue image features and ZHENG by learning through examples. The experimental results obtained over a set of 263 gastritis patients, most of whom suffering Cold Zheng or Hot ZHENG, and a control group of 48 healthy volunteers demonstrate an excellent performance of our proposed system.


title: Real-Time Human Objects Tracking for Smart Surveillance at the Edge

abstract: Allowing computation to be performed at the edge of a network, edge computing has been recognized as a promising approach to address some challenges in the cloud computing paradigm, particularly to the delay-sensitive and mission-critical applications like real-time surveillance. Prevalence of networked cameras and smart mobile devices enable video analytics at the network edge. However, human objects detection and tracking are still conducted at cloud centers, as real-time, online tracking is computationally expensive. In this paper, we investigated the feasibility of processing surveillance video streaming at the network edge for real-time, uninterrupted moving human objects tracking. Moving human detection based on Histogram of Oriented Gradients (HOG) and linear Support Vector Machine (SVM) is illustrated for features extraction, and an efficient multi-object tracking algorithm based on Kernelized Correlation Filters (KCF) is proposed. Implemented and tested on Raspberry Pi 3, our experimental results are very encouraging, which validated the feasibility of the proposed approach toward a real-time surveillance solution at the edge of networks.


title: BotDet: A System for Real Time Botnet Command and Control Traffic Detection

abstract: Over the past decade, the digitization of services transformed the healthcare sector leading to a sharp rise in cybersecurity threats. Poor cybersecurity in the healthcare sector, coupled with high value of patient records attracted the attention of hackers. Sophisticated advanced persistent threats and malware have significantly contributed to increasing risks to the health sector. Many recent attacks are attributed to the spread of malicious software, e.g., ransomware or bot malware. Machines infected with bot malware can be used as tools for remote attack or even cryptomining. This paper presents a novel approach, called BotDet, for botnet Command and Control (C&C) traffic detection to defend against malware attacks in critical ultrastructure systems. There are two stages in the development of the proposed system: 1) we have developed four detection modules to detect different possible techniques used in botnet C&C communications and 2) we have designed a correlation framework to reduce the rate of false alarms raised by individual detection modules. Evaluation results show that BotDet balances the true positive rate and the false positive rate with 82.3% and 13.6%, respectively. Furthermore, it proves BotDet capability of real time detection.


title: Towards a Robust Interactive and Learning Social Robot

abstract: Pepper is a humanoid robot, specifically designed for social interaction, that has been deployed in a variety of public environments. A programmable version of Pepper is also available, enabling our focused research on perception and behavior robustness and capabilities of an interactive social robot. We address Pepper perception by integrating state-of-the-art vision and speech recognition systems and experimentally analyzing their effectiveness. As we recognize limitations of the individual perceptual modalities, we introduce a multi-modality approach to increase the robustness of human social interaction with the robot. We combine vision, gesture, speech, and input from an onboard tablet, a remote mobile phone, and external microphones. Our approach includes the proactive seeking of input from a different modality, adding robustness to the failures of the separate components. We also introduce a learning algorithm to improve communication capabilities over time, updating speech recognition through social interactions. Finally, we realize the rich robot body-sensory data and introduce both a nearest-neighbor and a deep learning approach to enable Pepper to classify and speak up a variety of its own body motions. We view the contributions of our work to be relevant both to Pepper specifically and to other general social robots.


Towards Opinion Summarization of Customer Reviews


Recovering concept prerequisite relations from university course dependencies


title: OntoSearch: A Full-Text Search Engine for the Semantic Web

abstract: OntoSearch, a full-text search engine that exploits ontological knowledge for document retrieval, is presented in this paper. Different from other ontology based search engines, OntoSearch does not require a user to specify the associated concepts of his/her queries. Domain ontology in OntoSearch is in the form of a semantic network. Given a keyword based query, OntoSearch infers the related concepts through a spreading activation process in the domain ontology. To provide personalized information access, we further develop algorithms to learn and exploit user ontology model based on a customized view of the domain ontology. The proposed system has been applied to the domain of searching scientific publications in the ACM Digital Library. The experimental results support the efficacy of the OntoSearch system by using domain ontology and user ontology for enhanced search performance.


