title: Biometric cryptosystems: issues and challenges

abstract: In traditional cryptosystems, user authentication is based on possession of secret keys; the method falls apart if the keys are not kept secret (i.e., shared with non-legitimate users). Further, keys can be forgotten, lost, or stolen and, thus, cannot provide non-repudiation. Current authentication systems based on physiological and behavioral characteristics of persons (known as biometrics), such as fingerprints, inherently provide solutions to many of these problems and may replace the authentication component of traditional cryptosystems. We present various methods that monolithically bind a cryptographic key with the biometric template of a user stored in the database in such a way that the key cannot be revealed without a successful biometric authentication. We assess the performance of one of these biometric key binding/generation algorithms using the fingerprint biometric. We illustrate the challenges involved in biometric key generation primarily due to drastic acquisition variations in the representation of a biometric identifier and the imperfect nature of biometric feature extraction and matching algorithms. We elaborate on the suitability of these algorithms for digital rights management systems.


title: Multi-objective optimization in rule-based design space exploration

abstract: Design space exploration (DSE) aims to find optimal design candidates of a domain with respect to different objectives where design candidates are constrained by complex structural and numerical restrictions. Rule-based DSE aims to find such candidates that are reachable from an initial model by applying a sequence of exploration rules. Solving a rule-based DSE problem is a difficult challenge due to the inherently dynamic nature of the problem. In the current paper, we propose to integrate multi-objective optimization techniques by using Non-dominated Sorting Genetic Algorithms (NSGA) to drive rule-based design space exploration. For this purpose, finite populations of the most promising design candidates are maintained wrt. different optimization criteria. In our context, individuals of a generation are defined as a sequence of rule applications leading from an initial model to a candidate model. Populations evolve by mutation and crossover operations which manipulate (change, extend or combine) rule execution sequences to yield new individuals. Our multi-objective optimization approach for rule-based DSE is domain independent and it is automated by tooling built on the Eclipse framework. The main added value is to seamlessly lift multi-objective optimization techniques to the exploration process preserving both domain independence and a high-level of abstraction. Design candidates will still be represented as models and the evolution of these models as rule execution sequences. Constraints are captured by model queries while objectives can be derived both from models or rule applications.


title: Montague Meets Markov: Deep Semantics with Probabilistic Logical Form

abstract: We combine logical and distributional representations of natural language meaning by transforming distributional similarity judgments into weighted inference rules using Markov Logic Networks (MLNs). We show that this framework supports both judging sentence similarity and recognizing textual entailment by appropriately adapting the MLN implementation of logical connectives. We also show that distributional phrase similarity, used as textual inference rules created on the fly, improves its performance.


title: Stochastic gradient adaptation under general error criteria

abstract: Examines a family of adaptive filter algorithms of the form W/sub k+1/=W/sub k/+/spl mu/f(d/sub k/-W/sub k//sup t/X/sub k/)X/sub k/ in which f(/spl middot/) is a memoryless odd-symmetric nonlinearity acting upon the error. Such algorithms are a generalization of the least-mean-square (LMS) adaptive filtering algorithm for even-symmetric error criteria. For this algorithm family, the authors derive general expressions for the mean and mean-square convergence of the filter coefficients For both arbitrary stochastic input data and Gaussian input data. They then provide methods for optimizing the nonlinearity to minimize the algorithm misadjustment for a given convergence rate. Using the calculus of variations, it is shown that the optimum nonlinearity to minimize misadjustment near convergence under slow adaptation conditions is independent of the statistics of the input data and can be expressed as -p'(x)/p(x), where p(x) is the probability density function of the uncorrelated plant noise. For faster adaptation under the white Gaussian input and noise assumptions, the nonlinearity is shown to be x/{1+/spl mu//spl lambda/x/sup 2///spl sigma//sub k//sup 2/}, where /spl lambda/ is the input signal power and /spl sigma//sub k//sup 2/ is the conditional error power. Thus, the optimum stochastic gradient error criterion for Gaussian noise is not mean-square. It is shown that the equations governing the convergence of the nonlinear algorithm are exactly those which describe the behavior of the optimum scalar data nonlinear adaptive algorithm for white Gaussian input. Simulations verify the results for a host of noise interferences and indicate the improvement using non-mean-square error criteria. >


title: Making business sense of the Internet.

abstract: For managers in large, well-established businesses, the Internet is a tough nut to crack. It is very simple to set up a Web presence and very difficult to create a Web-based business model. Established businesses that over decades have carefully built brands and physical distribution relationships risk damaging all they have created when they pursue commerce through the Net. Still, managers can't avoid the impact of electronic commerce on their businesses. They need to understand the opportunities available to them and recognize how their companies may be vulnerable if rivals seize those opportunities first. Broadly speaking, the Internet presents four distinct types of opportunities. First, it links companies directly to customers, suppliers, and other interested parties. Second, it lets companies bypass other players in an industry's value chain. Third, it is a tool for developing and delivering new products and services to new customers. Fourth, it will enable certain companies to dominate the electronic channel of an entire industry or segment, control access to customers, and set business rules. As he elaborates on these four points, the author gives established companies a systematic way to sort through the risks and rewards of doing business in cyberspace.


title: Automatic 3-Language Cross-Language Information Retrieval with Latent Semantic Indexing

abstract: Our retrieval method is conpletely automatic and we are able to use it to create 3-way English-French-German information retrieval system. No query tranlation is required. Queries in a language can retrieve documents in others languages (as well as the original language)


title: Temperament and personality in dogs (Canis familiaris): A review and evaluation of past research

abstract: Abstract Spurred by theoretical and applied goals, the study of dog temperament has begun to garner considerable research attention. The researchers studying temperament in dogs come from varied backgrounds, bringing with them diverse perspectives, and publishing in a broad range of journals. This paper reviews and evaluates the disparate work on canine temperament. We begin by summarizing general trends in research on canine temperament. To identify specific patterns, we propose several frameworks for organizing the literature based on the methods of assessment, the breeds examined, the purpose of the studies, the age at which the dogs were tested, the breeding and rearing environment, and the sexual status of the dogs. Next, an expert-sorting study shows that the enormous number of temperament traits examined can be usefully classified into seven broad dimensions. Meta-analyses of the findings pertaining to inter-rater agreement, test–retest reliability, internal consistency, and convergent validity generally support the reliability and validity of canine temperament tests but more studies are needed to support these preliminary findings. Studies examining discriminant validity are needed, as preliminary findings on discriminant validity are mixed. We close by drawing 18 conclusions about the field, identifying the major theoretical and empirical questions that remain to be addressed.


Dynamic Bayesian Network Modeling of Hippocampal Subfields Connectivity with 7T fMRI: A Case Study


title: Nodular fasciitis of the breast simulating breast cancer on imaging

abstract: SUMMARY ::: ::: Nodular fasciitis is a rare benign soft tissue tumour of the breast that clinically and radiologically can mimic invasive duct carcinoma. The clinical, radiological and pathological findings of nodular fasciitis of the breast in a 38-year-old woman, who presented with a palpable lesion in the upper inner aspect of the left breast, are described. The tumour is characterized histologically by a stellate spindle cell tumour with a focal myxoid background containing scattered inflammatory cells and microhaemorrhages. Pathological assessment of the lesion is essential in making the diagnosis.


title: Placeless Place-Recognition

abstract: Place recognition is a core competency for any visual simultaneous localization and mapping system. Identifying previously visited places enables the creation of globally accurate maps, robust relocalization, and multi-user mapping. To match one place to another, most state-of-the-art approaches must decide a priori what constitutes a place, often in terms of how many consecutive views should overlap, or how many consecutive images should be considered together. Unfortunately, depending on thresholds such as these, limits their generality to different types of scenes. In this paper, we present a placeless place recognition algorithm using a novel vote-density estimation technique that avoids heuristically discretizing the space. Instead, our approach considers place recognition as a problem of continuous matching between image streams, automatically discovering regions of high vote density that represent overlapping trajectory segments. The resulting algorithm has a single free parameter and all remaining thresholds are set automatically using well-studied statistical tests. We demonstrate the efficiency and accuracy of our methodology on three outdoor sequences: A comprehensive evaluation against ground-truth from publicly available datasets shows that our approach outperforms several state-of-the-art algorithms for place recognition.


title: Inferring weak references for fixing Java memory leaks

abstract: Lots of efforts have yet been devoted to the detection of memory leaks. However, very few work concerns on the problem of memory leak fixing. In this paper, we propose a dynamic approach for the weak reference based memory leak fixing. The approach can automatically infer the weakenable references in a program, rank them and report them to the user. The user can then weaken references according to the reports to fix the memory leaks.


title: Detecting"Smart"Spammers On Social Network: A Topic Model Approach

abstract: Spammer detection on social network is a challenging problem. The rigid anti-spam rules have resulted in emergence of "smart" spammers. They resemble legitimate users who are difficult to identify. In this paper, we present a novel spammer classification approach based on Latent Dirichlet Allocation (LDA), a topic model. Our approach extracts both the local and the global information of topic distribution patterns, which capture the essence of spamming. Tested on one benchmark dataset and one self-collected dataset, our proposed method outperforms other stateof-the-art methods in terms of averaged F1score.


