title: Energy-Efficient CNN Implementation on a Deeply Pipelined FPGA Cluster

abstract: Recently, FPGA-based CNN accelerators have demonstrated superior energy efficiency compared to high-performance devices like GPGPUs. However, due to the constrained on-chip resource and many other factors, single-board FPGA designs may have difficulties in achieving optimal energy efficiency. In this paper we present a deeply pipelined multi-FPGA architecture that expands the design space for optimal performance and energy efficiency. A dynamic programming algorithm is proposed to map the CNN computing layers efficiently to different FPGA boards. To demonstrate the potential of the architecture, we built a prototype system with seven FPGA boards connected with high-speed serial links. The experimental results on AlexNet and VGG-16 show that the prototype can achieve up to 21x and 2x energy efficiency compared to optimized multi-core CPU and GPU implementations, respectively.


title: Securing Email

abstract: Email is the most ubiquitous and interoperable form of online communication today. However, email was not conceived with strong security guarantees, and the ensuing security enhancements are, by contrast, lacking in both ubiquity and interoperability. This state of affairs motivates our work. Our contributions include: (1) a systematization of secure email approaches taken by industry, academia, and independent developers; (2) an evaluation framework for proposed or deployed email security enhancements and a measurement of their security, deployability, and usability; (3) a stakeholder analysis and informed discussion of the current state of secure email; and (4) a research agenda highlighting open problems judged to be useful in pursuing secure email.


title: Matrix co-factorization for recommendation with rich side information and implicit feedback

abstract: Most recommender systems focus on the areas of leisure activities. As the Web evolves into omnipresent utility, recommender systems penetrate more serious applications such as those in online scientific communities. In this paper, we investigate the task of recommendation in online scientific communities which exhibit two characteristics: 1) there exists very rich information about users and items; 2) The users in the scientific communities tend not to give explicit ratings to the resources, even though they have clear preference in their minds. To address the above two characteristics, we propose matrix factorization techniques to incorporate rich user and item information into recommendation with implicit feedback. Specifically, the user information matrix is decomposed into a shared subspace with the implicit feedback matrix, and so does the item information matrix. In other words, the subspaces between multiple related matrices are jointly learned by sharing information between the matrices. The experiments on the testbed from an online scientific community (i.e., Nanohub) show that the proposed method can effectively improve the recommendation performance.


title: Online RGB-D gesture recognition with extreme learning machines

abstract: Gesture recognition is needed in many applications such as human-computer interaction and sign language recognition. The challenges of building an actual recognition system do not lie only in reaching an acceptable recognition accuracy but also with requirements for fast online processing. In this paper, we propose a method for online gesture recognition using RGB-D data from a Kinect sensor. Frame-level features are extracted from RGB frames and the skeletal model obtained from the depth data, and then classified by multiple extreme learning machines. The outputs from the classifiers are aggregated to provide the final classification results for the gestures. We test our method on the ChaLearn multi-modal gesture challenge data. The results of the experiments demonstrate that the method can perform effective multi-class gesture recognition in real-time.


title: SO-Net: Self-Organizing Network for Point Cloud Analysis

abstract: This paper presents SO-Net, a permutation invariant architecture for deep learning with orderless point clouds. The SO-Net models the spatial distribution of point cloud by building a Self-Organizing Map (SOM). Based on the SOM, SO-Net performs hierarchical feature extraction on individual points and SOM nodes, and ultimately represents the input point cloud by a single feature vector. The receptive field of the network can be systematically adjusted by conducting point-to-node k nearest neighbor search. In recognition tasks such as point cloud reconstruction, classification, object part segmentation and shape retrieval, our proposed network demonstrates performance that is similar with or better than state-of-the-art approaches. In addition, the training speed is significantly faster than existing point cloud recognition networks because of the parallelizability and simplicity of the proposed architecture. Our code is available at the project website. https://github.com/lijx10/SO-Net


title: Using Automatic Refactoring to Improve Energy Efficiency of Android Apps

abstract: The ever-growing popularity of mobile phones has brought additional challenges to the software development lifecycle. Mobile applications (apps, for short) ought to provide the same set of features as conventional software, with limited resources: such as, limited processing capabilities, storage, screen and, not less important, power source. Although energy efficiency is a valuable requirement, developers often lack knowledge of best practices. In this paper, we study whether or not automatic refactoring can aid developers ship energy efficient apps. We leverage a tool, Leafactor, with five energy code smells that tend to go unnoticed. We use Leafactor to analyze code smells in 140 free and open source apps. As a result, we detected and fixed code smells in 45 apps, from which 40% have successfully merged our changes into the official repository.


title: Ranking in evolving complex networks

abstract: Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Well-established ranking algorithms (such as the popular Google's PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. The recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of real network traffic, prediction of future links, and identification of highly-significant nodes.


title: SandUSB: An installation-free sandbox for USB peripherals

abstract: This work investigates two emerging attacks — Human Interface Device (HID) attack and Juice Jacking attack — that leverage USB peripherals, and proposes countermeasures to defend against them. These attacks can be easily reproduced using low-cost IoT prototyping boards (e.g., Raspberry Pi) and can bypass commercial antivirus tools. Although several research prototypes can effectively mitigate Juice Jacking and HID attacks, these prototypes encounter two challenges with respect to deployability: 1) Some require installation on host computers, which is inconvenient and users may lack permission to install software; 2) Some assume cryptographic keys for authentication, but such cryptographic operations may not be supported by legacy USB peripherals and hosts. To address these challenges, this paper presents the design and implementation of SandUSB, an installation-free and user-controllable security gadget for USB peripherals. Since SandUSB acts as an intermediary between the USB host and device, SandUSB can perform efficient scanning and analysis without changing USB devices or hosts. In addition, SandUSB provides a simple user interface (UI) to control and monitor connected USB devices, enabling users to identify malicious peripherals that masquerade as another type. This UI is complementary to the automatic defensive measures that SandUSB supports or cryptographic-based authentication. Our evaluation demonstrates that SandUSB can effectively defend against various USB attacks, including the HID attack and Juice Jacking, using affordable and easily accessible hardware.


title: Design methodologies for high-performance noise-tolerant XOR-XNOR circuits

abstract: Scaling down to deep submicrometer (DSM) technology has made noise a metric of equal importance as compared to power, speed, and area. Smaller feature size, lower supply voltage, and higher frequency are some of the characteristics for DSM circuits that make them more vulnerable to noise. New designs and circuit techniques are required in order to achieve robustness in presence of noise. Novel methodologies for designing energy-efficient noise-tolerant exclusive-OR-exclusive- NOR circuits that can operate at low-supply voltages with good signal integrity and driving capability are proposed. The circuits designed, after applying the proposed methodologies, are characterized and compared with previously published circuits for reliability, speed and energy efficiency. To test the driving capability of the proposed circuits, they are embedded in an existing 5-2 compressor design. The average noise threshold energy (ANTE) is used for quantifying the noise immunity of the proposed circuits. Simulation results show that, compared with the best available circuit in literature, the proposed circuits exhibit better noise-immunity, lower power-delay product (PDP) and good driving capability. All of the proposed circuits prove to be faster and successfully work at all ranges of supply voltage starting from 3.3 V down to 0.6 V. The savings in the PDP range from 94% to 21% for the given supply voltage range respectively and the average improvement in the ANTE is 2.67X.


Using Neural Networks to Predict Emoji Usage from Twitter Data


title: Detection of Websites Based on PhishingWebsites Characteristics

abstract: Phishing is a web-based attack that uses social engineering techniques to exploit internet users and acquire sensitive data. Most phishing attacks work by creating a fake version of the real site's web interface to gain the user's trust.. We applied different methods for detecting phishing using known as well as new features. In this we used the heuristic-based approach to handle phishing attacks, in this approached several website features are collected and used to identify the type of the website. The heuristic-based approach can recognize newly created fake websites in real-time. One intelligent approach based on genetic algorithm seems a potential solution that may effectively detect phishing websites with high accuracy and prevent it by blocking them.


title: Capacitor Voltage Balancing Schemes in Flying Capacitor Multilevel Inverters

abstract: This paper focuses on the development of capacitor voltage balancing methods in a flying capacitor multilevel inverter (FCMLI). Three such strategies have been described and have been applied to a five-level CMLI. The sinusoidal pulse width modulation technique has been used while making the proposals. The first scheme works on the principle of selection of the most favorable switching state depending on the preferential charging and discharging of capacitors. It uses a two-level comparator for each capacitor state detection. In the second scheme, capacitor voltages are controlled in a three-level comparator to achieve better performance. In the third scheme, a sampling frequency-based approach for controlling the capacitor voltages has been proposed to achieve further improved performance. The performance of proposed strategies is confirmed through both simulation and experimental investigations.


title: The CLSA Model: A Novel Framework for Concept-Level Sentiment Analysis

abstract: Hitherto, sentiment analysis has been mainly based on algorithms relying on the textual representation of online reviews and microblogging posts. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling, and counting their words. But when it comes to interpreting sentences and extracting opinionated information, their capabilities are known to be very limited. Current approaches to sentiment analysis are mainly based on supervised techniques relying on manually labeled samples, such as movie or product reviews, where the overall positive or negative attitude was explicitly indicated. However, opinions do not occur only at document-level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a review. In order to overcome this and many other issues related to sentiment analysis, we propose a novel framework, termed concept-level sentiment analysis (CLSA) model, which takes into account all the natural-language-processing tasks necessary for extracting opinionated information from text, namely: microtext analysis, semantic parsing, subjectivity detection, anaphora resolution, sarcasm detection, topic spotting, aspect extraction, and polarity detection.


title: Broadband 4×4 Butler matrix using wideband 90° hybrid couplers and crossovers for beamforming networks

abstract: The design and simulation of a wideband four-port steerable beamforming networks using a 4×4 Butler matrix for the 2.4 GHz applications is presented. The aim of this work is to develop an antenna array feeding network based on Butler matrix with a large bandwidth in order to cover the IEEE 802.11b/g/n band. In order to achieve wideband features, the matrix uses two-section branch-line couplers and four-section branch-line crossovers instead of the standard ones. The proposed circuit exhibits a high isolation and wideband 4×4 Butler matrix designed and simulated by using a single-layer FR-4 substrate, which covers the frequency band ranging from 2.1 GHz to 2.75 GHz. The return losses and the isolation are better than 28 dB and 30 dB, respectively, with its good performance is more suitable for IEEE 802.11b/g/n and ISM applications.