Thiagarajar College of Engineering, Madurai Tamil Nadu, India HJSA: A HIERARCHICAL JOB SCHEDULING ALGORITHM FOR COST OPTIMIZATION IN CLOUD COMPUTING ENVIRONMENT


title: Formal Specification with the Java Modeling Language

abstract: This text is a general, self contained, and tool independent introduction into the Java Modeling Language, JML. It appears in a book about the KeY approach and tool, because JML is the dominating starting point of KeY style Java verification. However, this chapter does not depend on KeY, nor any other specific tool, nor on any specific verification methodology. With this text, the authors aim to provide, for the time being, the definitive, general JML tutorial.


title: Learning to recognize shadows in monochromatic natural images

abstract: This paper addresses the problem of recognizing shadows from monochromatic natural images. Without chromatic information, shadow classification is very challenging because the invariant color cues are unavailable. Natural scenes make this problem even harder because of ambiguity from many near black objects. We propose to use both shadow-variant and shadow-invariant cues from illumination, textural and odd order derivative characteristics. Such features are used to train a classifier from boosting a decision tree and integrated into a Conditional random Field, which can enforce local consistency over pixel labels. The proposed approach is evaluated using both qualitative and quantitative results based on a novel database of hand-labeled shadows. Our results show shadowed areas of an image can be identified using proposed monochromatic cues.


title: A property of neural networks of associative memory with replacing units

abstract: A memory capacity exists for artificial neural networks of associative memory. The addition of new memories beyond the capacity overloads the network system and makes all learned memories irretrievable (catastrophic forgetting) unless there is a provision for forgetting old memories. This article describes a property of associative memory networks in which a number of units are replaced when networks learn. In our network, every time the network learns a new item or pattern, a number of units are erased and the same number of units are added. It is shown that the memory capacity of the network depends on the number of replaced units, and that there exists a optimal number of replaced units in which the memory capacity is maximized. The optimal number of replaced units is small, and seems to be independent of the network size.


title: Towards an intelligent textbook: eye gaze based attention extraction on materials for learning and instruction in physics

abstract: In this paper, we propose the attention extraction method on a textbook of physics by using eye tracking glasses. We prepare a document including text and tasks in physics, and record reading behavior on the document from 6-grade students. The result confirms that students pay attention to a different region depending on situations (using the text as a material to learn the content, or using it as hints to solve tasks) and their comprehension.


title: Long range eye gaze tracking system for a large screen

abstract: Eye gaze tracking system has been widely researched for the replacement of the conventional computer interfaces such as the mouse and keyboard. In this paper, we propose the long range binocular eye gaze tracking system that works from 1.5 m to 2.5 m with allowing a head displacement in depth. The 3D position of the user's eye is obtained from the two wide angle cameras. A high resolution image of the eye is captured using the pan, tilt, and focus controlled narrow angle camera. The angles for maneuvering the pan and tilt motor are calculated by the proposed calibration method based on virtual camera model. The performance of the proposed calibration method is verified in terms of speed and convenience through the experiment. The narrow angle camera keeps tracking the eye while the user moves his head freely. The point-of-gaze (POG) of each eye onto the screen is calculated by using a 2D mapping based gaze estimation technique and the pupil center corneal reflection (PCCR) vector. PCCR vector modification method is applied to overcome the degradation in accuracy with displacements of the head in depth. The final POG is obtained by the average of the two POGs. Experimental results show that the proposed system robustly works for a large screen TV from 1.5 m to 2.5 m distance with displacements of the head in depth (+20 cm) and the average angular error is 0.69°.