title: Through-Wall Imaging of Moving Targets Using UWB Random Noise Radar

abstract: For a through-wall ultrawideband (UWB) random noise radar using array antennas, subtraction of successive frames of the cross-correlation signals between each received element signal and the transmitted signal is able to isolate moving targets in heavy clutter. Images of moving targets are subsequently obtained using the back projection (BP) algorithm. This technique is not constrained to noise radar, but can also be applied to other kinds of radar systems. Different models based on the finite-difference time-domain (FDTD) algorithm are set up to simulate different through-wall scenarios of moving targets. Simulation results show that the heavy clutter is suppressed, and the signal-to-clutter ratio (SCR) is greatly enhanced using this approach. Multiple moving targets can be detected, localized, and tracked for any random movement.


title: A VLSI architecture for high-performance, low-cost, on-chip learning

abstract: The motivation for the X1 architecture described was to develop inexpensive commercial hardware suitable for solving large, real-world problems. Such an architecture must be systems oriented and flexible enough to execute any neural network algorithm and work cooperatively with existing hardware and software. The early application of neural networks must proceed in conjunction with existing technologies, both hardware and software. Using state-of-the-art technology and innovative architectural techniques, the author's architecture approaches the speed and cost of analog systems while retaining much of the flexibility of large, general-purpose parallel machines. The author has aimed at a particular set of applications and has made cost-performance tradeoffs accordingly. The goal is an architecture that could be considered a general-purpose microprocessor for neurocomputing


title: Automatic classification and prediction models for early Parkinson's disease diagnosis from SPECT imaging

abstract: Propose methods for very accurate classification of early PD using only 4 features.Used public database which is large and diverse making the developed models robust.First study to develop accurate prognostic model based on SBR features for early PD. Early and accurate diagnosis of Parkinson's disease (PD) is important for early management, proper prognostication and for initiating neuroprotective therapies once they become available. Recent neuroimaging techniques such as dopaminergic imaging using single photon emission computed tomography (SPECT) with 123I-Ioflupane (DaTSCAN) have shown to detect even early stages of the disease. In this paper, we use the striatal binding ratio (SBR) values that are calculated from the 123I-Ioflupane SPECT scans (as obtained from the Parkinson's progression markers initiative (PPMI) database) for developing automatic classification and prediction/prognostic models for early PD. We used support vector machine (SVM) and logistic regression in the model building process. We observe that the SVM classifier with RBF kernel produced a high accuracy of more than 96% in classifying subjects into early PD and healthy normal; and the logistic model for estimating the risk of PD also produced high degree of fitting with statistical significance indicating its usefulness in PD risk estimation. Hence, we infer that such models have the potential to aid the clinicians in the PD diagnostic process.


title: Neural Network Matrix Factorization

abstract: Data often comes in the form of an array or matrix. Matrix factorization techniques attempt to recover missing or corrupted entries by assuming that the matrix can be written as the product of two low-rank matrices. In other words, matrix factorization approximates the entries of the matrix by a simple, fixed function---namely, the inner product---acting on the latent feature vectors for the corresponding row and column. Here we consider replacing the inner product by an arbitrary function that we learn from the data at the same time as we learn the latent feature vectors. In particular, we replace the inner product by a multi-layer feed-forward neural network, and learn by alternating between optimizing the network for fixed latent features, and optimizing the latent features for a fixed network. The resulting approach---which we call neural network matrix factorization or NNMF, for short---dominates standard low-rank techniques on a suite of benchmark but is dominated by some recent proposals that take advantage of the graph features. Given the vast range of architectures, activation functions, regularizers, and optimization techniques that could be used within the NNMF framework, it seems likely the true potential of the approach has yet to be reached.


omparative analysis of strategies for feature extraction and lassification in SSVEP BCIs


title: Automatic Privacy Prediction to Accelerate Social Image Sharing

abstract: The manual process for privacy setting could be very time-consuming and challenging for common users. By assuming that there are hidden correlations between the visual properties of images (i.e., visual features) or object classes and the privacy settings for image sharing, an effective algorithm is developed in this paper to achieve automatic prediction of image privacy, so that the best-matching privacy setting can be recommended automatically for each single image being shared. Our algorithm for automatic image privacy prediction contains two approaches: (a) feature-based approach by learning more representative deep features and discriminative classifier for assigning each single image being shared into one of two categories: private vs. public, (b) object-based approach by detecting large numbers of privacy-sensitive object classes and events automatically and leveraging them to achieve more discriminative characterization of image privacy, so that we can support more explainable solution for automatic image privacy prediction. We have also conducted extensive experimental studies on large-scale social images, which have demonstrated both efficiency and effectiveness of our proposed algorithm.


title: Fingerprint Recognition with Artificial Neural Networks: Application to E-Learning

abstract: Fingerprint recognition is a mature biometric technique for identification or authentication application. In this work, we describe a method based on the use of neural network to authenticate people who want to accede to an automated fingerprint system for E-learning. The idea is to apply back propagation algorithm on a multilayer perceptron during the training stage. One of the advantages of this technique is the use of a hidden layer which allows the network to make comparison by calculating probabilities on template which are invariant to translation and rotation. Results come both from the NIST special database 4 and a local database, and show that a proposed method gives good results in some cases.


title: Improving the Quality of Linked Data Using Statistical Distributions

abstract: Linked Data on the Web is either created from structured data sources (such as relational databases), from semi-structured sources (such as Wikipedia), or from unstructured sources (such as text). In the latter two cases, the generated Linked Data will likely be noisy and incomplete. In this paper, we present two algorithms that exploit statistical distributions of properties and types for enhancing the quality of incomplete and noisy Linked Data sets: SDType adds missing type statements, and SDValidate identifies faulty statements. Neither of the algorithms uses external knowledge, i.e., they operate only on the data itself. We evaluate the algorithms on the DBpedia and NELL knowledge bases, showing that they are both accurate as well as scalable. Both algorithms have been used for building the DBpedia 3.9 release: With SDType, 3.4 million missing type statements have been added, while using SDValidate, 13,000 erroneous RDF statements have been removed from the knowledge base.


title: Cooperative Manipulation and Transportation with Aerial Robots

abstract: In this paper we consider the problem of controlling multiple robots manipulating and transporting a payload in three dimensions via cables. We develop robot configurations that ensure static equilibrium of the payload at a desired pose while respecting constraints on the tension and provide analysis of payload stability for these configurations. We demonstrate our methods on a team of aerial robots via simulation and experimentation.


title: An experience in blending the traditional and Agile methodologies to assist in a small software development project

abstract: Agile methodology has been adopted for many software development projects, due to its ability to deal with changing product requirements, while Traditional methods are better suited to dealing with projects that have clearly defined requirements. Because of the differences in the two methodologies and their diverse approaches to solving development problems, it is necessary to understand their individual approaches in order for the benefits to be compared and synthesized. This study is necessary to understand the differences and diversities of the two methodologies using the checklist table to select and adopt the appropriate methodology for particular development projects. Results of this study not only provide positive answers but also offer suggestions for better integration.


title: A broadband series power divider using zero-degree metamaterial phase-shifting lines

abstract: A metamaterial 1:4 series power divider that provides equal power split to all four output ports over a large bandwidth is presented, which can be extended to an arbitrary number of output ports. The divider comprises four nonradiating metamaterial lines in series, incurring a zero insertion phase over a large bandwidth, while simultaneously maintaining a compact length of /spl lambda//sub 0//8. Compared to a series power divider employing conventional one-wavelength long meandered transmission lines to provide in-phase signals at the output ports, the metamaterial divider provides a 165% increase in the input return-loss bandwidth and a 155% and 154% increase in the through-power bandwidth to ports 3 and 4, respectively. In addition, the metamaterial divider is significantly more compact, occupying only 2.6% of the area that the transmission line divider occupies. The metamaterial and transmission line dividers exhibit comparable insertion losses.


title: Exploring individuals' subjective well-being and loyalty towards social network sites from the perspective of network externalities: The Facebook case

abstract: Abstract This study aims to investigate users’ subjective well-being and loyalty towards social network sites (SNSs). Despite the growing role of network externalities in SNS continuance decisions, the SNS usage literature has paid scant attention to the relationship between network externalities, SNS identification, and users’ subjective well-being. In this study, we identify four components of network externalities: perceived network size, external prestige, compatibility, and complementarity. In the research model, both network size and external prestige are hypothesized positively to affect SNS identification. Perceived compatibility and perceived complementarity are hypothesized positively to affect user satisfaction. Satisfaction and SNS identification are hypothesized positively to affect user subjective well-being and loyalty towards the SNS. Users’ subjective well-being is hypothesized positively to affect their loyalty towards the SNS. Data collected from 615 valid users of Facebook provide strong support for most of these hypotheses. The findings indicate that perceived network size negatively affects users’ SNS identifications. Other components of network externalities have positive effects on SNS identification and satisfaction, which in turn have positive effects on users’ subjective well-being and loyalty towards SNS. Implications for theory and practice and suggestions for future research are also discussed.