Contrastive Logic


title: FPGA implementations of fast fourier transforms for real-time signal and image processing

abstract: Applications based on Fast Fourier Transform (FFT) such as signal and image processing require high computational power, plus the ability to experiment with algorithms. Reconfigurable hardware devices in the form of Field Programmable Gate Arrays (FPGAs) have been proposed as a way of obtaining high performance at an economical price. At present, however, users must program FPGAs at a very low level and have a detailed knowledge of the architecture of the device being used. To try to reconcile the dual requirements of high performance and ease of development, this paper reports on the design and realisation of a High Level framework for the implementation of 1-D and 2-D FFTs for real-time applications. Results show that the parallel implementation of 2-D FFT achieves virtually linear speed-up and real-time performance for large matrix sizes. Finally, an FPGA-based parametrisable environment based on the developed parallel 2-D FFT architecture is presented as a solution for frequency-domain image filtering application.


title: Fast and accurate mining of correlated heavy hitters

abstract: The problem of mining Correlated Heavy Hitters (CHH) from a two-dimensional data stream has been introduced recently, and a deterministic algorithm based on the use of the Misra--Gries algorithm has been proposed by Lahiri et al. to solve it. In this paper we present a new counter-based algorithm for tracking CHHs, formally prove its error bounds and correctness and show, through extensive experimental results, that our algorithm outperforms the Misra--Gries based algorithm with regard to accuracy and speed whilst requiring asymptotically much less space.


title: Review Paper Corporate Governance in Emerging Economies: A Review of the Principal–Principal Perspective

abstract: Instead of traditional principal-agent conflicts espoused in most research dealing with developed economies, principal-principal conflicts have been identified as a major concern of corporate governance in emerging economies. Principal-principal conflicts between controlling shareholders and minority shareholders result from concentrated ownership, extensive family ownership and control, business group structures, and weak legal protection of minority shareholders. Such principal-principal conflicts alter the dynamics of the corporate governance process and, in turn, require remedies different from those that deal with principal-agent conflicts. This article reviews and synthesizes recent research from strategy, finance, and economics on principal-principal conflicts with an emphasis on their institutional antecedents and organizational consequences. The resulting integration provides a foundation upon which future research can continue to build.


title: Traffic Load Minimization in Software Defined Wireless Sensor Networks

abstract: The emerging software defined networking enables the separation of control plane and data plane and saves the resource consumption of the network. Breakthrough in this area has opened up a new dimension to the design of software defined method in wireless sensor networks (WSNs). However, the limited routing strategy in software defined WSNs (SDWSNs) imposes a great challenge in achieving the minimum traffic load. In this paper, we propose a flow splitting optimization (FSO) algorithm for solving the problem of traffic load minimization (TLM) in SDWSNs by considering the selection of optimal relay sensor node and the transmission of optimal splitting flow. To this end, we first establish the model of different packet types and describe the TLM problem. We then formulate the TLM problem into an optimization problem which is constrained by the load of sensor nodes and the packet similarity between different sensor nodes. Afterwards, we present a Levenberg–Marquardt algorithm for solving the optimization problem of traffic load. We also provide the convergence analysis of the Levenberg–Marquardt algorithm. Finally, we implement the FSO algorithm in the NS-2 simulator and give extensive simulation results to verify the efficiency of FSO algorithm in SDWSNs.


title: Bilevel Programming for Hyperparameter Optimization and Meta-Learning

abstract: We introduce a framework based on bilevel programming that unifies gradient-based hyperparameter optimization and meta-learning. We show that an approximate version of the bilevel problem can be solved by taking into explicit account the optimization dynamics for the inner objective. Depending on the specific setting, the outer variables take either the meaning of hyperparameters in a supervised learning problem or parameters of a meta-learner. We provide sufficient conditions under which solutions of the approximate problem converge to those of the exact problem. We instantiate our approach for meta-learning in the case of deep learning where representation layers are treated as hyperparameters shared across a set of training episodes. In experiments, we confirm our theoretical findings, present encouraging results for few-shot learning and contrast the bilevel approach against classical approaches for learning-to-learn.


title: Enhancing thin dielectric remaining detection under polysilico plug of advanced DRAM by electron beam inspection

abstract: We’ve studied using negative mode e-beam inspection (EBI) to detect a thin layer of dielectric remain at the bottom of storage node contact (SNC) hole. A thin layer (~15 nm) of nitride at the bottom of some SNC holes was intentionally created, EBI were performed at two stages: post etch clean and after silicon nitride cap of SNC plug. Negative mode EBI after SNC nitride cap deposition successfully captured these contact open defects as bright voltage contrast (BVC) defects. The cross-section scanning electron microscope (SEM) confirmed the results. The inspection mechanism is also discussed in the paper. We found that negative mode EBI is very useful for in-line defect monitoring.


title: Coordinator Role Mobility Method for Increasing the Life Expectancy of Wireless Sensor Networks

abstract: Abstract The general problem of wireless sensor network nodes is the low-power batteries that significantly limit the life expectancy of a network. Nowadays the technical solutions related to energy resource management are being rapidly developed and integrated into the daily lives of people. The energy resource management systems use sensor networks for receiving and processing information during the realia time. The present paper proposes using a coordinator role mobility method for controlling the routing processes for energy balancing in nodes, which provides dynamic network reconfiguration possibilities. The method is designed to operate fully in the background and can be integrated into any exiting working system.


title: Overlaying communities and topics: an analysis on publication networks

abstract: Two layers of enriched information are constructed for communities: a paper-to-paper network based on shared author relations and a paper-to-paper network based on shared word relations. k-means and VOSviewer, a modularity-based clustering technique, are used to identify publication clusters in the two networks. Results show that a few research topics such as webometrics, bibliometric laws, and language processing, form their own research community; while other research topics contain different research communities, which may be caused by physical distance.


title: A Single-Planner Approach to Multi-Modal Humanoid Mobility

abstract: In this work, we present an approach to planning for humanoid mobility. Humanoid mobility is a challenging problem, as the configuration space for a humanoid robot is intractably large, especially if the robot is capable of performing many types of locomotion. For example, a humanoid robot may be able to perform such tasks as bipedal walking, crawling, and climbing. Our approach is to plan for all these tasks within a single search process. This allows the search to reason about all the capabilities of the robot at any point, and to derive the complete solution such that the plan is guaranteed to be feasible. A key observation is that we often can roughly decompose a mobility task into a sequence of smaller tasks, and focus planning efforts to reason over much smaller search spaces. To this end, we leverage the results of a recently developed framework for planning with adaptive dimensionality, and incorporate the capabilities of available controllers directly into the planning process. The resulting planner can also be run in an interleaved fashion alongside execution so that time spent idle is much reduced.


title: Commonsense for Generative Multi-Hop Question Answering Tasks

abstract: Reading comprehension QA tasks have seen a recent surge in popularity, yet most works have focused on fact-finding extractive QA. We instead focus on a more challenging multi-hop generative task (NarrativeQA), which requires the model to reason, gather, and synthesize disjoint pieces of information within the context to generate an answer. This type of multi-step reasoning also often requires understanding implicit relations, which humans resolve via external, background commonsense knowledge. We first present a strong generative baseline that uses a multi-attention mechanism to perform multiple hops of reasoning and a pointer-generator decoder to synthesize the answer. This model performs substantially better than previous generative models, and is competitive with current state-of-the-art span prediction models. We next introduce a novel system for selecting grounded multi-hop relational commonsense information from ConceptNet via a pointwise mutual information and term-frequency based scoring function. Finally, we effectively use this extracted commonsense information to fill in gaps of reasoning between context hops, using a selectively-gated attention mechanism. This boosts the model's performance significantly (also verified via human evaluation), establishing a new state-of-the-art for the task. We also show that our background knowledge enhancements are generalizable and improve performance on QAngaroo-WikiHop, another multi-hop reasoning dataset.