title: A hybrid model based on adaptive-network-based fuzzy inference system to forecast Taiwan stock market

abstract: Abstract In recent years, many academy researchers have proposed several forecasting models based on technical analysis to predict models such as Engle (1982) and Cheng, Chen, and Wei (2010) . After reviewing the literature, two major drawbacks are found in past models: (1) the forecasting models based on artificial intelligence algorithms (AI), such as neural networks (NN) and genetic algorithms (GAs), produce complex and unintelligible rules; and (2) statistic forecasting models, such as time series, require some basic assumptions for variables and build forecasting models based on mathematic equations, which are not easily understandable by stock investors. In order to refine these drawbacks of past models, this paper has proposed a model, based on adaptive-network-based fuzzy inference system which uses multi-technical indicators, to predict stock price trends. Three refined processes have proposed in the hybrid model for forecasting: (1) select essential technical indicators from popular indicators by a correlation matrix; (2) use the subtractive clustering method to partition technical indicator value into linguistic values based on an data discretization method; (3) employ a fuzzy inference system (FIS) to extract rules of linguistic terms from the dataset of the technical indicators, and optimize the FIS parameters based on an adaptive network to produce forecasts. A six-year period of the TAIEX is employed as experimental database to evaluate the proposed model with a performance indicator, root mean squared error (RMSE). The experimental results have shown that the proposed model is superior to two listing models (Chen’s and Yu’s models).


FLOW : The Psychology of Optimal Experience


title: Semantic Approach for the Building of User Profile for Recommender System

abstract: The major problem in the use of the Web is that of searching for relevant information that meets the expectations of a user. This problem increases every day and especially with the emergence of web 2.0 or social web. Our paper, therefore, ignores the disadvantage of social web and operates it to rich user profile.


title: A three-layer architecture for e-contract enforcement in an e-service environment

abstract: In an e-service environment, contracts are important for attaining business process interoperability and enforcing their proper enactment. An e-contract is the computerized facilitation or automation of a contract in a cross-organizational business process. We find that e-contract enforcement can be divided into multiple layers and perspectives, which has not been adequately addressed in the literature. This problem is challenging as it involves monitoring the enactment of business processes in counter parties outside an organization's boundary. This paper presents an architecture for e-contract enforcement with three layers, viz., document layer, business layer, and implementation layer. In the document layer, contracts are composed of different types of clauses. In the business layer, e-contract enforcement activities are defined through the realization of contract clauses as business rules in event-condition-action (ECA) form. In the implementation layer, cross-organizational e-contract enforcement interfaces are implemented with contemporary Enterprise Java Bean and Web services. We present a methodology for the engineering of e-contracts enforcement from a high-level document-view down to the implementation layer based on this architecture, using a supply-chain example. As a result, e-contracts can be seamlessly defined and enforced. Conceptual models of various layers are given in the Unified Modeling Language (UML).


title: Effects of disfluencies, predictability, and utterance position on word form variation in English conversation

abstract: Function words, especially frequently occurring ones such as (the, that, and, and of), vary widely in pronunciation. Understanding this variation is essential both for cognitive modeling of lexical production and for computer speech recognition and synthesis. This study investigates which factors affect the forms of function words, especially whether they have a fuller pronunciation (e.g., thi, thaet, aend, inverted-v v) or a more reduced or lenited pronunciation (e.g., thax, thixt, n, ax). It is based on over 8000 occurrences of the ten most frequent English function words in a 4-h sample from conversations from the Switchboard corpus. Ordinary linear and logistic regression models were used to examine variation in the length of the words, in the form of their vowel (basic, full, or reduced), and whether final obstruents were present or not. For all these measures, after controlling for segmental context, rate of speech, and other important factors, there are strong independent effects that made high-frequency monosyllabic function words more likely to be longer or have a fuller form (1) when neighboring disfluencies (such as filled pauses uh and um) indicate that the speaker was encountering problems in planning the utterance; (2) when the word is unexpected, i.e., less predictable in context; (3) when the word is either utterance initial or utterance final. Looking at the phenomenon in a different way, frequent function words are more likely to be shorter and to have less-full forms in fluent speech, in predictable positions or multiword collocations, and utterance internally. Also considered are other factors such as sex (women are more likely to use fuller forms, even after controlling for rate of speech, for example), and some of the differences among the ten function words in their response to the factors.