title: On a Formal Model of Safe and Scalable Self-driving Cars

abstract: In recent years, car makers and tech companies have been racing towards self driving cars. It seems that the main parameter in this race is who will have the first car on the road. The goal of this paper is to add to the equation two additional crucial parameters. The first is standardization of safety assurance --- what are the minimal requirements that every self-driving car must satisfy, and how can we verify these requirements. The second parameter is scalability --- engineering solutions that lead to unleashed costs will not scale to millions of cars, which will push interest in this field into a niche academic corner, and drive the entire field into a"winter of autonomous driving". In the first part of the paper we propose a white-box, interpretable, mathematical model for safety assurance. In the second part we describe a design of a system that adheres to our safety assurance requirements and is scalable to millions of cars.


title: A Virtual Assembly Design Environment

abstract: The Virtual Assembly Design Environment (VADE) is a virtual reality (VR) based engineering application which allows engineers to evaluate, analyze, and plan the assembly of mechanical systems. This system focuses on utilizing an immersive virtual environment tightly coupled with commercial computer aided design (CAD) systems. Salient features of VADE include: data integration (two-way) with a parametric CAD system; realistic interaction of the user with parts in the virtual environment; creation of valued design information in the virtual environment; reverse data transfer of design information back to the CAD system; significant interactivity in the virtual environment; collision detection; and physically-based modeling. This paper describes the functionality and applications of VADE. A discussion of the limitations of virtual assembly and a comparison with automated assembly planning systems are presented. Experiments conducted using real-world engineering models are also described.


title: LSISOM — A Latent Semantic Indexing Approach to Self-Organizing Maps of Document Collections

abstract: The Self Organizing Map (SOM) algorithm has been utilized, with much success, in a variety of applications for the automatic organization of full-text document collections. A great advantage of the SOM method is that document collections can be ordered in such a way so that documents with similar content are positioned at nearby locations of the 2-dimensional SOM lattice. The resulting ordered map thus presents a general view of the document collection which helps the exploration of information contained in the whole document space. The most notable example of such an application is the WEBSOM method where the document collection is ordered onto a map by utilizing word category histograms for representing the documents data vectors. In this paper, we introduce the LSISOM method which resembles WEBSOM in the sense that the document maps are generated from word category histograms rather than simple histograms of the words. However, a major difference between the two methods is that in WEBSOM the word category histograms are formed using statistical information of short word contexts whereas in LSISOM these histograms are obtained from the SOM clustering of the Latent Semantic Indexing representation of document terms.


title: Detecting offensive tweets via topical feature discovery over a large scale twitter corpus

abstract: In this paper, we propose a novel semi-supervised approach for detecting profanity-related offensive content in Twitter. Our approach exploits linguistic regularities in profane language via statistical topic modeling on a huge Twitter corpus, and detects offensive tweets using automatically these generated features. Our approach performs competitively with a variety of machine learning (ML) algorithms. For instance, our approach achieves a true positive rate (TP) of 75.1% over 4029 testing tweets using Logistic Regression, significantly outperforming the popular keyword matching baseline, which has a TP of 69.7%, while keeping the false positive rate (FP) at the same level as the baseline at about 3.77%. Our approach provides an alternative to large scale hand annotation efforts required by fully supervised learning approaches.


title: GPU-based Video Feature Tracking and Matching

abstract: This paper describes novel implementations of the KLT feature track- ing and SIFT feature extraction algorithms that run on the graphics processing unit (GPU) and is suitable for video analysis in real-time vision systems. While significant acceleration over standard CPU implementations is obtained by ex- ploiting parallelism provided by modern programmable graphics hardware, the CPU is freed up to run other computations in parallel. Our GPU-based KLT im- plementation tracks about a thousand features in real-time at 30 Hz on 1024 £ 768 resolution video which is a 20 times improvement over the CPU. It works on both ATI and NVIDIA graphics cards. The GPU-based SIFT implementation works on NVIDIA cards and extracts about 800 features from 640 £ 480 video at 10Hz which is approximately 10 times faster than an optimized CPU implementation.


title: Virtual screening strategies in drug design – methods and applications

abstract: Virtual screening (VS) overcomes the limitations of traditional high-throughput screening (HTS) by applying computer-based methods in drug discovery. VS takes advantage of fast algorithms to filter chemical space and successfully select potential drug candidates. A key aspect in structure-based VS is the sampling of ligand-receptor conformations and the evaluation of these poses to predict near-native binding modes. The development of fast and accurate algorithms during the last few years has allowed VS to become an important tool in drug discovery and design. Herein, an overview of widely used ligand-based (e.g., similarity, pharmacophore searches) and structurebased (protein-ligand docking) VS methods is discussed. Their strengths and limitations are described, along with many successful stories. This review not only serves as an introductory guide for inexperienced VS users but also presents a general overview of the current state and scope of these powerful tools.


title: Exploring the representation capabilities of the HOG descriptor

abstract: Object recognition strategies are increasingly based on regional descriptors such as SIFT or HOG at a sparse set of points or on a dense grid of points. Despite their success on databases such as PASCAL and CALTECH, the capability of such a representation in capturing the essential object content of the image is not well-understood: How large is the equivalence class of images sharing the same HOG descriptor? Are all these images from the same object category, and if not, do the non-category images resemble random images which cannot generically arise from imaged scenes? How frequently do images from two categories share the same HOG-based representation? These questions are increasingly more relevant as very large databases such as ImageNet and LabelMe are being developed where the current object recognition strategies show limited success. We examine these questions by introducing the metameric class of moments of HOG which allows for a target image to be morphed into an impostor image sharing the HOG representation of a source image while retaining the initial visual appearance. We report that two distinct images can be made to share the same HOG representation when the overlap between HOG patches is minimal, and the success of this method falls with increasing overlap. This paper is therefore a step in the direction of developing a sampling theorem for representing images by HOG features.


title: An i-vector based descriptor for alphabetical gesture recognition

abstract: An i-vector approach to extracting features for video camera based gesture recognition is proposed. Conventional low-level raw features, such as position, speed, and acceleration, are low-dimensional feature representations which often suffer from measurement noise and thus are not highly discriminative. High-level features, such as Fourier descriptor, usually take a global transformation on the whole raw features of a gesture, but local statistical information is seldom considered. Moreover, compared with speech recordings, video cameras used to capture data are often at a low frame rate such that it is challenging for proper modeling and recognition. In this paper, we show that the proposed i-vector framework can handle both local statistical information and sparse trajectory representations more efficiently under the sparse data scenarios for an in-car hand-gesturing English letter recognition system. Experimental results confirm the effectiveness of the proposed i-vector features, which can reduce the letter error rate by as much as 36-44% relatively from the results obtained with the conventional location based raw features.


title: Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses

abstract: A survey of pre/post-test data using the Halloun–Hestenes Mechanics Diagnostic test or more recent Force Concept Inventory is reported for 62 introductory physics courses enrolling a total number of students N=6542. A consistent analysis over diverse student populations in high schools, colleges, and universities is obtained if a rough measure of the average effectiveness of a course in promoting conceptual understanding is taken to be the average normalized gain 〈g〉. The latter is defined as the ratio of the actual average gain (%〈post〉−%〈pre〉) to the maximum possible average gain (100−%〈pre〉). Fourteen “traditional” (T) courses (N=2084) which made little or no use of interactive-engagement (IE) methods achieved an average gain 〈g〉T-ave=0.23±0.04 (std dev). In sharp contrast, 48 courses (N=4458) which made substantial use of IE methods achieved an average gain 〈g〉IE-ave=0.48±0.14 (std dev), almost two standard deviations of 〈g〉IE-ave above that of the traditional courses. Results for 30 (N=3259) of the a...


title: Constraints-driven scheduling and resource assignment

abstract: This paper describes a new method for modeling and solving different scheduling and resource assignment problems that are common in high-level synthesis (HLS) and system-level synthesis. It addresses assignment of resources for operations and tasks as well as their static, off-line scheduling. Different heterogeneous constraints are considered for these problems. These constraints can be grouped into two classes: problem-specific constraints and design-oriented constraints. They are uniformly modeled, in our approach, by finite domain (FD) constraints and solved using related constrained programming (CP) techniques. This provides a way to improve quality of final solutions. We have developed in Java a constraint solver engine, JaCoP (Java Constraint Programming), to evaluate this approach. This solver and a related framework make it possible to model different resource assignment and scheduling problems, and handle them uniformly. The JaCoP prototype system has been extensively evaluated on a number of HLS and system-level synthesis benchmarks. We have been able to obtain optimal results together with related proofs of optimality for all HLS scheduling benchmarks and for all explored design styles (except one functional pipeline design). Many system-level benchmarks can also be solved optimally. For large randomly generated task graphs, we have used heuristic search methods and obtained results that are 1--3p worse than lower bounds or optimal results. These experiments have proved the feasibility of the presented approach.


title: Safety climate in an oil refinery and in subcontracted firms

abstract: This paper examines the safety climate of a refinery in comparison to that of companies providing services in the area of maintenance, operating from the same refinery. The instrument used is a questionnaire that comprises ten subscales. In accordance with obtained data the employees of the subcontracting companies assess in a more negative way safety climate than employees of the contracting company. Additionally, we observed that temporary employees, presented in these companies, show greater negative perceptions of safety climate than their colleagues with a permanent contract. It was also identified significant correlations of perceptions of safety climate with the following variables: Age, Academic Qualifications, Professional Experience and having been an Accident Victim.