Root Gap Correction with a Deep Inpainting Model


title: The History of Histograms (abridged)

abstract: The history of histograms is long and rich, full of detailed information in every step. It includes the course of histograms in different scientific fields, the successes and failures of histograms in approximating and compressing information, their adoption by industry, and solutions that have been given on a great variety of histogram-related problems. In this paper and in the same spirit of the histogram techniques themselves, we compress their entire history (including their "future history" as currently anticipated) in the given/fixed space budget, mostly recording details for the periods, events, and results with the highest (personally-biased) interest. In a limited set of experiments, the semantic distance between the compressed and the full form of the history was found relatively small!


title: The Use of Psychoactive Drugs in Elderly Patients with Psychiatric Disorders: Survey Conducted in Twelve Veterans Administration Hospitals

abstract: A survey conducted at 12 VA hospitals included the collection of detailed information on the use of psychoactive drugs in 1,276 elderly psychiatric patients. On the day of the survey, 61 per cent of the patients were receiving psychoactive drugs. Prescription practices relating to the choice of drugs, prevalence of drug use, dosage, combination drug preparations, and antiparkinson agents are discussed in terms of such factors as the patient's age and the diagnosis. Also discussed is the literature on psychoactive drugs, particularly as it pertains to elderly populations.


title: The Mathematics Of Statistical Machine Translation: Parameter Estimation

abstract: We describe a series of five statistical models of the translation process and give algorithms for estimating the parameters of these models given a set of pairs of sentences that are translations of one another. We define a concept of word-by-word alignment between such pairs of sentences. For any given pair of such sentences each of our models assigns a probability to each of the possible word-by-word alignments. We give an algorithm for seeking the most probable of these alignments. Although the algorithm is suboptimal, the alignment thus obtained accounts well for the word-by-word relationships in the pair of sentences. We have a great deal of data in French and English from the proceedings of the Canadian Parliament. Accordingly, we have restricted our work to these two languages; but we feel that because our algorithms have minimal linguistic content they would work well on other pairs of languages. We also feel, again because of the minimal linguistic content of our algorithms, that it is reasonable to argue that word-by-word alignments are inherent in any sufficiently large bilingual corpus.


title: An Entity Based Model for Coreference Resolution

abstract: Recently, many advanced machine learning approaches have been proposed for coreference resolution; however, all of the discriminatively-trained models reason over mentions rather than entities. That is, they do not explicitly contain variables indicating the “canonical” values for each attribute of an entity (e.g., name, venue, title, etc.). This canonicalization step is typically implemented as a post-processing routine to coreference resolution prior to adding the extracted entity to a database. In this paper, we propose a discriminatively-trained model that jointly performs coreference resolution and canonicalization, enabling features over hypothesized entities. We validate our approach on two different coreference problems: newswire anaphora resolution and research paper citation matching, demonstrating improvements in both tasks and achieving an error reduction of up to 62% when compared to a method that reasons about mentions only.


title: We need an educationally relevant definition of mobile learning

abstract: In this paper we challenge current definitions of mobile learning and suggest that the direction of progress, both in theoretical/applied research as well as its role as a tool that serves social transformation and development, will be determined and even dictated by the availability of an adequate definition. A new framework for the definition of mobile learning is proposed, one that considers a repertoire of domains, and which embraces not only technical, methodological and educational aspects, but also considers social and philosophical dimensions.


Automatic Gaze-Based Detection of Mind Wandering during Film Viewing


title: Internal Models in the Cerebellum

abstract: This review will focus on the possibility that the cerebellum contains an internal model or models of the motor apparatus. Inverse internal models can provide the neural command necessary to achieve some desired trajectory. First, we review the necessity of such a model and the evidence, based on the ocular following response, that inverse models are found within the cerebellar circuitry. Forward internal models predict the consequences of actions and can be used to overcome time delays associated with feedback control. Secondly, we review the evidence that the cerebellum generates predictions using such a forward model. Finally, we review a computational model that includes multiple paired forward and inverse models and show how such an arrangement can be advantageous for motor learning and control.


title: A Brain-Computer Interface Based on a Few-Channel EEG-fNIRS Bimodal System

abstract: With the development of the wearable brain–computer interface (BCI), a few-channel BCI system is necessary for its application to daily life. In this paper, we proposed a bimodal BCI system that uses only a few channels of electroencephalograph (EEG) and functional near-infrared spectroscopy (fNIRS) signals to obtain relatively high accuracy. We developed new approaches for signal acquisition and signal processing to improve the performance of this few-channel BCI system. At the signal acquisition stage, source analysis was applied for both EEG and fNIRS signals to select the optimal channels for bimodal signal collection. At the feature extraction stage, phase-space reconstruction was applied to the selected three-channel EEG signals to expand them into multichannel signals, thus allowing the use of the traditional effective common spatial pattern to extract EEG features. For the fNIRS signal, the Hurst exponents for the selected ten channels were calculated and composed of the fNIRS data feature. At the classification stage, EEG and fNIRS features were joined and classified with the support vector machine. The averaged classification accuracy of 12 participants was 81.2% for the bimodal EEG-fNIRS signals, which was significantly higher than that for either single modality.


title: A Nonparametric Approach for Mild Cognitive Impairment to AD Conversion Prediction: Results on Longitudinal Data

abstract: The goal of this study is to introduce a nonparametric technique for predicting conversion from Mild Cognitive impairment (MCI)-to-Alzheimer's disease (AD). Progression of a slowly progressing disease such as AD benefits from the use of longitudinal data; however, research till now is limited due to the insufficient patient data and short follow-up time. A small dataset size invalidates the estimation of underlying disease progression model; hence, a supervised nonparametric method is proposed. While depicting a real-world setting, longitudinal data of three years are employed for training, whereas only the baseline visit's data is used for validation. The train set is preprocessed for extraction of two dense clusters representing the subjects who remain stable at MCI or progress to AD after three years of the baseline visit. Similarity between these clusters and the test point is calculated in Euclidean space. Multiple features from two modalities of biomarkers, i.e., neuropsychological measures (NM) and structural magnetic resonance imaging (MRI) morphometry are also analyzed. Due to the limited MCI dataset size (NM: 145, MRI: 52, NM+MRI: 29), leave-one-out cross validation setup is employed for performance evaluation. The algorithm performance is noted for both unimodal case and bimodal cases. Superior performance (accuracy: 89.66%, sensitivity: 87.50%, specificity: 92.31%, precision: 93.33%) is delivered by multivariate predictors. Three notable conclusions of this study are: 1) Longitudinal data are more powerful than the temporal data, 2) MRI is a better predictor of MCI-to-AD conversion than NM, and 3) multivariate predictors outperform single predictor models


title: Factors Associated With Desistence and Persistence of Childhood Gender Dysphoria: A Quantitative Follow-Up Study

abstract: Objective To examine the factors associated with the persistence of childhood gender dysphoria (GD), and to assess the feelings of GD, body image, and sexual orientation in adolescence. Method The sample consisted of 127 adolescents (79 boys, 48 girls), who were referred for GD in childhood ( Results We found a link between the intensity of GD in childhood and persistence of GD, as well as a higher probability of persistence among natal girls. Psychological functioning and the quality of peer relations did not predict the persistence of childhood GD. Formerly nonsignificant (age at childhood assessment) and unstudied factors (a cognitive and/or affective cross-gender identification and a social role transition) were associated with the persistence of childhood GD, and varied among natal boys and girls. Conclusion Intensity of early GD appears to be an important predictor of persistence of GD. Clinical recommendations for the support of children with GD may need to be developed independently for natal boys and for girls, as the presentation of boys and girls with GD is different, and different factors are predictive for the persistence of GD.


title: Mental Toughness and Individual Differences in Learning, Educational and Work Performance, Psychological Well-being, and Personality: A Systematic Review

abstract: Mental toughness (MT) is an umbrella term that entails positive psychological resources, which are crucial across a wide range of achievement contexts and in the domain of mental health. We systematically review empirical studies that explored the associations between the concept of MT and individual differences in learning, educational and work performance, psychological well-being, personality, and other psychological attributes. Studies that explored the genetic and environmental contributions to individual differences in MT are also reviewed. The findings suggest that MT is associated with various positive psychological traits, more efficient coping strategies and positive outcomes in education and mental health. Approximately 50% of the variation in MT can be accounted for by genetic factors. Furthermore, the associations between MT and psychological traits can be explained mainly by either common genetic or nonshared environmental factors. Taken together, our findings suggest a ‘mental toughness advantage’ with possible implications for developing interventions to facilitate achievement in a variety of settings.


