title: K-Nearest Neighbor Classification Approach for Face and Fingerprint at Feature Level Fusion

abstract: system that based on single biometric called uni- modal biometrics usually suffers from problems like imposter's attack or hacking, unacceptable error rate and low performance. So the need of using multimodal biometric system arises in such cases. The aim of this paper is to study the fusion at feature extraction level for face and fingerprint. The proposed system fuses the two traits at feature extraction level by first making the feature sets compatible for concatenation and then reducing the feature sets to handle the "problem of curse of dimensionality". After concatenation these features are classified. Features of both modalities are extracted using Gabor filter and Principal Component Analysis (PCA). K-Nearest Neighbour classifier is used to classify the different people in the database. The experimental results reveal that the fusion of more than one biometric trait at feature level fusion with the K-Nearest Neighbor technique exhibits robust performance and increases its performance with utmost level of accuracy.


CHAPTER 1 INTRODUCTION TO THE HILBERT HUANG TRANSFORM AND ITS RELATED MATHEMATICAL PROBLEMS


title: A learning evasive email-based P2P-like botnet

abstract: Nowadays, machine learning is widely used in malware detection system as a core component. The machine learning algorithm is designed under the assumption that all datasets follow the same underlying data distribution. But the real-world malware data distribution is not stable and changes with time. By exploiting the knowledge of the machine learning algorithm and malware data concept drift problem, we show a novel learning evasive botnet architecture and a stealthy and secure C&C mechanism. Based on the email communication channel, we construct a stealthy email-based P2P-like botnet that exploit the excellent reputation of email servers and a huge amount of benign email communication in the same channel. The experiment results show horizontal correlation learning algorithm is difficult to separate malicious email traffic from normal email traffic based on the volume features and time-related features with enough confidence. We discuss the malware data concept drift and possible defense strategies.


title: Hierarchical Cellular Automata for Visual Saliency

abstract: Saliency detection, finding the most important parts of an image, has become increasingly popular in computer vision. In this paper, we introduce Hierarchical Cellular Automata (HCA)—a temporally evolving model to intelligently detect salient objects. HCA consists of two main components: Single-layer Cellular Automata (SCA) and Cuboid Cellular Automata (CCA). As an unsupervised propagation mechanism, Single-layer Cellular Automata can exploit the intrinsic relevance of similar regions through interactions with neighbors. Low-level image features as well as high-level semantic information extracted from deep neural networks are incorporated into the SCA to measure the correlation between different image patches. With these hierarchical deep features, an impact factor matrix and a coherence matrix are constructed to balance the influences on each cell’s next state. The saliency values of all cells are iteratively updated according to a well-defined update rule. Furthermore, we propose CCA to integrate multiple saliency maps generated by SCA at different scales in a Bayesian framework. Therefore, single-layer propagation and multi-scale integration are jointly modeled in our unified HCA. Surprisingly, we find that the SCA can improve all existing methods that we applied it to, resulting in a similar precision level regardless of the original results. The CCA can act as an efficient pixel-wise aggregation algorithm that can integrate state-of-the-art methods, resulting in even better results. Extensive experiments on four challenging datasets demonstrate that the proposed algorithm outperforms state-of-the-art conventional methods and is competitive with deep learning based approaches.


Introducing institutions


title: BPD'S INTERPERSONAL HYPERSENSITIVITY PHENOTYPE: A GENE-ENVIRONMENT- DEVELOPMENTAL MODEL

abstract: Abstract This paper explores the development of BPD as it might emerge in the child's early interpersonal reactions and how such reactions might evolve into the interpersonal pattern that typifies BPD. It begins to bridge the relevant bodies of clinical literature on the borderline's prototypic interpersonal problems with the concurrently expanding relevant literature on early child development. We will start by considering how a psychobiological disposition to BPD is likely to include a constitutional diathesis for relational reactivity, that is, for hypersensitivity to interpersonal stressors. Data relevant to this disposition's manifestations in adult clinical samples and to its heritability and neurobiology will be reviewed. We then consider how such a psychobiological disposition for interpersonal reactivity might contribute to the development of a disorganized-ambivalent form of attachment, noting especially the likely contributions of both the predisposed child and of parents who are themselves pre...


title: Learning Where to Look: Data-Driven Viewpoint Set Selection for 3D Scenes

abstract: The use of rendered images, whether from completely synthetic datasets or from 3D reconstructions, is increasingly prevalent in vision tasks. However, little attention has been given to how the selection of viewpoints affects the performance of rendered training sets. In this paper, we propose a data-driven approach to view set selection. Given a set of example images, we extract statistics describing their contents and generate a set of views matching the distribution of those statistics. Motivated by semantic segmentation tasks, we model the spatial distribution of each semantic object category within an image view volume. We provide a search algorithm that generates a sampling of likely candidate views according to the example distribution, and a set selection algorithm that chooses a subset of the candidates that jointly cover the example distribution. Results of experiments with these algorithms on SUNCG indicate that they are indeed able to produce view distributions similar to an example set from NYUDv2 according to the earth mover's distance. Furthermore, the selected views improve performance on semantic segmentation compared to alternative view selection algorithms.


