title: Densely Connected Convolutional Networks

abstract: Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections - one between each layer and its subsequent layer - our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less computation to achieve high performance. Code and pre-trained models are available at this https URL .


title: Fast running experiments involving a humanoid robot

abstract: The present paper describes an implementation of fast running motions involving a humanoid robot. Two important technologies are described: a motion generation and a balance control. The motion generation is a unified way to design both walking and running and can generate the trajectory with the vertical conditions of the Center Of Mass (COM) in short calculation time. The balance control enables a robot to maintain balance by changing the positions of the contact foot dynamically when the robot is disturbed. This control consists of 1) compliance control without force sensors, in which the joints are made compliant by feed-forward torques and adjustment of gains of position control, and 2) feedback control, which uses the measured orientation of the robot's torso used in the motion generation as an initial condition to decide the foot positions. Finally, a human-sized humanoid robot that can run forward at 7.0 [km/h] is presented.


title: Disaster detection from aerial imagery with convolutional neural network

abstract: In recent years, analysis of remote sensing imagery is imperatives in the domain of environmental and climate monitoring primarily for the application of detecting and managing a natural disaster. Satellite imagery or aerial imagery is beneficial because it can widely capture the condition of the surface ground and provides a massive amount of information in a piece of satellite imagery. Since obtaining satellite imagery or aerial imagery is getting more ease in recent years, landslide detection and flood detection is highly in demand. In this paper, we propose automatic natural disaster detection particularly for landslide and flood detection by implementing convolutional neural network (CNN) in extracting the feature of disaster more effectively. CNN is robust to shadow, able to obtain the characteristic of disaster adequately and most importantly able to overcome misdetection or misjudgment by operators, which will affect the effectiveness of disaster relief. The neural network consists of 2 phases: training phase and testing phase. We created training data patches of pre-disaster and post-disaster by clipping and resizing aerial imagery obtained from Google Earth Aerial Imagery. We are currently focusing on two countries which are Japan and Thailand. Training dataset for both landslide and flood consist of 50000 patches. All patches are trained in CNN to extract region where changes occurred or known as disaster region occurred without delay. We obtained accuracy of our system in around 80%–90% of both disaster detections. Based on the promising results, the proposed method may assist in our understanding of the role of deep learning in disaster detection.


title: Design of a novel 6/5 segmental rotor type switched reluctance motor

abstract: This paper presents a novel 6/5 segmental rotor type switched reluctance motor (SRM) for cooling fan application. Different from conventional structures, the proposed structure of rotor consists of a series of discrete segments, and the stator is constructed from two types of stator poles: exciting and auxiliary poles. Furthermore, in this structure short flux paths are taken and no flux reversion exists in the stator. Compared with conventional SRM, the proposed structure increases the electrical utilization of the machine and reduces the core losses, which may leads to a high efficiency. To verify the proposed structure, finite element method (FEM) is employed to get static and dynamic characteristics. Meanwhile, conventional 12/8 SRM is also analyzed for comparison.


title: Nourishing the ground for sustainable HCI: considerations from ecologically engaged art

abstract: Sustainable HCI is now a recognized area of human-computer interaction drawing from a variety of disciplinary approaches, including the arts. How might HCI researchers working on sustainability productively understand the discourses and practices of ecologically engaged art as a means of enriching their own activities? We argue that an understanding of both the history of ecologically engaged art, and the art-historical and critical discourses surrounding it, provide a fruitful entry-point into a more critically aware sustainable HCI. We illustrate this through a consideration of frameworks from the arts, looking specifically at how these frameworks act more as generative devices than prescriptive recipes. Taking artistic influences seriously will require a concomitant rethinking of sustainable HCI standpoints - a potentially useful exercise for HCI research in general.


title: Learning from Demonstrations for Real World Reinforcement Learning

abstract: Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor. This may be acceptable for a simulator, but it severely limits the applicability of deep RL to many real-world tasks, where the agent must learn in the real environment. In this paper we study a setting where the agent may access data from previous control of the system. We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages this data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism. DQfD works by combining temporal difference updates with supervised classification of the demonstrator's actions. We show that DQfD has better initial performance than Prioritized Dueling Double Deep Q-Networks (PDD DQN) as it starts with better scores on the first million steps on 41 of 42 games and on average it takes PDD DQN 82 million steps to catch up to DQfD's performance. DQfD learns to out-perform the best demonstration given in 14 of 42 games. In addition, DQfD leverages human demonstrations to achieve state-of-the-art results for 17 games. Finally, we show that DQfD performs better than three related algorithms for incorporating demonstration data into DQN.


title: Multi Sensor Data Fusion Architectures for Air Traffic Control Applications

abstract: Nowadays, the radar is no longer the sole technology which is able to ensure the surveillance of air traffic. The extensive deployment of satellite systems and air-to-ground data links leads to the emergence of complementary means and techniques on which a great deal of research and experiments have been carried out over the past ten years. In such an environment, the sensor data processing, which is a key element in any Air Traffic Control (ATC) centre, has been continuously upgraded so as to follow the sensor technology evolution and in the meantime improves the quality in term of continuity, integrity and accuracy criteria. This book chapter proposes a comprehensive description of the state of art and the roadmap for the future of the multi sensor data fusion architectures and techniques in use in ATC centres. The first part of the chapter describes the background of ATC centres, while the second part of the chapter points out various data fusion techniques. Multi radar data processing architecture is analysed and a brief definition of internal core tracking algorithms is given as well as a comparative benchmark based on their respective advantages and drawbacks. The third part of the chapter focuses on the most recent evolution that leads from a Multi Radar Tracking System to a Multi Sensor Tracking System. The last part of the chapter deals with the sensor data processing that will be put in operation in the next ten years. The main challenge will be to provide the same level of services in both surface and air surveillance areas in order to offer: • highly accurate air and surface situation awareness to air traffic controllers, • situational awareness via Traffic Information System – Broadcast (TIS-B) services to pilots and vehicle drivers, and • new air and surface safety, capacity and efficiency applications to airports and airlines.


title: Painting-91: a large scale database for computational painting categorization

abstract: Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly, such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency estimation algorithms.


MMTF-14 K : A Multifaceted Movie Trailer Dataset for Recommendation and Retrieval


title: Software defined optical networks technology and infrastructure: Enabling software-defined optical network operations

abstract: Software-defined networking (SDN) enables programmable SDN control and management functions at a number of layers, allowing applications to control network resources or information across different technology domains, e.g., Ethernet, wireless, and optical. Current cloud-based services are pushing networks to new boundaries by deploying cutting edge optical technologies to provide scalable and flexible services. SDN combined with the latest optical transport technologies, such as elastic optical networks, enables network operators and cloud service providers to customize their infrastructure dynamically to user/application requirements and therefore minimize the extra capital and operational costs required for hosting new services. In this paper a unified control plane architecture based on OpenFlow for optical SDN tailored to cloud services is introduced. Requirements for its implementation are discussed considering emerging optical transport technologies. Implementations of the architecture are proposed and demonstrated across heterogeneous state-of-the-art optical, packet, and IT resource integrated cloud infrastructure. Finally, its performance is evaluated using cloud use cases and its results are discussed.


title: Identity, privacy and security challenges with Ontario's enhanced driver's licence

abstract: This paper examines the development in Ontario of the ‘enhanced driver's licence’ as a passport substitute for entering the US, including the public discussion surrounding it. We discuss the significant security, privacy, and identity risks that outweigh the benefits claimed, and call for more effective public participation in decision making over future ID schemes.


History of Mysticism: The Unchanging Testament


title: A Taxonomy of Customer Relationship Management Analyses for Data Warehousing

abstract: Customer Relationship Management (CRM) is a strategy that supports an organization's decision-making process to retain long-term and profitable relationships with its customers. Effective CRM analyses require a detailed data warehouse model that can support various CRM analyses and deep understanding on CRM-related business questions. In this paper, we present a taxonomy of CRM analysis categories. Our CRM taxonomy includes CRM strategies, CRM category analyses, CRM business questions, their potential uses, and key performance indicators (KPIs) for those analysis types. Our CRM taxonomy can be used in selecting and evaluating a data schema for CRM analyses, CRM vendors, CRM strategies, and KPIs.


title: Vision-based UAVs Aerial Image Localization: A Survey

abstract: Unmanned aerial vehicles (UAVs) have been increasingly used in earth observation, public safety, military and civilian applications due to its portability, high mobility and flexibility. In some GPS-denied environments, accurate drone position cannot be obtained due to occlusion, multi-path interference and other factors. While understanding and localization the content of the images is vital for earth observation, map revision, multi-source image fusion, disaster relief, smart city and other applications. The progress of computer vision and convolutional neural networks(CNNs) in image processing provide a promising solution to locate UAVs aerial image and mapping to the large-scale reference image. Firstly, key localization techniques based on image retrieval-----image description, image matching and position mapping are summarized considering the characteristics of UAVs aerial images. And then, image localization based on extracting deep semantic features and image localization based on classification method by subdividing areas are recommended. Throughout this paper, we will have an insight into the prospect of the UAVs image localization and the challenges to be faced.


