title: Adapting ranking SVM to document retrieval

abstract: The paper is concerned with applying learning to rank to document retrieval. Ranking SVM is a typical method of learning to rank. We point out that there are two factors one must consider when applying Ranking SVM, in general a "learning to rank" method, to document retrieval. First, correctly ranking documents on the top of the result list is crucial for an Information Retrieval system. One must conduct training in a way that such ranked results are accurate. Second, the number of relevant documents can vary from query to query. One must avoid training a model biased toward queries with a large number of relevant documents. Previously, when existing methods that include Ranking SVM were applied to document retrieval, none of the two factors was taken into consideration. We show it is possible to make modifications in conventional Ranking SVM, so it can be better used for document retrieval. Specifically, we modify the "Hinge Loss" function in Ranking SVM to deal with the problems described above. We employ two methods to conduct optimization on the loss function: gradient descent and quadratic programming. Experimental results show that our method, referred to as Ranking SVM for IR, can outperform the conventional Ranking SVM and other existing methods for document retrieval on two datasets.


title: Cloud Analytics for Capacity Planning and Instant VM Provisioning

abstract: The popularity of cloud service spurs the increasing demands of virtual resources to the service vendors. Along with the promising business opportunities, it also brings new technique challenges such as effective capacity planning and instant cloud resource provisioning. In this paper, we describe our research efforts on improving the service quality for the capacity planning and instant cloud resource provisioning problem. We first formulate both of the two problems as a generic cost-sensitive prediction problem. Then, considering the highly dynamic environment of cloud, we propose an asymmetric and heterogeneous measure to quantify the prediction error. Finally, we design an ensemble prediction mechanism by combining the prediction power of a set of prediction techniques based on the proposed measure. To evaluate the effectiveness of our proposed solution, we design and implement an integrated prototype system to help improve the service quality of the cloud. Our system considers many practical situations of the cloud system, and is able to dynamically adapt to the changing environment. A series of experiments on the IBM Smart Cloud Enterprise (SCE) trace data demonstrate that our method can significantly improve the service quality by reducing the resource provisioning time while maintaining a low cloud overhead.


title: Precise Indoor Positioning Using UWB: A Review of Methods, Algorithms and Implementations

abstract: The demand and growth of indoor positioning has increased rapidly in the past few years for a diverse range of applications. Various innovative techniques and technologies have been introduced but precise and reliable indoor positioning still remains a challenging task due to dependence on a large number of factors and limitations of the technologies. Positioning technologies based on radio frequency (RF) have many advantages over the technologies utilizing ultrasonic, optical and infrared devices. Both narrowband and wideband RF systems have been implemented for short range indoor positioning/real-time locating systems. Ultra wideband (UWB) technology has emerged as a viable candidate for precise indoor positioning due its unique characteristics. This article presents a comparison of UWB and narrowband RF technologies in terms of modulation, throughput, transmission time, energy efficiency, multipath resolving capability and interference. Secondly, methods for measurement of the positioning parameters are discussed based on a generalized measurement model and, in addition, widely used position estimation algorithms are surveyed. Finally, the article provides practical UWB positioning systems and state-of-the-art implementations. We believe that the review presented in this article provides a structured overview and comparison of the positioning methods, algorithms and implementations in the field of precise UWB indoor positioning, and will be helpful for practitioners as well as for researchers to keep abreast of the recent developments in the field.


title: Application-aware software-defined EPON access network

abstract: As bandwidth requirements of access networks continue to increase rapidly, especially due to the growth of streaming traffic, the need for resolving bandwidth contention among competing users and applications becomes more compelling. The proposed Software-Defined Ethernet Passive Optical Network (EPON) architecture utilizes an application-level feedback from the client side (for video users) to the network through a Software-Defined-Network (SDN) controller to achieve better client-and-service-level differentiation in the downstream direction of an EPON access network Numerical results obtained from simulation experiments demonstrate that the proposed architecture with downstream resource allocation for video streaming applications considerably reduce video stalls and increase video buffer level at the client applications leading to better Quality of Experience (QoE) for users along with better client-and-service-level differentiation.


title: Planning and application of Electric Vehicle with MATLAB®/Simulink®

abstract: Vehicular emissions have major impact on global warming. Many countries have had serious considerations about it and have delved into developing alternative fuel technologies. The most optimistic choice for powering electric vehicles at present and the immediate future is battery electric power. The global automobile manufacturers are investing heavily in research of battery run efficient electric vehicles. This paper demonstrates step-by-step mathematical calculations and simulation modeling of Electric Vehicle (EV). In this paper, EV namely “Mahindra e2o” from a leading automobile manufacturer of India, Mahindra & Mahindra has been simulated and analyzed in MATLAB®/Simulink® platform. The simulation model is equipped with recuperation capability of EV. Its performance has been investigated by simulation results for various vehicle velocity inputs and summarized in terms of vehicle range and Battery State of Charge (SOC). Furthermore, vehicle range and SOC are evaluated and summarized with variation of input parameters at the end of this paper. This summary can give significant insight to improve the range of this EV. This paper is developed in such a way that it can act as ready reference for the design, simulation and analysis of EV.


title: DAILY PEAK LOAD ELECTRICITY FORECASTING USING ARTIFICIAL NEURAL NETWORKS (ANNs)

abstract: This paper presents a report on the implementation of an artificial neural network (ANN) that is to be used to forecast the daily peak load at the Pennsylvania New Jersey Maryland Interconnect Association (PJM). This paper examines the process of determining the appropriate ANN inputs and architectures and compares the results to the current mainframe-based pattern matching program. It is unique in that it uses real rather than simulated data to compare the ANN performance and also because it is looking at peak load forecasting for a large network of generating plants.


title: Ground influence on the input impedance of transient dipole and bow-tie antennas

abstract: In this paper, the influence of a lossy ground on the input impedance of dipole and bow-tie antennas excited by a short pulse is investigated. It is shown that the ground influence on the input impedance of transient dipole and bow-tie antennas is significant only for elevations smaller than 1/5 of the wavelength that corresponds to the central frequency of the exciting pulse. Furthermore, a principal difference between the input impedance due to traveling-wave and standing-wave current distributions is pointed out.


title: ε-Approximations with Minimum Packing Constraint Violation (Extended Abstract)

abstract: We present efficient new randomized and deterministic methods for transforming optimal solutions for a type of relaxed integer linear program into provably good solutions for the corresponding NP-hard discrete optimization problem. Without any constraint violation, the epsilon-approximation problem for many problems of this type is itself NP-hard. Our methods provide polynomial-time epsilon-approximations while attempting to minimize the packing constraint violation. Our methods lead to the first known approximation algorithms with provable performance guarantees for the s-median problem, the tree pruning problem, and the generalized assignment problem. These important problems have numerous applications to data compression, vector quantization, memory-based learning, computer graphics, image processing, clustering, regression, network location, scheduling, protocol testing, and communication. We provide evidence via reductions that our approximation algorithms are nearly optimal in terms of the packing constraint violation. We also discuss some recent applications of our techniques to scheduling problems.


title: Planning Safe and Legible Hand-over Motions for Human-Robot Interaction

abstract: Human-Robot interaction brings new challenges to motion planning. The human, who is generally considered as an obstacle for the robot, needs to be considered as a separate entity that has a position, a posture, a field of view and an activity. These properties can be represented as new constraints to the motion generation mechanisms. In this paper we present three human related constraints to the motion planning for object hand over scenarios. We also describe a new planning method to consider these constraints. The resulting system automatically computes where the object should be transferred to the human, and the motion of the whole robot considering human's comfort.


title: Stochastic Optimization for Large-scale Optimal Transport

abstract: Optimal transport (OT) defines a powerful framework to compare probability distributions in a geometrically faithful way. However, the practical impact of OT is still limited because of its computational burden. We propose a new class of stochastic optimization algorithms to cope with large-scale problems routinely encountered in machine learning applications. These methods are able to manipulate arbitrary distributions (either discrete or continuous) by simply requiring to be able to draw samples from them, which is the typical setup in high-dimensional learning problems. This alleviates the need to discretize these densities, while giving access to provably convergent methods that output the correct distance without discretization error. These algorithms rely on two main ideas: (a) the dual OT problem can be re-cast as the maximization of an expectation ; (b) entropic regularization of the primal OT problem results in a smooth dual optimization optimization which can be addressed with algorithms that have a provably faster convergence. We instantiate these ideas in three different setups: (i) when comparing a discrete distribution to another, we show that incremental stochastic optimization schemes can beat Sinkhorn's algorithm, the current state-of-the-art finite dimensional OT solver; (ii) when comparing a discrete distribution to a continuous density, a semi-discrete reformulation of the dual program is amenable to averaged stochastic gradient descent, leading to better performance than approximately solving the problem by discretization ; (iii) when dealing with two continuous densities, we propose a stochastic gradient descent over a reproducing kernel Hilbert space (RKHS). This is currently the only known method to solve this problem, apart from computing OT on finite samples. We backup these claims on a set of discrete, semi-discrete and continuous benchmark problems.