title: Using Expectation-Maximization for Reinforcement Learning

abstract: We discuss Hinton's (1989) relative payoff procedure (RPP), a static reinforcement learning algorithm whose foundation is not stochastic gradient ascent. We show circumstances under which applying the RPP is guaranteed to increase the mean return, even though it can make large changes in the values of the parameters. The proof is based on a mapping between the RPP and a form of the expectation-maximization procedure of Dempster, Laird, and Rubin (1977).


title: Real-time 3D Ball Recognition using Perspective and Catadioptric Cameras

abstract: A combined strike indicator and hook setter adaptable to a fishing line and independent from a fishing pole having of a base, a removable spring arm with a releasable line holding means attached at one end, and a sear mechanism for actuating the removable spring arm from a set to a releasable position.


title: Automated torch path planning using polygon subdivision for solid freeform fabrication based on welding

abstract: A continuous path is one of the most important requirements for solid freeform fabrication (SFF) based on welding. This paper proposes a method for torch path planning applicable to SFF based on welding with an emphasis on minimum human intervention. The suggested approach describes a method based on the subdivision of a two-dimensional (2-D) polygonal section into a set of monotone polygons to generate a continuous path for material deposition. A two-dimensional contour is subdivided into smaller polygons (subpolygons). The path for each individual subpolygon is generated. The final torch path is obtained by connecting individual paths for all the subpolygons and trimming along the points of intersection of the paths for individual subpolygons. The final path is a closed loop; therefore, any point can be selected as the starting point for material deposition. The proposed method can be used to develop the toolpath for CNC milling.


title: An Overview of Biomolecular Event Extraction from Scientific Documents

abstract: This paper presents a review of state-of-the-art approaches to automatic extraction of biomolecular events from scientific texts. Events involving biomolecules such as genes, transcription factors, or enzymes, for example, have a central role in biological processes and functions and provide valuable information for describing physiological and pathogenesis mechanisms. Event extraction from biomedical literature has a broad range of applications, including support for information retrieval, knowledge summarization, and information extraction and discovery. However, automatic event extraction is a challenging task due to the ambiguity and diversity of natural language and higher-level linguistic phenomena, such as speculations and negations, which occur in biological texts and can lead to misunderstanding or incorrect interpretation. Many strategies have been proposed in the last decade, originating from different research areas such as natural language processing, machine learning, and statistics. This review summarizes the most representative approaches in biomolecular event extraction and presents an analysis of the current state of the art and of commonly used methods, features, and tools. Finally, current research trends and future perspectives are also discussed.


title: Piecewise convexity of artificial neural networks

abstract: Although artificial neural networks have shown great promise in applications including computer vision and speech recognition, there remains considerable practical and theoretical difficulty in optimizing their parameters. The seemingly unreasonable success of gradient descent methods in minimizing these non-convex functions remains poorly understood. In this work we offer some theoretical guarantees for networks with piecewise affine activation functions, which have in recent years become the norm. We prove three main results. Firstly, that the network is piecewise convex as a function of the input data. Secondly, that the network, considered as a function of the parameters in a single layer, all others held constant, is again piecewise convex. Finally, that the network as a function of all its parameters is piecewise multi-convex, a generalization of biconvexity. From here we characterize the local minima and stationary points of the training objective, showing that they minimize certain subsets of the parameter space. We then analyze the performance of two optimization algorithms on multi-convex problems: gradient descent, and a method which repeatedly solves a number of convex sub-problems. We prove necessary convergence conditions for the first algorithm and both necessary and sufficient conditions for the second, after introducing regularization to the objective. Finally, we remark on the remaining difficulty of the global optimization problem. Under the squared error objective, we show that by varying the training data, a single rectifier neuron admits local minima arbitrarily far apart, both in objective value and parameter space.


title: Towards intelligent lower limb wearable robots: Challenges and perspectives - State of the art

abstract: Recent technological advances made necessary the use of the robots in various types of applications. Currently, the traditional robot-like scenarios dedicated to industrial applications with repetitive tasks, were replaced by applications which require human interaction. The main field of such applications concerns the rehabilitation and aid of elderly persons. In this study, we present a state-of-the-art of the main research advances in lower limbs actuated orthosis/wearable robots in the literature. This will include a review on researches covering full limb exoskeletons, lower limb exoskeletons and particularly the knee joint orthosis. Rehabilitation using treadmill based device and use of Functional Electrical Stimulation (FES) are also investigated. We discuss finally the challenges not yet solved such as issues related to portability, energy consumption, social constraints and high costs of theses devices.


title: Study of single motor unit discharge patterns using 1/f process model.