title: A VLC Smartphone Camera Based Indoor Positioning System

abstract: We present a real-time indoor visible light positioning system based on the optical camera communication, where the coordinate data in the ON–OFF keying format is transmitted via light-emitting diode-based lights and captured using a smartphone camera. The position of the camera is estimated using a novel perspective- $n$ -point problem algorithm, which determines the position of a calibrated camera from $n~3\text{D}$ -to-2D point correspondences. The experimental results show that the proposed system offers mean position errors of 4.81 and 6.58 cm for the heights of 50 and 80 cm, respectively.


title: A Taxonomy of Biologically Inspired Research in Computer Networking

abstract: The natural world is enormous, dynamic, incredibly diverse, and highly complex. Despite the inherent challenges of surviving in such a world, biological organisms evolve, self-organize, self-repair, navigate, and flourish. Generally, they do so with only local knowledge and without any centralized control. Our computer networks are increasingly facing similar challenges as they grow larger in size, but are yet to be able to achieve the same level of robustness and adaptability. Many research efforts have recognized these parallels, and wondered if there are some lessons to be learned from biological systems. As a result, biologically inspired research in computer networking is a quickly growing field. This article begins by exploring why biology and computer network research are such a natural match. We then present a broad overview of biologically inspired research, grouped by topic, and classified in two ways: by the biological field that inspired each topic, and by the area of networking in which the topic lies. In each case, we elucidate how biological concepts have been most successfully applied. In aggregate, we conclude that research efforts are most successful when they separate biological design from biological implementation - that is to say, when they extract the pertinent principles from the former without imposing the limitations of the latter.


title: A magnetostatic 2-axis MEMS scanner with I-section rib-reinforcement and slender permanent magnet patterns

abstract: This study demonstrates the 2-axis epitaxial silicon scanner driven by the coil-less magnetostatic force using electroplated permanent magnet (CoNiMnP) film. The present approach has four merits: (1) the process employs the cheap silicon wafer with epitaxial layer; and the electrochemical etching stop technique is used to precisely control the thickness of scanner; (2) the I-section rib-reinforced structure is implemented to provide high stiffness of the mirror plate; (3) the magnetostatic driving force on scanner is increased by electroplated permanent magnet film with slender patterns; (4) the size of packaged scanner is reduced since the assembled permanent magnets are not required.


title: Design and Performance of Electrical Propulsion System of Extended Range Electric Vehicle (EREV) Chevrolet Volt

abstract: This paper presents the design and performance details of the Chevrolet Volt electric propulsion system. The propulsion system has two machines: One machine is operating mostly as a motor while the other machine is operating mostly as a generator. Both machines of the Volt electric drive system are permanent-magnet ac synchronous machine types with the magnets buried inside the rotor. The motor has distributed windings. However, as opposed to a conventional stranded winding, the Chevrolet Volt motor has bar-wound construction to improve the motor performance, particularly in the low to medium speed range. At higher speed, the skin and proximity effects in the stator bars lead to increased stator winding losses but are addressed in the design. The bar-wound construction also has excellent thermal performance, in both the steady-state and transient conditions, necessary for full electric vehicle (EV) driving. The generator uses concentrated windings. The concentrated winding construction has good slot fill and short end-turn length. These features resulted in good performance in the intended operational region and were an enabler for machine packaging inside the transmission. Both the machines exhibit excellent efficiency and exceptionally smooth and quiet operation. The machine design and construction details, as well as the measured thermal, electromagnetic, and acoustic noise performances, are presented in this paper.


title: LilyPad in the Wild: How Hardwareʼs Long Tail is Supporting New Engineering and Design Communities

abstract: This paper examines the distribution, adoption, and evolution of an open-source toolkit we developed called the LilyPad Arduino. We track the two-year history of the kit and its user community from the time the kit was commercially introduced, in October of 2007, to November of 2009. Using sales data, publicly available project documentation and surveys, we explore the relationship between the LilyPad and its adopters. We investigate the community of developers who has adopted the kit---paying special attention to gender---explore what people are building with it, describe how user feedback impacted the development of the kit and examine how and why people are contributing their own LilyPad-inspired tools back to the community. What emerges is a portrait of a new technology and a new engineering/design community in co-evolution.


title: Model Agnostic Supervised Local Explanations

abstract: Model interpretability is an increasingly important component of practical machine learning. Some of the most common forms of interpretability systems are example-based, local, and global explanations. One of the main challenges in interpretability is designing explanation systems that can capture aspects of each of these explanation types, in order to develop a more thorough understanding of the model. We address this challenge in a novel model called MAPLE that uses local linear modeling techniques along with a dual interpretation of random forests (both as a supervised neighborhood approach and as a feature selection method). MAPLE has two fundamental advantages over existing interpretability systems. First, while it is effective as a black-box explanation system, MAPLE itself is a highly accurate predictive model that provides faithful self explanations, and thus sidesteps the typical accuracy-interpretability trade-off. Specifically, we demonstrate, on several UCI datasets, that MAPLE is at least as accurate as random forests and that it produces more faithful local explanations than LIME, a popular interpretability system. Second, MAPLE provides both example-based and local explanations and can detect global patterns, which allows it to diagnose limitations in its local explanations.


title: Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling

abstract: Recommendation is crucial in both academia and industry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic regression, factorization machines, neural networks and multi-armed bandits. However, most of the previous studies suffer from two limitations: (1) considering the recommendation as a static procedure and ignoring the dynamic interactive nature between users and the recommender systems, (2) focusing on the immediate feedback of recommended items and neglecting the long-term rewards. To address the two limitations, in this paper we propose a novel recommendation framework based on deep reinforcement learning, called DRR. The DRR framework treats recommendation as a sequential decision making procedure and adopts an "Actor-Critic" reinforcement learning scheme to model the interactions between the users and recommender systems, which can consider both the dynamic adaptation and long-term rewards. Furthermore, a state representation module is incorporated into DRR, which can explicitly capture the interactions between items and users. Three instantiation structures are developed. Extensive experiments on four real-world datasets are conducted under both the offline and online evaluation settings. The experimental results demonstrate the proposed DRR method indeed outperforms the state-of-the-art competitors.


title: Dart: A Fast Heuristic Algebraic Reconstruction Algorithm for Discrete Tomography

abstract: Discrete tomography (DT) is concerned with the tomographic reconstruction of images that consist of only a small number of gray levels. DT reconstruction problems are usually underdetermined. Therefore, incorporation of heuristic rules to guide the reconstruction algorithm towards an optimal as well as intuitive solution would be valuable. In this paper, we introduce DART: a new, heuristic DT algorithm that is based on an iterative algebraic reconstruction method. Starting from a continuous reconstruction, a discrete image is reconstructed by consistent updating of border pixels. Using simulation experiments, it is shown that the DART algorithm is capable of computing high quality reconstructions from substantially fewer projections than required for conventional continuous tomography.


title: Gaming addiction, definition and measurement: A large-scale empirical study

abstract: Although the general public appears to have embraced the term 'videogame addiction', the scientific debate as to whether 'gaming addiction' can actually be considered an addiction similar to substance addictions of DSM-IV is still unsettled. To date, research on gaming addiction has focused on problematic behavior from the gaming activity itself and there has been little empirical research related to pathological personality patterns that usually are associated with substance addictions. Therefore, the current study examined how game exposure and 'problematic gaming behavior' are related to personality patterns associated with addiction by means of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2). A large-scale survey study was performed among 1004 adolescent boys (age-range 11-18, M=14.18, SD=1.36) measuring problematic gaming behavior, physical game-related symptoms, gaming behavior and three MMPI-2 subscales measuring personality patterns usually associated with substance addiction (MAC-R, APS, AAS). Results showed that problematic gaming and physical symptoms were positively related to all MMPI-2 subscales, while game exposure was not related to the indirect measures of addictive personality patterns (i.e., MAC-R, APS). Thus, problematic gaming should be clearly distinguished from high game exposure. High game exposure merely indicates enthusiasm for some although it may be psychopathological for others.


title: A filter design algorithm for subpixel rendering on matrix displays

abstract: The subpixel rendering is attractive in that it improves effective resolutions of displays by processing at the subpixel level. But it lies on a risk of chrominance error called color fringing effect while restoring the degraded shape of restrictive resolution. We propose a filter design method for subpixel rendering. We first generate a virtual image model, which describes how human visual system perceives color at each subpixel position. Then, we derive the filter that minimizes the difference between the original image and the corresponding virtual image. While other algorithm is achieved in frequency domain, our algorithm use simpler model for image perception and obtain the filter directly in the spatial domain. We experimentally demonstrate that the proposed filter provides smooth edges, while reducing color fringing effect.