title: HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition

abstract: We present an algorithm for simultaneous face detection, landmarks localization, pose estimation and gender recognition using deep convolutional neural networks (CNN). The proposed method called, HyperFace, fuses the intermediate layers of a deep CNN using a separate CNN followed by a multi-task learning algorithm that operates on the fused features. It exploits the synergy among the tasks which boosts up their individual performances. Additionally, we propose two variants of HyperFace: (1) HyperFace-ResNet that builds on the ResNet-101 model and achieves significant improvement in performance, and (2) Fast-HyperFace that uses a high recall fast face detector for generating region proposals to improve the speed of the algorithm. Extensive experiments show that the proposed models are able to capture both global and local information in faces and performs significantly better than many competitive algorithms for each of these four tasks.


title: Mixture of Expert Agents for Handling Imbalanced Data Sets

abstract: Many real-world data sets exhibit skewed class distributions in which almost all cases are allotted to a class and far fewer cases to a smaller, usually more interesting class. A classifier induced from an imbalanced data set has, typically, a low error rate for the majority class and an unacceptable error rate for the minority class. This paper firstly provides a systematic study on the various methodologies that have tried to handle this problem. Finally, it presents an experimental study of these methodologies with a proposed mixture of expert agents and it concludes that such a framework can be a more effective solution to the problem. Our method seems to allow improved identification of difficult small classes in predictive analysis, while keeping the classification ability of the other classes in an acceptable level.


title: Side-channel analysis of cryptographic RFIDs with analog demodulation

abstract: As most modern cryptographic Radio Frequency Identification (RFID) devices are based on ciphers that are secure from a purely theoretical point of view, e.g., (Triple-)DES or AES, adversaries have been adopting new methods to extract secret information and cryptographic keys from contactless smartcards: Side-Channel Analysis (SCA) targets the physical implementation of a cipher and allows to recover secret keys by exploiting a side-channel, for instance, the electro-magnetic (EM) emanation of an Integrated Circuit (IC). In this paper we present an analog demodulator specifically designed for refining the SCA of contactless smartcards. The customized analogue hardware increases the quality of EM measurements, facilitates the processing of the side-channel leakage and can serve as a plug-in component to enhance any existing SCA laboratory. Employing it to obtain power profiles of several real-world cryptographic RFIDs, we demonstrate the effectiveness of our measurement setup and evaluate the improvement of our new analog technique compared to previously proposed approaches. Using the example of the popular Mifare DESFire MF3ICD40 contactless smartcard, we show that commercial RFID devices are susceptible to the proposed SCA methods. The security analyses presented in this paper do not require expensive equipment and demonstrate that SCA poses a severe threat to many real-world systems. This novel attack vector has to be taken into account when employing contactless smartcards in security-sensitive applications, e.g., for wireless payment or identification.


title: Getting started on natural language processing with Python

abstract: The term Natural Language Processing encompasses a broad set of techniques for automated generation, manipulation and analysis of natural or human languages. Although most NLP techniques inherit largely from Linguistics and Artificial Intelligence, they are also influenced by relatively newer areas such as Machine Learning, Computational Statistics and Cognitive Science. Before we see some examples of NLP techniques, it will be useful to introduce some very basic terminology. Please note that as a side effect of keeping things simple, these definitions may not stand up to strict linguistic scrutiny.


title: Noise disturbance in open-plan study environments: a field study on noise sources, student tasks and room acoustic parameters.

abstract: The aim of this study is to gain more insight in the assessment of noise in open-plan study environments and to reveal correlations between noise disturbance experienced by students and the noise sources they perceive, the tasks they perform and the acoustic parameters of the open-plan study environment they work in. Data were collected in five open-plan study environments at universities in the Netherlands. A questionnaire was used to investigate student tasks, perceived sound sources and their perceived disturbance, and sound measurements were performed to determine the room acoustic parameters. This study shows that 38% of the surveyed students are disturbed by background noise in an open-plan study environment. Students are mostly disturbed by speech when performing complex cognitive tasks like studying for an exam, reading and writing. Significant but weak correlations were found between the room acoustic parameters and noise disturbance of students. Practitioner Summary: A field study was conducted to gain more insight in the assessment of noise in open-plan study environments at universities in the Netherlands. More than one third of the students was disturbed by noise. An interaction effect was found for task type, source type and room acoustic parameters.


title: Bayesian Reasoning and Machine Learning

abstract: Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.


title: Procedural Modeling for Digital Cultural Heritage

abstract: The rapid development of computer graphics and imaging provides the modern archeologist with several tools to realistically model and visualize archeological sites in 3D. This, however, creates a tension between veridical and realistic modeling. Visually compelling models may lead people to falsely believe that there exists very precise knowledge about the past appearance of a site. In order to make the underlying uncertainty visible, it has been proposed to encode this uncertainty with different levels of transparency in the rendering, or of decoloration of the textures. We argue that procedural modeling technology based on shape grammars provides an interesting alternative to such measures, as they tend to spoil the experience for the observer. Both its efficiency and compactness make procedural modeling a tool to produce multiple models, which together sample the space of possibilities. Variations between the different models express levels of uncertainty implicitly, while letting each individual model keeping its realistic appearance. The underlying, structural description makes the uncertainty explicit. Additionally, procedural modeling also yields the flexibility to incorporate changes as knowledge of an archeological site gets refined. Annotations explaining modeling decisions can be included. We demonstrate our procedural modeling implementation with several recent examples.


title: Aligning 3D models to RGB-D images of cluttered scenes

abstract: The goal of this work is to represent objects in an RGB-D scene with corresponding 3D models from a library. We approach this problem by first detecting and segmenting object instances in the scene and then using a convolutional neural network (CNN) to predict the pose of the object. This CNN is trained using pixel surface normals in images containing renderings of synthetic objects. When tested on real data, our method outperforms alternative algorithms trained on real data. We then use this coarse pose estimate along with the inferred pixel support to align a small number of prototypical models to the data, and place into the scene the model that fits best. We observe a 48% relative improvement in performance at the task of 3D detection over the current state-of-the-art [34], while being an order of magnitude faster.


title: A Chebyshev-Response Step-Impedance Phase-Inverter Rat-Race Coupler Directly on Lossy Silicon Substrate and Its Gilbert Mixer Application

abstract: This paper focuses on the analysis and the design methodology of the step-impedance phase-inverter rat-race coupler on a silicon-based process. The issues of impedance limitation and bandwidth are discussed in detail. Our proposed concept utilizes a high silicon dielectric constant, phase-inverter structure, step-impedance technique, and Chebyshev response to make the rat-race coupler more compact (~ 64% reduction) and highly balanced over a wide operating bandwidth. Moreover, the inter-digital coplanar stripline used in the step-impedance section effectively reduces the characteristic impedance of the transmission line for large size shrinkage and insertion-loss reduction. The demonstrated step-impedance rat-race coupler directly on silicon substrate has 6- ~ 7-dB insertion loss from 5 to 15 GHz and small variations in amplitude/phase balance. Compared with our previous work, the proposed rat-race coupler achieves a 3-dB improvement in the insertion loss. Thus, a 0.13-μm CMOS Gilbert down-converter with a miniature phase-inverter rat-race coupler at the RF path for wideband single-to-differential signal conversion achieves a noise figure of 16 dB.


title: Big data's impact on privacy, security and consumer welfare

abstract: The purpose of this paper is to highlight the costs, benefits, and externalities associated with organizations' use of big data. Specifically, it investigates how various inherent characteristics of big data are related to privacy, security and consumer welfare. The relation between characteristics of big data and privacy, security and consumer welfare issues are examined from the standpoints of data collection, storing, sharing and accessibility. The paper also discusses how privacy, security and welfare effects of big data are likely to vary across consumers of different levels of sophistication, vulnerability and technological savviness. There are various costs, benefits, and externalities associated with organizations' use of big data.Different characteristics of big data are tightly related to privacy, security and welfare effects.Privacy, security and welfare effects of big data are likely to vary across consumers of different levels of sophistication, vulnerability and technological savviness.


title: Privacy-Preserving Deep Learning via Additively Homomorphic Encryption

abstract: We present a privacy-preserving deep learning system in which many learning participants perform neural network-based deep learning over a combined dataset of all, without revealing the participants’ local data to a central server. To that end, we revisit the previous work by Shokri and Shmatikov (ACM CCS 2015) and show that, with their method, local data information may be leaked to an honest-but-curious server. We then fix that problem by building an enhanced system with the following properties: 1) no information is leaked to the server and 2) accuracy is kept intact, compared with that of the ordinary deep learning system also over the combined dataset. Our system bridges deep learning and cryptography: we utilize asynchronous stochastic gradient descent as applied to neural networks, in combination with additively homomorphic encryption. We show that our usage of encryption adds tolerable overhead to the ordinary deep learning system.