title: Concepts of cooperation in artificial life

abstract: We have built some simple, but useful, cooperative artificial life agents. Based on this experience and by contrasting our work with computer viruses, we argue that artificial life (the simulation of life including evolution) can only remain reliably and indefinitely cooperative if it adheres to explicitly-specified social conventions. Breaking or neglecting these conventions results in systems that are worse than useless; in fact, malicious with respect to human social values. >


title: Improving Code Review by Predicting Reviewers and Acceptance of Patches

abstract: Code reviews are an important part of software development because they help to increase code quality and reliability. For this paper, we observed the review processes of two open-source projects, Firefox and Mozilla Core. We noticed that code reviews are mostly organized manually. In particular, finding appropriate reviewers is a complex and time-consuming task and, surprisingly, impacts the review outcome: review requests without an initial reviewer assignment have lower chances to be accepted (and take longer). Based on our observations we propose two improvements: (1) predict whether a given patch is acceptable and (2) suggest reviewers for a patch. We implemented and tested both approaches for the Firefox and Mozilla Core projects. In our experiments, the prediction accuracy was 73% for the the review outcome and 51‐80% for the reviewer recommendation. The values for accuracy are higher than those of comparable approaches and are high enough to be useful and applicable in practice.


title: Citation prediction in heterogeneous bibliographic networks

abstract: To reveal information hiding in link space of bibliographical networks, link analysis has been studied from different perspectives in recent years. In this paper, we address a novel problem namely citation prediction, that is: given information about authors, topics, target publication venues as well as time of certain research paper, finding and predicting the citation relationship between a query paper and a set of previous papers. Considering the gigantic size of relevant papers, the loosely connected citation network structure as well as the highly skewed citation relation distribution, citation prediction is more challenging than other link prediction problems which have been studied before. By building a meta-path based prediction model on a topic discriminative search space, we here propose a two-phase citation probability learning approach, in order to predict citation relationship effectively and efficiently. Experiments are performed on real-world dataset with comprehensive measurements, which demonstrate that our framework has substantial advantages over commonly used link prediction approaches in predicting citation relations in bibliographical networks.


title: Modeling and simulation of FPGA-based variable-speed drives using Simulink

abstract: This paper presents a novel approach for the design and simulation of variable-speed drives when the control algorithm is to be implemented in an Field Programmable Gate Array (FPGA). The specialized Simulink tools used and the design procedure are presented. An application example using a Direct Torque Control (DTC) induction motor drive is presented and discussed.


title: Human Preferences in Industrial Human-Robot Interactions

abstract: Joint-action is one of the key research areas in robotics and especially important in physical human-robot interaction. The two main criteria for robots, which should be integrated in everyday life, are safety and efficiency. Therefore, it is of particular interest to understand how humans work together in order to transfer the resulting facts from these studies to direct human-robot interaction. In this work, we investigate a simple case of physical human-robot interaction, i.e. the handing over of small objects from a robot to the human. Experiments, in which six cubes were handed over from the robot to the human, were performend with two different robot systems, a robot arm in a humanoid set-up and a typical industrial set-up. Two different velocity profiles were integrated in the robot systems, a trapezoidal velocity profile in joint coordinates and a human inspired minimum jerk profile in cartesian coordinates. In both set-ups the use of the minimum jerk profile lead to shorter reaction times of the humans for the interaction. The humanoid setup showed with both profiles shorter reaction times than the industrial setup. It was also investigated in the experiments, whether the human body position adopts during the experiments to an optimal position for the hand-over. During the experiments the body spatial position stayed largely invariant, which indicates, that the subjects were not frightened and felt comfortable with the given hand over position. The result of our experiments along with the given comparison to natural human-human behaviour provides a solid basis for more efficiency of collaboration of humans and assistive robot systems.


title: Road marking detection and classification using machine learning algorithms

abstract: This paper presents a novel approach for road marking detection and classification based on machine learning algorithms. Road marking recognition is an important feature of an intelligent transportation system (ITS). Previous works are mostly developed using image processing and decisions are often made using empirical functions, which makes it difficult to be generalized. Hereby, we propose a general framework for object detection and classification, aimed at video-based intelligent transportation applications. It is a two-step approach. The detection is carried out using binarized normed gradient (BING) method. PCA network (PCANet) is employed for object classification. Both BING and PCANet are among the latest algorithms in the field of machine learning. Practically the proposed method is applied to a road marking dataset with 1,443 road images. We randomly choose 60% images for training and use the remaining 40% images for testing. Upon training, the system can detect 9 classes of road markings with an accuracy better than 96.8%. The proposed approach is readily applicable to other ITS applications.


title: Multi-Hop Cluster based IEEE 802.11p and LTE Hybrid Architecture for VANET Safety Message Dissemination

abstract: This paper proposes a hybrid architecture, namely VMaSC-LTE, combining IEEE 802.11p based multi-hop clustering and the fourth generation cellular system, Long Term Evolution (LTE), with the goal of achieving high data packet delivery ratio and low delay while keeping the usage of the cellular architecture at the minimum level. In VMaSC-LTE, vehicles are clustered based on a novel approach named VMaSC: Vehicular Multi-hop algorithm for Stable Clustering. From the clustered topology, elected cluster heads operate as dual-interface nodes with the functionality of IEEE 802.11p and LTE interface to link VANET to LTE network. Using various key metrics of interest including data packet delivery ratio, delay, control overhead and clustering stability, we demonstrate superior performance of the proposed architecture compared to both previously proposed hybrid architectures and alternative routing mechanisms including flooding and cluster based routing via extensive simulations in ns-3 with the vehicle mobility input from the Simulation of Urban Mobility (SUMO). The proposed architecture also allows achieving higher required reliability of the application quantified by the data packet delivery ratio at the cost of higher LTE usage determined by the number of cluster heads in the network.


title: DESIGN OF A CLINICAL DECISION SUPPORT FOR DETERMINING VENTILATOR WEANING USING SUPPORT VECTOR MACHINE

abstract: Ventilator weaning is the process of discontinuing mechanical ventilators from patients with respiratory failure. This study designed a clinical decision support system (CDSS) using support vector machine (SVM) to predict if a patient can be weaned from mechanical ventilator successfully. Alter method based on logistic regression anal- ysis (LRA) and a wrapper method based on recursive feature elimination (RFE) were adopted to select salient features in 27 variables, including demographic information, physiology and disease factors, and care and treatment factors for CDSS. Data of 348 patients were collected at four different periods from all-purpose respiratory care cen- ter. Seven signicant variables ( p < 0 :05 ) using LRA contrasted to eleven variables using RFE algorithm were selected. The predictive accuracy under cross-validation is 88.33% (LRA) and 92.73% (RFE), respectively. The systems outperform predictors (75- 78%) built using frequency-to-tid al volume ratio (


title: Machine learning \&artificial intelligence in the quantum domain

abstract: Quantum information technologies, and intelligent learning systems, are both emergent technologies that will likely have a transforming impact on our society. The respective underlying fields of research -- quantum information (QI) versus machine learning (ML) and artificial intelligence (AI) -- have their own specific challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question to what extent these fields can learn and benefit from each other. QML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently, we have witnessed breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups in ML, critical in our "big data" world. Conversely, ML already permeates cutting-edge technologies, and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been demonstrated for interactive learning, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments, and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement, researchers have also broached the fundamental issue of quantum generalizations of ML/AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is described by quantum mechanics. In this review, we describe the main ideas, recent developments, and progress in a broad spectrum of research investigating machine learning and artificial intelligence in the quantum domain.


Distributed Representations of Tuples for Entity Resolution


title: Learning Intrusion Detection: Supervised or Unsupervised?

abstract: Application and development of specialized machine learning techniques is gaining increasing attention in the intrusion detection community. A variety of learning techniques proposed for different intrusion detection problems can be roughly classified into two broad categories: supervised (classification) and unsupervised (anomaly detection and clustering). In this contribution we develop an experimental framework for comparative analysis of both kinds of learning techniques. In our framework we cast unsupervised techniques into a special case of classification, for which training and model selection can be performed by means of ROC analysis. We then investigate both kinds of learning techniques with respect to their detection accuracy and ability to detect unknown attacks.


title: Singular value decomposition on GPU using CUDA

abstract: Linear algebra algorithms are fundamental to many computing applications. Modern GPUs are suited for many general purpose processing tasks and have emerged as inexpensive high performance co-processors due to their tremendous computing power. In this paper, we present the implementation of singular value decomposition (SVD) of a dense matrix on GPU using the CUDA programming model. SVD is implemented using the twin steps of bidiagonalization followed by diagonalization. It has not been implemented on the GPU before. Bidiagonalization is implemented using a series of Householder transformations which map well to BLAS operations. Diagonalization is performed by applying the implicitly shifted QR algorithm. Our complete SVD implementation outperforms the MATLAB and Intel ®Math Kernel Library (MKL) LAPACK implementation significantly on the CPU. We show a speedup of upto 60 over the MATLAB implementation and upto 8 over the Intel MKL implementation on a Intel Dual Core 2.66GHz PC on NVIDIA GTX 280 for large matrices. We also give results for very large matrices on NVIDIA Tesla S1070.


title: Mining knowledge-sharing sites for viral marketing

abstract: Viral marketing takes advantage of networks of influence among customers to inexpensively achieve large changes in behavior. Our research seeks to put it on a firmer footing by mining these networks from data, building probabilistic models of them, and using these models to choose the best viral marketing plan. Knowledge-sharing sites, where customers review products and advise each other, are a fertile source for this type of data mining. In this paper we extend our previous techniques, achieving a large reduction in computational cost, and apply them to data from a knowledge-sharing site. We optimize the amount of marketing funds spent on each customer, rather than just making a binary decision on whether to market to him. We take into account the fact that knowledge of the network is partial, and that gathering that knowledge can itself have a cost. Our results show the robustness and utility of our approach.