abstract: We developed a technique to study temporal discharge patterns of single motor units using 1/f process model, whose fractional parameter gamma was shown to be a useful indicator for distinguishing between discharge behaviors of single motor units of normal subjects and patients with upper motor neuron lesions. We have studied a total of 47 motor units in 3 normal subjects, and 41 in 3 patients with upper motor neuron lesions from biceps and extensor digitorum communis muscles during steady contraction. The parameter gamma was estimated with an algorithm based on wavelet analysis. The mean value of gamma in patients was 1.52, and the mean value of gamma in normal subjects was -0.06. These results suggest that 1/f process can be used to document the impaired motor control mechanisms at single motor unit level.


title: Analytical method for estimating palaeowave climate and water depth from wave ripple marks

abstract: Analytical expressions are developed for evaluating palaeodepth and palaeowave conditions from wave ripple marks. Palaeodepth and palaeowave climate estimates can be extremely powerful predictors in reconstructions of vertical movements related to basin subsidence, local and regional ancient wave climate trends and the basin shape. ::: ::: ::: ::: The analytical expressions developed herein are applied to wave ripple marks preserved in the Lower Marine Molasse of Switzerland, western Austria and southern Germany. The results suggest that, in contrast to traditional concepts of an eustatic sea-level change, the Lower Marine Molasse regression was a result of mainly depositional processes. The subsidence rate greatly exceeded the rate of sea-level fall over a wide geographical area. Furthermore, the widening of the Lower Marine Molasse basin to the ENE is well documented by the increase of the ancient wave period and wave power, calculated from wave ripple marks found in storm deposits.


title: Future Thought and the Self-Regulation of Energization

abstract: The chapter explores how different forms of thinking about the future affect the self-regulation of energy mobilization for goal pursuit as assessed by its physiological underpinnings (i.e., cardiovascular response). Counter to what the popular self-help literature proposes, positive thinking can be detrimental to energy mobilization (i.e., energization) if it comes in the form of fantasies (free thoughts and images about the desired future) rather than beliefs (expectations). Indeed, fantasizing positively about a desired future leads to a decreased energization as indicated by a dampened cardiovascular response. Fantasy realization theory, however, specifies how positive fantasies can be used to wisely self-regulate energization for goal pursuit. According to the theory, the strategy of mental contrasting future and reality will increase or decrease energization, depending on a person’s high versus low expectations of success, respectively. Indeed, mental contrasting leads to an increased or decreased energization (measured by cardiovascular response and self-report) depending on expectations of success. The increased or decreased cardiovascular response in turn predicted performance in goal pursuit. Moreover, drawing on Hull’s conception of energization as an unspecific activation state, a recent series of studies found that energization triggered by mental contrasting in one domain may carry-over to another domain to fuel goal striving behavior with regard to an unrelated task. We discuss the relation of the findings to other models of physiological self-regulation, such as Brehm’s theory of motivation, the biopsychosocial model of arousal regulation, and excitation transfer theory. Finally, implications for interventions geared at improving self-regulation of effort are discussed.


title: Using augmented reality to treat phobias

abstract: Virtual reality (VR) is useful for treating several psychological problems, including phobias such as fear of flying, agoraphobia, claustrophobia, and phobia to insects and small animals. We believe that augmented reality (AR) could also be used to treat some psychological disorders. AR and VR share some advantages over traditional treatments. However, AR gives a greater feeling of presence (the sensation of being there) and reality judgment (judging an experience as real) than VR because the environment and the elements the patient uses to interact with the application are real. Moreover, in AR users see their own hands, feet, and so on, whereas VR only simulates this experience. With these differences in mind, the question arises as to the kinds of psychological treatments AR and VR are most suited for. In our system, patients see their own hands, feet, and so on. They can touch the table that animals are crossing or seeing their feet while the animals are running on the floor. They can also hold a marker with a dead spider or cockroach or pick up a flyswatter, a can of insecticide, or a dustpan.


title: Social Perception of Self-Enhancement Bias and Error

abstract: Abstract. How do social observers perceive and judge individuals who self-enhance (vs. not)? Using a decision-theoretic framework, we distinguish between self-enhancement bias and error, where the former comprises both correct and incorrect self-perceptions of being better than average. The latter occurs when a claim to be better than others is found to be false. In two studies, we find that when judging people’s competence, observers are sensitive to the accuracy of self-perception. When judging their morality, however, they tend to respond negatively to any claims of being better than average. These findings are further modulated by the domain of performance (intelligence vs. moral aptitude). Implications for the strategic use of self-enhancement claims are discussed.


title: Semantic Sentiment Analysis Challenge at ESWC2017

abstract: Sentiment Analysis is a widely studied research field in both research and industry, and there are different approaches for addressing sentiment analysis related tasks. Sentiment Analysis engines implement approaches spanning from lexicon-based techniques, to machine learning, or involving syntactical rules analysis. Such systems are already evaluated in international research challenges. However, Semantic Sentiment Analysis approaches, which take into account or rely also on large semantic knowledge bases and implement Semantic Web best practices, are not under specific experimental evaluation and comparison by other international challenges. Such approaches may potentially deliver higher performance, since they are also able to analyze the implicit, semantics features associated with natural language concepts. In this paper, we present the fourth edition of the Semantic Sentiment Analysis Challenge, in which systems implementing or relying on semantic features are evaluated in a competition involving large test sets, and on different sentiment tasks. Systems merely based on syntax/word-count or just lexicon-based approaches have been excluded by the evaluation. Then, we present the results of the evaluation for each task and show the winner of the most innovative approach award, that combines several knowledge bases for addressing the sentiment analysis task.