title: An ontology-based framework for XML semantic integration

abstract: XML is becoming the standard for data interchange on the Web. However, XML and its schema languages do not express semantics but rather structure, such as nesting information. Therefore, semantically equivalent documents often present different document structures. We provide an ontology-based framework that aims to make two XML documents intemperate at the semantic level while retaining their nesting structure. In our global-as-view approach, we generate an RDF ontology for each of the participating XML documents, which preserves the nesting structure of the document. An RDF global ontology is the result of merging the individual ontologies. The global ontology unifies the query access and establishes semantic connections among the underlying individual databases. We consider two types of queries: those that are posed on the global ontology and those that are posed on any of the XML documents, in a P2P fashion. The former type is processed using query translation from an RDF query to an XML query. The latter type entails bidirectional query processing: the translation from an XML query to an RDF query followed by the translation from an RDF query to an XML query. To ensure the correctness of the answer to the query in the latter case, we introduce the concept of reversibility of the query translation.


title: Finding causes of program output with the Java Whyline

abstract: Debugging and diagnostic tools are some of the most important software development tools, but most expect developers choose the right code to inspect. Unfortunately, this rarely occurs. A new tool called the Whyline is described which avoids such speculation by allowing developers to select questions about a program's output. The tool then helps developers work backwards from output to its causes. The prototype, which supports Java programs, was evaluated in an experiment in which participants investigated two real bug reports from an open source project using either the Whyline or a breakpoint debugger. Whyline users were successful about three times as often and about twice as fast compared to the control group, and were extremely positive about the tool's ability to simplify diagnostic tasks in software development work.


title: Information systems security design methods: implications for information systems development

abstract: The security of information systems is a serious issue because computer abuse is increasing. It is important, therefore, that systems analysts and designers develop expertise in methods for specifying information systems security. The characteristics found in three generations of general information system design methods provide a framework for comparing and understanding current security design methods. These methods include approaches that use checklists of controls, divide functional requirements into engineering partitions, and create abstract models of both the problem and the solution. Comparisons and contrasts reveal that advances in security methods lag behind advances in general systems development methods. This analysis also reveals that more general methods fail to consider security specifications rigorously.


title: Pairwise interaction tensor factorization for personalized tag recommendation

abstract: Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning. In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation.


Data Mining Techniques in EDM for Predicting the Performance of Students Ajay Kumar Pal


Simple gesture-based error correction interface for smartphone speech recognition.


title: Analytic Algorithm for Strand Slot Leakage Reactance of the Transposition Bar in an AC Machine

abstract: Calculation of circulating current in transposition bars is the key problem in the design of stator windings, which will determine the transposition type of stator windings. The circuit equation method, in which the calculation of strand slot leakage reactance in transposition bars is the premise, is widely used in engineering to calculate circulating current. This paper describes an analytical algorithm for the calculation of strand slot leakage reactance in transposition bars called discrete integral method. The presented method is based on Ampere's circuit law. The slot leakage reactance of strands in transposition bars is calculated by the integral of the transposition path discretized into many key points under the proper coordinate system. Contrary to the existing calculation formula, the discrete integral method is a general method for the calculation of slot leakage reactance, which can simulate the transposition path of different transposition types. In order to validate the discrete integral method, the circulating current generated by slot leakage magnetic field of two typical transposition bars is calculated by circuit equation method and finite-element method.


title: A Study of Reinforcement Learning for Neural Machine Translation

abstract: Recent studies have shown that reinforcement learning (RL) is an effective approach for improving the performance of neural machine translation (NMT) system. However, due to its instability, successfully RL training is challenging, especially in real-world systems where deep models and large datasets are leveraged. In this paper, taking several large-scale translation tasks as testbeds, we conduct a systematic study on how to train better NMT models using reinforcement learning. We provide a comprehensive comparison of several important factors (e.g., baseline reward, reward shaping) in RL training. Furthermore, to fill in the gap that it remains unclear whether RL is still beneficial when monolingual data is used, we propose a new method to leverage RL to further boost the performance of NMT systems trained with source/target monolingual data. By integrating all our findings, we obtain competitive results on WMT14 English- German, WMT17 English-Chinese, and WMT17 Chinese-English translation tasks, especially setting a state-of-the-art performance on WMT17 Chinese-English translation task.


title: Achieving"Massive MIMO"Spectral Efficiency with a Not-so-Large Number of Antennas

abstract: Time-Division Duplexing (TDD) allows to estimate the downlink channels for an arbitrarily large number of base station antennas from a finite number of orthogonal uplink pilot signals, by exploiting channel reciprocity. Based on this observation, a recently proposed "Massive MIMO" scheme was shown to achieve unprecedented spectral efficiency in realistic conditions of distance-dependent pathloss and channel coherence time and bandwidth. The main focus and contribution of this paper is an improved Network-MIMO TDD architecture achieving spectral efficiencies comparable with "Massive MIMO", with one order of magnitude fewer antennas per active user per cell (roughly, from 500 to 50 antennas). The proposed architecture is based on a family of Network-MIMO schemes defined by small clusters of cooperating base stations, zero-forcing multiuser MIMO precoding with suitable inter-cluster interference mitigation constraints, uplink pilot signals allocation and frequency reuse across cells. The key idea consists of partitioning the users into equivalence classes, optimizing the Network-MIMO scheme for each equivalence class, and letting a scheduler allocate the channel time-frequency dimensions to the different classes in order to maximize a suitable network utility function that captures a desired notion of fairness. This results in a mixed-mode Network-MIMO architecture, where different schemes, each of which is optimized for the served user equivalence class, are multiplexed in time-frequency. In order to carry out the performance analysis and the optimization of the proposed architecture in a systematic and computationally efficient way, we consider the large-system regime where the number of users, the number of antennas, and the channel coherence block length go to infinity with fixed ratios.


title: The psychosocial impact of Hurricane Katrina: Contextual differences in psychological symptoms, social support, and discrimination

abstract: This study tested a contextual model of disaster reaction by examining regional differences in the psychosocial impact of Hurricane Katrina. A total of 386 individuals participated in this study. All were recruited in the primary areas affected by Hurricane Katrina and included residents of metropolitan New Orleans (Orleans Parish, Louisiana), Greater New Orleans (i.e., Metairie, Kenner, Gretna), and the Mississippi Gulf Coast (i.e., cities along the coast from Waveland to Ocean Springs, Mississippi). Participants were assessed for posttraumatic stress disorder (PTSD) symptoms, other psychological symptoms, perceptions of discrimination, perceptions of social support, evacuation distance, and the extent to which they experienced hurricane-related stressful events. Results were consistent with previous research on the impact of disasters on mental health symptoms. Findings extended research on individual differences in the response to trauma and indicated that regional context predicted unique variance in the experience of discrimination, social support, and emotional symptoms consistent with the theoretical model presented.


title: LibD: Scalable and Precise Third-Party Library Detection in Android Markets

abstract: With the thriving of the mobile app markets, third-party libraries are pervasively integrated in the Android applications. Third-party libraries provide functionality such as advertisements, location services, and social networking services, making multi-functional app development much more productive. However, the spread of vulnerable or harmful third-party libraries may also hurt the entire mobile ecosystem, leading to various security problems. The Android platform suffers severely from such problems due to the way its ecosystem is constructed and maintained. Therefore, third-party Android library identification has emerged as an important problem which is the basis of many security applications such as repackaging detection and malware analysis. According to our investigation, existing work on Android library detection still requires improvement in many aspects, including accuracy and obfuscation resilience. In response to these limitations, we propose a novel approach to identifying third-party Android libraries. Our method utilizes the internal code dependencies of an Android app to detect and classify library candidates. Different from most previous methods which classify detected library candidates based on similarity comparison, our method is based on feature hashing and can better handle code whose package and method names are obfuscated. Based on this approach, we have developed a prototypical tool called LibD and evaluated it with an update-to-date and large-scale dataset. Our experimental results on 1,427,395 apps show that compared to existing tools, LibD can better handle multi-package third-party libraries in the presence of name-based obfuscation, leading to significantly improved precision without the loss of scalability.


title: Extending and Formalizing UML 2.0 Activity Diagrams for the specification of time-constrained business processes

abstract: In real world, most business processes are subject to time constraints. Equally important as the execution of a process is the time in which it is to be performed. A time constrained business process can be defined as a process whose correct behavior depends not only on the results obtained, but also on the time it is carried out. UML has become an important tool for model-driven software development, perhaps partly thanks to its visually oriented modeling concepts, and it is increasingly being used in business process modeling too. In this paper, we extend UML 2.0 Activity Diagrams with time annotations and new modelling constructs inspired of the Timed CSP language. Furthermore, in order to overcome the lack of formal semantics that these diagrams suffer from, we introduce a set of mapping rules to derive formal specifications in terms of Timed CSP processes from models in the extended Activity Diagrams being proposed. Timed CSP has several features that make it well suited to give a formal semantics for extended Activity Diagrams (e.g., compositionality, well defined denotational and operational semantics, etc.). The proposed mapping rules enable checking whether the model described in an Activity Diagram satisfies such properties as timeliness, deadlock freeness, liveness, etc. In order to show the applicability of our approach, we present a case study from a manufacturing domain.