title: Wearable E-Textile Technologies: A Review on Sensors, Actuators and Control Elements

abstract: Wearable e-textiles are able to perform electronic functions and are perceived as a way to add features into common wearable textiles, building competitive market advantages. The e-textile production has become not only a research effort but also an industrial production challenge. It is important to know how to use existing industrial processes or to develop new ones that are able to scale up production, ensuring the behavior and performance of prototypes. Despite the technical challenges, there are already some examples of wearable e-textiles where sensors, actuators, and production techniques were used to seamlessly embed electronic features into traditional wearable textiles, which allow for daily use without a bionic stigma.


title: Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation

abstract: What is the right object representation for manipulation? We would like robots to visually perceive scenes and learn an understanding of the objects in them that (i) is task-agnostic and can be used as a building block for a variety of manipulation tasks, (ii) is generally applicable to both rigid and non-rigid objects, (iii) takes advantage of the strong priors provided by 3D vision, and (iv) is entirely learned from self-supervision. This is hard to achieve with previous methods: much recent work in grasping does not extend to grasping specific objects or other tasks, whereas task-specific learning may require many trials to generalize well across object configurations or other tasks. In this paper we present Dense Object Nets, which build on recent developments in self-supervised dense descriptor learning, as a consistent object representation for visual understanding and manipulation. We demonstrate they can be trained quickly (approximately 20 minutes) for a wide variety of previously unseen and potentially non-rigid objects. We additionally present novel contributions to enable multi-object descriptor learning, and show that by modifying our training procedure, we can either acquire descriptors which generalize across classes of objects, or descriptors that are distinct for each object instance. Finally, we demonstrate the novel application of learned dense descriptors to robotic manipulation. We demonstrate grasping of specific points on an object across potentially deformed object configurations, and demonstrate using class general descriptors to transfer specific grasps across objects in a class.


Modeling Web Maintenance Centers through Queue Models


title: ����� � � ���� � ������������� � ��� � ����� � � ��� � ��� � ��������� � ���� � � Evaluating the Potential of Texture and Color Descriptors for Remote Sensing Image Retrieval and Classification

abstract: Classifying Remote Sensing Images (RSI) is a hard task. There are automatic approaches whose results normally need to be revised. The identification and polygon extraction tasks usually rely on applying classification strategies that exploit visual aspects related to spectral and texture patterns identified in RSI regions. There are a lot of image descriptors proposed in the literature for content-based image retrieval purposes that can be useful for RSI classification. This paper presents a comparative study to evaluate the potential of using successful color and texture image descriptors for remote sensing retrieval and classification. Seven descriptors that encode texture information and twelve color descriptors that can be used to encode spectral information were selected. We perform experiments to evaluate the effectiveness of these descriptors, considering image retrieval and classification tasks. To evaluate descriptors in classification tasks, we also propose a methodology based on KNN classifier. Experiments demonstrate that Joint Auto-Correlogram (JAC), Color Bitmap, Invariant Steerable Pyramid Decomposition (SID) and Quantized Compound Change Histogram (QCCH) yield the


Bidirectional LSTM-RNN for Improving Automated Assessment of Non-Native Children's Speech.


title: Intelligent Management Systems for Energy Efficiency in Buildings: A Survey

abstract: In recent years, reduction of energy consumption in buildings has increasingly gained interest among researchers mainly due to practical reasons, such as economic advantages and long-term environmental sustainability. Many solutions have been proposed in the literature to address this important issue from complementary perspectives, which are often hard to capture in a comprehensive manner. This survey article aims at providing a structured and unifying treatment of the existing literature on intelligent energy management systems in buildings, with a distinct focus on available architectures and methodology supporting a vision transcending the well-established smart home vision, in favor of the novel Ambient Intelligence paradigm. Our exposition will cover the main architectural components of such systems, beginning with the basic sensory infrastructure, moving on to the data processing engine where energy-saving strategies may be enacted, to the user interaction interface subsystem, and finally to the actuation infrastructure necessary to transfer the planned modifications to the environment. For each component, we will analyze different solutions, and we will provide qualitative comparisons, also highlighting the impact that a single design choice can have on the rest of the system.


Graphical Causal Models


title: Analysis of Insertion Loss and Impedance Compatibility of Hybrid EMI Filter Based on Equivalent Circuit Model

abstract: The hybrid active electromagnetic interference filter (HAEF) is considered one of the best options for improving power quality for a number of considerations. A systematic classification and identification of HAEF configurations is given, including their insertion losses, impedance compatibilities, potential applications, and comparative features. It is aimed at providing a broad perspective on the status of HAEF technology to researchers and application engineers dealing with EMI issues. The basis of discussion is an equivalent circuit model that includes all possible combinations of active and passive elements and matches these to the desirable attributes. The evaluation indicates those topologies that are known and those topologies that are relatively new.


title: The insider threat to information systems and the effectiveness of ISO17799

abstract: Insider threat is widely recognised as an issue of utmost importance for IS security management. In this paper, we investigate the approach followed by ISO17799, the dominant standard in IS security management, in addressing this type of threat. We unfold the criminology theory that has designated the measures against insider misuse suggested by the standard, i.e. the General Deterrence Theory, and explore the possible enhancements to the standard that could result from the study of more recent criminology theories. The paper concludes with supporting the argument for a multiparadigm and multidisciplinary approach towards IS security management and insider threat mitigation.


title: A compliant, underactuated hand for robust manipulation

abstract: This paper introduces the i-HY Hand, an underactuated hand driven by 5 actuators that is capable of performing a wide range of grasping and in-hand manipulation tasks. This hand was designed to address the need for a durable, inexpensive, moderately dexterous hand suitable for use on mobile robots. The primary focus of this paper will be on the novel minimalistic design of i-HY, which was developed by choosing a set of target tasks around which the design of the hand was optimized. Particular emphasis is placed on the development of underactuated fingers that are capable of both firm power grasps and low- stiffness fingertip grasps using only the passive mechanics of the finger mechanism. Experimental results demonstrate successful grasping of a wide range of target objects, the stability of fingertip grasping, as well as the ability to adjust the force exerted on grasped objects using the passive finger mechanics.


title: On the Sustained Use of a Test-Driven Development Practice at IBM

abstract: Test-driven development (TDD) is an agile practice that is widely accepted and advocated by most agile methods and methodologists. In this paper, we report on a post hoc analysis of the results of an IBM team who has sustained use of TDD for five years and over ten releases of a Java-implemented product. The team worked from a design and wrote tests incrementally before or while they wrote code and, in the process, developed a significant asset of automated tests. The IBM team realized sustained quality improvement relative to a pre-TDD project and consistently had defect density below industry standards. As a result, our data indicate that the TDD practice can aid in the production of high quality products. This quality improvement would compensate for the moderate perceived productivity losses. Additionally, our data indicates that the use of TDD may decrease the degree to which code complexity increases as software ages, as measured by cyclomatic complexity metric.


title: QAque: faceted query expansion techniques for exploratory search using community QA resources

abstract: Recently, query suggestions have become quite useful in web searches. Most provide additional and correct terms based on the initial query entered by users. However, query suggestions often recommend queries that differ from the user's search intentions due to different contexts. In such cases, faceted query expansions and their usages are quite efficient. In this paper, we propose faceted query expansion methods using the resources of Community Question Answering (CQA), which is social network service (SNS) that shares user knowledge. In a CQA site, users can post questions in a suitable category. Others answer them based on the category framework. Thus, the CQA "category" makes a "facet" of the query expansion. In addition, the time of year when the question was posted plays an important role in understanding its context. Thus, such seasonality creates another "facet" of the query expansion. We implement two-dimensional faceted query expansion methods based on the results of the Latent Dirichlet Allocation (LDA) analysis of CQA resources. The question articles deriving query expansion are provided for choosing appropriate terms by users. Our sophisticated evaluations using actual and long-term CQA resources, such as "Yahoo! CHIEBUKURO," demonstrate that most parts of the CQA questions are posted in periodicity and in bursts.


title: A Low-Latency and Low-Power Hybrid Scheme for On-Chip Networks

abstract: Network-on-chip (NoC) has emerged as a vital factor that determines the performance and power consumption of many-core systems. This paper proposes a hybrid scheme for NoCs, which aims at obtaining low latency and low power consumption. In the presented hybrid scheme, a novel switching mechanism, called virtual circuit switching, is proposed to intermingle with circuit switching and packet switching. Flits traveling in virtual circuit switching can traverse the router with only one stage. In addition, multiple virtual circuit-switched (VCS) connections are allowed to share a common physical channel. Moreover, a path allocation algorithm is proposed in this paper to determine VCS connections and circuit-switched connections on a mesh-connected NoC, such that both communication latency and power are optimized. A set of synthetic and real traffic workloads are exploited to evaluate the effectiveness of the proposed hybrid scheme. The experimental results show that our proposed hybrid scheme can efficiently reduce the communication latency and power. For instance, for real traffic workloads, an average of 20.3% latency reduction and 33.2% power saving can be obtained when compared with the baseline NoC. Moreover, when compared with the NoC with virtual point-to-point connections (VIP), the proposed hybrid scheme can reduce the latency by 6.8% with the power decreasing by 11.3% averagely.