title: An IoT based fire alarming and authentication system for workhouse using Raspberry Pi 3

abstract: Ensuring minimum rights and safety of the garment workers has become a burning issue nowadays. The workers of garment factories are facing some labyrinths and broken out of fire is surely one of them. The investors are losing their interest and the prominence of this sector is getting toneless. In this paper, we have propounded a system which is capable to detect fire and can provide the location of the affected region. Raspberry Pi 3 has been used to control multiple Arduino which are integrated with a couple of sensors and camera. A 360° relay motor is assembled with the camera so that it can snap the image in whatever angle the fire is detected. We have provided a confirmation of the fire suspecting system to avoid any false alarm. The system will immediately send a message along with the image of the affected spot and Arduino's location. An admin can confirm or deny the impeachment and if the admin confirms the situation as a breaking out of fire, then the system will immediately raise an alarm and an automatic message will be sent to the nearby fire brigade.


title: DRL-cloud: Deep reinforcement learning-based resource provisioning and task scheduling for cloud service providers

abstract: Cloud computing has become an attractive computing paradigm in both academia and industry. Through virtualization technology, Cloud Service Providers (CSPs) that own data centers can structure physical servers into Virtual Machines (VMs) to provide services, resources, and infrastructures to users. Profit-driven CSPs charge users for service access and VM rental, and reduce power consumption and electric bills so as to increase profit margin. The key challenge faced by CSPs is data center energy cost minimization. Prior works proposed various algorithms to reduce energy cost through Resource Provisioning (RP) and/or Task Scheduling (TS). However, they have scalability issues or do not consider TS with task dependencies, which is a crucial factor that ensures correct parallel execution of tasks. This paper presents DRL-Cloud, a novel Deep Reinforcement Learning (DRL)-based RP and TS system, to minimize energy cost for large-scale CSPs with very large number of servers that receive enormous numbers of user requests per day. A deep Q-learning-based two-stage RP-TS processor is designed to automatically generate the best long-term decisions by learning from the changing environment such as user request patterns and realistic electric price. With training techniques such as target network, experience replay, and exploration and exploitation, the proposed DRL-Cloud achieves remarkably high energy cost efficiency, low reject rate as well as low runtime with fast convergence. Compared with one of the state-of-the-art energy efficient algorithms, the proposed DRL-Cloud achieves up to 320% energy cost efficiency improvement while maintaining lower reject rate on average. For an example CSP setup with 5,000 servers and 200,000 tasks, compared to a fast round-robin baseline, the proposed DRL-Cloud achieves up to 144% runtime reduction.


title: A subdivision-based algorithm for the sparse resultant

abstract: Multivariate resultants generalize the Sylvester resultant of two polynomials and characterize the solvability of a polynomial system. They also reduce the computation of all common roots to a problem in linear algebra. We propose a determinantal formula for the sparse resultant of an arbitrary system of n + 1 polynomials in n variables. This resultant generalizes the classical one and has significantly lower degree for polynomials that are sparse in the sense that their mixed volume is lower than their Bezout number. Our algorithm uses a mixed polyhedral subdivision of the Minkowski sum of the Newton polytopes in order to construct a Newton matrix. Its determinant is a nonzero multiple of the sparse resultant and the latter equals the GCD of at most n + 1 such determinants. This construction implies a restricted version of an effective sparse Nullstellensatz. For an arbitrary specialization of the coefficients, there are two methods that use one extra variable and yield the sparse resultant. This is the first algorithm to handle the general case with complexity polynomial in the resultant degree and simply exponential in n . We conjecture its extension to producing an exact rational expression for the sparse resultant.


title: Visceral pain : the ins and outs , the ups and downs

abstract: Purpose of reviewVisceral pain represents a major clinical problem, yet far less is known about its mechanisms compared with somatic pains, for example, from cutaneous and muscular structures.Recent findingsIn this review, we describe the neuroanatomical bases of visceral pain signalling in the peri


title: Agent-based virtual organization architecture

abstract: The purpose of this paper is to present the applicability of THOMAS, an architecture specially designed to model agent-based virtual organizations, in the development of a multiagent system for managing and planning routes for clients in a mall. In order to build virtual organizations, THOMAS offers mechanisms to take into account their structure, behaviour, dynamic, norms and environment. Moreover, one of the primary characteristics of the THOMAS architecture is the use of agents with reasoning and planning capabilities. These agents can perform a dynamic reorganization when they detect changes in the environment. The proposed architecture is composed of a set of related modules that are appropriate for developing systems in highly volatile environments similar to the one presented in this study. This paper presents THOMAS as well as the results obtained after having applied the system to a case study.


title: Attribute-Based Messaging: Access Control and Confidentiality

abstract: Attribute-Based Messaging (ABM) enables messages to be addressed using attributes of recipients rather than an explicit list of recipients. Such messaging offers benefits of efficiency, exclusiveness, and intensionality, but faces challenges in access control and confidentiality. In this article we explore an approach to intraenterprise ABM based on providing access control and confidentiality using information from the same attribute database exploited by the addressing scheme. We show how to address three key challenges. First, we demonstrate a manageable access control system based on attributes. Second, we demonstrate use of attribute-based encryption to provide end-to-end confidentiality. Third, we show that such a system can be efficient enough to support ABM for mid-size enterprises. Our implementation can dispatch confidential ABM messages approved by XACML policy review for an enterprise of at least 60,000 users with only seconds of latency.


title: A brief comparative review of primate posterior parietal cortex: A novel hypothesis on the human toolmaker

abstract: Abstract The primate visual system contains two major cortical pathways: a ventral-temporal pathway that has been associated with object processing and recognition, and a dorsal-parietal pathway that has been associated with spatial processing and action guidance. Our understanding of the role of the dorsal pathway, in particular, has greatly evolved within the framework of the two-pathway hypothesis since its original conception. Here, we present a comparative review of the primate dorsal pathway in humans and monkeys based on electrophysiological, neuroimaging, neuropsychological, and neuroanatomical studies. We consider similarities and differences across species in terms of the topographic representation of visual space; specificity for eye, reaching, or grasping movements; multi-modal response properties; and the representation of objects and tools. We also review the relative anatomical location of functionally- and topographically-defined regions of the posterior parietal cortex. An emerging theme from this comparative analysis is that non-spatial information is represented to a greater degree, and with increased complexity, in the human dorsal visual system. We propose that non-spatial information in the primate parietal cortex contributes to the perception-to-action system aimed at manipulating objects in peripersonal space. In humans, this network has expanded in multiple ways, including the development of a dorsal object vision system mirroring the complexity of the ventral stream, the integration of object information with parietal working memory systems, and the emergence of tool-specific object representations in the anterior intraparietal sulcus and regions of the inferior parietal lobe. We propose that these evolutionary changes have enabled the emergence of human-specific behaviors, such as the sophisticated use of tools.


title: Oblix: An Efficient Oblivious Search Index

abstract: Search indices are fundamental building blocks of many systems, and there is great interest in running them on encrypted data. Unfortunately, many known schemes that enable search queries on encrypted data achieve efficiency at the expense of security, as they reveal access patterns to the encrypted data. In this paper we present Oblix, a search index for encrypted data that is oblivious (provably hides access patterns), is dynamic (supports inserts and deletes), and has good efficiency. Oblix relies on a combination of novel oblivious-access techniques and recent hardware enclave platforms (e.g., Intel SGX). In particular, a key technical contribution is the design and implementation of doubly-oblivious data structures, in which the client's accesses to its internal memory are oblivious, in addition to accesses to its external memory at the server. These algorithms are motivated by hardware enclaves like SGX, which leak access patterns to both internal and external memory. We demonstrate the usefulness of Oblix in several applications: private contact discovery for Signal, private retrieval of public keys for Key Transparency, and searchable encryption that hides access patterns and result sizes.


5G Radio Access: Requirements, Concept and Experimental Trials


title: Plant Disease Detection Using Machine Learning

abstract: Crop diseases are a noteworthy risk to sustenance security, however their quick distinguishing proof stays troublesome in numerous parts of the world because of the non attendance of the important foundation. Emergence of accurate techniques in the field of leaf-based image classification has shown impressive results. This paper makes use of Random Forest in identifying between healthy and diseased leaf from the data sets created. Our proposed paper includes various phases of implementation namely dataset creation, feature extraction, training the classifier and classification. The created datasets of diseased and healthy leaves are collectively trained under Random Forest to classify the diseased and healthy images. For extracting features of an image we use Histogram of an Oriented Gradient (HOG). Overall, using machine learning to train the large data sets available publicly gives us a clear way to detect the disease present in plants in a colossal scale.


title: Polarization-agile antennas

abstract: A polarization-agile antenna is a type of antenna the polarization state of which can be changed dynamically, i.e., it can have either linear (vertical or horizontal) or circular polarization (left hand or right hand), depending on the requirements of its specific application. This special characteristic offers great flexibility for antenna systems, because a single antenna could be used to satisfy the different requirements of several systems. This paper gives a comprehensive review of polarization-agile antennas. The basics of polarization in antennas and polarization-agile antennas are explained. Various examples of recent developments are then provided, and the antenna configurations and design principles are illustrated and discussed. Finally, the future development of polarization-agile antennas for wireless systems is discussed


title: In-Situ ISAR Imaging of Wind Turbines

abstract: Measurement and processing methodologies are developed to capture the in-situ inverse synthetic aperture radar (ISAR) image of an operating wind turbine. In-situ measurement data of a small three-blade wind turbine are collected using an ultrawideband radar, and the measured signatures are compared with simulation results based on physical optics. The backscattering phenomenology is examined in the sinogram, spectrogram, and ISAR image domains. It is shown that the resulting ISAR images provide revealing geometrical insight into the blade scattering phenomenology which is not as apparent in either the range profile or Doppler spectrogram alone. Subsequently, the same methodologies are applied to generate the in-situ ISAR imagery of an 18-blade windmill and a 1.7-MW utility-class wind turbine.