title: Energy Efficient Resource Optimization for a Downlink NOMA Heterogeneous Small-Cell Network

abstract: Non-orthogonal multiple access (NOMA) is considered to be a key technology for fifth-generation mobile communication networks due to its superior spectral efficiency. In this paper, the problem of energy efficient subchannel and power allocation is investigated for a downlink NOMA heterogeneous network. The energy efficient resource allocation problem is first formulated as a mixed integer nonconvex optimization problem. By exploiting convex relaxation and dual decomposition techniques, a closed-form expression is derived via a Lagrangian approach. Simulation results show that the proposed algorithm can converge within ten iterations and obtain high system energy efficiency.


title: Emergence of Communication in an Interactive World with Consistent Speakers

abstract: Training agents to communicate with one another given task-based supervision only has attracted considerable attention recently, due to the growing interest in developing models for human-agent interaction. Prior work on the topic focused on simple environments, where training using policy gradient was feasible despite the non-stationarity of the agents during training. In this paper, we present a more challenging environment for testing the emergence of communication from raw pixels, where training using policy gradient fails. We propose a new model and training algorithm, that utilizes the structure of a learned representation space to produce more consistent speakers at the initial phases of training, which stabilizes learning. We empirically show that our algorithm substantially improves performance compared to policy gradient. We also propose a new alignment-based metric for measuring context-independence in emerged communication and find our method increases context-independence compared to policy gradient and other competitive baselines.


title: Fructosyltransferase activity and fructan accumulation during development in wheat exposed to terminal drought

abstract: Fructans act as storage carbohydrates in wheat (Triticum aestivum L.) stems, and published data indicate that these can account for up to 70% or more of grain dry matter under conditions of drought. The activity of enzymes involved in fructan synthesis (fructosyltransferases) in wheat was measured during development of three high-yielding wheat cultivars (cvv. Kauz, Westonia and Attila A) exposed to rainfed conditions, and one cultivar (cv. Westonia) exposed to irrigated conditions. Fructan concentration was on average 2.5-fold higher in the stems of rainfed wheat compared with irrigated samples, but average fructosyltransferase activity was similar in both. There was a weak positive correlation (r 2 =0.35-0.38) between fructan concentration and fructosyltransferase activity across development in the stems of both rainfed and irrigated wheat. Soon after anthesis, 31% of accumulated fructans in rainfed Westonia stems were located in the penultimate internode, although fructosyltransferase activity was five times higher in the bottom two internodes than the penultimate internode.


title: ENHANCED RSSI-BASED HIGH ACCURACY REAL-TIME USER LOCATION TRACKING SYSTEM FOR INDOOR AND OUTDOOR ENVIRONMENTS

abstract: Existing researches on location tracking focus either entirely on indoor or entirely on outdoor by using different devices and techniques. Several solutions have been proposed to adopt a single location sensing technology that fits in both situations. This paper aims to track a user position in both indoor and outdoor environments by using a single wireless device with minimal tracking error. RSSI (Received Signal Strength Indication) technique together with enhancement algorithms is proposed to cater this solution. The proposed RSSI-based tracking technique is divided into two main phases, namely the calibration of RSSI coefficients (deterministic phase) and the distance along with position estimation of user location by iterative trilateration (probabilistic phase). A low complexity RSSI smoothing algorithm is implemented to minimize the dynamic fluctuation of radio signal received from each reference node when the target node is moving. Experiment measurements are carried out to analyze the sensitivity of RSSI. The results reveal the feasibility of these algorithms in designing a more accurate real-time position monitoring system.