title: Energy-Efficient Cooperative Spectrum Sensing With Reporting Errors in Hybrid Spectrum Sharing CRNs

abstract: Recently, cooperative spectrum sensing (CSS) in cognitive radio networks has been extensively researched. However, most of the studies mainly focus on maximizing the spectral efficiency while the energy consumption is generally ignored. However, since the secondary users (SUs) are usually battery-powered devices, energy saving is very important. This paper studies the mean energy efficiency (EE) maximization problem of the CSS system using the hybrid spectrum sharing (HSS) scheme. Specifically, the effects of imperfect spectrum sensing and reporting channel errors on the EE are considered. Our goal is to maximize the mean EE of SUs while maintaining the detection accuracy by jointly optimizing the sensing time and the number of cooperative SUs, subject to the SUs’ average/peak transmission power constraints (ATPC/PTPC) and minimum data rate constraint, and the average interference power constraint of primary user. To address the non-convexity of the optimization problem, an energy-efficient iterative power allocation algorithm is developed. Simulations compare the achievable mean EE under ATPC/PTPC with three hard combining fusing rules using the HSS scheme and the opportunistic spectrum access (OSA) scheme, respectively; the results show that our proposed HSS scheme can obtain higher mean EE than the conventional OSA scheme and that the EE achieved under ATPC is better than that under PTPC. Moreover, among the three hard combining fusing rules, the Majority rule has the best EE.


title: Clothoid-based model predictive control for autonomous driving

abstract: This paper presents a novel linear time-varying model predictive controller (LTV-MPC) using a sparse clothoid-based path description: a LTV-MPCC. Clothoids are used world-wide in road design since they allow smooth driving associated with low jerk values. The formulation of the MPC controller is based on the fact that the path of a vehicle traveling at low speeds defines a segment of clothoids if the steering angle is chosen to vary piecewise linearly. Therefore, we can compute the vehicle motion as clothoid parameters and translate them to vehicle inputs. We present simulation results that demonstrate the ability of the controller to produce a very comfortable and smooth driving while maintaining a tracking accuracy comparable to that of a regular LTV-MPC. While the regular MPC controllers use path descriptions where waypoints are close to each other, our LTV-MPCC has the ability of using paths described by very sparse waypoints. In this case, each pair of waypoints describes a clothoid segment and the cost function minimization is performed in a more efficient way allowing larger prediction distances to be used. This paper also presents a novel algorithm that addresses the problem of path sparsification using a reduced number of clothoid segments. The path sparsification enables a path description using few waypoints with almost no loss of detail. The detail of the reconstruction is an adjustable parameter of the algorithm. The higher the required detail, the more clothoid segments are used.


title: REScala: bridging between object-oriented and functional style in reactive applications

abstract: Traditionally, object-oriented software adopts the Observer pattern to implement reactive behavior. Its drawbacks are well-documented and two families of alternative approaches have been proposed, extending object-oriented languages with concepts from functional reactive and dataflow programming, respectively event-driven programming. The former hardly escape the functional setting; the latter do not achieve the declarativeness of more functional approaches. In this paper, we present REScala, a reactive language which integrates concepts from event-based and functional-reactive programming into the object-oriented world. REScala supports the development of reactive applications by fostering a functional declarative style which complements the advantages of object-oriented design.


title: Cognitive radio network architecture: part I -- general structure

abstract: Cognitive radio has been considered as a key technology for future wireless communications and mobile computing. We note the cognitive radios can form cognitive radio networks (CRN) by extending the radio link features to network layer functions and above. We categorize CRN architecture into several structures and classify the unidirectional links in such structures, to pave the way for future systematic CRN research.


title: Legged Self-Manipulation

abstract: This paper introduces self-manipulation as a new formal design methodology for legged robots with varying ground interactions. The term denotes a set of modeling choices that permit a uniform and body-centric representation of the equations of motion - essentially a guide to the selection and configuration of coordinate frames. We present the hybrid system kinematics, dynamics, and transitions in the form of a consistently structured representation that simplifies and unites the account of these, otherwise bewilderingly diverse differential algebraic equations. Cleaving as closely as possible to the modeling strategies developed within the mature manipulation literature, self-manipulation models can leverage those insights and results where applicable, while clarifying the fundamental differences. Our primary motivation is not to facilitate numerical simulation but rather to promote design insight. We instantiate the abstract formalism for a simplified model of RHex, and illustrate its utility by applying a variety of analytical and computational techniques to derive new results bearing on behaviors, controllers, and platform design. For each example, we present empirical results documenting the specific benefits of the new insight into the robot's transitions from standing to moving in place and to leaping.


title: Deep hierarchical parsing for semantic segmentation

abstract: This paper proposes a learning-based approach to scene parsing inspired by the deep Recursive Context Propagation Network (RCPN). RCPN is a deep feed-forward neural network that utilizes the contextual information from the entire image, through bottom-up followed by top-down context propagation via random binary parse trees. This improves the feature representation of every super-pixel in the image for better classification into semantic categories. We analyze RCPN and propose two novel contributions to further improve the model. We first analyze the learning of RCPN parameters and discover the presence of bypass error paths in the computation graph of RCPN that can hinder contextual propagation. We propose to tackle this problem by including the classification loss of the internal nodes of the random parse trees in the original RCPN loss function. Secondly, we use an MRF on the parse tree nodes to model the hierarchical dependency present in the output. Both modifications provide performance boosts over the original RCPN and the new system achieves state-of-the-art performance on Stanford Background, SIFT-Flow and Daimler urban datasets.


title: Interpersonal forgiving in close relationships: II. Theoretical elaboration and measurement.

abstract: Interpersonal forgiving was conceptualized in the context of a 2-factor motivational system that governs people's responses to interpersonal offenses. Four studies were conducted to examine the extent to which forgiving could be predicted with relationship-level variables such as satisfaction, commitment, and closeness; offense-level variables such as apology and impact of the offense; and social-cognitive variables such as offunder-focused empathy and rumination about the offense. Also described is the development of the transgression-related interpersonal motivations inventory--a self-report measure designed to assess the 2-component motivational system (Avoidance and Revenge) posited to underlie forgiving. The measure demonstrated a variety of desirable psychometric properties, commending its use for future research. As predicted, empathy, apology, rumination, and several indexes of relationship closeness were associated with self-reported forgiving.


title: QuickFind: Fast and contact-free object detection using a depth sensor

abstract: Pervasive computing applications have long used contact free object detection to process images collected from smartphone, tablet and wearable cameras. Two key challenges encountered in uncontrolled environments are the detection accuracy and speed, more so on computationally limited embedded systems. Recent technological advances have led to the emergence of depth sensors being integrated in a variety of embedded devices such as smartphones and wearables. Depth sensors are contact free and avoid some of the pitfalls caused by variations in lighting and background conditions that are typically associated with images from conventional cameras. This paper presents QuickFind, a fast and lightweight algorithm for object detection using only depth data. QuickFind is fast and particularly suited to run on embedded platforms as it uses a quick method for segmentation and makes use of low overhead features. We demonstrate empirically the performance of QuickFind by benchmarking on two embedded systems: Raspberry Pi and Intel Edison. We also demonstrate the wide applicability of QuickFind by developing two proof-of-concept pervasive computing applications.


M-16 rifle wounds in Vietnam.


Sentiment Lexicon Expansion Based on Neural PU Learning, Double Dictionary Lookup, and Polarity Association


title: RGB-D multi-view object detection with object proposals and shape context

abstract: We propose a novel approach for multi-view object detection in 3D scenes reconstructed from RGB-D sensor. We utilize shape based representation using local shape context descriptors along with the voting strategy which is supported by unsupervised object proposals generated from 3D point cloud data. Our algorithm starts with a single-view object detection where object proposals generated in 3D space are combined with object specific hypotheses generated by the voting strategy. To tackle the multi-view setting, the data association between multiple views enabled view registration and 3D object proposals. The evidence from multiple views is combined in simple bayesian setting. The approach is evaluated on the Washington RGB-D scenes datasets [1], [2] containing several classes of objects in a table top setting. We evaluated our approach against the other state-of-the-art methods and demonstrated superior performance on the same dataset.


title: Learning Discrete Hashing Towards Efficient Fashion Recommendation

abstract: In our daily life, how to match clothing well is always a troublesome problem especially when we are shopping online to select a pair of matched pieces of clothing from tens of thousands available selections. To help common customers overcome selection issues, recent studies in the recommender system area have started to infer the fashion matching results automatically. The traditional fashion recommendation is normally achieved by considering visual similarity of clothing items or/and item co-purchase history from existing shopping transactions. Due to the high complexity of visual features and the lack of historical item purchase records, most of the existing work is unlikely to make an efficient and accurate recommendation. To address the problem, in this paper, we propose a new model called Discrete Supervised Fashion Coordinates Hashing. Its main objective is to learn meaningful yet compact high-level features of clothing items, which are represented as binary hash codes. In detail, this learning process is supervised by a clothing matching matrix, which is initially constructed based on limited known matching pairs and subsequently on the self-augmented ones. The proposed model jointly learns the intrinsic matching patterns from the matching matrix and the binary representations from the clothing items’ images, where the visual feature of each clothing item is discretized into a fixed-length binary vector. The binary representation learning significantly reduces the memory cost and accelerates the recommendation speed. The experiments compared with several state-of-the-art approaches have evidenced the superior performance of the proposed approach on efficient fashion recommendation.