title: To Relay or Not to Relay: Learning Device-to-Device Relaying Strategies in Cellular Networks

abstract: We consider a cellular network where mobile transceiver devices that are owned by self-interested users are incentivized to cooperate with each other using tokens, which they exchange electronically to “buy” and “sell” downlink relay services, thereby increasing the network's capacity compared to a network that only supports base station-to-device (B2D) communications. We investigate how an individual device in the network can learn its optimal cooperation policy online , which it uses to decide whether or not to provide downlink relay services for other devices in exchange for tokens. We propose a supervised learning algorithm that devices can deploy to learn their optimal cooperation strategies online given their experienced network environment. We then systematically evaluate the learning algorithm in various deployment scenarios. Our simulation results suggest that devices have the greatest incentive to cooperate when the network contains (i) many devices with high energy budgets for relaying, (ii) many highly mobile users (e.g., users in motor vehicles), and (iii) neither too few nor too many tokens. Additionally, within the token system, self-interested devices can effectively learn to cooperate online, and achieve up to 20 percent throughput gains on average compared to B2D communications alone, all while selfishly maximizing their own utilities.


title: Recurrent miscarriage: aetiology, management and prognosis.

abstract: Recurrent miscarriage (RM) is a heterogeneous condition. A large number of studies has recently been published, yet many of them have conflicting conclusions. The various aetiological factors, management, prognostic features and outcomes of a subsequent pregnancy in women with RM are reviewed.


title: A Brief Survey on 5G Wireless Mobile Network

abstract: The new upcoming technology of the fifth generation wireless mobile network is advertised as lightning speed internet, everywhere, for everything, for everyone in the nearest future. There are a lot of efforts and research carrying on many aspects, e.g. millimetre wave (mmW) radio transmission, massive multiple input and multiple output (Massive-MIMO) new antenna technology, the promising technique of SDN architecture, Internet of Thing (IoT) and many more. In this brief survey, we highlight some of the most recent developments towards the 5G mobile network.


title: Perspectives of fuzzy systems and control

abstract: Although fuzzy control was initially introduced as a model-free control design method based on the knowledge of a human operator, current research is almost exclusively devoted to model-based fuzzy control methods that can guarantee stability and robustness of the closed-loop system. State-of-the-art techniques for identifying fuzzy models and designing model-based controllers are reviewed in this article. Attention is also paid to the role of fuzzy systems in higher levels of the control hierarchy, such as expert control, supervision and diagnostic systems. Open issues are highlighted and an attempt is made to give some directions for future research.


title: Design and Construction of a Historical Financial Database of the Italian Stock Market 1973--2011

abstract: This article presents the technical aspects of designing and building a historical database of the Italian Stock Market. The database contains daily market data from 1973 to 2011 and is constructed by merging two main digital sources and several other hand-collected data sources. We analyzed and developed semiautomatic tools to deal with problems related to time-series matchings, quality of data, and numerical errors. We also developed a concatenation structure to allow the handling of company name changes, mergers, and spin-offs without artificially altering numerical series. At the same time, we maintained the transparency of the historical information on each individual company listed. Thanks to the overlapping of digital and hand-collected data, the completed database has a very high level of detail and accuracy. The dataset is particularly suited for any empirical research in financial economics and for more practically oriented numerical applications and forecasting simulations.


title: “How Old Do You Think I Am?”: A Study of Language and Age in Twitter

abstract: In this paper we focus on the connection between age and language use, exploring age prediction of Twitter users based on their tweets. We discuss the construction of a fine-grained annotation effort to assign ages and life stages to Twitter users. Using this dataset, we explore age prediction in three different ways: classifying users into age categories, by life stages, and predicting their exact age. We find that an automatic system achieves better performance than humans on these tasks and that both humans and the automatic systems have difficulties predicting the age of older people. Moreover, we present a detailed analysis of variables that change with age. We find strong patterns of change, and that most changes occur at young ages.


A WLAN Fingerprinting Based Indoor Localization Technique


title: An Exploration of Unreliable News Classification in Brazil and The U.S

abstract: The propagation of unreliable information is on the rise in many places around the world. This expansion is facilitated by the rapid spread of information and anonymity granted by the Internet. The spread of unreliable information is a wellstudied issue and it is associated with negative social impacts. In a previous work, we have identified significant differences in the structure of news articles from reliable and unreliable sources in the US media. Our goal in this work was to explore such differences in the Brazilian media. We found significant features in two data sets: one with Brazilian news in Portuguese and another one with US news in English. Our results show that features related to the writing style were prominent in both data sets and, despite the language difference, some features have a universal behavior, being significant to both US and Brazilian news articles. Finally, we combined both data sets and used the universal features to build a machine learning classifier to predict the source type of a news article as reliable or unreliable.


title: A NAT Traversal Mechanism for Peer-To-Peer Networks

abstract: NAT (Network Address Translation) are widely used to isolate the private networks from internet. Unfortunately, NATs block the data transmitted directly between two Nodes under different private networks in many peer-to-peer applications. A protocol, STUN (Simple Traversal of UDP over NATs), has been proposed by IETF to solve the problem, but it costs great network bandwidth and transmission delays when the number of Nodes is large. In addition, it is less flexible between different network conditions. To solve this problem, this paper makes many practices to improve STUN and proposes a novel NAT traversal technology to establish peer-to-peer connections, enabling load on the sever to be remarkably reduced. Our algorithm called C_STUN classifies Node’s area and select proper traversal method dynamically, which can accelerate Nodes’ connection in a large and complicated peer-to-peer network.


title: One Vector is Not Enough: Entity-Augmented Distributed Semantics for Discourse Relations

abstract: Discourse relations bind smaller linguistic units into coherent texts. Automatically identifying discourse relations is difficult, because it requires understanding the semantics of the linked arguments. A more subtle challenge is that it is not enough to represent the meaning of each argument of a discourse relation, because the relation may depend on links between lower-level components, such as entity mentions. Our solution computes distributed meaning representations for each discourse argument by composition up the syntactic parse tree. We also perform a downward compositional pass to capture the meaning of coreferent entity mentions. Implicit discourse relations are then predicted from these two representations, obtaining substantial improvements on the Penn Discourse Treebank.


title: Cosero, Find My Keys! Object Localization and Retrieval Using Bluetooth Low Energy Tags

abstract: Personal robots will contribute mobile manipulation capabilities to our future smart homes. In this paper, we propose a low-cost object localization system that uses static devices with Bluetooth capabilities, which are distributed in an environment, to detect and localize active Bluetooth beacons and mobile devices. This system can be used by a robot to coarsely localize objects in retrieval tasks. We attach small Bluetooth low energy tags to objects and require at least four static Bluetooth receivers. While commodity Bluetooth devices could be used, we have built low-cost receivers from Raspberry Pi computers. The location of a tag is estimated by lateration of its received signal strengths. In experiments, we evaluate accuracy and timing of our approach, and report on the successful demonstration at the RoboCup German Open 2014 competition in Magdeburg.


title: Sizing Energy Storage to Mitigate Wind Power Forecast Error Impacts by Signal Processing Techniques

abstract: This paper proposes to use discrete Fourier transform (DFT) and discrete wavelet transform (DWT) methods to schedule grid-scale energy storage systems to mitigate wind power forecast error impacts while considering energy storage properties. This is accomplished by decomposing the wind forecast error signal to different time-varying periodic components to schedule sodium sulfur (NaS) batteries, compressed air energy storage (CAES), and conventional generators. The advantage of signal processing techniques is that the resultant decomposed components are appropriate for cycling of each energy storage technology. It is also beneficial for conventional generators, which are more efficient to operate close to rated capacity. The tradeoff between installing more energy storage units and decreasing the wind spillage, back-up energy, and the standard deviation of residual forecast error signal is analyzed. The NaS battery life cycle analysis and CAES contribution on increasing NaS battery lifetime are studied. The impact of considering the frequency bias constant to allow small frequency deviations is also investigated. To showcase the applicability of the proposed approach, a simulation case study based on a real-world 5-min interval wind data from Bonneville Power Administration (BPA) in 2013 is presented.


title: Investigating NLP-Based Approaches for Predicting Manual Test Case Failure

abstract: System-level manual acceptance testing is one of the most expensive testing activities. In manual testing, typically, a human tester is given an instruction to follow on the software. The results as "passed" or "failed" will be recorded by the tester, according to the instructions. Since this is a labourintensive task, any attempt in reducing the amount of this type of expensive testing is essential, in practice. Unfortunately, most of the existing heuristics for reducing test executions (e.g., test selection, prioritization, and reduction) are either based on source code or specification of the software under test, which are typically not being accessed during manual acceptance testing. In this paper, we propose a test case failure prediction approach for manual testing that can be used as a noncode/ specifcation-based heuristic for test selection, prioritization, and reduction. The approach uses basic Information Retrieval (IR) methods on the test case descriptions, written in natural language. The IR-based measure is based on the frequency of terms in the manual test scripts. We show that a simple linear regression model using the extracted natural language/IR-based feature together with a typical history-based feature (previous test execution results) can accurately predict the test cases' failure in new releases. We have conducted an extensive empirical study on manual test suites of 41 releases of Mozilla Firefox over three projects (Mobile, Tablet, Desktop). Our comparison of several proposed approaches for predicting failure shows that a) we can accurately predict the test case failure and b) the NLP-based feature can improve the prediction models.