title: Design and hardware implementation of reconfigurable nano satellite communication system using FPGA based SDR for FM/FSK demodulation and BPSK modulation

abstract: Communication system is one of the major areas in which digital signal processing finds direct application. Recent advances in signal processing have helped in enormously reducing the complexity of communication system design and also in improving the performance of the system. Software Defined Radio (SDR) enables in-orbit re-configurability of frequency, modulation scheme, data rate, bandwidth and channel coding in the case of satellite communication systems where component change is not possible after launch. This paper describes the design and hardware implementation of SDR type communication system based on FPGA for a nano satellite. The major functions carried out onboard are FM/FSK demodulation for tele command uplink and BPSK modulation with raised cosine filtering for telemetry downlink. The full system is designed and implemented based on Microsemi Smartfusion2 FPGA. For hardware evaluation of the system, Virtex-6 FPGA with high speed analog daughter card is employed. Test results are also provided at the end of the paper along with implementation of re-configurability.


title: Didn't you see my message?: predicting attentiveness to mobile instant messages

abstract: Mobile instant messaging (e.g., via SMS or WhatsApp) often goes along with an expectation of high attentiveness, i.e., that the receiver will notice and read the message within a few minutes. Hence, existing instant messaging services for mobile phones share indicators of availability, such as the last time the user has been online. However, in this paper we not only provide evidence that these cues create social pressure, but that they are also weak predictors of attentiveness. As remedy, we propose to share a machine-computed prediction of whether the user will view a message within the next few minutes or not. For two weeks, we collected behavioral data from 24 users of mobile instant messaging services. By the means of machine-learning techniques, we identified that simple features extracted from the phone, such as the user's interaction with the notification center, the screen activity, the proximity sensor, and the ringer mode, are strong predictors of how quickly the user will attend to the messages. With seven automatically selected features our model predicts whether a phone user will view a message within a few minutes with 70.6% accuracy and a precision for fast attendance of 81.2%


title: Convolutional Self Organizing Map

abstract: Recently, deep learning became very popular, and was applied to many fields. The convolutional neural networks are often used for representing the layers for deep learning. In this paper, we propose Convolutional Self Organizing Map, which can be applicable to deep learning. Conventional Self Organizing Map uses single layered architecture, and can visualizes and classifies the input data on 2 dimensional map. SOMs which uses multiple layers are already proposed. In this paper, we propose Self Organizing Map algorithms which include convolutional layers. 2 types of convolution methods, which are based on conventional method and inspired from Self Organizing Map algorithm are proposed, and the performance of both method is examined in the experiments of clustering image data.


title: Characteristics of Excessive Cellular Phone Use in Korean Adolescents

abstract: Abstract The objective of this study was to evaluate the possible psychological problems related to excessive cellular phone use in adolescents. Results from 595 participants showed that the potentially excessive user group had a tendency to identify themselves with their cellular phones and to have difficulties in controlling usage. They expressed more depressive symptoms, higher interpersonal anxiety, and lower self-esteem. A positive correlation was also observed between excessive cellular phone use and Internet addiction.


title: Fast Two Dimensional Pattern Matching

abstract: An algorithm for searching for a two-dimensional m×m pattern in a two-dimensional n×n text is presented. It performs on the average less comparisons than the size of the text: n2m using m2 extra space. Basically, it uses multiple string matching on only nm rows of the text. It runs in at most 2n2 time and is close to the optimal n2 time for many patterns. It steadily extends to an alphabet-independent algorithm with a similar worst case. Experimental results are included for a practical version.


title: Membrane: Operating system support for restartable file systems

abstract: We introduce Membrane, a set of changes to the operating system to support restartable file systems. Membrane allows an operating system to tolerate a broad class of file system failures, and does so while remaining transparent to running applications; upon failure, the file system restarts, its state is restored, and pending application requests are serviced as if no failure had occurred. Membrane provides transparent recovery through a lightweight logging and checkpoint infrastructure, and includes novel techniques to improve performance and correctness of its fault-anticipation and recovery machinery. We tested Membrane with ext2, ext3, and VFAT. Through experimentation, we show that Membrane induces little performance overhead and can tolerate a wide range of file system crashes. More critically, Membrane does so with little or no change to existing file systems, thus improving robustness to crashes without mandating intrusive changes to existing file-system code.