title: Distributed Asynchronous Constrained Stochastic Optimization

abstract: In this paper, we study two problems which often occur in various applications arising in wireless sensor networks. These are the problem of reaching an agreement on the value of local variables in a network of computational agents and the problem of cooperative solution to a convex optimization problem, where the objective function is the aggregate sum of local convex objective functions. We incorporate the presence of a random communication graph between the agents in our model as a more realistic abstraction of the gossip and broadcast communication protocols of a wireless network. An added ingredient is the presence of local constraint sets to which the local variables of each agent is constrained. Our model allows for the objective functions to be nondifferentiable and accommodates the presence of noisy communication links and subgradient errors. For the consensus problem we provide a diminishing step size algorithm which guarantees asymptotic convergence. The distributed optimization algorithm uses two diminishing step size sequences to account for communication noise and subgradient errors. We establish conditions on these step sizes under which we can achieve the dual task of reaching consensus and convergence to the optimal set with probability one. In both cases we consider the constant step size behavior of the algorithm and establish asymptotic error bounds.


title: B2B E-commerce: Frameworks for e-readiness assessment

abstract: This paper looks into the development of e- readiness model for B2B e-commerce of Small and Medium Enterprises (SMEs). Drawing on existing research on e-readiness models of ICT (information, Communications Technology) specifically B2B e-commerce technologies, this paper formulates a conceptual framework of B2B e-commerce assessment constructs together with the specific research approach towards the development of a more robust e-readiness model. The research presents a conceptual model and framework that highlight the key factors in B2B e-commerce implementation, index score formulations, focus group evaluations and the recommendation approaches to improve B2B e-commerce readiness.


title: A Machine Learning Approach to Improve Contactless Heart Rate Monitoring Using a Webcam

abstract: Unobtrusive, contactless recordings of physiological signals are very important for many health and human-computer interaction applications. Most current systems require sensors which intrusively touch the user's skin. Recent advances in contact-free physiological signals open the door to many new types of applications. This technology promises to measure heart rate (HR) and respiration using video only. The effectiveness of this technology, its limitations, and ways of overcoming them deserves particular attention. In this paper, we evaluate this technique for measuring HR in a controlled situation, in a naturalistic computer interaction session, and in an exercise situation. For comparison, HR was measured simultaneously using an electrocardiography device during all sessions. The results replicated the published results in controlled situations, but show that they cannot yet be considered as a valid measure of HR in naturalistic human-computer interaction. We propose a machine learning approach to improve the accuracy of HR detection in naturalistic measurements. The results demonstrate that the root mean squared error is reduced from 43.76 to 3.64 beats/min using the proposed method.


15 Adaptive Decision Fusion for Audio-Visual Speech Recognition


MRI Image Denoising Approach based on TV and Neural Network Filter


title: When Null Hypothesis Significance Testing Is Unsuitable for Research: A Reassessment

abstract: Null hypothesis significance testing (NHST) has several shortcomings that are likely contributing factors behind the widely debated replication crisis of (cognitive) neuroscience, psychology and biomedical science in general. We review these shortcomings and suggest that, after about 60 years of negative experience, NHST should no longer be the default, dominant statistical practice of all biomedical and psychological research. If theoretical predictions are weak we should not rely on all or nothing hypothesis tests. Different inferential methods may be most suitable for different types of research questions. Whenever researchers use NHST they should justify its use, and publish pre-study power calculations and effect sizes, including negative findings. Hypothesis-testing studies should be pre-registered and optimally raw data published. The current statistics lite educational approach for students that has sustained the widespread, spurious use of NHST should be phased out. Instead, we should encourage either more in-depth statistical training of more researchers and/or more widespread involvement of professional statisticians in all research.


title: Machine Learning with Brain Graphs: Predictive Modeling Approaches for Functional Imaging in Systems Neuroscience

abstract: The observation and description of the living brain has attracted a lot of research over the past centuries. Many noninvasive imaging modalities have been developed, such as topographical techniques based on the electromagnetic field potential [i.e., electroencephalography (EEG) and magnetoencephalography (MEG)], and tomography approaches including positron emission tomography and magnetic resonance imaging (MRI). Here we will focus on functional MRI (fMRI) since it is widely deployed for clinical and cognitive neurosciences today, and it can reveal brain function due to neurovascular coupling (see ?From Brain Images to fMRI Time Series?). It has led to a much better understanding of brain function, including the description of brain areas with very specialized functions such as face recognition. These neuroscientific insights have been made possible by important methodological advances in MR physics, signal processing, and mathematical modeling.


title: A Compact SIW Mixer For Millimeter-Wave Applications

abstract: The present paper highlights an innovative broadband millimeter-wave single balanced diode mixer which is designed using a newly designed 90 substrate integrated waveguide (SIW) coupler and two cavity (SIW) filters. The mixer covers RF/LO operating frequency range which fluctuates between 10 to 12 GHz and IF port covers 2GHz. The proposed mixer exhibits a fairly low conversion loss of less than 10 dB and high port to-port isolations over the frequency band of interest as the simulated results make clear. Furthermore, the two cavities SIW filter is embedded to achieve a better image frequency suppression of about 28dB. DOI: http://dx.doi.org/10.11591/ijece.v4i6.6684 Full Text: PDF


title: Experiments in automatic statistical thesaurus construction

abstract: A well constructed thesaurus has long been recognized as a valuable tool in the effective operation of an information retrieval system. This paper reports the results of experiments designed to determine the validity of an approach to the automatic construction of global thesauri (described originally by Crouch in [1] and [2] based on a clustering of the document collection. The authors validate the approach by showing that the use of thesauri generated by this method results in substantial improvements in retrieval effectiveness in four test collections. The term discrimination value theory, used in the thesaurus generation algorithm to determine a term's membership in a particular thesaurus class, is found not to be useful in distinguishing a “good” from an “indifferent” or “poor” thesaurus class). In conclusion, the authors suggest an alternate approach to automatic thesaurus construction which greatly simplifies the work of producing viable thesaurus classes. Experimental results show that the alternate approach described herein in some cases produces thesauri which are comparable in retrieval effectiveness to those produced by the first method at much lower cost.


title: Proportional fairness-based user pairing and power allocation for non-orthogonal multiple access

abstract: The non-orthogonal multiple access (NOMA) has been investigated recently as a candidate radio access technology in future mobile networks. It is able to implement power-domain user multiplexing based on successive interference cancellation (SIC). This paper focuses on the user pairing and power allocation problem in the 2-user NOMA system. The optimal solution in closed-form is derived with the proportional fairness objective and is used for the design of the user pair power allocation scheme. The prerequisites for user pairing are also formulated in order to avoid unnecessary comparison of candidate user pairs. The performance of the proposed scheme is evaluated by system-level simulations and results in higher gains than the search-based transmission power allocation. On the other hand, the computational complexity is reduced by the proposed scheme effectively.


title: A model of type theory in cubical sets

abstract: We present a model of type theory with dependent product, sum, and ::: identity, in cubical sets. We describe a universe and explain how ::: to transform an equivalence between two types into an equality. We ::: also explain how to model propositional truncation and the circle. ::: While not expressed internally in type theory, the model is expressed in a constructive metalogic. Thus it is a step towards a computational interpretation of Voevodsky's Univalence Axiom.


title: Resilience or hope? Incremental and convergent validity of the resilience scale for adults (RSA) and the Herth hope scale (HHS) in the prediction of anxiety and depression

abstract: Background ::: Hope and resilience protect against inner vulnerabilities or harsh life circumstances; they explain individual differences in physical or mental health outcomes under high stress. They have been studied in complementary or competing theoretical frameworks; therefore, the study of measures of hope and resilience should be undertaken prior to explore if they are truly value-added for research. This study investigates the convergent and incremental validity of the Resilience Scale for Adults (RSA) and the Herth Hope Scale (HHS), in the prediction of anxiety and depression (HSCL-25).


title: Performance of a high frequency quasi-resonant inverter with variable-frequency output for induction heating

abstract: This paper presents the performance of a high frequency quasi-resonant inverter using power Metal Oxide Semiconductor Field Effect Transistor (MOSFETs) as switching devices is described. This inverter circuit can output the variable frequency from 160kHz to 400kHz by power MOSFET for induction heating. The induction heating is often used for the heat-treatment of a metal work-piece. In hardening of a gear, since the bottom or tips are heated at lower frequency f L or higher frequency f H respectively, the heating at two different frequencies for uniform heat-treatment is required. Therefore, two inverters are necessary to output different resonant frequency each other. To solve this problem a single adjustable frequency quasi-resonant inverter circuits. This inverter circuit includes the first resonant capacitor and that of the second with a one-way short-circuit switch. Synthetic series capacitance is varied by manipulating the switch and then the resonant frequency can be adjusted. The power MOSFET has the disadvantages of withstand voltage and current. To solve this problem by the methods of connecting switching devices to series [2]. Voltage across the series connected power MOSFETs are balanced by magnetically coupling all gate lines using a simple two-winding transformer.


title: Flip-chip package with integrated antenna on a polyimide substrate for a 122-GHz bistatic radar IC

abstract: This paper presents the packaging technology and the integrated antenna design for a miniaturized 122-GHz radar sensor. The package layout and the assembly process are shortly explained. Measurements of the antenna including the flip chip interconnect are presented that have been achieved by replacing the IC with a dummy chip that only contains a through-line. Afterwards, radiation pattern measurements are shown that were recorded using the radar sensor as transmitter. Finally, details of the fully integrated radar sensor are given, together with results of the first Doppler measurements.