title: H6 Transformerless Full-Bridge PV Grid-Tied Inverters

abstract: Transformerless inverters are widely used in grid-tied photovoltaic (PV) generation systems, due to the benefits of achieving high efficiency and low cost. Various transformerless inverter topologies have been proposed to meet the safety requirement of leakage currents, such as specified in the VDE-4105 standard. In this paper, a family of H6 transformerless inverter topologies with low leakage currents is proposed, and the intrinsic relationship between H5 topology, highly efficient and reliable inverter concept (HERIC) topology, and the proposed H6 topology has been discussed as well. One of the proposed H6 inverter topologies is taken as an example for detail analysis with operation modes and modulation strategy. The power losses and power device costs are compared among the H5, the HERIC, and the proposed H6 topologies. A universal prototype is built for these three topologies mentioned for evaluating their performances in terms of power efficiency and leakage currents characteristics. Experimental results show that the proposed H6 topology and the HERIC achieve similar performance in leakage currents, which is slightly worse than that of the H5 topology, but it features higher efficiency than that of H5 topology.


title: ECOFEN: An End-to-end energy Cost mOdel and simulator For Evaluating power consumption in large-scale Networks

abstract: Wired networks are increasing in size and their power consumption is becoming a matter of concern. Evaluating the end-to-end electrical cost of new network architectures and protocols is difficult due to the lack of monitored realistic infrastructures. We propose an End-to-End energy Cost mOdel and simulator For Evaluating power consumption in large-scale Networks (ECOFEN) whose user's entries are the network topology and traffic. Based on configurable measurement of different network components (routers, switches, NICs, etc.), it provides the power consumption of the overall network including the end-hosts as well as the power consumption of each equipment over time.


title: Transfer Learning with Partial Observability Applied to Cervical Cancer Screening

abstract: Cervical cancer remains a significant cause of mortality in low-income countries. As in many other diseases, the existence of several screening/diagnosis methods and subjective physician preferences creates a complex ecosystem for automated methods. In order to diminish the amount of labeled data from each modality/expert we propose a regularization-based transfer learning strategy that encourages source and target models to share the same coefficient signs. We instantiated the proposed framework to predict cross-modality individual risk and cross-expert subjective quality assessment of colposcopic images for different modalities. Thus, we are able to transfer knowledge gained from one expert/modality to another.


title: Review of fish swimming modes for aquatic locomotion

abstract: Several physico-mechanical designs evolved in fish are currently inspiring robotic devices for propulsion and maneuvering purposes in underwater vehicles. Considering the potential benefits involved, this paper presents an overview of the swimming mechanisms employed by fish. The motivation is to provide a relevant and useful introduction to the existing literature for engineers with an interest in the emerging area of aquatic biomechanisms. The fish swimming types are presented, following the well-established classification scheme and nomenclature originally proposed by Breder. Fish swim either by body and/or caudal fin (BCF) movements or using median and/or paired fin (MPF) propulsion. The latter is generally employed at slow speeds, offering greater maneuverability and better propulsive efficiency, while BCF movements can achieve greater thrust and accelerations. For both BCF and MPF locomotion, specific swimming modes are identified, based on the propulsor and the type of movements (oscillatory or undulatory) employed for thrust generation. Along with general descriptions and kinematic data, the analytical approaches developed to study each swimming mode are also introduced. Particular reference is made to lunate tail propulsion, undulating fins, and labriform (oscillatory pectoral fin) swimming mechanisms, identified as having the greatest potential for exploitation in artificial systems.


title: Leveraging Word Embeddings for Spoken Document Summarization

abstract: Owing to the rapidly growing multimedia content available on the Internet, extractive spoken document summarization, with the purpose of automatically selecting a set of representative sentences from a spoken document to concisely express the most important theme of the document, has been an active area of research and experimentation. On the other hand, word embedding has emerged as a newly favorite research subject because of its excellent performance in many natural language processing (NLP)-related tasks. However, as far as we are aware, there are relatively few studies investigating its use in extractive text or speech summarization. A common thread of leveraging word embeddings in the summarization process is to represent the document (or sentence) by averaging the word embeddings of the words occurring in the document (or sentence). Then, intuitively, the cosine similarity measure can be employed to determine the relevance degree between a pair of representations. Beyond the continued efforts made to improve the representation of words, this paper focuses on building novel and efficient ranking models based on the general word embedding methods for extractive speech summarization. Experimental results demonstrate the effectiveness of our proposed methods, compared to existing state-of-the-art methods.


title: Virtual experiment system for electrician training based on Kinect and Unity3D

abstract: In order to establish low-cost and strongly-immersive desktop virtual experiment system, a solution based on Kinect and Unity3D engine technology was herein proposed, with a view to applying Kinect gesture recognition and triggering more spontaneous human-computer interactions in three-dimensional virtual environment. A kind of algorithm tailored to the detection of concave-convex points of fingers is put forward to identify various gestures and interaction semantics. In the context of Unity3D, Finite-State Machine (FSM) programming was applied in intelligent management for experimental logic tasks. A “Virtual Experiment System for Electrician Training” was designed and put into practice by these methods. The applications of “Lighting Circuit” module prove that these methods can be satisfyingly helpful to complete virtual experimental tasks and improve user experience. Compared with traditional WIMP interaction, Kinect somatosensory interaction is combined with Unity3D so that three-dimensional virtual system with strong immersion can be established.


Experimental and numerical investigation of fracturing mechanism in concrete using deflagration agent


title: HyperSafe: A Lightweight Approach to Provide Lifetime Hypervisor Control-Flow Integrity

abstract: Virtualization is being widely adopted in today’s computing systems. Its unique security advantages in isolating and introspecting commodity OSes as virtual machines (VMs) have enabled a wide spectrum of applications. However, a common, fundamental assumption is the presence of a trustworthy hypervisor. Unfortunately, the large code base of commodity hypervisors and recent successful hypervisor attacks (e.g., VM escape) seriously question the validity of this assumption. In this paper, we present HyperSafe, a lightweight approach that endows existing Type-I bare-metal hypervisors with a unique self-protection capability to provide lifetime control flow integrity. Specifically, we propose two key techniques. The first one, non-bypassable memory lockdown, reliably protects the hypervisor’s code and static data from being compromised even in the presence of exploitable memory corruption bugs (e.g., buffer overflows), therefore successfully providing hypervisor code integrity. The second one, restricted pointer indexing, introduces one layer of indirection to convert the control data into pointer indexes. These pointer indexes are restricted such that the corresponding call/return targets strictly follow the hypervisor control flow graph, hence expanding protection to control-flow integrity. We have built a prototype and used it to protect two open-source Type-I hypervisors: BitVisor and Xen. The experimental results with synthetic hypervisor exploits and benchmarking programs show HyperSafe can reliably enable the hypervisor self-protection and provide the integrity guarantee with a small performance overhead.


title: Modeling user knowledge with dynamic Bayesian networks in interactive narrative environments

abstract: Recent years have seen a growing interest in interactive narrative systems that dynamically adapt story experiences in response to users' actions, preferences, and goals. However, relatively little empirical work has investigated runtime models of user knowledge for informing interactive narrative adaptations. User knowledge about plot scenarios, story environments, and interaction strategies is critical in a range of interactive narrative contexts, such as mystery and detective genre stories, as well as narrative scenarios for education and training. This paper proposes a dynamic Bayesian network approach for modeling user knowledge in interactive narrative environments. A preliminary version of the model has been implemented for the CRYSTAL ISLAND interactive narrative-centered learning environment. Results from an initial empirical evaluation suggest several future directions for the design and evaluation of user knowledge models for guiding interactive narrative generation and adaptation.


title: A brief history of bioinformatics

abstract: It is easy for today's students and researchers to believe that modern bioinformatics emerged recently to assist next-generation sequencing data analysis. However, the very beginnings of bioinformatics occurred more than 50 years ago, when desktop computers were still a hypothesis and DNA could not yet be sequenced. The foundations of bioinformatics were laid in the early 1960s with the application of computational methods to protein sequence analysis (notably, de novo sequence assembly, biological sequence databases and substitution models). Later on, DNA analysis also emerged due to parallel advances in (i) molecular biology methods, which allowed easier manipulation of DNA, as well as its sequencing, and (ii) computer science, which saw the rise of increasingly miniaturized and more powerful computers, as well as novel software better suited to handle bioinformatics tasks. In the 1990s through the 2000s, major improvements in sequencing technology, along with reduced costs, gave rise to an exponential increase of data. The arrival of 'Big Data' has laid out new challenges in terms of data mining and management, calling for more expertise from computer science into the field. Coupled with an ever-increasing amount of bioinformatics tools, biological Big Data had (and continues to have) profound implications on the predictive power and reproducibility of bioinformatics results. To overcome this issue, universities are now fully integrating this discipline into the curriculum of biology students. Recent subdisciplines such as synthetic biology, systems biology and whole-cell modeling have emerged from the ever-increasing complementarity between computer science and biology.