title: Performance Management: A model and research agenda

abstract: Performance Management deals with the challenge organizations face in defining, measuring and stimulating employee performance with the ultimate goal to improve organizational performance. Thus, Performance Management involves multiple levels of analysis and is clearly linked to the topics studied in strategic HRM as well as performance appraisal. This paper presents a model for Performance Management combining insights from strategic HRM and I/O psychology. The model incorporates multi-level elements, and adds to previous models by explicitly incorporating employee perceptions, the role of direct supervisors and possible reversed causality. Challenges for future research are also presented.


title: Embarrassingly Parallel Variational Inference in Nonconjugate Models

abstract: We develop a parallel variational inference (VI) procedure for use in data-distributed settings, where each machine only has access to a subset of data and runs VI independently, without communicating with other machines. This type of "embarrassingly parallel" procedure has recently been developed for MCMC inference algorithms; however, in many cases it is not possible to directly extend this procedure to VI methods without requiring certain restrictive exponential family conditions on the form of the model. Furthermore, most existing (nonparallel) VI methods are restricted to use on conditionally conjugate models, which limits their applicability. To combat these issues, we make use of the recently proposed nonparametric VI to facilitate an embarrassingly parallel VI procedure that can be applied to a wider scope of models, including to nonconjugate models. We derive our embarrassingly parallel VI algorithm, analyze our method theoretically, and demonstrate our method empirically on a few nonconjugate models.


title: Magic Bullets for Mental Disorders: The Emergence of the Concept of an “Antipsychotic” Drug

abstract: When “antipsychotic” drugs were introduced into psychiatry in the 1950s, they were thought to work by inducing a state of neurological suppression, which reduced behavioral disturbance as well as psychotic symptoms. This view was reflected in the name “neuroleptic.” Within a few years, however, the idea that the drugs were a disease-specific treatment for schizophrenia or psychosis, and that they worked by modifying the underlying pathology of the condition, replaced this earlier view, and they became known as “antipsychotics.” This transformation of views about the drugs’ mode of action occurred with little debate or empirical evaluation in the psychiatric literature and obscured earlier evidence about the nature of these drugs. Drug advertisements in the British Journal of Psychiatry reflect the same changes, although the nondisease-specific view persisted for longer. It is suggested that professional interests rather than scientific merit facilitated the rise of the disease-specific view of drug action. The increasing popularity of atypical antipsychotics makes it important to examine the origins of the assumptions on which modern drug treatment is based.


title: Bejo: Behavior Based Job Classification for Resource Consumption Prediction in the Cloud

abstract: Resource prediction (e.g. CPU/memory utilization) of cloud computing jobs has attracted substantial amount of attention. Existing works use regression methods based on historical information of jobs, with an impractical assumption that the job to be predicted has the same class as the historical jobs. To address this problem, we propose to take the category of the jobs into consideration for effective resource prediction. Existing works on job classification either ignores the temporal variance of resource consumption during job execution or use it in a naive way, resulting in unsatisfactory classification accuracy and/or slow speed. In this paper, we introduce a new and efficient job classification approach, called Bejo. Inspired by the textual document classification methods, which use distribution of text words to describe and classify a document, Bejo treats the job as a document, assigns each collected resource consumption snapshot to a certain "resource word", and uses the distribution of the words to describe and classify a job. An l1 norm minimization formulation is used to assign each resource snapshot to a resource word, to especially address the unique challenges of high noise and tight time budget of cloud job classification. We collect a comprehensive dataset for job classification and resource consumption prediction on cloud platforms, and demonstrate superior quality and efficiency of Bejo over state-of-the-art algorithms. Experiments also show the relative error of resource consumption prediction can be dramatically reduced by adding an extra job classification step to the existing regression methods.


Use of Neural Networks to Forecast Time Series: River Flow Modeling


Laser Welded Stainless Steel and Aluminum Alloys for Lithium-Ion Cell Casings


title: dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs

abstract: Objective assessment of image quality is fundamentally important in many image processing tasks. In this paper, we focus on learning blind image quality assessment (BIQA) models, which predict the quality of a digital image with no access to its original pristine-quality counterpart as reference. One of the biggest challenges in learning BIQA models is the conflict between the gigantic image space (which is in the dimension of the number of image pixels) and the extremely limited reliable ground truth data for training. Such data are typically collected via subjective testing, which is cumbersome, slow, and expensive. Here, we first show that a vast amount of reliable training data in the form of quality-discriminable image pairs (DIPs) can be obtained automatically at low cost by exploiting large-scale databases with diverse image content. We then learn an opinion-unaware BIQA (OU-BIQA, meaning that no subjective opinions are used for training) model using RankNet, a pairwise learning-to-rank (L2R) algorithm, from millions of DIPs, each associated with a perceptual uncertainty level, leading to a DIP inferred quality (dipIQ) index. Extensive experiments on four benchmark IQA databases demonstrate that dipIQ outperforms the state-of-the-art OU-BIQA models. The robustness of dipIQ is also significantly improved as confirmed by the group MAximum Differentiation competition method. Furthermore, we extend the proposed framework by learning models with ListNet (a listwise L2R algorithm) on quality-discriminable image lists (DIL). The resulting DIL inferred quality index achieves an additional performance gain.