title: The Testing Effect and the Retention Interval

abstract: People learn from tests. Providing tests often enhances retention more than additional study opportunities, but is this testing effect mediated by processes related to retrieval that are fundamentally different from study processes? Some previous studies have reported that testing enhances retention relative to additional studying, but only after a relatively long retention interval. To the extent that this interaction with retention interval dissociates the effects of studying and testing, it may provide crucial evidence for different underlying processes. However, these findings can be questioned because of methodological differences between the study and the test conditions. In two experiments, we eliminated or minimized the confounds that rendered the previous findings equivocal and still obtained the critical interaction. Our results strengthen the evidence for the involvement of different processes underlying the effects of studying and testing, and support the hypothesis that the testing effect is ...


title: Real-Time Tracking via On-line Boosting

abstract: Thermally stable light distillate turbine fuel composition containing 1) a substituted carbamate and 2) an aldehyde-amine condensation product, and a method for operating a turbine engine.


title: User acceptance of a G2B system: a case of electronic procurement system in Malaysia

abstract: Purpose – The purpose of this paper is to determine the factors that influence the intention to use and actual usage of a G2B system such as electronic procurement system (EPS) by various ministries in the Government of Malaysia.Design/methodology/approach – The research uses an extension of DeLone and McLean's model of IS success by including trust, facilitating conditions, and web design quality. The model is tested using an empirical approach. A questionnaire was designed and responses from 358 users from various ministries were collected and analyzed using structural equation modeling (SEM).Findings – The findings of the study indicate that: perceived usefulness, perceived ease of use, assurance of service by service providers, responsiveness of service providers, facilitating conditions, web design (service quality) are strongly linked to intention to use EPS; and intention to use is strongly linked to actual usage behavior.Practical implications – Typically, governments of developing countries spend...


title: Modeling snow crystal growth II: A mesoscopic lattice map with plausible dynamics

abstract: We present a local lattice model for the evolution of snow crystals that combines diffusion-limited aggregation with anisotropic attachment kinetics and an idealized quasi-liquid layer. Despite a voluminous modeling literature, this is apparently the first approach that successfully captures the essential form of core and tip instabilities, branch faceting, and other aspects of real snow crystal growth. As parameters are varied, our nearest neighbor system reproduces the basic features of most of the observed varieties of planar snowflakes and offers new insights into their genesis.


title: P-V-T-C equation for epoxy molding compound

abstract: The isothermal and isobaric volume shrinkage is measured by a single-plunger-type dilatometer for epoxy molding compound (EMC). This device has been found suitable for measuring volume change of thermosetting materials such as commercial EMC under isothermal and isobaric conditions. Moreover, the degree of cure (conversion) was determined by a differential scanning calorimetry (DSC). Combining volume change and conversion, a mathematic pressure-volume-temperature-cure (P-V-T-C) model is proposed to describe the relationship between volume shrinkage, pressure, temperature and conversion. The P-V-T-C equation can be simply expressed as VS(P,T,C)=F/sub 1/(P,T)/spl middot/C/sup F2(P,T)/. This equation can well describe historical profiles of volume shrinkage under specified isothermal and isobaric states. From the predicted results, volume shrinkage under different pressure levels in any specified temperature can be approximated as and it obeys the principle of linearity. With the help of this model, together with three-dimensional mold filling simulation, engineers will be able to predict warpage and residual stresses for a package after molding.


title: Limitations, definitions, principles and methods of risk analysis

abstract: Decisions on veterinary biologicals involve large uncertainties, complexities which cut across many scientific and technical disciplines, and large potential adverse impacts on public health and on important sectors of the economy. How should risk assessment help to guide the decision process on veterinary biologicals? How can risk assessment practices be harmonized internationally, given the different regulatory traditions and institutions of different countries? A broad view of risk assessment is needed, that risk assessment is a framework for summarizing applicable scientific judgement in support of regulatory decision-making. Support for this view of risk assessment is found in the major reports which have defined risk assessment as currently practised by many regulatory agencies in the United States of America (USA). However, some interested and affected parties perceive risk assessment in the USA as overly quantitative and narrowly focused on regulatory standards for carcinogens. An example of risk assessment for microbial contamination indicates how quantitative methods can be used when data are sparse and decisions must be made in the face of great uncertainty. Such quantitative methods can be used to improve communication about risk, to promote consensus in support of controversial decisions, and to identify valuable opportunities for research to reduce the important sources of uncertainty.


title: Scale optimization for full-image-CNN vehicle detection

abstract: Many state-of-the-art general object detection methods make use of shared full-image convolutional features (as in Faster R-CNN). This achieves a reasonable test-phase computation time while enjoys the discriminative power provided by large Convolutional Neural Network (CNN) models. Such designs excel on benchmarks which contain natural images but which have very unnatural distributions, i.e. they have an unnaturally high-frequency of the target classes and a bias towards a "friendly" or "dominant" object scale. In this paper we present further study of the use and adaptation of the Faster R-CNN object detection method for datasets presenting natural scale distribution and unbiased real-world object frequency. In particular, we show that better alignment of the detector scale sensitivity to the extant distribution improves vehicle detection performance. We do this by modifying both the selection of Region Proposals, and through using more scale-appropriate full-image convolution features within the CNN model. By selecting better scales in the region proposal input and by combining feature maps through careful design of the convolutional neural network, we improve performance on smaller objects. We significantly increase detection AP for the KITTI dataset car class from 76.3% on our baseline Faster R-CNN detector to 83.6% in our improved detector.


title: Salinity tolerance of cumin ( Cuminum cyminum L.) genotypes during germination

abstract: A laboratory experiment was conducted to identify cumin (Cuminum cyminum L.) genotypes tolerant to salt at germination stage. Four salinity levels having electrical conductivity of 2.61 (control), 5.17 (low), S.10 (medium) and 12.15 (high) dSm-1 were tested. There is an increase in the suppression of seed germination with increase in salinity in all the genotypes tested. The genotype UC-20S showed maximum salt tolerance, followed by UC-209 and UC-2IS.


title: Electrolyte-Gated Transistors for Organic and Printed Electronics

abstract: Here we summarize recent progress in the development of electrolyte-gated transistors (EGTs) for organic and printed electronics. EGTs employ a high capacitance electrolyte as the gate insulator; the high capacitance increases drive current, lowers operating voltages, and enables new transistor architectures. Although the use of electrolytes in electronics is an old concept going back to the early days of the silicon transistor, new printable, fast-response polymer electrolytes are expanding the potential applications of EGTs in flexible, printed digital circuits, rollable displays, and conformal bioelectronic sensors. This report introduces the structure and operation mechanisms of EGTs and reviews key developments in electrolyte materials for use in printed electronics. The bulk of the article is devoted to electrical characterization of EGTs and emerging applications.


title: Numerical investigation on stable crack growth in plane stress

abstract: Large deformation finite element analysis has been carried out to investigate the stress-strain fields ahead of a growing crack for compact tension (a/W=0.5) and three-point bend (a/W=0.1 and 0.5) specimens under plane stress condition. The crack growth is controlled by the experimental J-integral resistance curves measured by Sun et al. The results indicate that the distributions of opening stress, equivalent stress and equivalent strain ahead of a growing crack are not sensitive to specimen geometry. For both stationary and growing cracks, similar distributions of opening stress and triaxiality can be found along the ligament. During stable crack growth, the crack- tip opening displacement (CTOD) resistance curve and the cohesive fracture energy in the fracture process zone are independent of specimen geometry and may be suitable criteria for characterizing stable crack growth in plane stress.


title: Sequence-Level Knowledge Distillation

abstract: Neural machine translation (NMT) offers a novel alternative formulation of translation that is potentially simpler than statistical approaches. However to reach competitive performance, NMT models need to be exceedingly large. In this paper we consider applying knowledge distillation approaches (Bucila et al., 2006; Hinton et al., 2015) that have proven successful for reducing the size of neural models in other domains to the problem of NMT. We demonstrate that standard knowledge distillation applied to word-level prediction can be effective for NMT, and also introduce two novel sequence-level versions of knowledge distillation that further improve performance, and somewhat surprisingly, seem to eliminate the need for beam search (even when applied on the original teacher model). Our best student model runs 10 times faster than its state-of-the-art teacher with little loss in performance. It is also significantly better than a baseline model trained without knowledge distillation: by 4.2/1.7 BLEU with greedy decoding/beam search. Applying weight pruning on top of knowledge distillation results in a student model that has 13 times fewer parameters than the original teacher model, with a decrease of 0.4 BLEU.


title: Partial duplication of HRX in acute leukemia with trisomy 11.

abstract: The HRX gene has recently been shown to be involved in most of the chromosomal abnormalities of band 11q23 frequently present in human hematological malignancies. Rearrangements are strikingly diverse, but most affect a restricted area of the HRX gene and lead to gene fusion between HRX and a gene located on the partner chromosome. Another kind of HRX alteration seen in human acute leukemia is a partial duplication of the NH2 part of the HRX locus. We have characterized two cases of partial HRX duplication in acute leukemias bearing trisomy 11 as the sole chromosomal abnormality. In one patient analyzed at the genomic level, an Alu repeat was involved within exon 6 but not within intron 1. Splicing of exon 6 to exon 2 was observed in this patient while splicing of exon 8 to exon 2 was observed in the other. Our data indicated that HRX duplication is highly similar to the translocation affecting the HRX locus both in the restricted diversity of the fusion points and the involvement of Alu repeats within the breakpoint cluster region (exon 5 to 10).


title: Pitfalls and Best Practices in Algorithm Configuration

abstract: Good parameter settings are crucial to achieve high performance in many areas of artificial intelligence (AI), such as propositional satisfiability solving, AI planning, scheduling, and machine learning (in particular deep learning). Automated algorithm configuration methods have recently received much attention in the AI community since they replace tedious, irreproducible and error-prone manual parameter tuning and can lead to new state-of-the-art performance. However, practical applications of algorithm configuration are prone to several (often subtle) pitfalls in the experimental design that can render the procedure ineffective. We identify several common issues and propose best practices for avoiding them. As one possibility for automatically handling as many of these as possible, we also propose a tool called GenericWrapper4AC.