title: Lest we remember: cold-boot attacks on encryption keys

abstract: Contrary to widespread assumption, dynamic RAM (DRAM), the main memory in most modern computers, retains its contents for several seconds after power is lost, even at room temperature and even if removed from a motherboard. Although DRAM becomes less reliable when it is not refreshed, it is not immediately erased, and its contents persist sufficiently for malicious (or forensic) acquisition of usable full-system memory images. We show that this phenomenon limits the ability of an operating system to protect cryptographic key material from an attacker with physical access to a machine. It poses a particular threat to laptop users who rely on disk encryption: we demonstrate that it could be used to compromise several popular disk encryption products without the need for any special devices or materials. We experimentally characterize the extent and predictability of memory retention and report that remanence times can be increased dramatically with simple cooling techniques. We offer new algorithms for finding cryptographic keys in memory images and for correcting errors caused by bit decay. Though we discuss several strategies for mitigating these risks, we know of no simple remedy that would eliminate them.


title: Linear model predictive control of the locomotion of Pepper, a humanoid robot with omnidirectional wheels

abstract: The goal of this paper is to present a new realtime controller based on linear model predictive control for an omnidirectionnal wheeled humanoid robot. It is able to control both the mobile base of the robot and its body, while taking into account dynamical constraints. It makes it possible to have high velocity and acceleration motions by predicting the dynamic behavior of the robot in the future. Experimental results are proposed on the robot Pepper made by Aldebaran, showing good performance in terms of robustness and tracking control, efficiently managing kinematic and dynamical constraints.


title: Automated demand response strategies using home energy management system in a RES-based smart grid

abstract: Demand Response (DR) applications via Home Energy Management System (HEMS) at the building level in a strategically integrated RES-based electricity grid can help reduce the building peak demand as well as energy consumption and the power inefficiency in the grid respectively. Distributed Energy Generation (DEG) based on Renewable Energy Sources (RES) are seen as a reliable alternative and more efficient grid optimization to the traditional fossil energy sources-based grid. But a proper combination of demand response and the DEG can significantly make a revolutionary change in the electricity grid. This paper focuses on the impact of integrated control of Solar PV and the Wind Turbine on improving the supply-side and coordinated control strategy for automated demand response by ensuring efficient use of home appliances including Electric Vehicle (EV), air cooling, water heating etc. based on HEMS in order to reduce the peak demand and overall power inefficiency in the grid. For that purpose and analysis, a highly accurate aggregated model of Solar PV, Wind turbine and the home appliances has been developed in the Simulink (MATLAB) and an iterative algorithm based on Load Shifting technique has been established for HEMS controller with embedded MATLAB code. The obtained simulation result illustrates the effectiveness of the proposed system.


Spanning seven orders of magnitude: a challenge for cognitive modeling


title: Identifying Customer Needs from User-Generated Content

abstract: We evaluate user-generated content as a source of customer needs and propose and test a machine-learning approach for identifying customer needs more efficiently.


title: DAG-Recurrent Neural Networks for Scene Labeling

abstract: In image labeling, local representations for image units are usually generated from their surrounding image patches, thus long-range contextual information is not effectively encoded. In this paper, we introduce recurrent neural networks (RNNs) to address this issue. Specifically, directed acyclic graph RNNs (DAG-RNNs) are proposed to process DAG-structured images, which enables the network to model long-range semantic dependencies among image units. Our DAG-RNNs are capable of tremendously enhancing the discriminative power of local representations, which significantly benefits the local classification. Meanwhile, we propose a novel class weighting function that attends to rare classes, which phenomenally boosts the recognition accuracy for non-frequent classes. Integrating with convolution and deconvolution layers, our DAG-RNNs achieve new state-of-the-art results on the challenging SiftFlow, CamVid and Barcelona benchmarks.


title: Design frame of a leg exoskeleton for load-carrying augmentation

abstract: This paper presents a design frame of a leg ex-oskeleton. The performance of an exoskeleton was analyzed by studying a model of a linear coupled 1-DOF human-exoskeleton system. The results showed that a low-impedance, anthropomorphic and well attached mechanical structure is benefit for the control of exoskeleton, and a direct force feedback control could also be implemented to further reduce the impedance. Three kinds of applied sensor interface were compared. A leg exoskeleton called ELEBOT was developed for validating control algorithm. The ELEBOT included four main parts: mechanical structure, hydraulic actuator, sensor and control system and power. The function of such a load-carrying robot is to reduce the injuries associated with the back and lower limbs after a long heavy walk. Experiment results with a simple force controller confirmed that ELEBOT could efficiently assist people walking with 30 kg payload now.


title: Bayesian inference, entropy, and the multinomial distribution

abstract: Instead of maximum-likelihood or MAP, Bayesian inference encourages the use of predictive densities and evidence scores. This is illustrated in the context of the multinomial distribution, where predictive estimates are often used but rarely described as Bayesian. By using an entropy approximation to the evidence, many Bayesian quantities can be expressed in information-theoretic terms. For example, testing whether two samples come from the same distribution or testing whether two variables are independent boils down to a mutual information score (with appropriate smoothing). The same analysis can be applied to discrete Markov chains to get a test for Markovianity.


title: A proposal for a Fully Distributed Flight Control System design

abstract: Since the delivery of the first A320 airliner with a Fly-by-Wire Flight Control System (FCS) in 1988, aircraft avionics architecture evolved significantly. Federated Architecture applied in the A320 family of aircraft presumed one computer per function. Limits regarding weight and space availability were reached and new generation of aircraft designed in early 2000s were equipped with Distributed Integrated Modular Avionics Architecture consisting of shared hardware resources running separate software modules according to aircraft priorities. Flight Control Computer functions were assumed by the Flight Control Module. While reducing the cost, weight and number of computers on board the aircraft, problems with troubleshooting and system modifications emerged. Proving that critical Systems perform within certain certification safety requirements became infeasible due to unpredictable dependencies between software modules. Abovementioned problems are addressed in this paper with a proposal of a Fully Distributed Flight Control System (FDFCS) design. Main contribution is that aircraft stability and trajectory control logic is distributed to a network of independent Control Units (CU) collocated on actuators collaborating to control the aircraft with respect to common goal. This paper outlines design for FDFCS and its CUs. Problems that distributed FCS implies and solves are identified. Finally, requirements for planned FDFCS Hardware in the Loop Simulator are set.


title: KeyGraph: automatic indexing by co-occurrence graph based on building construction metaphor

abstract: Presents an algorithm for extracting keywords representing the asserted main point in a document, without relying on external devices such as natural-language processing tools or a document corpus. Our algorithm, KeyGraph, is based on the segmentation of a graph, representing the co-occurrence between terms in a document, into clusters. Each cluster corresponds to a concept on which an author's idea is based, and the top-ranked terms are selected as keywords using a statistic based on each term's relationship to these clusters. This strategy comes from considering that a document is constructed like a building for expressing new ideas based on traditional concepts. The experimental results show that the thus-extracted terms match the author's main point quite accurately, even though KeyGraph does not use each term's average frequency in a corpus, i.e. KeyGraph is a content-sensitive, domain-independent indexing device.


title: Reflections on outreach programs in CS classes: learning objectives for "unplugged" activities

abstract: To provide a unified view of any scientific field, outreach programs need to realistically portray the subject in question. Consequently, topics and methods actually taught in Computer Science courses should to be touched upon in Computer Science outreach programs or, conversely, elements from successful Computer Science outreach programs can be used to enrich established courses in Computer Science. We follow up on the latter aspect and investigate how outreach material might be used as a teaching resource in lower secondary Computer Science. In particular, we extract and classify learning objectives from the activities of the well-received Computer Science Unplugged program. Based upon this classification, we comment on where and to which extent these activities can be used to enrich teaching Computer Science in secondary education.


title: RoboEarth Action Recipe Execution

abstract: The ability of reusing existing task execution plans is an important step towards autonomous behavior. Today, the reuse of sophisticated services allowing robots to act autonomous is usually limited to identical robot platforms and to very similar application scenarios. The approach presented in this paper proposes a way to mitigate this limitation by storing and reusing task plans on a global accessible database. We describe the task execution engine for the RoboEarth project [28] to demonstrate its ability to execute tasks in a flexible and reliable way.


title: Modeling disease progression via multi-task learning

abstract: Abstract Alzheimer's disease (AD), the most common type of dementia, is a severe neurodegenerative disorder. Identifying biomarkers that can track the progress of the disease has recently received increasing attentions in AD research. An accurate prediction of disease progression would facilitate optimal decision-making for clinicians and patients. A definitive diagnosis of AD requires autopsy confirmation, thus many clinical/cognitive measures including Mini Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-Cog) have been designed to evaluate the cognitive status of the patients and used as important criteria for clinical diagnosis of probable AD. In this paper, we consider the problem of predicting disease progression measured by the cognitive scores and selecting biomarkers predictive of the progression. Specifically, we formulate the prediction problem as a multi-task regression problem by considering the prediction at each time point as a task and propose two novel multi-task learning formulations. We have performed extensive experiments using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Specifically, we use the baseline MRI features to predict MMSE/ADAS-Cog scores in the next 4 years. Results demonstrate the effectiveness of the proposed multi-task learning formulations for disease progression in comparison with single-task learning algorithms including ridge regression and Lasso. We also perform longitudinal stability selection to identify and analyze the temporal patterns of biomarkers in disease progression. We observe that cortical thickness average of left middle temporal, cortical thickness average of left and right Entorhinal, and white matter volume of left Hippocampus play significant roles in predicting ADAS-Cog at all time points. We also observe that several MRI biomarkers provide significant information for predicting MMSE scores for the first 2 years, however very few are shown to be significant in predicting MMSE score at later stages. The lack of predictable MRI biomarkers in later stages may contribute to the lower prediction performance of MMSE than that of ADAS-Cog in our study and other related studies.