DEVELOPMENT OF IMMUNOCHROMATOGRAPHIC STRIP TEST FOR DENGUE VIRUS DETECTION


title: A Low Profile Printed Quadrifilar Helical Antenna for Handheld UHF RFID Reader

abstract: This paper proposes a compact printed quadrifilar helix antenna (PQHA) that has quarter wavelength inverted-F resonators at a low cost. The proposed PQHA is fed by a four-port quadrature phase system which consists of a 180°power splitter and two couplers implemented by lumped-element circuit. The fabricated and miniaturized PQHA measures only.The measured impedance bandwidth is about 21% and the 3-dB axial ratio bandwidth range from 800-1000MHz can cover the whole UHF RFID bands. The measured maximum gain is around 3.6dBic and the 3-dB beam width is about 120°at 920 MHz, which small size and good performance is enough for practical handheld RFID applications.


title: A Collaborative Key Management Protocol in Ciphertext Policy Attribute-Based Encryption for Cloud Data Sharing

abstract: Ciphertext policy attribute-based encryption (CP-ABE) is a promising cryptographic technique for fine-grained access control of outsourced data in the cloud. However, some drawbacks of key management hinder the popularity of its application. One drawback in urgent need of solution is the key escrow problem. We indicate that front-end devices of clients like smart phones generally have limited privacy protection, so if private keys are entirely held by them, clients risk key exposure that is hardly noticed but inherently existed in previous research. Furthermore, enormous client decryption overhead limits the practical use of ABE. In this paper, we propose a collaborative key management protocol in CP-ABE. Our construction realizes distributed generation, issue and storage of private keys without adding any extra infrastructure. A fine-grained and immediate attribute revocation is provided for key update. The proposed collaborative mechanism effectively solves not only key escrow problem but also key exposure. Meanwhile, it helps markedly reduce client decryption overhead. A comparison with other representative CP-ABE schemes demonstrates that our scheme has somewhat better performance in terms of cloud-based outsourced data sharing on mobile devices. Finally, we provide proof of security for the proposed protocol.


title: Chunk-based resource allocation in OFDMA systems - part I: chunk allocation

abstract: In this paper, throughput performance analysis of the chunk-based subcarrier allocation is presented by considering the average bit-error-rate (BER) constraint over a chunk in downlink multiuser orthogonal frequency division multiplexing (OFDM) transmission. The outage probabilities per subcarrier are compared between the average BER-constraint based chunk allocation and the average signal-to-noise-ratio (SNR) based chunk allocation. The effects of system parameters, such as the number of users, the number of subcarriers per chunk, and the coherence bandwidth, are evaluated. The numerical results show that, when the chunk bandwidth is smaller than the coherence bandwidth, the average downlink throughput of the chunk-based subcarrier allocation is very close to that of the single-subcarrier-based allocation. When the number of users is small, the average throughput increases dramatically with increasing the number of users due to multiuser diversity, whereas when the number of users is large, the multiuser diversity gain is saturated. The effective throughput of the average BER-constraint based chunk allocation is higher than that of the average SNR based chunk allocation, especially when the number of users is large or when the ratio of the chunk bandwidth to the coherence bandwidth is large.


title: Multi-Task Learning for Multiple Language Translation

abstract: In this paper, we investigate the problem of learning a machine translation model that can simultaneously translate sentences from one source language to multiple target languages. Our solution is inspired by the recently proposed neural machine translation model which generalizes machine translation as a sequence learning problem. We extend the neural machine translation to a multi-task learning framework which shares source language representation and separates the modeling of different target language translation. Our framework can be applied to situations where either large amounts of parallel data or limited parallel data is available. Experiments show that our multi-task learning model is able to achieve significantly higher translation quality over individually learned model in both situations on the data sets publicly available.


Mega enterprise chooses smart cards.


title: Learning Discrete Representations via Information Maximizing Self Augmented Training

abstract: Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation. The task includes clustering and hash learning as special cases. Deep neural networks are promising to be used because they can model the non-linearity of data and scale to large datasets. However, their model complexity is huge, and therefore, we need to carefully regularize the networks in order to learn useful representations that exhibit intended invariance for applications of interest. To this end, we propose a method called Information Maximizing Self-Augmented Training (IMSAT). In IMSAT, we use data augmentation to impose the invari-ance on discrete representations. More specifically, we encourage the predicted representations of augmented data points to be close to those of the original data points in an end-to-end fashion. At the same time, we maximize the information-theoretic dependency between data and their predicted discrete representations. Extensive experiments on benchmark datasets show that IMSAT produces state-of-the-art results for both clustering and unsupervised hash learning.


I Have Seen Enough: Transferring Parts Across Categories.


title: Conceptualizing “Smart Cities”

abstract: “Smart City” has become a buzzword. Much is being written about smart cities as we speak, most of it promotional and uncritical. The goal of this article is not to criticize smart cities, nor is it to promote them. Rather, we would like to make a contribution to the conceptualization of smart cities and, by doing so, help the concept to become intellectually more solid. We think that this will also contribute to developing a more realistic and ultimately more practical view of what smart cities can achieve ... and what they cannot. In this article, we will proceed along the following five steps: in a first step, we will conceptualize cities as complex and dynamic socio-technical systems, a conceptualization without which “smart cities” – i. e., the penetration of cities by the information and communication technologies (a phenomenon also called digitalization) – cannot really be understood. In a second step, we will then define such digitalization much more precisely. Such a definition will be necessary in order to understand, in a third step, what digitalization exactly does to cities. In a fourth section, we will then discuss what such digitalization means for cities. In a fifth section, we will further elaborate on this and discuss the different perspectives on smart cities made possible by the digitalization of urban systems.


title: What’s So Different about Blockchain? — Blockchain is a Probabilistic State Machine

abstract: Blockchain is a distributed timestamp server technology introduced for realization of Bitcoin, a digital cash system. It has been attracting much attention especially in the areas of financial and legal applications. But such applications would fail if they are designed without knowledge of the fundamental differences in blockchain from existing technology. We show that blockchain is a probabilistic state machine in which participants can never commit on decisions, we also show that this probabilistic nature is necessarily deduced from the condition where the number of participants remains unknown. This work provides useful abstractions to think about blockchain, and raises discussion for promoting the better use of the technology.


title: Functional connectivity in the brain—is it an elusive concept?

abstract: Even though functional brain connectivity is an influential concept in modern cognitive neuroscience, it is a very controversial notion. This is why further theoretical and methodological clarification are needed to help define precisely what is meant by functional connectivity and to help frame-associated issues. In this review we present the neurophysiological concept of functional connectivity, which utilizes in a plausible manner the notion of neural assemblies, as well as local and large-scale levels of description. Here functional connectivity is the mechanism for the coordination of activity between different neural assemblies in order to achieve a complex cognitive task or perceptual process. Our theoretical and empirical findings offer new insights into possible implications of the concept of functional connectivity for cognitive neuroscience.


title: A Robust Image Authentication Method Distinguishing JPEG Compression from Malicious Manipulation

abstract: Image authentication verifies the originality of an image by detecting malicious manipulations. Its goal is different from that of image watermarking, which embeds into the image a signature surviving most manipulations. Most existing methods for image authentication treat all types of manipulation equally (i.e., as unacceptable). However, some practical applications demand techniques that can distinguish acceptable manipulations (e.g., compression) from malicious ones. In this paper, we present an effective technique for image authentication which can prevent malicious manipulations but allow JPEG lossy compression. The authentication signature is based on the invariance of the relationships between discrete cosine transform (DCT) coefficients at the same position in separate blocks of an image. These relationships are preserved when DCT coefficients are quantized in JPEG compression. Our proposed method can distinguish malicious manipulations from JPEG lossy compression regardless of the compression ratio or the number of compression iterations. We describe adaptive methods with probabilistic guarantee to handle distortions introduced by various acceptable manipulations such as integer rounding, image filtering, image enhancement, or scaling-recaling. We also present theoretical and experimental results to demonstrate the effectiveness of the technique.


title: Opening the can of worms: an exploration tool for vortical flows

abstract: Gaining a comprehensive understanding of turbulent flows still poses one of the great challenges in fluid dynamics. A well-established approach to advance this research is the analysis of the vortex structures contained in the flow. In order to be able to perform this analysis efficiently, supporting visualization tools with clearly defined requirements are needed. In this paper, we present a visualization system which matches these requirements to a large extent. The system consists of two components. The first component analyzes the flow by means of a novel combination of vortex core line detection and the /spl lambda//sub 2/ method. The second component is a vortex browser which allows for an interactive exploration and manipulation of the vortices detected and separated during the first phase. Our system improves the reliability and applicability of existing vortex detection methods and allows for a more efficient study of vortical flows which is demonstrated in an evaluation performed by experts.