Using PAPI for Hardware Performance Monitoring on Linux Systems


title: A Nearly Optimal Contextual Bandit Algorithm

abstract: We investigate the contextual multi-armed bandit problem in an adversarial setting and introduce an online algorithm that asymptotically achieves the performance of the best contextual bandit arm selection strategy under certain conditions. We show that our algorithm is highly efficient and provides significantly improved performance with a guaranteed performance upper bound in a strong mathematical sense. We have no statistical assumptions on the context vectors and the loss of the bandit arms, hence our results are guaranteed to hold even in adversarial environments. We use a tree notion in order to partition the space of context vectors in a nested structure. Using this tree, we construct a large class of context dependent bandit arm selection strategies and adaptively combine them to achieve the performance of the best strategy. We use the hierarchical nature of introduced tree to implement this combination with a significantly low computational complexity, thus our algorithm can be efficiently used in applications involving big data. Through extensive set of experiments involving synthetic and real data, we demonstrate significant performance gains achieved by the proposed algorithm with respect to the state-of-the-art adversarial bandit algorithms.


title: A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems

abstract: The area under the ROC curve, or the equivalent Gini index, is a widely used measure of performance of supervised classification rules. It has the attractive property that it side-steps the need to specify the costs of the different kinds of misclassification. However, the simple form is only applicable to the case of two classes. We extend the definition to the case of more than two classes by averaging pairwise comparisons. This measure reduces to the standard form in the two class case. We compare its properties with the standard measure of proportion correct and an alternative definition of proportion correct based on pairwise comparison of classes for a simple artificial case and illustrate its application on eight data sets. On the data sets we examined, the measures produced similar, but not identical results, reflecting the different aspects of performance that they were measuring. Like the area under the ROC curve, the measure we propose is useful in those many situations where it is impossible to give costs for the different kinds of misclassification.


title: Facilitating Cognitive Presence in Online Learning: Interaction Is Not Enough

abstract: This study assessed the depth of online learning, with a focus on the nature of online interaction in four distance education course designs. The Study Process Questionnaire was used to measure the shift in students' approach to learning from the beginning to the end of the courses. Design had a significant impact on the nature of the interaction and whether students approached learning in a deep and meaningful manner. Structure and leadership were found to be crucial for online learners to take a deep and meaningful approach to learning.


title: Automatic Detection and Quantification of WBCs and RBCs Using Iterative Structured Circle Detection Algorithm

abstract: Segmentation and counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs) and red blood cells (RBCs) in microscopic images is an extremely tedious, time consuming, and inaccurate process. Automatic analysis will allow hematologist experts to perform faster and more accurately. The proposed method uses an iterative structured circle detection algorithm for the segmentation and counting of WBCs and RBCs. The separation of WBCs from RBCs was achieved by thresholding, and specific preprocessing steps were developed for each cell type. Counting was performed for each image using the proposed method based on modified circle detection, which automatically counted the cells. Several modifications were made to the basic (RCD) algorithm to solve the initialization problem, detecting irregular circles (cells), selecting the optimal circle from the candidate circles, determining the number of iterations in a fully dynamic way to enhance algorithm detection, and running time. The validation method used to determine segmentation accuracy was a quantitative analysis that included Precision, Recall, and F-measurement tests. The average accuracy of the proposed method was 95.3% for RBCs and 98.4% for WBCs.


title: Recording of brain activity across spatial scales

abstract: Brain activity reveals exquisite coordination across spatial scales, from local microcircuits to brain-wide networks. Understanding how the brain represents, transforms and communicates information requires simultaneous recordings from distributed nodes of whole brain networks with single-cell resolution. Realizing multi-site recordings from communicating populations is hampered by the need to isolate clusters of interacting cells, often on a day-to-day basis. Chronic implantation of multi-electrode arrays allows long-term tracking of activity. Lithography on thin films provides a means to produce arrays of variable resolution, a high degree of flexibility, and minimal tissue displacement. Sequential application of surface arrays to monitor activity across brain-wide networks and subsequent implantation of laminar arrays to target specific populations enables continual refinement of spatial scale while maintaining coverage.


title: Effects of Training on the Electrophysiologic Properties of Atrium and Accessory Pathway in Athletes with Wolff-Parkinson-White Syndrome

abstract: Twenty-two subjects with Wolff-Parkinson-White (WPW) electrocardiographic pattern performing agonistic physical activity were referred to our laboratory to assess arrhythmogenic risk (group 1). This allowed us to evaluate a less known aspect, namely that of effects of training on the electrophysiologic properties of the atrium and accessory pathway. This was done utilizing a control group of 10 WPW patients who did not perform agonistic physical activity (group 2). All subejcts were symptom free, and without signs of associated cardiopathy if we exclude 1 patient of group 1 who presented moderate mitral valve prolapse. Group 1 patients showed significantly higher mean values for basic cycle length (p


title: Voltage and Power Balance Control for a Cascaded H-Bridge Converter-Based Solid-State Transformer

abstract: The solid-state transformer (SST) is an interface device between ac distribution grids and dc distribution systems. The SST consists of a cascaded multilevel ac/dc rectifier stage, a dual active bridge (DAB) converter stage with high-frequency transformers to provide a regulated 400-V dc distribution, and an optional dc/ac stage that can be connected to the 400-V dc bus to provide residential 120/240 V $_{\rm ac}$ . However, due to dc-link voltage and power unbalance in the cascaded modules, the unbalanced dc-link voltages and power increase the stress of the semiconductor devices and cause overvoltage or overcurrent issues. This paper proposes a new voltage and power balance control for the cascaded H-Bridge converter-based SST. Based on the single-phase dq model, a novel voltage and the power control strategy is proposed to balance the rectifier capacitor voltages and the real power through parallel DAB modules. Furthermore, the intrinsic power constraints of the cascaded H-Bridge voltage balance control are derived and analyzed. With the proposed control methods, the dc-link voltage and the real power through each module can be balanced. The SST switching model simulation and the prototype experiments are presented to verify the performance of the proposed voltage and power balance controller.


title: Scanning and tracking dynamic objects with commodity depth cameras

abstract: The 3D data collected using state-of-the-art algorithms often suffers from various problems, such as incompletion and inaccuracy. Using temporal information has been proven effective for improving the reconstruction quality; for example, KinectFusion [21] shows significant improvements for static scenes. In this work, we present a system that uses commodity depth and color cameras, such as Microsoft Kinects, to fuse the 3D data captured over time for dynamic objects to build a complete and accurate model, and then tracks the model to match later observations. The key ingredients of our system include a nonrigid matching algorithm that aligns 3D observations of dynamic objects by using both geometry and texture measurements, and a volumetric fusion algorithm that fuses noisy 3D data. We demonstrate that the quality of the model improves dramatically by fusing a sequence of noisy and incomplete depth data of human and that by deforming this fused model to later observations, noise-and-hole-free 3D models are generated for the human moving freely.


combination techniques . defined by radiometric drug-inhibitor Potential drug targets for Mycobacterium avium


title: Automated screening of research studies for systematic reviews using study characteristics

abstract: BACKGROUND ::: Screening candidate studies for inclusion in a systematic review is time-consuming when conducted manually. Automation tools could reduce the human effort devoted to screening. Existing methods use supervised machine learning which train classifiers to identify relevant words in the abstracts of candidate articles that have previously been labelled by a human reviewer for inclusion or exclusion. Such classifiers typically reduce the number of abstracts requiring manual screening by about 50%. ::: ::: ::: METHODS ::: We extracted four key characteristics of observational studies (population, exposure, confounders and outcomes) from the text of titles and abstracts for all articles retrieved using search strategies from systematic reviews. Our screening method excluded studies if they did not meet a predefined set of characteristics. The method was evaluated using three systematic reviews. Screening results were compared to the actual inclusion list of the reviews. ::: ::: ::: RESULTS ::: The best screening threshold rule identified studies that mentioned both exposure (E) and outcome (O) in the study abstract. This screening rule excluded 93.7% of retrieved studies with a recall of 98%. ::: ::: ::: CONCLUSIONS ::: Filtering studies for inclusion in a systematic review based on the detection of key study characteristics in abstracts significantly outperformed standard approaches to automated screening and appears worthy of further development and evaluation.