title: Detecting Visual Relationships with Deep Relational Networks

abstract: Relationships among objects play a crucial role in image understanding. Despite the great success of deep learning techniques in recognizing individual objects, reasoning about the relationships among objects remains a challenging task. Previous methods often treat this as a classification problem, considering each type of relationship (e.g. ride) or each distinct visual phrase (e.g. person-ride-horse) as a category. Such approaches are faced with significant difficulties caused by the high diversity of visual appearance for each kind of relationships or the large number of distinct visual phrases. We propose an integrated framework to tackle this problem. At the heart of this framework is the Deep Relational Network, a novel formulation designed specifically for exploiting the statistical dependencies between objects and their relationships. On two large data sets, the proposed method achieves substantial improvement over state-of-the-art.


title: A module map showing conditional activity of expression modules in cancer

abstract: Data on human genetic variation help scientists to understand human origins, susceptibility to illness and genetic causes of disease. Destructive episodes in the history of genetic research make it crucial to consider the ethical and social implications of research in genomics, especially human genetic variation. The analysis of ethical, legal and social implications should be integrated into genetic research, with the participation of scientists who can anticipate and monitor the full range of possible applications of the research from the earliest stages. The design and implementation of research directs the ways in which its results can be used, and data and technology, rather than ethical considerations or social needs, drive the use of science in unintended ways. Here we examine forensic genetics and argue that all geneticists should anticipate the ethical and social issues associated with nonmedical applications of genetic variation research.


title: Adaptive computation offloading for energy conservation on battery-powered systems

abstract: This paper considers the problem of extending the battery lifetime for a portable computer by off loading its computation to a server. Depending on the inputs, computation time for different instances of a program can vary significantly and they are often difficult to predict. Different from previous studies on computation off loading, our approach does not require estimating the computation time before the execution. We execute the program initially on the portable client with a timeout. If the computation is not completed after the timeout, it is off loaded to the server. We first set the timeout to be the minimum computation time that can benefit from off loading. This method is proved to be 2- competitive. We further consider collecting online statistics of the computation time and find the statistically optimal timeout. Finally, we provide guidelines to construct programs with computation off loading. Experiments show that our methods can save up to 17% more energy than existing approaches.


title: Bridging the digital divide in older adults: a study from an initiative to inform older adults about new technologies

abstract: Purpose ::: In a society where technology progresses at an exponential rate, older adults are often unaware of the existence of different kinds of information and communication technologies (ICTs). To bridge the gap, we launched a 2-year project, during which we conducted focus groups (FGs) with demonstrations of ICTs, allowing older adults to try them out and to share their opinions. This study aimed at investigating how participants perceived this kind of initiative and how they reacted to different kinds of ICTs.


title: Evolution of three-dimensional nonlinear pulses in weakly dispersive media

abstract: Abstract Evolution of three-dimensional axially symmetric nonlinear pulses in weakly dispersive and dissipative media is studied numerically. The master equation is a generalization of the Zabolotskaya-Khokhlov and Kadomtsev-Petviashvili equations with a Kawahara term. We show that the latter plays a significant role in preventing collapse when it may appear according to the three-dimensional Kadomtsev-Petviashvili equation.


title: The performance of universal encoding

abstract: Universal coding theory is surveyed from the viewpoint of the interplay between delay and redundancy. The price for universality turns out to be acceptably small.


title: Graph OLAP: a multi-dimensional framework for graph data analysis

abstract: Databases and data warehouse systems have been evolving from handling normalized spreadsheets stored in relational databases, to managing and analyzing diverse application-oriented data with complex interconnecting structures. Responding to this emerging trend, graphs have been growing rapidly and showing their critical importance in many applications, such as the analysis of XML, social networks, Web, biological data, multimedia data and spatiotemporal data. Can we extend useful functions of databases and data warehouse systems to handle graph structured data? In particular, OLAP (On-Line Analytical Processing) has been a popular tool for fast and user-friendly multi-dimensional analysis of data warehouses. Can we OLAP graphs? Unfortunately, to our best knowledge, there are no OLAP tools available that can interactively view and analyze graph data from different perspectives and with multiple granularities. In this paper, we argue that it is critically important to OLAP graph structured data and propose a novel Graph OLAP framework. According to this framework, given a graph dataset with its nodes and edges associated with respective attributes, a multi-dimensional model can be built to enable efficient on-line analytical processing so that any portions of the graphs can be generalized/specialized dynamically, offering multiple, versatile views of the data. The contributions of this work are three-fold. First, starting from basic definitions, i.e., what are dimensions and measures in the Graph OLAP scenario, we develop a conceptual framework for data cubes on graphs. We also look into different semantics of OLAP operations, and classify the framework into two major subcases: informational OLAP and topological OLAP. Second, we show how a graph cube can be materialized by calculating a special kind of measure called aggregated graph and how to implement it efficiently. This includes both full materialization and partial materialization where constraints are enforced to obtain an iceberg cube. As we can see, due to the increased structural complexity of data, aggregated graphs that depend on the underlying “network” properties of the graph dataset are much harder to compute than their traditional OLAP counterparts. Third, to provide more flexible, interesting and informative OLAP of graphs, we further propose a discovery-driven multi-dimensional analysis model to ensure that OLAP is performed in an intelligent manner, guided by expert rules and knowledge discovery processes. We outline such a framework and discuss some challenging research issues for discovery-driven Graph OLAP.


title: NeuroNER: an easy-to-use program for named-entity recognition based on neural networks

abstract: Named-entity recognition (NER) aims at identifying entities of interest in a text. Artificial neural networks (ANNs) have recently been shown to outperform existing NER systems. However, ANNs remain challenging to use for non-expert users. In this paper, we present NeuroNER, an easy-to-use named-entity recognition tool based on ANNs. Users can annotate entities using a graphical web-based user interface (BRAT): the annotations are then used to train an ANN, which in turn predict entities' locations and categories in new texts. NeuroNER makes this annotation-training-prediction flow smooth and accessible to anyone.


title: The Bag of Communities: Identifying Abusive Behavior Online with Preexisting Internet Data

abstract: Since its earliest days, harassment and abuse have plagued the Internet. Recent research has focused on in-domain methods to detect abusive content and faces several challenges, most notably the need to obtain large training corpora. In this paper, we introduce a novel computational approach to address this problem called Bag of Communities (BoC)---a technique that leverages large-scale, preexisting data from other Internet communities. We then apply BoC toward identifying abusive behavior within a major Internet community. Specifically, we compute a post's similarity to 9 other communities from 4chan, Reddit, Voat and MetaFilter. We show that a BoC model can be used on communities "off the shelf" with roughly 75% accuracy---no training examples are needed from the target community. A dynamic BoC model achieves 91.18% accuracy after seeing 100,000 human-moderated posts, and uniformly outperforms in-domain methods. Using this conceptual and empirical work, we argue that the BoC approach may allow communities to deal with a range of common problems, like abusive behavior, faster and with fewer engineering resources.


Effect of Leakage Inductance on Torque Capability of Field Oriented Controlled Induction Machine in Field Weakening Region


Teaching Business Ethically in the “New” South Africa:


title: GraphLab: A New Framework for Parallel Machine Learning

abstract: Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design challenges. By targeting common patterns in ML, we developed GraphLab, which improves upon abstractions like MapReduce by compactly expressing asynchronous iterative algorithms with sparse computational dependencies while ensuring data consistency and achieving a high degree of parallel performance. We demonstrate the expressiveness of the GraphLab framework by designing and implementing parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and Compressed Sensing. We show that using GraphLab we can achieve excellent parallel performance on large scale real-world problems.


title: Order-Based Disparity Refinement Including Occlusion Handling for Stereo Matching

abstract: Accurate stereo matching is still challenging in case of weakly textured areas, discontinuities, and occlusions. Besides, occlusion recovery is often regarded as a subordinate problem and simply handled. To obtain dense high-accuracy depth maps, this letter proposes an efficient multistep disparity refinement framework with occlusion handling. The framework is implemented by classifying the outliers into leftmost occlusions, nonborder occlusions, as well as mismatches, and employing different strategies to recover them. To recover occlusions, a filling order is specially introduced to avoid error propagation and surface decision based on local image content is performed when more than one background surface exists. The evaluations on Middlebury datasets and comparisons with other refinement algorithms show the superiority and robustness of our method.