Towards a Multi-Feature Enabled Approach for Optimized Expert Seeking


Synchrotron radiation: earth, environmental and material sciences applications


title: Glioma Segmentation with Cascaded Unet

abstract: MRI analysis takes central position in brain tumor diagnosis and treatment, thus it's precise evaluation is crucially important. However, it's 3D nature imposes several challenges, so the analysis is often performed on 2D projections that reduces the complexity, but increases bias. On the other hand, time consuming 3D evaluation, like, segmentation, is able to provide precise estimation of a number of valuable spatial characteristics, giving us understanding about the course of the disease.\newline Recent studies, focusing on the segmentation task, report superior performance of Deep Learning methods compared to classical computer vision algorithms. But still, it remains a challenging problem. In this paper we present deep cascaded approach for automatic brain tumor segmentation. Similar to recent methods for object detection, our implementation is based on neural networks; we propose modifications to the 3D UNet architecture and augmentation strategy to efficiently handle multimodal MRI input, besides this we introduce approach to enhance segmentation quality with context obtained from models of the same topology operating on downscaled data. We evaluate presented approach on BraTS 2018 dataset and discuss results.


title: Motor-learning-related changes in piano players and non-musicians revealed by functional magnetic-resonance signals

abstract: In this study, we investigated blood-flow-related magnetic-resonance (MR) signal changes and the time course underlying short-term motor learning of the dominant right hand in ten piano players (PPs) and 23 non-musicians (NMs), using a complex finger-tapping task. The activation patterns were analyzed for selected regions of interest (ROIs) within the two examined groups and were related to the subjects’ performance. A functional learning profile, based on the regional blood-oxygenation-level-dependent (BOLD) signal changes, was assessed in both groups. All subjects achieved significant increases in tapping frequency during the training session of 35 min in the scanner. PPs, however, performed significantly better than NMs and showed increasing activation in the contralateral primary motor cortex throughout motor learning in the scanner. At the same time, involvement of secondary motor areas, such as bilateral supplementary motor area, premotor, and cerebellar areas, diminished relative to the NMs throughout the training session. Extended activation of primary and secondary motor areas in the initial training stage (7–14 min) and rapid attenuation were the main functional patterns underlying short-term learning in the NM group; attenuation was particularly marked in the primary motor cortices as compared with the PPs. When tapping of the rehearsed sequence was performed with the left hand, transfer effects of motor learning were evident in both groups. Involvement of all relevant motor components was smaller than after initial training with the right hand. Ipsilateral premotor and primary motor contributions, however, showed slight increases of activation, indicating that dominant cortices influence complex sequence learning of the non-dominant hand. In summary, the involvement of primary and secondary motor cortices in motor learning is dependent on experience. Interhemispheric transfer effects are present.


title: Recurrent Gaussian Processes

abstract: We define Recurrent Gaussian Processes (RGP) models, a general family of Bayesian nonparametric models with recurrent GP priors which are able to learn dynamical patterns from sequential data. Similar to Recurrent Neural Networks (RNNs), RGPs can have different formulations for their internal states, distinct inference methods and be extended with deep structures. In such context, we propose a novel deep RGP model whose autoregressive states are latent, thereby performing representation and dynamical learning simultaneously. To fully exploit the Bayesian nature of the RGP model we develop the Recurrent Variational Bayes (REVARB) framework, which enables efficient inference and strong regularization through coherent propagation of uncertainty across the RGP layers and states. We also introduce a RGP extension where variational parameters are greatly reduced by being reparametrized through RNN-based sequential recognition models. We apply our model to the tasks of nonlinear system identification and human motion modeling. The promising obtained results indicate that our RGP model maintains its highly flexibility while being able to avoid overfitting and being applicable even when larger datasets are not available.


title: PID: the Pathway Interaction Database

abstract: The Pathway Interaction Database (PID, http://pid.nci.nih.gov) is a freely available collection of curated and peer-reviewed pathways composed of human molecular signaling and regulatory events and key cellular processes. Created in a collaboration between the US National Cancer Institute and Nature Publishing Group, the database serves as a research tool for the cancer research community and others interested in cellular pathways, such as neuroscientists, developmental biologists and immunologists. PID offers a range of search features to facilitate pathway exploration. Users can browse the predefined set of pathways or create interaction network maps centered on a single molecule or cellular process of interest. In addition, the batch query tool allows users to upload long list(s) of molecules, such as those derived from microarray experiments, and either overlay these molecules onto predefined pathways or visualize the complete molecular connectivity map. Users can also download molecule lists, citation lists and complete database content in extensible markup language (XML) and Biological Pathways Exchange (BioPAX) Level 2 format. The database is updated with new pathway content every month and supplemented by specially commissioned articles on the practical uses of other relevant online tools.


title: Learning with covariate shift-detection and adaptation in non-stationary environments: Application to brain-computer interface

abstract: Learning in the presence of dataset shifts in non-stationary environments is a major challenge. Dataset shifts in the form of covariate shifts commonly occur in a broad range of real-world systems such as, electroencephalogram (EEG) based brain-computer interfaces (BCIs). Under covariate shifts, the properties of the input data distribution may shift over time from training to test/operating phase. In such systems, there is a need for continuous monitoring of the process behavior and tracking the state of the shifts to decide about initiating adaptation in a timely manner. This paper presents a covariate shift-detection and adaptation methodology, and its application to motor-imagery based BCIs. An exponential weighted moving average (EWMA) model based test is used for the covariate shift-detection in the features of EEG signals. The proposed algorithm initiates the adaptation by reconfiguring the knowledge-base of the classifier. Its performance is evaluated through experiments using a real-world dataset i.e. BCI Competition IV dataset 2A. Results show that the proposed methodology effectively performs covariate-shift-detection and adaptation and it can help to realize adaptive BCI systems.


title: Directional Coupler Compensation With Optimally Positioned Capacitances

abstract: An accurate design synthesis for the phase velocity compensation in coupled line microstrip couplers by means of parallel capacitances is presented. In contrast to previous approaches, an a priori arbitrary placement of the capacitances along the coupled line structure is considered. By optimizing these positions, the directivity-bandwidth performance is significantly improved. Cases with two and three capacitances are treated extensively. The findings are used to generalize the compensation scheme to any number of capacitances. Throughout the analysis, the parasitic even-mode capacitance is taken into account using a realistic model. Several design examples are presented. Simulation results are confirmed by measurements. They compare favorably with those reported previously.


title: <u>Correction</u> to "A Formal Basis for the Heuristic Determination of Minimum Cost Paths"

abstract: Our paper on the use of heuristic information in graph searching defined a path-finding algorithm, A*, and proved that it had two important properties. In the notation of the paper, we proved that if the heuristic function n (n) is a lower bound on the true minimal cost from node n to a goal node, then A* is admissible; i.e., it would find a minimal cost path if any path to a goal node existed. Further, we proved that if the heuristic function also satisfied something called the consistency assumption, then A* was optimal; i.e., it expanded no more nodes than any other admissible algorithm A no more informed than A*. These results were summarized in a book by one of us.


title: Transformer-Coupled Multiport ZVS Bidirectional DC–DC Converter With Wide Input Range

abstract: Multiport dc-dc converters are particularly interesting for sustainable energy generation systems where diverse sources and storage elements are to be integrated. This paper presents a zero-voltage switching (ZVS) three-port bidirectional dc-dc converter. A simple and effective duty ratio control method is proposed to extend the ZVS operating range when input voltages vary widely. Soft-switching conditions over the full operating range are achievable by adjusting the duty ratio of the voltage applied to the transformer winding in response to the dc voltage variations at the port. Keeping the volt-second product (half-cycle voltage-time integral) equal for all the windings leads to ZVS conditions over the entire operating range. A detailed analysis is provided for both the two-port and the three-port converters. Furthermore, for the three-port converter a dual-PI-loop based control strategy is proposed to achieve constant output voltage, power flow management, and soft-switching. The three-port converter is implemented and tested for a fuel cell and supercapacitor system.


title: Localized hyperthermia induced by microwave diathermy in osteoarthritis of the knee: a randomized placebo-controlled double-blind clinical trial

abstract: Purpose ::: To investigate the effects of hyperthermia on knee osteoarthritis (OA) in a randomized placebo-controlled double-blind clinical trial.