title: Robust power control for QoS guarantee in two-tier femtocell networks

abstract: In this paper, we propose a power control scheme in which a macrocell user and femtocell users are subject to the probability constraints with packets delivery rate and time delay, respectively. This scheme aims at maximizing the net benefits among the network users. Since channel gains are uncertain, a probability density function is used to transform the uncertain constraints into certain ones. An iteration algorithm is derived to solve this optimization problem. Sub-gradient method is adopted to search the optimal solution for the equivalent optimization problem. Numerical results are given to illustrate the performance of the power control scheme.


title: Aggregated Recommendation through Random Forests

abstract: Aggregated recommendation refers to the process of suggesting one kind of items to a group of users. Compared to user-oriented or item-oriented approaches, it is more general and, therefore, more appropriate for cold-start recommendation. In this paper, we propose a random forest approach to create aggregated recommender systems. The approach is used to predict the rating of a group of users to a kind of items. In the preprocessing stage, we merge user, item, and rating information to construct an aggregated decision table, where rating information serves as the decision attribute. We also model the data conversion process corresponding to the new user, new item, and both new problems. In the training stage, a forest is built for the aggregated training set, where each leaf is assigned a distribution of discrete rating. In the testing stage, we present four predicting approaches to compute evaluation values based on the distribution of each tree. Experiments results on the well-known MovieLens dataset show that the aggregated approach maintains an acceptable level of accuracy.


title: Energy-Efficient Hybrid Analog and Digital Precoding for MmWave MIMO Systems With Large Antenna Arrays

abstract: Millimeter wave (mmWave) MIMO will likely use hybrid analog and digital precoding, which uses a small number of RF chains to reduce the energy consumption associated with mixed signal components like analog-to-digital components not to mention baseband processing complexity. However, most hybrid precoding techniques consider a fully connected architecture requiring a large number of phase shifters, which is also energy-intensive. In this paper, we focus on the more energy-efficient hybrid precoding with subconnected architecture, and propose a successive interference cancelation (SIC)-based hybrid precoding with near-optimal performance and low complexity. Inspired by the idea of SIC for multiuser signal detection, we first propose to decompose the total achievable rate optimization problem with nonconvex constraints into a series of simple subrate optimization problems, each of which only considers one subantenna array. Then, we prove that maximizing the achievable subrate of each subantenna array is equivalent to simply seeking a precoding vector sufficiently close (in terms of Euclidean distance) to the unconstrained optimal solution. Finally, we propose a low-complexity algorithm to realize SIC-based hybrid precoding, which can avoid the need for the singular value decomposition (SVD) and matrix inversion. Complexity evaluation shows that the complexity of SIC-based hybrid precoding is only about 10% as complex as that of the recently proposed spatially sparse precoding in typical mmWave MIMO systems. Simulation results verify that SIC-based hybrid precoding is near-optimal and enjoys higher energy efficiency than the spatially sparse precoding and the fully digital precoding.


New Flow-based Heuristic for Search Algorithms Solving Multi-agent Path Finding


ANTUSD: A Large Chinese Sentiment Dictionary.


title: Particle swarm optimization implementation for minimal transmission power providing a fully-connected cluster for the Internet of Things

abstract: Everyday, more sensor nodes become interconnected to make the Internet of Things real. However, since Wireless Sensor Networks comprise energy-limited devices, techniques to save energy have become a trend in research. Since energy-saving comes at a cost to the network, its consequences must be thoroughly considered in order to keep their communication effective. In this work, the use of particle swarm optimization (PSO) is proposed to calculate different transmission powers for each node, without creating disconnected areas in a sensors cluster. The achieved results show that the proposed PSO algorithm is able to save sensors energy when compared to the common deployment of nodes with a single transmission power.


Practical Relay Attack on Contactless Transactions by Using NFC Mobile Phones. IACR Cryptology ePrint Archive


title: Robust local ternary patterns for texture categorization

abstract: This paper proposes a new image representation for texture categorization, which is based on extension of local binary patterns (LBP). As we know LBP can achieve effective description ability with appearance invariance and adaptability of patch matching based methods. However, LBP only thresholds the differential values between neighborhood pixels and the focused one to 0 or 1, which is very sensitive to noise existing in the processed image. This study extends LBP to local ternary patterns (LTP), which considers the differential values between neighborhood pixels and the focused one as negative or positive stimulus if the absolute differential value is large; otherwise no stimulus (set as 0). With the ternary values of all neighbored pixels, we can achieve a pattern index for each local patch, and then extract the pattern histogram for image representation. Experiments on two texture datasets: Brodats32 and KTH TIPS2-a validate that the robust LTP can achieve much better performances than the conventional LBP and the state-of-the-art methods.


title: Efficient privacy-preserving biometric identification in cloud computing

abstract: Biometric identification is a reliable and convenient way of identifying individuals. The widespread adoption of biometric identification requires solid privacy protection against possible misuse, loss, or theft of biometric data. Existing techniques for privacy-preserving biometric identification primarily rely on conventional cryptographic primitives such as homomorphic encryption and oblivious transfer, which inevitably introduce tremendous cost to the system and are not applicable to practical large-scale applications. In this paper, we propose a novel privacy-preserving biometric identification scheme which achieves efficiency by exploiting the power of cloud computing. In our proposed scheme, the biometric database is encrypted and outsourced to the cloud servers. To perform a biometric identification, the database owner generates a credential for the candidate biometric trait and submits it to the cloud. The cloud servers perform identification over the encrypted database using the credential and return the result to the owner. During the identification, cloud learns nothing about the original private biometric data. Because the identification operations are securely outsourced to the cloud, the realtime computational/communication costs at the owner side are minimal. Thorough analysis shows that our proposed scheme is secure and offers a higher level of privacy protection than related solutions such as kNN search in encrypted databases. Real experiments on Amazon cloud, over databases of different sizes, show that our computational/communication costs at the owner side are several magnitudes lower than the existing biometric identification schemes.


title: Rough Terrain 3D Mapping and Navigation Using a Continuously Rotating 2D Laser Scanner

abstract: Mapping, real-time localization, and path planning are prerequisites for autonomous robot navigation. These functions also facilitate situation awareness of remote operators. In this paper, we propose methods for efficient 3D mapping and real-time 6D pose tracking of autonomous robots using a continuously rotating 2D laser scanner. We have developed our approach in the context of the DLR SpaceBot Cup robotics challenge. Multi-resolution surfel representations allow for compact maps and efficient registration of local maps. Real-time pose tracking is performed by a particle filter observing individual laser scan lines. Terrain drivability is assessed within a global environment map and used for planning feasible paths. Our approach is evaluated using challenging real environments.


ROLE OF ORGANIZATIONAL CULTURE ON PROJECT SUCCESS


title: Fall Detection Based on Movement and Smart Phone Technology

abstract: Nowadays, recognizing human activities is an important subject; it is exploited widely and applied to many fields in real-life, especially health care or context aware application. Research achievements are mainly focused on activities of daily living which are useful for suggesting advises to health care applications. Falling event is one of the biggest risks to the health and well being of the elderly especially in independent living because falling accidents may be caused from heart attack. Recognizing this activity still remains in difficult research area. Many systems which equip wearable sensors have been proposed but they are not useful if users forget to wear the clothes or lack ability to adapt themselves to mobile systems without specific wearable sensors. In this paper, we develop novel method based on analyzing the change of acceleration, orientation when the fall occurs. In this study, we recruit five volunteers in our experiment including various fall categories. The results are effective for recognizing fall activity. Our system is implemented on Google Android smart phone which already plugged accelerometer and orientation sensors. The popular phone is used to get data from accelerometer and results show the feasibility of our method and contribute significantly to fall detection in Health care.


title: Does metabolic syndrome have an impact on the quality of life and mood of hemodialysis patients?

abstract: OBJECTIVE ::: Little is known about the association between metabolic syndrome (MetSyn), health-related quality of life (HRQoL), and depressive symptoms in hemodialysis (HD) patients. We hypothesized that MetSyn may be associated with lower HRQoL and depression in HD patients. ::: ::: ::: DESIGN ::: This was a cross-sectional study. ::: ::: ::: SETTING ::: The trial involved HD patients at a tertiary-care hospital. ::: ::: ::: PATIENTS ::: We evaluated 115 patients (41 women and 74 men; mean age, 48.4 +/- SD 11.9 years SD). ::: ::: ::: METHODS ::: MetSyn was defined according to National Cholesterol Education Panel criteria. The Medical Outcomes Study Short Form-36 (SF-36) and Beck Depression Inventory (BDI) were used to assess HRQoL and signs of depression, respectively. We compared HRQoL and clinical and psychosocial characteristics among participants with and without MetSyn. ::: ::: ::: RESULTS ::: Fifty patients (43.5%) had MetSyn, and 65 patients (56.5%) were free of MetSyn. Comparisons of SF-36 and BDI scores between HD patients with and without MetSyn revealed no statistically significant differences. The Physical Component Summary Score (PCS) of SF-36 was independently associated with HD duration (beta = -0.274, P = .002), age (beta = -0.206, P = .024), sleep disturbance (beta = -0.175, P = .045), albumin (beta = +0.252, P = .006), and hemoglobin (beta = +0.270, P = .002) in stepwise linear regression analysis. The MetSyn was not associated with PCS. The Mental Component Summary Score of SF-36 was independently associated with hemoglobin (beta = +0.235, P = .016) and BDI score (beta = -0.218, P = .025). ::: ::: ::: CONCLUSIONS ::: The presence of MetSyn was not associated with HRQoL according to the Mental Component Summary Score. In HD patients, HRQoL and depressive behaviors were not influenced by MetSyn, but by various other factors.