title: The TileDB Array Data Storage Manager

abstract: We present a novel storage manager for multi-dimensional arrays that arise in scientific applications, which is part of a larger scientific data management system called TileDB. In contrast to existing solutions, TileDB is optimized for both dense and sparse arrays. Its key idea is to organize array elements into ordered collections called fragments. Each fragment is dense or sparse, and groups contiguous array elements into data tiles of fixed capacity. The organization into fragments turns random writes into sequential writes, and, coupled with a novel read algorithm, leads to very efficient reads. TileDB enables parallelization via multi-threading and multi-processing, offering thread-/process-safety and atomicity via lightweight locking. We show that TileDB delivers comparable performance to the HDF5 dense array storage manager, while providing much faster random writes. We also show that TileDB offers substantially faster reads and writes than the SciDB array database system with both dense and sparse arrays. Finally, we demonstrate that TileDB is considerably faster than adaptations of the Vertica relational column-store for dense array storage management, and at least as fast for the case of sparse arrays.


title: Analysing qualitative data.

abstract: EvERyoNE who has ever undertaken qualitative research knows that there are multiple complex challenges throughout all stages of the research process. After the initial idea for a research project has emerged and developed, the researcher needs to make important decisions and choices about what methods to use, how to collect the data, how to analyse that data and, in the end, how to present the data in a meaningful way. whatever methodological decisions are made and however the data is collected, the result is usually a large amount of data and it is the analysis of that data that can prove most challenging and can be the most time consuming part of the entire research project. Those new to qualitative research often underestimate both the volume of data they will collect and the complexity involved in the analysis of it. Even for experienced researchers data analysis can be the most difficult part of the research process, but it gets easier with practice and by drawing on the experiences of others. There are many good methodology books describing the multiple stages of qualitative research methods, which can prove valuable for researchers when planning a new project. however, some of the most useful guidance and advice comes from the writing of those who have been through the process and have described their own practical experiences. Reading about the experiences of others brings the research into the real world and extends beyond theoretical perspectives. In this edition of Nurse Researcher the three themed papers offer a useful insight into different aspects of qualitative data analysis. They focus on some of the common challenges facing researchers and how these might be overcome.


title: Listen, Attend and Spell

abstract: We present Listen, Attend and Spell (LAS), a neural network that learns to transcribe speech utterances to characters. Unlike traditional DNN-HMM models, this model learns all the components of a speech recognizer jointly. Our system has two components: a listener and a speller. The listener is a pyramidal recurrent network encoder that accepts filter bank spectra as inputs. The speller is an attention-based recurrent network decoder that emits characters as outputs. The network produces character sequences without making any independence assumptions between the characters. This is the key improvement of LAS over previous end-to-end CTC models. On a subset of the Google voice search task, LAS achieves a word error rate (WER) of 14.1% without a dictionary or a language model, and 10.3% with language model rescoring over the top 32 beams. By comparison, the state-of-the-art CLDNN-HMM model achieves a WER of 8.0%.


title: Join-Graph Propagation Algorithms

abstract: The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl's belief propagation algorithm (BP). We start with the bounded inference mini-clustering algorithm and then move to the iterative scheme called Iterative Join-Graph Propagation (IJGP), that combines both iteration and bounded inference. Algorithm IJGP belongs to the class of Generalized Belief Propagation algorithms, a framework that allowed connections with approximate algorithms from statistical physics and is shown empirically to surpass the performance of mini-clustering and belief propagation, as well as a number of other state-of-the-art algorithms on several classes of networks. We also provide insight into the accuracy of iterative BP and IJGP by relating these algorithms to well known classes of constraint propagation schemes


title: Development of single leg version of HAL for hemiplegia

abstract: Our goal is to try to enhance the QoL of persons with hemiplegia by the mean of an active motion support system based on the HAL’s technology. The HAL (Hybrid Assistive Limb) in its standard version is an exoskeleton-based robot suit to support and enhance the human motor functions. The purpose of the research presented in this paper is the development of a new version of the HAL to be used as an assistive device providing walking motion support to persons with hemiplegia. It includes the realization of the single leg version of the HAL and the redesign of the original HAL’s Autonomous Controller to execute human-like walking motions in an autonomous way. Clinical trials were conducted in order to assess the effectiveness of the developed system. The first stage of the trials described in this paper involved the participation of one hemiplegic patient who has difficulties to flex his right knee. As a result, the knee flexion support for walking provided by the HAL appeared to improve the subject’s walking (longer stride and faster steps). The first evaluation of the system with one subject showed promising results for the future developments.


title: A problem solving model for collaborative agents

abstract: This paper describes a model of problem solving for use in collaborative agents. It is intended as a practical model for use in implemented systems, rather than a study of the theoretical underpinnings of collaborative action. The model is based on our experience in building a series of interactive systems in different domains, including route planning, emergency management, and medical advising. It is currently being used in an implemented, end-to- end spoken dialogue system in which the system assists a person in managing their medications. While we are primarily focussed on human-machine collaboration, we believe that the model will equally well apply to interactions between sophisticated software agents that need to coordinate their activities.