title: Plagiarism detection on bigdata using modified map-reduced based SCAM algorithm

abstract: Plagiarism is one of the biggest problems of scientific research and engineering. Plagiarism is understood as presenting, intentionally or otherwise, someone else's words, thoughts, analyses, argumentation, pictures, techniques, computer programmers etc. Plagiarism has a wider meaning, paraphrasing someone else's texts by replacing a few words by synonyms or interchanging some sentences in own way is also plagiarism. Even reproducing in your own words a reasoning or analysis made by someone else may constitute plagiarism if you do not add any content of your own; in so doing, you create the opinion that you have invented the argumentation yourself while this is not the case. The same still applies if you bring together bits of work by various authors without mentioning the sources. Plagiarism has also increased with the use of internet and large amount of big data available. Plagiarism detection techniques are applied by making a distinction between natural and programming languages. A similarity score is determined for each pair of documents which match significantly. We have a SCAM (Standard Copy Analysis Mechanism) plagiarism detection algorithm which calculates relative measure to detect overlap by making comparison on asset of words that are common between test document and registered document. Our proposed detection process is based on natural language by comparing documents. We have implemented Map-Reduce based SCAM algorithm for processing big data using Hadoop and detect plagiarism in big data. Normal Scam algorithm is suitable for normal data processing not for big data processing.


title: Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network

abstract: Recently, Generative Adversarial Network (GAN) has been found wide applications in style transfer, image-to-image translation and image super-resolution. In this paper, a color-depth conditional GAN is proposed to concurrently resolve the problems of depth super-resolution and color super-resolution in 3D videos. Firstly, given the low-resolution depth image and low-resolution color image, a generative network is proposed to leverage mutual information of color image and depth image to enhance each other in consideration of the geometry structural dependency of color-depth image in the same scene. Secondly, three loss functions, including data loss, total variation loss, and 8-connected gradient difference loss are introduced to train this generative network in order to keep generated images close to the real ones, in addition to the adversarial loss. Experimental results demonstrate that the proposed approach produces high-quality color image and depth image from low-quality image pair, and it is superior to several other leading methods. Besides, the applications of the proposed method in other tasks are image smoothing and edge detection at the same time.


title: Balanced Coupled-Resonator Bandpass Filters Using Multisection Resonators for Common-Mode Suppression and Stopband Extension

abstract: Novel fourth-order balanced coupled-resonator bandpass filters are proposed using suitably designed half-wavelength (lambda/2) multisection resonators for common-mode suppression. By properly designing the input/output (I/O) resonators associated with the filter composed of four bi-section resonators, a balanced filter with good common-mode suppression is realized, but its rejection bandwidth is rather limited. To widen the rejection bandwidth, the I/O bi-section resonators are replaced by the tri-section ones so that a balanced filter with good common-mode suppression and wide rejection bandwidth may be realized by suitably arranging the composed bi-/tri-section resonators. Specifically, a stopband-extended balanced filter with good common-mode suppression (>50 dB) within the differential-mode passband is implemented and its stopbands are also extended up to 5f0 d with a rejection level of 30 dB, where f0 d is the center frequency in differential-mode operation.


title: Robust speaker recognition based on DNN/i-vectors and speech separation

abstract: Recent research shows that the i-vector framework for speaker recognition can significantly benefit from phonetic information. A common approach is to use a deep neural network (DNN) trained for automatic speech recognition to generate a universal background model (UBM). Studies in this area have been done in relatively clean conditions. However, strong background noise is known to severely reduce speaker recognition performance. This study investigates a phonetically-aware i-vector system in noisy conditions. We propose a front-end to tackle the noise problem by performing speech separation and examine its performance for both verification and identification tasks. The proposed separation system trains a DNN to estimate the ideal ratio mask of the noisy speech. The separated speech is then used to extract enhanced features for the i-vector framework. We compare the proposed system against a multi-condition trained baseline and a traditional GMM-UBM i-vector system. Our proposed system provides an absolute average improvement of 8% in identification accuracy and 1.2% in equal error rate.


title: Flexible Data Access Control Based on Trust and Reputation in Cloud Computing

abstract: Cloud computing offers a new way of services and has become a popular service platform. Storing user data at a cloud data center greatly releases storage burden of user devices and brings access convenience. Due to distrust in cloud service providers, users generally store their crucial data in an encrypted form. But in many cases, the data need to be accessed by other entities for fulfilling an expected service, e.g., an eHealth service. How to control personal data access at cloud is a critical issue. Various application scenarios request flexible control on cloud data access based on data owner policies and application demands. Either data owners or some trusted third parties or both should flexibly participate in this control. However, existing work hasn't yet investigated an effective and flexible solution to satisfy this demand. On the other hand, trust plays an important role in data sharing. It helps overcoming uncertainty and avoiding potential risks. But literature still lacks a practical solution to control cloud data access based on trust and reputation. In this paper, we propose a scheme to control data access in cloud computing based on trust evaluated by the data owner and/or reputations generated by a number of reputation centers in a flexible manner by applying Attribue-Based Encryption and Proxy Re-Encryption. We integrate the concept of context-aware trust and reputation evaluation into a cryptographic system in order to support various control scenarios and strategies. The security and performance of our scheme are evaluated and justified through extensive analysis, security proof, comparison and implementation. The results show the efficiency, flexibility and effectiveness of our scheme for data access control in cloud computing.


title: Fast Single-image Defogging

abstract: Bad weather conditions such as fog, haze, and dust often reduce the performance of outdoor cameras. To improve the effectiveness of surveillance and in-vehicle cameras under such conditions, we propose a method based on a dark channel prior for quickly defogging images. It first estimates the intensity of the atmospheric light by searching the sky area in the foggy image. Then it estimates the transmission map by refining a coarse map from a fine map. Finally, it produces a clearer image from the foggy image by using the estimated intensity and the transmission map. When implemented on a notebook PC with a graphics processing unit (GPU), it was able to process 50 images (720 × 480 pixels) per second. It can thus be used for real-time processing of surveillance and in-vehicle system images.


title: Large-scale image categorization with explicit data embedding

abstract: Kernel machines rely on an implicit mapping of the data such that non-linear classification in the original space corresponds to linear classification in the new space. As kernel machines are difficult to scale to large training sets, it has been proposed to perform an explicit mapping of the data and to learn directly linear classifiers in the new space. In this paper, we consider the problem of learning image categorizers on large image sets (e.g. > 100k images) using bag-of-visual-words (BOV) image representations and Support Vector Machine classifiers. We experiment with three approaches to BOV embedding: 1) kernel PCA (kPCA) [15], 2) a modified kPCA we propose for additive kernels and 3) random projections for shift-invariant kernels [14]. We report experiments on 3 datasets: Cal-tech101, VOC07 and ImageNet. An important conclusion is that simply square-rooting BOV vectors – which corresponds to an exact mapping for the Bhattacharyya kernel – already leads to large improvements, often quite close to the best results obtained with additive kernels. Another conclusion is that, although it is possible to go beyond additive kernels, the embedding comes at a much higher cost.


title: Intrinsically Switched Varactor-Tuned Filters and Filter Banks

abstract: Intrinsically switched tunable filters are switched on and off using the tuning elements that tune their center frequencies and/or bandwidths, without requiring an increase in the tuning range of the tuning elements. Because external RF switches are not needed, substantial improvements in insertion loss, linearity, dc power consumption, control complexity, size, and weight are possible compared to conventional approaches. An intrinsically switched varactor-tuned bandstop filter and bandpass filter bank are demonstrated here for the first time. The intrinsically switched bandstop filter prototype has a second-order notch response with more than 50 dB of rejection continuously tunable from 665 to 1000 MHz (50%) with negligible passband ripple in the intrinsic off state. The intrinsically switched tunable bandpass filter bank prototype, comprised of three third-order bandpass filters, has a constant 50-MHz bandwidth response continuously tunable from 740 to 1644 MHz (122%) with less than 5 dB of passband insertion loss and more than 40 dB of isolation between bands.


title: Technology Acceptance of Cloud Computing: Empirical Evidence from German IT Departments

abstract: In this article, we examine the technology acceptance of cloud computing by analyzing empirical data from 100 CIOs and IT managers from stock indexed companies using factor analysis. For a valid methodological background we use a modified version of the common Technology Acceptance Model. The outcomes indicate that user acceptance of cloud computing can be explained and predicted by various non monetary variables concerning social influence and cognitive instrumental process. In particular, factors such as image, job relevance and perceived usefulness play an important role in cloud computing acceptance. These findings advance theory and contribute to the foundation for future research aimed at improving our understanding of technology adoption behavior. They also provide a structured and more systematic approach for cloud service providers on how to build their services and promoting them to the market.


title: Design of an underwater cable fault location device to precisely locate submarine cable faults

abstract: The objective of the paper is to develop a robust and reliable method to pinpoint submarine cable faults. Submarine cables have existed for over 100 years but there is still no single method used to determine the precise location of a submarine cable fault. The various methods used to pinpoint cable faults, both submarine and non-submarine were researched and investigated to produce a method which would accurately pinpoint submarine cable faults. From this, the idea of a hydrophone string was derived from a non-submarine pinpointing method currently used. The hydrophone string was tested and it accurately pinpointed the location of the cable fault.