title: Sensemaking in Organizations: Taking Stock and Moving Forward

abstract: Sensemaking is the process through which people work to understand issues or events that are novel, ambiguous, confusing, or in some other way violate expectations. As an activity central to organizing, sensemaking has been the subject of considerable research which has intensified over the last decade. We begin this review with a historical overview of the field, and develop a definition of sensemaking rooted in recurrent themes from the literature. We then review and integrate existing theory and research, focusing on two key bodies of work. The first explores how sensemaking is accomplished, unpacking the sensemaking process by examining how events become triggers for sensemaking, how intersubjective meaning is created, and the role of action in sensemaking. The second body considers how sensemaking enables the accomplishment of other key organizational processes, such organizational change, learning, and creativity and innovation. The final part of the chapter draws on areas of difference and debate h...


title: A Novel Generalized Fuzzy Canonical Correlation Analysis Framework for Feature Fusion and Recognition

abstract: In this paper, a novel CCA-based dimensionality reduction method called generalized fuzzy canonical correlation analysis (GFCCA) is proposed. GFCCA combines the generalized canonical correlation analysis and fuzzy set theory. GFCCA redefines the fuzzy between-class and within-class scatter matrices that relate directly to the samples distribution information. For nonlinear separated problems, we extend the kernel extension of GFCCA with positive definite kernels and indefinite kernels. Experiments on real-world data sets are performed to test and evaluate the effectiveness of the proposed algorithms.


title: A longitudinal study of the relation between adolescent boys and girls' computer use with friends and friendship quality: Support for the social compensation or the rich-get-richer hypothesis?

abstract: Using computers with friends either in person or online has become ubiquitous in the life of most adolescents; however, little is known about the complex relation between this activity and friendship quality. This study examined direct support for the social compensation and rich-get-richer hypotheses among adolescent girls and boys by including social anxiety as a moderating factor. A sample of 1050 adolescents completed a survey in grade 9 and then again in grades 11 and 12. For girls, there was a main effect of using computers with friends on friendship quality; providing support for both hypotheses. For adolescent boys, however, social anxiety moderated this relation, supporting the social compensation hypothesis. These findings were identical for online communication and were stable throughout adolescence. Furthermore, participating in organized sports did not compensate for social anxiety for either adolescent girls or boys. Therefore, characteristics associated with using computers with friends may create a comfortable environment for socially anxious adolescents to interact with their peers which may be distinct from other more traditional adolescent activities.


title: EASYLOCAL++: an object-oriented framework for flexible design of local search algorithms

abstract: Local search is a paradigm for search and optimization problems, which has recently evidenced to be very effective for a large number of combinatorial problems. Despite the increasing interest of the research community in this subject, there is still a lack of a widely-accepted software tools for local search.We propose EASYLOCAL++, an object-oriented framework for the design and the analysis of local-search algorithms. The abstract classes that compose the framework specify and implement the invariant part of the algorithm and are meant to be specialized by concrete classes that supply the problem-dependent part. The framework provides the full control structures of the algorithms, and the user has only to write the problem-specific code. Furthermore, the framework comes with some tools that simplify the analysis of the algorithms.The architecture of EASYLOCAL++ provides a principled modularization for the solution of combinatorial problems by local search and helps the user by deriving a neat conceptual scheme of the application. It also supports the design of combinations of basic techniques and/or neighborhood structures.The framework has been tested in some applicative domains and has proved to be flexible enough in the implementation of algorithms for the solution of various scheduling problems.


title: Are the Dorsal and Ventral Hippocampus Functionally Distinct Structures?

abstract: One literature treats the hippocampus as a purely cognitive structure involved in memory; another treats it as a regulator of emotion whose dysfunction leads to psychopathology. We review behavioral, anatomical, and gene expression studies that together support a functional segmentation into three hippocampal compartments: dorsal, intermediate, and ventral. The dorsal hippocampus, which corresponds to the posterior hippocampus in primates, performs primarily cognitive functions. The ventral (anterior in primates) relates to stress, emotion, and affect. Strikingly, gene expression in the dorsal hippocampus correlates with cortical regions involved in information processing, while genes expressed in the ventral hippocampus correlate with regions involved in emotion and stress (amygdala and hypothalamus).


title: Core stability training on lower limb balance strength

abstract: AbstractThis study aimed to assess the effects of core stability training on lower limbs’ muscular asymmetries and imbalances in team sport. Twenty footballers were divided into two groups, either core stability or control group. Before each daily practice, core stability group (n = 10) performed a core stability training programme, while control group (n = 10) did a standard warm-up. The effects of the core stability training programme were assessed by performing isokinetic tests and single-leg countermovement jumps. Significant improvement was found for knee extensors peak torque at 3.14 rad · s−1 (14%; P < 0.05), knee flexors peak torque at 1.05 and 3.14 rad · s−1 (19% and 22% with P < 0.01 and P < 0.01, respectively) and peak torque flexors/extensors ratios at 1.05 and 3.14 rad · s−1 (7.7% and 8.5% with P < 0.05 and P < 0.05, respectively) only in the core stability group. The jump tests showed a significant reduction in the strength asymmetries in core stability group (−71.4%; P = 0.02) while a concu...


title: Oscillation criteria and growth of nonoscillatory solutions of higher order differential equations

abstract: We look for conditions under which all solutions of the nonlinear ordinary differential equation y(n) + f(t, y) = 0, t ⩾ 0, −∞ < y < ∞, are oscillatory, as well as consider the asymptotic behaviour of the nonoscillatory solutions.


title: Effects of Instructional Guidance and Sequencing of Manipulatives and Written Symbols on Second Graders’ Numeration Knowledge

abstract: Concrete objects used to illustrate mathematical ideas are commonly known as manipulatives. Manipulatives are ubiquitous in North American elementary classrooms in the early years, and although they can be beneficial, they do not guarantee learning. In the present study, the authors examined two factors hypothesized to impact second-graders’ learning of place value and regrouping with manipulatives: (a) the sequencing of concrete (base-ten blocks) and abstract (written symbols) representations of the standard addition algorithm; and (b) the level of instructional guidance on the structural relations between the representations. Results from a classroom experiment with second-grade students (N = 87) indicated that place value knowledge increased from pre-test to post-test when the base-ten blocks were presented before the symbols, but only when no instructional guidance was offered. When guidance was given, only students in the symbols-first condition improved their place value knowledge. Students who received instruction increased their understanding of regrouping, irrespective of representational sequence. No effects were found for iterative sequencing of concrete and abstract representations. Practical implications for teaching mathematics with manipulatives are considered.


A Review of Relation Extraction


title: The Saccharomyces cerevisiae ADE1 gene: structure, overexpression and possible regulation by general amino acid control

abstract: Abstract The ADE1 gene of the yeast Saccharomyces cerevisiae has been cloned by complementation of the ade1 mutation. The nucleotide sequence has been determined for the 918-bp coding region, 240-bp 5′-noncoding region and 292-bp 3′-noncoding region. The sequenced region includes a single large open reading frame coding for a protein of 306 amino acid (aa) residues. The promoter of the ADE1 gene contains a copy of the 5′ - TGACTC hexanucleotide, a feature characteristic of promoters under general aa control. Subsequent search of other published purine biosynthesis gene sequences revealed that all of them also contain general aa control signals in their promoter regions. An expression plasmid containing the ADE1 coding region under control of the PHO5 promoter produced N -succinyl-5-aminoimidazole-4-carboxamide ribotide (SAICAR) synthetase in yeast cells at a level of 40% of total cellular protein. One-step purification resulted in an almost homogeneous preparation of SAICAR synthetase.


title: A Lightened CNN for Deep Face Representation

abstract: Convolution neural network (CNN) has significantly pushed forward the development of face recognition techniques. To achieve ultimate accuracy, CNN models tend to be deeper or multiple local facial patch ensemble, which result in a waste of time and space. To alleviate this issue, this paper studies a lightened CNN framework to learn a compact embedding for face representation. First, we introduce the concept of maxout in the fully connected layer to the convolution layer, which leads to a new activation function, named Max-Feature-Map (MFM). Compared with widely used ReLU, MFM can simultaneously capture compact representation and competitive information. Then, one shallow CNN model is constructed by 4 convolution layers and totally contains about 4M parameters; and the other is constructed by reducing the kernel size of convolution layers and adding Network in Network (NIN) layers between convolution layers based on the previous one. These models are trained on the CASIA-WebFace dataset and evaluated on the LFW and YTF datasets. Experimental results show that the proposed models achieve state-of-the-art results. At the same time, a reduction of computational cost is reached by over 9 times in comparison with the released VGG model.