title: Representation Learning with Contrastive Predictive Coding

abstract: While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments.


title: RF Bandaid: A Fully-Analog and Passive Wireless Interface for Wearable Sensors

abstract: This paper presents a passive wireless RF sensor platform (RFSP), with only analog components, that harvests energy from an RF source and reflects data as a direct subcarrier modulation, thus making it battery free. A fully-analog architecture results in an ultra-low power device (under 200 μW) with a low component count, reducing the physical footprint. We envision such a platform to enable medical sensing systems that fit on a small bandaid like flexible structure, require no-battery, or charging and are able to provide continuous physiological monitoring. To realize this vision, we have developed and optimized a novel RF architecture that 1) directly maps sensor output to frequency modulation and transmits it to a remote receiver processing unit (RPU). This direct frequency mapping allows all further digitization and computation to be moved to the RPU -- reducing power and size requirements on the RFSP; 2) harvests energy from the carrier signal transmitted by a simple continuous wave transmitter, thereby requiring no batteries or supercap; and 3) uses backscatter to communicate with the RPU enabling ultra-low power requirements. The total power consumption of our prototype device leveraging this architecture was measured to be between 35 μW and 160 μW. We demonstrate that the RFSP can harvest sufficient power, sense, and communicate continuously without necessity for energy storage at a distance of 4 m from a transmitter emitting a 915 MHz continuous wave at 26 dBm (0.39 W). Prior backscatter systems typically have power budgets of 1 mW and require energy storage (battery or supercap), RFSP's sub 200 μW power consumption provides a significant improvement and longer range for a given TX power. To demonstrate applicability to real-world health sensing and the flexibility to adapt to different sensors, this paper presents results from breathing, heart rate, temperature, and sound sensing applications.


title: Audio Scene Classification with Deep Recurrent Neural Networks

abstract: We introduce in this work an efficient approach for audio scene classification using deep recurrent neural networks. A scene audio signal is firstly transformed into a sequence of high-level label tree embedding feature vectors. The vector sequence is then divided into multiple subsequences on which a deep GRU-based recurrent neural network is trained for sequence-to-label classification. The global predicted label for the entire sequence is finally obtained via aggregation of subsequence classification outputs. We will show that our approach obtain an F1-score of 97.7% on the LITIS Rouen dataset, which is the largest dataset publicly available for the task. Compared to the best previously reported result on the dataset, our approach is able to reduce the relative classification error by 35.3%.


title: Nasal tip refinement in primary rhinoplasty: the cephalic trim cap graft.

abstract: BACKGROUND ::: Commonly used techniques for achieving nasal tip projection and refinement are adequate for most primary rhinoplasty patients, but they may result in undesirable tip bifidity and visible lower lateral cartilage angularity, especially in patients with thin skin. ::: ::: ::: OBJECTIVE ::: We report the use of "like" local tissues, cephalic trim cartilage remnants of the lower lateral cartilages, as invisible tip grafts to soften any angular cartilage edges or tip bifidity. ::: ::: ::: METHODS ::: The cartilaginous framework was exposed by using the open rhinoplasty approach. Lower lateral cartilages were separated from upper lateral cartilages, the caudal septum at the anterior septal angle, and from each other. A cephalic trim was performed as necessary, with the cartilaginous segments preserved for use as a cap graft. Interdomal sutures and transdermal sutures were used either alone or in combination to set the desired tip projection. If tip bifidity was visible through thin nasal tip skin, a cephalic trim cap graft was placed. ::: ::: ::: RESULTS ::: The procedure can achieve a well-unified nasal tip with no evidence of bifidity, angularity, or cartilage graft visibility. ::: ::: ::: CONCLUSION ::: Use of this technique to improve nasal tip projection can avoid undesirable tip bifidity and visible lower lateral cartilage angularity in patients with thin nasal skin. (Aesthetic Surg J 2002;22:39-45.).


title: The Infeasibility of Free Trade in Classical Theory: Ricardo's Comparative Advantage Parable has no Solution

abstract: In this paper, formal models of Ricardo's comparative advantage parable, which include general forms of consumer price response behavior, are constructed from a detailed textual exegesis of Ricardo's story. Using these models, the comparative advantage parable is shown to be mathematically over-determined and therefore generally unsolvable. To reinforce this conclusion, a numerical solution is derived for a constant elasticity version of the model. A necessary condition for the existence of a solution to the constant elasticity model is that two price elasticities of demand must be functions of the other two price elasticities of demand. General formulas are derived expressing this dependency. When realistic elasticities for wine are set, the model can only be solved if Portuguese demand for English cloth is unrealistically elastic. This demonstrates that sustainable and mutually beneficial trade between England and Portugal can only be realized through managed trade.