title: CellProfiler Analyst: interactive data exploration, analysis and classification of large biological image sets

abstract: Summary: CellProfiler Analyst allows the exploration and visualization of image-based data, together with the classification of complex biological phenotypes, via an interactive user interface designed for biologists and data scientists. CellProfiler Analyst 2.0, completely rewritten in Python, builds on these features and adds enhanced supervised machine learning capabilities (Classifier), as well as visualization tools to overview an experiment (Plate Viewer and Image Gallery). ::: ::: Availability and Implementation: CellProfiler Analyst 2.0 is free and open source, available at http://www.cellprofiler.org and from GitHub (https://github.com/CellProfiler/CellProfiler-Analyst) under the BSD license. It is available as a packaged application for Mac OS X and Microsoft Windows and can be compiled for Linux. We implemented an automatic build process that supports nightly updates and regular release cycles for the software. ::: ::: Contact: gro.etutitsnidaorb@enna ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.


title: A Decision Tree Approach for Predicting Students Academic Performance

abstract: This research is on the use of a decision tree approach for predicting students’ academic performance. Education is the platform on which a society improves the quality of its citizens. To improve on the quality of education, there is a need to be able to predict academic performance of the students. The IBM Statistical Package for Social Studies (SPSS) is used to apply the Chi-Square Automatic Interaction Detection (CHAID) in producing the decision tree structure. Factors such as the financial status of the students, motivation to learn, gender were discovered to affect the performance of the students. 66.8% of the students were predicted to have passed while 33.2% were predicted to fail. It is observed that much larger percentage of the students were likely to pass and there is also a higher likely of male students passing than female students.


title: Joint Deep Learning for Pedestrian Detection

abstract: Feature extraction, deformation handling, occlusion handling, and classification are four important components in pedestrian detection. Existing methods learn or design these components either individually or sequentially. The interaction among these components is not yet well explored. This paper proposes that they should be jointly learned in order to maximize their strengths through cooperation. We formulate these four components into a joint deep learning framework and propose a new deep network architecture. By establishing automatic, mutual interaction among components, the deep model achieves a 9% reduction in the average miss rate compared with the current best-performing pedestrian detection approaches on the largest Caltech benchmark dataset.


title: On the modeling of error functions as high dimensional landscapes for weight initialization in learning networks

abstract: Next generation deep neural networks for classification hosted on embedded platforms will rely on fast, efficient, and accurate learning algorithms. Initialization of weights in learning networks has a great impact on the classification accuracy. In this paper we focus on deriving good initial weights by modeling the error function of a deep neural network as a high-dimensional landscape. We observe that due to the inherent complexity in its algebraic structure, such an error function may conform to general results of the statistics of large systems. To this end we apply some results from Random Matrix Theory to analyse these functions. We model the error function in terms of a Hamiltonian in N-dimensions and derive some theoretical results about its general behavior. These results are further used to make better initial guesses of weights for the learning algorithm.


title: A dynamic operating system for sensor nodes

abstract: Sensor network nodes exhibit characteristics of both embedded systems and general-purpose systems. They must use little energy and be robust to environmental conditions, while also providing common services that make it easy to write applications. In TinyOS, the current state of the art in sensor node operating systems, reusable components implement common services, but each node runs a single statically-linked system image, making it hard to run multiple applications or incrementally update applications. We present SOS, a new operating system for mote-class sensor nodes that takes a more dynamic point on the design spectrum. SOS consists of dynamically-loaded modules and a common kernel, which implements messaging, dynamic memory, and module loading and unloading, among other services. Modules are not processes: they are scheduled cooperatively and there is no memory protection. Nevertheless, the system protects against common module bugs using techniques such as typed entry points, watchdog timers, and primitive resource garbage collection. Individual modules can be added and removed with minimal system interruption. We describe SOS's design and implementation, discuss tradeoffs, and compare it with TinyOS and with the Mate virtual machine. Our evaluation shows that despite the dynamic nature of SOS and its higher-level kernel interface, its long term total usage nearly identical to that of systems such as Mate and TinyOS.


title: Current-Fed Switched Inverter

abstract: High-boost dc-ac inverters are used in solar photovoltaic (PV), fuel cell, wind energy, and uninterruptible power supply systems. High step-up and step-down capabilities and shoot-through immunity are some of the desired properties of an inverter for a reliable, versatile, and low-distortion ac inversion. The recently developed Z-source inverter (ZSI) possesses these qualities. However, the realization of ZSI comes at a cost of higher passive component count as it needs two sets of passive filters. A switched boost inverter (SBI) has similar properties as ZSI, and it has one L-C pair less compared to ZSI, but its gain is less than ZSI. This paper proposes the current-fed switched inverter (CFSI) which combines the high-gain property of ZSI and low passive component count of SBI. The proposed inverter uses only one L-C filter and three switches apart from the inverter structure. The inverter topology is based on current-fed dc/dc topology. Steady-state analysis of the inverter is presented to establish the relation between the dc input and the ac output. A pulse width modulation (PWM) control strategy is devised for the proposed inverter. An experimental prototype is built to validate the proposed inverter circuit in both buck and boost modes of operation. A 353-V dc-link and a 127 V (rms) ac are obtained from a 35.3-V dc input to demonstrate the boost mode of operation. A 200-V dc-link and a 10.5-V (rms) ac are obtained from a 37.8-V dc input to verify the buck mode of operation of CFSI.


Fun with ChromaKey and MATLAB Automatic person segmentation in webcam video for ChromaKey application


title: Hybrid multi-layer deep CNN/aggregator feature for image classification

abstract: Deep Convolutional Neural Networks (DCNN) have established a remarkable performance benchmark in the field of image classification, displacing classical approaches based on hand-tailored aggregations of local descriptors. Yet DCNNs impose high computational burdens both at training and at testing time, and training them requires collecting and annotating large amounts of training data. Supervised adaptation methods have been proposed in the literature that partially re-learn a transferred DCNN structure from a new target dataset. Yet these require expensive bounding-box annotations and are still computationally expensive to learn. In this paper, we address these shortcomings of DCNN adaptation schemes by proposing a hybrid approach that combines conventional, unsupervised aggregators such as Bag-of-Words (BoW), with the DCNN pipeline by treating the output of intermediate layers as densely extracted local descriptors. We test a variant of our approach that uses only intermediate DCNN layers on the standard PASCAL VOC 2007 dataset and show performance significantly higher than the standard BoW model and comparable to Fisher vector aggregation but with a feature that is 150 times smaller. A second variant of our approach that includes the fully connected DCNN layers significantly outperforms Fisher vector schemes and performs comparably to DCNN approaches adapted to Pascal VOC 2007, yet at only a small fraction of the training and testing cost.


title: Facebook and body image concern in adolescent girls: A prospective study

abstract: Objective: The primary aim of the study was to examine the relationship across time between Facebook use and body image concern in adolescent girls. Method: A sample of 438 girls in the first two years (Years 8 and 9) of high school (aged 13-15 years) at Time 1 completed questionnaire measures of Facebook consumption and body image concerns, and again two years later (Time 2). Results: Facebook involvement increased substantially over the two year time period. Body image concerns also increased. Number of Facebook friends was found to prospectively predict the observed increase in drive for thinness. On the other hand, internalisation and body surveillance prospectively predicted the observed increase in number of Facebook friends. Discussion: It was concluded that Facebook ‘friendships’ represent a potent socio-cultural force in the body image of adolescent girls.


title: Wearable Activity Tracking in Car Manufacturing

abstract: A context-aware wearable computing system could support a production or maintenance worker by recognizing the worker's actions and delivering just-in-time information about activities to be performed.


title: Skyline queries: An introduction

abstract: During the two past decades, skyline queries were used in several multi-criteria decision support applications. Given a dominance relationship in a dataset, a skyline query returns the objects that cannot be dominated by any other objects. Skyline queries were studied extensively in multidimensional spaces, in subspaces, in metric spaces, in dynamic spaces, in streaming environments, and in time-series data. Several algorithms were proposed for skyline query processing, such as window-based, progressive, distributed, geometric-based, index-based, divide- and-conquer, and dynamic programming algorithms. Moreover, several variations were proposed to solve application-specific problems like k-dominant skylines, top-k dominating queries, spatial skyline queries, and others. As the number of objects that are returned in a skyline query may become large, there is also an extensive study for the cardinality of skyline queries. This extensive research depicts the importance of skyline queries and their variations in modern applications.


title: Movement Primitive Segmentation for Human Motion Modeling: A Framework for Analysis

abstract: Movement primitive segmentation enables long sequences of human movement observation data to be segmented into smaller components, termed movement primitives , to facilitate movement identification, modeling, and learning. It has been applied to exercise monitoring, gesture recognition, human–machine interaction, and robot imitation learning. This paper proposes a segmentation framework to categorize and compare different segmentation algorithms considering segment definitions, data sources, application-specific requirements, algorithm mechanics, and validation techniques. The framework is applied to human motion segmentation methods by grouping them into online, semionline, and offline approaches. Among the online approaches, distance-based methods provide the best performance, while stochastic dynamic models work best in the semionline and offline settings. However, most algorithms to date are tested with small datasets, and algorithm generalization across participants and to movement changes remains largely untested.


title: Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain

abstract: One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease (AD), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method (SyN) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler-Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast SyN's performance with a related elastic method and with the standard ITK implementation of Thirion's Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of FTD and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.