title: Improving PCM Endurance with a Constant-Cost Wear Leveling Design

abstract: Improving PCM endurance is a fundamental issue when it is considered as an alternative to replace DRAM as main memory. Memory-based wear leveling (WL) is an effective way to improve PCM endurance, but its major challenge is how to efficiently determine the appropriate memory pages for allocation or swapping. In this article, we present a constant-cost WL design that is compatible with existing memory management. Two implementations, namely bucket-based and array-based WL, with constant-time (or nearly zero) search cost are proposed to be integrated into the OS layer and the hardware layer, respectively, as well as to trade between time and space complexity. The results of experiments conducted based on an implementation in Android, as well as simulations with popular benchmarks, to evaluate the effectiveness of the proposed design are very encouraging.


title: ORIGINAL CONTRIBUTION Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets

abstract: Abstract A neural network learning procedure has been applied to the classification of sonar returns from two undersea targets, a metal cylinder and a similarly shaped rock. Networks with an intermediate layer of hidden processing units achieved a classification accuracy as high as 100% on a training set of 104 returns. These networks correctly classified up to 90.4% of 104 test returns not contained in the training set. This performance was better than that of a nearest neighbor classifier, which was 82.7%, and was close to that of an optimal Bayes classifier. Specific signal features extracted by hidden units in a trained network were identified and related to coding schemes in the pattern of connection strengths between the input and the hidden units. Network performance and classification strategy was comparable to that of trained human listeners.


title: Assured Cloud-Based Data Analysis with ClusterBFT

abstract: The shift to cloud technologies is a paradigm change that offers considerable financial and administrative gains. However governmental and business institutions wanting to tap into these gains are concerned with security issues. The cloud presents new vulnerabilities and is dominated by new kinds of applications, which calls for new security solutions.


title: Reducing Bias in Production Speech Models

abstract: Replacing hand-engineered pipelines with end-to-end deep learning systems has enabled strong results in applications like speech and object recognition. However, the causality and latency constraints of production systems put end-to-end speech models back into the underfitting regime and expose biases in the model that we show cannot be overcome by "scaling up", i.e., training bigger models on more data. In this work we systematically identify and address sources of bias, reducing error rates by up to 20% while remaining practical for deployment. We achieve this by utilizing improved neural architectures for streaming inference, solving optimization issues, and employing strategies that increase audio and label modelling versatility.


abstract: Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. ::: Educational Technology & Society, 15(3), 42–57.


title: Friction compensation design for velocity-controlled feed drive motions of CNC machines

abstract: Feedforward friction torque compensation structure is usually used for friction compensation on the basis of the given position commands, velocity commands, and an integrated friction model. However, for mechanical systems that are actuated by velocity-controlled servomotor packs, the torque compensation structure cannot be applied directly to reduce the effects of friction, and thus a velocity-based friction compensator is required in the motion control design. In this study, a new velocity-based friction compensation structure with an additional velocity input was developed to overcome the adverse effects induced by friction to an extent. A number of circle motion tests with different feedrates were carried out on a three-axis CNC milling machine and the experimental results indicate that a maximum reduction of 58.71% is achieved in the absolute contouring errors during circular motions by the friction compensation structure developed in this study.


title: Games in the Brain: Neural Substrates of Gambling Addiction

abstract: As a popular form of recreational risk taking, gambling games offer a paradigm for decision neuroscience research. As an individual behavior, gambling becomes dysfunctional in a subset of the population, with debilitating consequences. Gambling disorder has been recently reconceptualized as a “behavioral addiction” in the DSM-5, based on emerging parallels with substance use disorders. Why do some individuals undergo this transition from recreational to disordered gambling? The biomedical model of problem gambling is a “brain disorder” account that posits an underlying neurobiological abnormality. This article first delineates the neural circuitry that underpins gambling-related decision making, comprising ventral striatum, ventromedial prefrontal cortex, dopaminergic midbrain, and insula, and presents evidence for pathophysiology in this circuitry in gambling disorder. These biological dispositions become translated into clinical disorder through the effects of gambling games. This influence is better ar...


Machine Learning Approach to Identifying the Dataset Threshold for the Performance Estimators in Supervised Learning


title: Electromagnetic Radiations of FPGAs: High Spatial Resolution Cartography and Attack on a Cryptographic Module

abstract: Since the first announcement of a Side Channel Analysis (SCA) about ten years ago, considerable research has been devoted to studying these attacks on Application Specific Integrated Circuits (ASICs), such as smart cards or TPMs. In this article, we compare power-line attacks with ElectroMagnetic (EM) attacks, specifically targeting Field Programmable Gate Array devices (FPGAs), as they are becoming widely used for sensitive applications involving cryptography. We show experimentally that ElectroMagnetic Analysis (EMA) is always faster than the historical Differential Power Analysis (DPA) in retrieving keys of symmetric ciphers. In addition, these analyses prove to be very convenient to conduct, as they are totally non-invasive. Research reports indicate that EMA can be conducted globally, typically with macroscopic home-made coils circling the device under attack, with fair results. However, as accurate professional EM antennas are now becoming more accessible, it has become commonplace to carry out EM analyses locally. Cartography has been carried out by optical means on circuits realized with technology greater than 250 nanometers. Nonetheless, for deep submicron technologies, the feature size of devices that are spied upon is too small to be visible with photographic techniques. In addition, the presence of the 6+ metallization layers obviously prevents a direct observation of the layout. Therefore, EM imaging is emerging as a relevant means to discover the underlying device structure. In this article, we present the first images of deep-submicron FPGAs. The resolution is not as accurate as photographic pictures: we notably compare the layout of toy design examples placed at the four corners of the FPGAs with the EM images we collected. We observe that EM imaging has the advantage of revealing active regions, which can be useful in locating a particular processor (visible while active---invisible when inactive). In the context of EM attacks, we stress that the exact localization of the cryptographic target is not necessary: the coarse resolution we obtain is sufficient. We note that the EM imaging does not reveal the exact layout of the FPGA, but instead directly guides the attacker towards the areas which are leaking the most. We achieve attacks with an accurate sensor, both far from (namely on a SMC capacitor on the board) and close to (namely directly over the FPGA) the encryption co-processor. As compared to the previously published attacks, we report a successful attack on a DES module in fewer than 6,300 measurements, which is currently the best cracking performance against this encryption algorithm implemented in FPGAs.


title: A rate-disortion perspective on local differential privacy

abstract: Local differential privacy is a model for privacy in which an untrusted statistician collects data from individuals who mask their data before revealing it. While randomized response has shown to be a good strategy when the statistician's goal is to estimate a parameter of the population, we consider instead the problem of locally private data publishing, in which the data collector must publish a version of the data it has collected. We model utility by a distortion measure and consider privacy mechanisms that act via a memoryless channnel operating on the data. If we consider a the source distribution to be unknown but in a class of distributions, we arrive at a robust-rate distortion model for the privacy-distortion tradeoff. We show that under Hamming distortions, the differential privacy risk is lower bounded for all nontrivial distortions, and that the lower bound grows logarithmically in the alphabet size.


title: Chemical accidents in academic institutions - A critical commentary on accidents reported in the University of California system

abstract: A summary of laboratory accidents reported in the University of California system and what can be learned from them.


title: Multi-level Adaptive Active Learning for Scene Classification

abstract: Semantic scene classification is a challenging problem in computer vision. In this paper, we present a novel multi-level active learning approach to reduce the human annotation effort for training robust scene classification models. Different from most existing active learning methods that can only query labels for selected instances at the target categorization level, i.e., the scene class level, our approach establishes a semantic framework that predicts scene labels based on a latent object-based semantic representation of images, and is capable to query labels at two different levels, the target scene class level (abstractive high level) and the latent object class level (semantic middle level). Specifically, we develop an adaptive active learning strategy to perform multi-level label query, which maintains the default label query at the target scene class level, but switches to the latent object class level whenever an “unexpected” target class label is returned by the labeler. We conduct experiments on two standard scene classification datasets to investigate the efficacy of the proposed approach. Our empirical results show the proposed adaptive multi-level active learning approach can outperform both baseline active learning methods and a state-of-the-art multi-level active learning method.


title: The Anatomy of Large Facebook Cascades

abstract: When users post photos on Facebook, they have the option of allowing their friends, followers, or anyone at all to subsequently reshare the photo. A portion of the billions of photos posted to Facebook generates cascades of reshares, enabling many additional users to see, like, comment, and reshare the photos. In this paper we present characteristics of such cascades in aggregate, finding that a small fraction of photos account for a significant proportion of reshare activity and generate cascades of non-trivial size and depth. We also show that the true influence chains in such cascades can be much deeper than what is visible through direct attribution. To illuminate how large cascades can form, we study the diffusion trees of two widely distributed photos: one posted on President Barack Obama’s page following his reelection victory, and another posted by an individual Facebook user hoping to garner enough likes for a cause. We show that the two cascades, despite achieving comparable total sizes, are markedly different in their time evolution, reshare depth distribution, predictability of subcascade sizes, and the demographics of users who propagate them. The findings suggest not only that cascades can achieve considerable size but that they can do so in distinct ways.