title: An Indoor and Outdoor, Multimodal, Multispectral and Multi-Illuminant Database for Face Recognition

abstract: In this effort, we describe a face database obtained by using an involved acquisition system for the collection of multimodal and multispectral image data under various illumination conditions. The database of facial images may aid in exploring new avenues in face recognition, especially when involving multi-band visible and thermal information. The database has applications in experimentation with human identification, authentication, and face modeling. We describe the abilities of the acquisition system wherein the imaging sensors, system considerations and registration are discussed. For each data record in the database, we have twenty five images spanning the visible spectrum, one visible RGB/monochromatic image, one thermal image and the spectral distribution of the illumination of the scene. The complete dataset has so far 2624 face images and the corresponding illumination information for each data record. Moreover, we present a multispectral data fusion approach for illumination adjustment. The database will be made public to the research community for perusal towards illumination invariant face related research.


title: Controversy Detection in Wikipedia Using Collective Classification

abstract: Concerns over personalization in IR have sparked an interest in detection and analysis of controversial topics. Accurate detection would enable many beneficial applications, such as alerting search users to controversy. Wikipedia's broad coverage and rich metadata offer a valuable resource for this problem. We hypothesize that intensities of controversy among related pages are not independent; thus, we propose a stacked model which exploits the dependencies among related pages. Our approach improves classification of controversial web pages when compared to a model that examines each page in isolation, demonstrating that controversial topics exhibit homophily. Using notions of similarity to construct a subnetwork for collective classification, rather than using the default network present in the relational data, leads to improved classification with wider applications for semi-structured datasets, with the effects most pronounced when a small set of neighbors is used.


title: Efficient cell segmentation and tracking of developing plant meristem

abstract: Analysis of Confocal Laser Scanning Microscopy (CLSM) images is gaining popularity in developmental biology for understanding growth dynamics. The automated analysis of such images is highly desirable for efficiency and accuracy. The first step in this process is segmentation and tracking leading to computation of cell lineages. In this paper, we present efficient, accurate, and robust segmentation and tracking algorithms for cells and detection of cell divisions in a 4D spatio-temporal image stack of a growing plant meristem. We show how to optimally choose the parameters in the watershed algorithm for high quality segmentation results. This yields high quality tracking results using cell correspondence evaluation functions. We show segmentation and tracking results on Confocal laser scanning microscopy data captured for 72 hours at every 3 hour intervals. Compared to recent results in this area, the proposed algorithms provide significantly longer cell lineages and more comprehensive identification of cell divisions.


title: Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid

abstract: Three-dimensional surface registration transforms multiple three-dimensional data sets into the same coordinate system so as to align overlapping components of these sets. Recent surveys have covered different aspects of either rigid or nonrigid registration, but seldom discuss them as a whole. Our study serves two purposes: 1) To give a comprehensive survey of both types of registration, focusing on three-dimensional point clouds and meshes and 2) to provide a better understanding of registration from the perspective of data fitting. Registration is closely related to data fitting in which it comprises three core interwoven components: model selection, correspondences and constraints, and optimization. Study of these components 1) provides a basis for comparison of the novelties of different techniques, 2) reveals the similarity of rigid and nonrigid registration in terms of problem representations, and 3) shows how overfitting arises in nonrigid registration and the reasons for increasing interest in intrinsic techniques. We further summarize some practical issues of registration which include initializations and evaluations, and discuss some of our own observations, insights and foreseeable research trends.


title: Look, Listen and Learn - A Multimodal LSTM for Speaker Identification

abstract: Speaker identification refers to the task of localizing the face of a person who has the same identity as the ongoing voice in a video. This task not only requires collective perception over both visual and auditory signals, the robustness to handle severe quality degradations and unconstrained content variations are also indispensable. In this paper, we describe a novel multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies both visual and auditory modalities from the beginning of each sequence input. The key idea is to extend the conventional LSTM by not only sharing weights across time steps, but also sharing weights across modalities. We show that modeling the temporal dependency across face and voice can significantly improve the robustness to content quality degradations and variations. We also found that our multimodal LSTM is robustness to distractors, namely the non-speaking identities. We applied our multimodal LSTM to The Big Bang Theory dataset and showed that our system outperforms the state-of-the-art systems in speaker identification with lower false alarm rate and higher recognition accuracy.


title: A location-based smart shopping system with IoT technology

abstract: Localization is one important part of Internet of Things(IoT) where the Location of Everything (LoE) system plays a important role to improve most services in IoT area. On the other hand, data mining techniques are essential analyses when we have big data from IoT platforms. Indeed, integration of location-based methods and data mining analysis process can make a smart system service for IoT scenarios and applications. For this purpose, we design a smart shopping platform including four components, location of everything component, data collection component, data filtering/analysing component and data mining component. Then a novel accurate localization scheme named “location orbital” is developed that estimates the current location of mobile objects (users or everything) based on both current and the previous locations. Finally, an implementation of the experiment in a shopping mall is conducted to practically examine performance evaluation of the location-based scheme. The experimental results show that the proposed scheme could achieve significant higher precision than other localization techniques.


title: Closed-Loop Analysis and Cascade Control of a Nonminimum Phase Boost Converter

abstract: In this paper, a cascade controller is designed and analyzed for a boost converter. The fast inner current loop uses sliding-mode control. The slow outer voltage loop uses the proportional-integral (PI) control. Stability analysis and selection of PI gains are based on the nonlinear closed-loop error dynamics. It is proven that the closed-loop system has a nonminimum phase behavior. The voltage transients and reference voltage are predictable. The current ripple and system sensitivity are studied. The controller is validated by a simulation circuit with nonideal circuit parameters, different circuit parameters, and various maximum switching frequencies. The simulation results show that the reference output voltage is well tracked under parametric changes, system uncertainties, or external disturbances with fast dynamic transients, confirming the validity of the proposed controller.


title: Long-term intensive training induced brain structural changes in world class gymnasts

abstract: Many previous studies suggested that both short-term and long-term motor training can modulate brain structures. However, little evidence exists for such brain anatomical changes in top-level gymnasts. Using diffusion-weighted and structural magnetic resonance images of the human brain, we applied voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) as well as FA-VBA (voxel-based analysis of fractional anisotropy, a VBM-style analysis) methods to quantitatively compare the brain structural differences between the world class gymnasts (WCG) and the non-athlete groups. In order to reduce the rate of false positive findings, we first determined that the clusters defined at a threshold of t > 2.3 and a cluster significance of p < 0.05 (FWE-corrected) across all subjects were the brain regions that showed significant differences in a between-group comparison. We then constructed several between-group comparisons based on the repeated diffusion or structural MRI data and created the intersecting regions from multiple between-group comparisons. Thus, we found significantly decreased fractional anisotropy (FA) not only in the white matter of the WCG in areas that included the bilateral superior longitudinal fasciculus, inferior longitudinal fasciculus, and inferior occipito-frontal fascicle, but also in the gray matter of the WCG in areas that included the bilateral middle cingulum, bilateral postcentral gyri, and bilateral motor regions. We also identified significantly increased gray matter density in the WCG, especially in the left inferior frontal gyrus, bilateral inferior and superior parietal lobule, bilateral superior lateral occipital cortex, left cuneus, left angular gyrus, and right postcentral gyrus. We speculate that the brain changes of the WCG may reflect the gymnasts’ extraordinary ability to estimate the direction of their movements, their speed of execution, and their identification of their own and surrounding objects’ locations. Our findings suggest that our method of constructing intersecting regions from multiple between-group comparison can considerably reduce the false positives, and our results provide new insights into the brain structure changes induced by long-term intensive gymnastic training.


title: A narrative review on haptic devices: relating the physiology and psychophysical properties of the hand to devices for rehabilitation in central nervous system disorders

abstract: Purpose: This article provides rehabilitation professionals and engineers with a theoretical and pragmatic rationale for the inclusion of haptic feedback in the rehabilitation of central nervous system disorders affecting the hand. Method: A narrative review of haptic devices used in sensorimotor hand rehabilitation was undertaken. Presented papers were selected to outline and clarify the underlying somatosensory mechanisms underpinning these technologies and provide exemplars of the evidence to date. Results: Haptic devices provide kinaesthetic and/or tactile stimulation. Kinaesthetic haptics are beginning to be incorporated in central nervous system rehabilitation; however, there has been limited development of tactile haptics. Clinical research in haptic rehabilitation of the hand is embryonic but initial findings indicate potential clinical benefit. Conclusions: Haptic rehabilitation offers the potential to advance sensorimotor hand rehabilitation but both scientific and pragmatic developments are nee...


title: Combining tDCS and Working Memory Training to Down Regulate State Rumination: A Single-Session Double Blind Sham-Controlled Trial

abstract: Rumination has been associated with reduced working memory and dorsolateral prefrontal cortex activity. This study explored whether single session anodal transcranial direct current stimulation (tDCS) applied to the dorsolateral prefrontal cortex and/or working memory training can transiently ameliorate working memory and down regulate state rumination. Sixty-six participants were randomly allocated to three conditions: (1) control training + tDCS, (2) working memory training + sham tDCS and (3) working memory training + tDCS. Before and after manipulation participants performed working memory tasks and state rumination was measured with self-report and heart rate variability. Participants who received real tDCS were significantly faster in switching between information in working memory than participants who received sham tDCS. No effects on self-reported state rumination were found. However, both groups receiving working memory training showed a higher increase in heart rate variability than the control training group, indicating more adaptive self-regulation.