title: Using Research Instruments: A Guide for Researchers

abstract: Introduction 1. Questionnaires 2. Interviews 3. Content Analysis 4. Focus Groups 5. Observation 6. Researching the Things People Say and Do Bibliography


title: Intra Coding of the HEVC Standard

abstract: This paper provides an overview of the intra coding techniques in the High Efficiency Video Coding (HEVC) standard being developed by the Joint Collaborative Team on Video Coding (JCT-VC). The intra coding framework of HEVC follows that of traditional hybrid codecs and is built on spatial sample prediction followed by transform coding and postprocessing steps. Novel features contributing to the increased compression efficiency include a quadtree-based variable block size coding structure, block-size agnostic angular and planar prediction, adaptive pre- and postfiltering, and prediction direction-based transform coefficient scanning. This paper discusses the design principles applied during the development of the new intra coding methods and analyzes the compression performance of the individual tools. Computational complexity of the introduced intra prediction algorithms is analyzed both by deriving operational cycle counts and benchmarking an optimized implementation. Using objective metrics, the bitrate reduction provided by the HEVC intra coding over the H.264/advanced video coding reference is reported to be 22% on average and up to 36%. Significant subjective picture quality improvements are also reported when comparing the resulting pictures at fixed bitrate.


title: Description of philophonetics counselling as expressive therapeutic modality for treating depression

abstract: Depression is ranked as most common type of mental illness by the World Health Organization (WHO in Depression and other common mental disorders: Global health estimates. World Health Organization, Geneva, 2017). Although cognitive behavioural therapy is recommended as the evidence-based psychological treatment of choice, this applies mostly to youthful, attractive, verbal, intelligent and successful persons with medical aid support in high income countries. More holistic counselling that includes holistic, verbal and non-verbal, expressive therapeutic modalities are more suitable for the planetary majority. Consequently, this study describes the process and effectiveness of philophonetics counselling with a sample of ten depressed clients (6 Black African, 2 Indian, 2 White; 7 women, 3 men; mean age 32, age range 22–43 years) seeking clinical psychological services in Zululand, South Africa, a region and country beset with considerable violence, crime illness and unemployment. Client reports of effectiveness of philophonetics counselling warrants further intervention and evaluative research, with special reference to wider community psychological applications of this expressive therapeutic modality via primary health care facilities, online courses and mobile technology.


title: Semi-supervised radio signal identification

abstract: Radio emitter recognition in dense multi-user environments is an important tool for optimizing spectrum utilization, identifying and minimizing interference, and enforcing spectrum policy. Radio data is readily available and easy to obtain from an antenna, but labeled and curated data is often scarce making supervised learning strategies difficult and time consuming in practice. We demonstrate that semi-supervised learning techniques can be used to scale learning beyond supervised datasets, allowing for discerning and recalling new radio signals by using sparse signal representations based on both unsupervised and supervised methods for nonlinear feature learning and clustering methods.


title: 28 GHz Millimeter-Wave Ultrawideband Small-Scale Fading Models in Wireless Channels

abstract: This paper presents small-scale fading measurements for 28 GHz outdoor millimeter-wave ultrawideband channels using directional horn antennas at the transmitter and receiver. Power delay profiles were measured at half-wavelength spatial increments over a local area (33 wavelengths) on a linear track in two orthogonal receiver directions in a typical base-to-mobile scenario with fixed transmitter and receiver antenna beam pointing directions. The voltage path amplitudes are shown to follow a Rician distribution, with K-factor ranging from 9 - 15 dB and 5 - 8 dB in line of sight (LOS) and non-line of sight (NLOS) for a vertical-to-vertical co- polarized antenna scenario, respectively, and from 3 - 7 dB in both LOS and NLOS vertical-to- horizontal cross-polarized antenna scenario. The average spatial autocorrelation functions of individual multipath components reveal that signal amplitudes reach a correlation of 0 after 2 and 5 wavelengths in LOS and NLOS co-polarized V-V antenna scenarios. The models provided are useful for recreating path gain statistics of millimeter- wave wideband channel impulse responses over local areas, for the study of multi-element antenna simulations and channel estimation algorithms.


title: Analysis of Emotional Effect on Speech-Body Gesture Interplay

abstract: In interpersonal interactions, speech and body gesture channels are internally coordinated towards conveying communicative intentions. The speech-gesture relationship is influenced by the internal emotion state underlying the communication. In this paper, we focus on uncovering the emotional effect on the interrelation between speech and body gestures. We investigate acoustic features describing speech prosody (pitch and energy) and vocal tract configuration (MFCCs), as well as three types of body gestures, viz., head motion, lower and upper body motions. We employ mutual information to measure the coordination between the two communicative channels, and analyze the quantified speech-gesture link with respect to distinct levels of emotion attributes, i.e., activation and valence. The results reveal that the speech-gesture coupling is generally tighter for low-level activation and high-level valence, compared to high-level activation and low-level valence. We further propose a framework for modeling the dynamics of speech-gesture interaction. Experimental studies suggest that such quantified coupling representations can well discriminate different levels of activation and valence, reinforcing that emotions are encoded in the dynamics of the multimodal link. We also verify that the structures of the coupling representations are emotiondependent using subspace-based analysis. Index Terms: emotion attributes, body gesture, speech prosody, speech-gesture interplay, mutual information


Optimization of Traffic Flow through Signalized Intersections using PSO


title: A Survey of Automated Web Service Composition Methods

abstract: In today’s Web, Web services are created and updated on the fly. It’s already beyond the human ability to analysis them and generate the composition plan manually. A number of approaches have been proposed to tackle that problem. Most of them are inspired by the researches in cross-enterprise workflow and AI planning. This paper gives an overview of recent research efforts of automatic Web service composition both from the workflow and AI planning research community.


title: Implanted antennas inside a human body: simulations, designs, and characterizations

abstract: Antennas implanted in a human body are largely applicable to hyperthermia and biotelemetry. To make practical use of antennas inside a human body, resonance characteristics of the implanted antennas and their radiation signature outside the body must be evaluated through numerical analysis and measurement setup. Most importantly, the antenna must be designed with an in-depth consideration given to its surrounding environment. In this paper, the spherical dyadic Green's function (DGF) expansions and finite-difference time-domain (FDTD) code are applied to analyze the electromagnetic characteristics of dipole antennas and low-profile patch antennas implanted in the human head and body. All studies to characterize and design the implanted antennas are performed at the biomedical frequency band of 402-405 MHz. By comparing the results from two numerical methodologies, the accuracy of the spherical DGF application for a dipole antenna at the center of the head is evaluated. We also consider how much impact a shoulder has on the performance of the dipole inside the head using FDTD. For the ease of the design of implanted low-profile antennas, simplified planar geometries based on a real human body are proposed. Two types of low-profile antennas, i.e., a spiral microstrip antenna and a planar inverted-F antenna, with superstrate dielectric layers are initially designed for medical devices implanted in the chest of the human body using FDTD simulations. The radiation performances of the designed low-profile antennas are estimated in terms of radiation patterns, radiation efficiency, and specific absorption rate. Maximum available power calculated to characterize the performance of a communication link between the designed antennas and an exterior antenna show how sensitive receivers are required to build a reliable telemetry link.


title: Extracting data records from the web using tag path clustering

abstract: Fully automatic methods that extract lists of objects from the Web have been studied extensively. Record extraction, the first step of this object extraction process, identifies a set of Web page segments, each of which represents an individual object (e.g., a product). State-of-the-art methods suffice for simple search, but they often fail to handle more complicated or noisy Web page structures due to a key limitation -- their greedy manner of identifying a list of records through pairwise comparison (i.e., similarity match) of consecutive segments. This paper introduces a new method for record extraction that captures a list of objects in a more robust way based on a holistic analysis of a Web page. The method focuses on how a distinct tag path appears repeatedly in the DOM tree of the Web document. Instead of comparing a pair of individual segments, it compares a pair of tag path occurrence patterns (called visual signals) to estimate how likely these two tag paths represent the same list of objects. The paper introduces a similarity measure that captures how closely the visual signals appear and interleave. Clustering of tag paths is then performed based on this similarity measure, and sets of tag paths that form the structure of data records are extracted. Experiments show that this method achieves higher accuracy than previous methods.