title: Optogenetics, Tools and Applications in Neurobiology

abstract: Comprehension of the brain function can be helpful for therapy of neurodegenerative diseases. The brain consists of various types of neuron sets, which organize in three-dimensional complex networks and form neural circuits underlying different behaviors. The circuits act based on the patterns that encode the brain functions. Recognition of the neural patterns requires methods to manipulate the neurons. Electrical stimulation may be the most common method. However, it has significant drawbacks including failure to identify specific neurons in experiments. As an alternative, optical stimulation is a new method that acts in combination with genetic approaches. The novel, optogenetic technology makes it feasible to manipulate either the specific cell types or the neural circuits. This is associated with minimum tissue damages as well as side effects. In this study, a new technology has been introduced, and then its optical and genetical tools have been investigated.


title: Development of maturity models: A systematic literature review

abstract: Background: Maturity models have been developed to assist organizations to enhance software quality, and the software engineering community has proposed numerous types of these, most of them based on CMM, ISO/IEC 15504 and CMMI-DEV. The great proliferation of maturity models leads to a need for systematic approaches to build them. Aim: To discover and analyze the existing methods and recommended practices for developing maturity models. Method: A systematic literature review (SLR) was conducted. Results: 1500 papers were returned as a result of the searches, from which 23 were chosen as candidates, and 7 of these, published between 2005 and 2010, were eventually selected as primary studies. Conclusions: The proposal of methods for maturity models development is an emerging topic in literature. Most published maturity models are based on practices and success factors from projects that showed good results in an organization or industry, but which lack a sound theoretical basis and methodology. The proposed methods highlight as main activities: to establish goals for maturity model development, to design model architecture, to set out capability levels and dimensions, to and pilot testing. However, the creation of an assessment questionnaire and specific activities for maintenance are poorly supported. The SLR results have served to propose a method for developing maturity models that is focused on product quality characteristics.


title: Event Studies in Information Systems Research: A Review

abstract: Event studies, which look at stock market reactions to assess corporate business events, represent a relatively new research approach in the information systems field. In this paper we present a systematic review of thirty event studies related to information technology. After a brief discussion of each of the papers included in our review, we call attention to several limitations of the published studies and propose possible future research avenues.


title: A Systematic Review of Topical Finasteride in the Treatment of Androgenetic Alopecia in Men and Women.

abstract: INTRODUCTION ::: Currently, only topical minoxidil (MNX) and oral finasteride (FNS) are approved by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) for the treatment of androgenetic alopecia. Although FNS is efficacious for hair regrowth, its systemic use is associated with side effects limiting long-term utilization. Exploring topical FNS as an alternative treatment regimen may prove promising. ::: ::: ::: METHODS ::: A search was conducted to identify studies regarding human in vivo topical FNS treatment efficacy including clinically relevant case reports, randomized controlled trials (RCTs), and prospective studies. ::: ::: ::: RESULTS ::: Seven articles were included in this systematic review. In all studies, there was significant decrease in the rate of hair loss, increase in total and terminal hair counts, and positive hair growth assessment with topical FNS. Both scalp and plasma DHT significantly decreased with application of topical FNS; no changes in serum testosterone were noted. ::: ::: ::: CONCLUSION ::: Preliminary results on the use of topical FNS are limited, but safe and promising. Continued research into drug-delivery, ideal topical concentration and application frequency, side effects, and use for other alopecias will help to elucidate the full extent of topical FNS' use. J Drugs Dermatol. 2018;17(4):457-463..


title: Information systems interoperability: What lies beneath?

abstract: Interoperability is the most critical issue facing businesses that need to access information from multiple information systems. Our objective in this research is to develop a comprehensive framework and methodology to facilitate semantic interoperability among distributed and heterogeneous information systems. A comprehensive framework for managing various semantic conflicts is proposed. Our proposed framework provides a unified view of the underlying representational and reasoning formalism for the semantic mediation process. This framework is then used as a basis for automating the detection and resolution of semantic conflicts among heterogeneous information sources. We define several types of semantic mediators to achieve semantic interoperability. A domain-independent ontology is used to capture various semantic conflicts. A mediation-based query processing technique is developed to provide uniform and integrated access to the multiple heterogeneous databases. A usable prototype is implemented as a proof-of-concept for this work. Finally, the usefulness of our approach is evaluated using three cases in different application domains. Various heterogeneous datasets are used during the evaluation phase. The results of the evaluation suggest that correct identification and construction of both schema and ontology-schema mapping knowledge play very important roles in achieving interoperability at both the data and schema levels.