No . 93-64 191531 / y IC S 2 O Years of Excellence MULTIPHASE COMPLETE EXCHANGE : A THEORETICAl . ANALYSIS


title: Unsupervised Pattern Discovery in Speech

abstract: We present a novel approach to speech processing based on the principle of pattern discovery. Our work represents a departure from traditional models of speech recognition, where the end goal is to classify speech into categories defined by a prespecified inventory of lexical units (i.e., phones or words). Instead, we attempt to discover such an inventory in an unsupervised manner by exploiting the structure of repeating patterns within the speech signal. We show how pattern discovery can be used to automatically acquire lexical entities directly from an untranscribed audio stream. Our approach to unsupervised word acquisition utilizes a segmental variant of a widely used dynamic programming technique, which allows us to find matching acoustic patterns between spoken utterances. By aggregating information about these matching patterns across audio streams, we demonstrate how to group similar acoustic sequences together to form clusters corresponding to lexical entities such as words and short multiword phrases. On a corpus of academic lecture material, we demonstrate that clusters found using this technique exhibit high purity and that many of the corresponding lexical identities are relevant to the underlying audio stream.


title: Art and Society in Italy 1350-1500

abstract: The Italian Renaissance was a pivotal period in the history of Western culture during which artists such as Masaccio, Donatello, Fra Angelico, and Leonardo created some of the world's most influential and exciting works in a variety of artistic fields. Here, Evelyn Welch presents a fresh picture of the Italian Renaissance by challenging traditional scholarship and placing emphasis on recreating the experience of contemporary Italians: the patrons who commissioned the works, the members of the public who viewed them, and the artists who produced them. Art and Society in Italy 1350-1500 dramatically revises the traditional story of the Renaissance and takes into account new issues that have greatly enriched our understanding of the period. From paintings and coins to sculptures and tapestries, Welch examines the issues of materials, workshop practices, and artist-patron relationships, and explores the ways in which visual imagery related to contemporary sexual, social, and political behavior.


title: Learning Receptive Field Size by Learning Filter Size

abstract: Covering various receptive fields within a layer is essential to effectively recognize the objects of various sizes and types at a specific layer for a convolutional neural network (CNN). In this work, we propose a novel adaptive learning method which learns the filter size (i.e. the kernel size of a convolutional filter) and distribution to learn the receptive field size. Directly optimizing with respect to the filter size is challenging because the filter size is discrete. To overcome this, we propose a masking technique, which enables the automatic allocation of resources over filters of different sizes and leads to efficient optimization. Through our proposed trainable formulation of the mask, the network self-organizes its structure through the standard backpropagation. The proposed adaptive CNN can be generalized to any single-path structures and multi-path structures as well. The effectiveness of our proposed approach is validated by several benchmark datasets compared with various previous structures on the image classification task for diverse network depths and widths. Furthermore, we demonstrate our adaptive CNN trained on a large-scale dataset can yield improved performance when applying to a transfer learning.


title: Secure and trusted telemedicine in Internet of Things IoT

abstract: Internet of Things (IoT) is a revolutionary technology for the modern society. IoT can connect every surrounding objects for various applications like security, medical fields, monitoring and other industrial applications. This paper considers the application of IoT in the field of medicine. IoT in E-medicine can take the advantage of emerging technologies to provide immediate treatment to the patient as well as monitors and keeps track of health record for healthy person. IoT then performs complex computations on these collected data and can provide health related advice. Though IoT can provide a cost effective medical services to any people of all age groups, there are several key issues that need to be addressed. System security, IoT interoperability, dynamic storage facility and unified access mechanisms are some of the many fundamental issues associated with IoT. This paper proposes a system level design solution for security and flexibility aspect of IoT. In this paper, the functional components are bound in security function group which ensures the management of privacy and secure operation of the system. The security function group comprises of components which offers secure communication using Ciphertext-Policy Attribute-Based Encryption (CP-ABE). Since CP-ABE are delegated to unconstrained devices with the assumption that these devices are trusted, the producer encrypts data using AES and the ABE scheme is protected through symmetric key solutions.


title: Learning to Teach

abstract: Teaching plays a very important role in our society, by spreading human knowledge and educating our next generations. A good teacher will select appropriate teaching materials, impact suitable methodologies, and set up targeted examinations, according to the learning behaviors of the students. In the field of artificial intelligence, however, one has not fully explored the role of teaching, and pays most attention to machine \emph{learning}. In this paper, we argue that equal attention, if not more, should be paid to teaching, and furthermore, an optimization framework (instead of heuristics) should be used to obtain good teaching strategies. We call this approach `learning to teach'. In the approach, two intelligent agents interact with each other: a student model (which corresponds to the learner in traditional machine learning algorithms), and a teacher model (which determines the appropriate data, loss function, and hypothesis space to facilitate the training of the student model). The teacher model leverages the feedback from the student model to optimize its own teaching strategies by means of reinforcement learning, so as to achieve teacher-student co-evolution. To demonstrate the practical value of our proposed approach, we take the training of deep neural networks (DNN) as an example, and show that by using the learning to teach techniques, we are able to use much less training data and fewer iterations to achieve almost the same accuracy for different kinds of DNN models (e.g., multi-layer perceptron, convolutional neural networks and recurrent neural networks) under various machine learning tasks (e.g., image classification and text understanding).


title: Mining Temporal Information to Improve Duplication Detection on Bug Reports

abstract: Due to the vast amount of bug reports in many large-scale open source projects, detecting duplicate reports has received much attention in recent years. Textual information and different kinds of categorical information have been leveraged in many detection methods. Although past studies have shown the influence of the temporal information in the submission interval of duplicate bug reports, only few methods consider the temporal information in its plain interval form. In this paper, we propose a temporal model to improve the detection performance by considering the submission time and the version information. We discuss the effectiveness by implementing it based on a prestigious BM25Fext-based method. Empirical experiments have been conducted on a dataset collected from the Eclipse open source project. Compared with the baseline BM25Fext-based method, the proposed scheme can obtain performance improvement consistently.


title: Incremental diversification for very large sets: a streaming-based approach

abstract: Result diversification is an effective method to reduce the risk that none of the returned results satisfies a user's query intention. It has been shown to decrease query abandonment substantially. On the other hand, computing an optimally diverse set is NP-hard for the usual objectives. Existing greedy diversification algorithms require random access to the input set, rendering them impractical in the context of large result sets or continuous data. To solve this issue, we present a novel diversification approach which treats the input as a stream and processes each element in an incremental fashion, maintaining a near-optimal diverse set at any point in the stream. Our approach exhibits a linear computation and constant memory complexity with respect to input size, without significant loss of diversification quality. In an extensive evaluation on several real-world data sets, we show the applicability and efficiency of our algorithm for large result sets as well as for continuous query scenarios such as news stream subscriptions.


title: Towards crowd density-aware video surveillance applications

abstract: Crowd density analysis is a crucial component in visual surveillance mainly for security monitoring. This paper proposes a novel approach for crowd density measure, in which local information at pixel level substitutes a global crowd level or a number of people per-frame. The proposed approach consists of generating automatic crowd density maps using local features as an observation of a probabilistic density function. It also involves a feature tracking step which excludes feature points belonging to the background. This process is favorable for the later density estimation as the influence of features irrelevant to the underlying crowd density is removed. Since the proposed crowd density conveys rich information about the local distributions of persons in the scene, we employ it as a side information to complement other tasks related to video surveillance in crowded scenes. First, since conventional detection and tracking methods are hard to be scalable to crowds, we use the proposed crowd density to enhance detection and tracking in videos of high density crowds. Second, we employ the local density together with regular motion patterns as crowd attributes for high level applications such as crowd change detection and event recognition. Third, we investigate the concept of crowd context-aware privacy protection by adjusting the obfuscation level according to the crowd density. In the experimental results, our proposed approach for crowd density estimation is evaluated on videos from different datasets, and the results demonstrate the effectiveness of feature tracks for crowd measurements. Moreover, the employment of crowd density in other applications demonstrate good performances for detection, tracking, behavior analysis, and privacy preservation.


EFFECTS OF AUGMENTED SITUATIONAL AWARENESS ON DRIVER TRUST IN SEMI-AUTONOMOUS VEHICLE OPERATION


title: Improving Preemptive Scheduling with Application-Transparent Checkpointing in Shared Clusters

abstract: Modern data center clusters are shifting from dedicated single framework clusters to shared clusters. In such shared environments, cluster schedulers typically utilize preemption by simply killing jobs in order to achieve resource priority and fairness during peak utilization. This can cause significant resource waste and delay job response time. In this paper, we propose using suspend-resume mechanisms to mitigate the overhead of preemption in cluster scheduling. Instead of killing preempted jobs or tasks, our approach uses a system level, application-transparent checkpointing mechanism to save the progress of jobs for resumption at a later time when resources are available. To reduce the preemption overhead and improve job response times, our approach uses adaptive preemption to dynamically select appropriate preemption mechanisms (e.g., kill vs. suspend, local vs. remote restore) according to the progress of a task and its suspend-resume overhead. By leveraging fast storage technologies, such as non-volatile memory (NVM), our approach can further reduce the preemption penalty to provide better QoS and resource efficiency. We implement the proposed approach and conduct extensive experiments via Google cluster trace-driven simulations and applications on a Hadoop cluster. The results demonstrate that our approach can significantly reduce the resource and power usage and improve application performance over existing approaches. In particular, our implementation on the next generation Hadoop YARN platform achieves up to a 67% reduction in resource wastage, 30% improvement in overall job response time times and 34% reduction in energy consumption over the current YARN scheduler.