title: Beyond Just Text: Semantic Emoji Similarity Modeling to Support Expressive Communication 👫📲😃

abstract: Emoji, a set of pictographic Unicode characters, have seen strong uptake over the last couple of years. All common mobile platforms and many desktop systems now support emoji entry, and users have embraced their use. Yet, we currently know very little about what makes for good emoji entry. While soft keyboards for text entry are well optimized, based on language and touch models, no such information exists to guide the design of emoji keyboards. In this article, we investigate of the problem of emoji entry, starting with a study of the current state of the emoji keyboard implementation in Android. To enable moving forward to novel emoji keyboard designs, we then explore a model for emoji similarity that is able to inform such designs. This semantic model is based on data from 21 million collected tweets containing emoji. We compare this model against a solely description-based model of emoji in a crowdsourced study. Our model shows good perfor mance in capturing detailed relationships between emoji.


title: Detecting tunneled video streams using traffic analysis

abstract: Detecting access to video streaming websites is the first step for an organization to regulate unwanted accesses to such sites by its employees. Adversaries often adopt circumvention techniques using proxy servers and Virtual Private Networks (VPNs) in order to avoid such detection. This paper presents a traffic analysis based technique that can detect such tunneled traffic at an organization's firewall using signatures found in traffic amount and timing in targeted video traffic. We present the detection results on the traffic data for several popular video streaming sites. Additional results are presented to validate the detection framework when detecting access to video streaming sites from a wide range of clients with a classifier trained with traffic data collected from a limited number of clients. The results show that the classifier works in both cases. It detects same-client traffic with high true positive rate, while it detects traffic from an unknown client with lower true positive rate but very low false positive rate. The results validate the effectiveness of traffic analysis based detection of video streaming sites.


title: ALMA: a layered model of affect

abstract: In this paper we introduce ALMA - A Layered Model of Affect. It integrates three major affective characteristics: emotions, moods and personality that cover short, medium, and long term affect. The use of this model consists of two phases: In the preparation phase appraisal rules and personality profiles for characters must be specified with the help of AffectML - our XML based affect modeling language. In the runtime phase, the specified appraisal rules are used to compute real-time emotions and moods as results of a subjective appraisal of relevant input. The computed affective characteristics are represented in AffectML and can be processed by sub-sequent modules that control the cognitive processes and physical behavior of embodied conversational characters. ALMA is part of the VirtualHuman project which develops interactive virtual characters that serve as dialog partners with human-like conversational skills. ALMA provides our virtual humans with a personality profile and with real-time emotions and moods. These are used by the multimodal behavior generation module to enrich the lifelike and believable qualities.


title: Overview and management of fillers complications

abstract: Filler injections are among the most popular cosmetic procedures performed worldwide. Although fillers have a safety profile, there has been a rise in litigation as a result of treatments in the USA. In the Brazilian scenario, the number of non-surgical procedures has increased in the past years, mainly due to the increase of filler options available in the Brazilian market, as well as in the type of professionals allowed to perform injectable procedures. Therefore we sought to review the related literature regarding semi-permanent and temporary fillers adverse effects and outline a practical guide for complications avoidance, diagnosis and management


title: A parallel workload model and its implications for processor allocation

abstract: We develop a workload model based on the observed behavior of parallel computers at the San Diego Supercomputer Center and the Cornell Theory Center. This model gives us insight into the performance of strategies for scheduling moldable jobs on space-sharing parallel computers. We find that Adaptive Static Partitioning (ASP), which has been reported to work well for other workloads, does not perform as well as strategies that adapt better to system load. The best of the strategies we consider is one that explicitly reduces allocations when load is high (a variation of Sevcik's (1989) A+ strategy).