title: A Multi-Touch DJ Interface with Remote Audience Feedback

abstract: Current DJ interfaces lack direct support for typical digital communication common in social media. We present a novel DJ interface for live internet broadcast performances with remote audience feedback integration. Our multi-touch interface is designed for a table top display, featuring a time-line based visualization. Two studies are presented involving seven DJs, culminating in four live broadcasts gathering and analyzing data to better understand both the DJ and audience perspective. This study is one of the first to look closer at DJs and remote audiences. We present useful insight for future interaction design between DJs and remote audiences, and interface integrated audience feedback.


title: Towards Scalable Deep Learning via I/O Analysis and Optimization

abstract: Deep learning systems have been growing in prominence as a way to automatically characterize objects, trends, and anomalies. Researchers have been investigating techniques to optimize such systems. An area of particular interest has been using supercomputing systems to quickly generate effective deep learning networks, a phase referred to as “training” of the deep neural network. As we scale deep learning frameworks-such as Caffe-on large-scale systems, we notice that parallelism can help improve the computation tremendously, leaving data I/O as the major bottleneck limiting the overall system scalability. In this paper, we present a detailed analysis of the performance bottlenecks of Caffe on large supercomputing systems. The analysis shows that Caffe's I/O subsystem-LMDB-relies on memory-mapped I/O, which can be highly inefficient on large-scale systems because of its interaction with the process-scheduling system and the network-based parallel filesystem. Based on this analysis, we present LMDBIO, an optimized I/O plugin for Caffe that takes into account the data access pattern in order to vastly improve I/O performance. Experimental results show that LMDBIO can improve the overall execution time of Caffe by nearly 20-fold in some cases.


title: Differentially private data analysis of social networks via restricted sensitivity

abstract: We introduce the notion of restricted sensitivity as an alternative to global and smooth sensitivity to improve accuracy in differentially private data analysis. The definition of restricted sensitivity is similar to that of global sensitivity except that instead of quantifying over all possible datasets, we take advantage of any beliefs about the dataset that a querier may have, to quantify over a restricted class of datasets. Specifically, given a query f and a hypothesis H about the structure of a dataset D, we show generically how to transform f into a new query f_H whose global sensitivity (over all datasets including those that do not satisfy H) matches the restricted sensitivity of the query f. Moreover, if the belief of the querier is correct (i.e., D is in H) then f_H(D) = f(D). If the belief is incorrect, then f_H(D) may be inaccurate. We demonstrate the usefulness of this notion by considering the task of answering queries regarding social-networks, which we model as a combination of a graph and a labeling of its vertices. In particular, while our generic procedure is computationally inefficient, for the specific definition of H as graphs of bounded degree, we exhibit efficient ways of constructing f_H using different projection-based techniques. We then analyze two important query classes: subgraph counting queries (e.g., number of triangles) and local profile queries (e.g., number of people who know a spy and a computer-scientist who know each other). We demonstrate that the restricted sensitivity of such queries can be significantly lower than their smooth sensitivity. Thus, using restricted sensitivity we can maintain privacy whether or not D is in H, while providing more accurate results in the event that H holds true.


title: Machine Translation Approach for Vietnamese Diacritic Restoration

abstract: The diacritic marks exist in many languages such as French, German, Slovak, Vietnamese, etc. However for some reasons, sometime they are omitted in writing. This phenomenon may lead to the ambiguity for reader when reading a non-diacritic text. The automatic diacritic restoration problem has been proposed and resolved in several languages using the character-based approach, word-based approach, point-wise approach, etc. However, these approaches lean heavily on the linguistics information, size of training corpus and sometime they are language dependent. In this paper, a simple and effective restoration method will be presented. The machine translation approach will be used as a new solution for this problem. The restoration method has been applied for Vietnamese language, and integrated in an Android application named VIVA (Vietnamese Voice Assistant) that reads out the content of incoming text messages on mobile phone. Our experiments show that the proposed restoration method can recover diacritic marks with a 99.0% accuracy rate.


title: Characterizing debate performance via aggregated twitter sentiment

abstract: Television broadcasters are beginning to combine social micro-blogging systems such as Twitter with television to create social video experiences around events. We looked at one such event, the first U.S. presidential debate in 2008, in conjunction with aggregated ratings of message sentiment from Twitter. We begin to develop an analytical methodology and visual representations that could help a journalist or public affairs person better understand the temporal dynamics of sentiment in reaction to the debate video. We demonstrate visuals and metrics that can be used to detect sentiment pulse, anomalies in that pulse, and indications of controversial topics that can be used to inform the design of visual analytic systems for social media events.


title: Contextual Language Model Adaptation for Conversational Agents

abstract: Statistical language models (LM) play a key role in Automatic Speech Recognition (ASR) systems used by conversational agents. These ASR systems should provide a high accuracy under a variety of speaking styles, domains, vocabulary and argots. In this paper, we present a DNN-based method to adapt the LM to each user-agent interaction based on generalized contextual information, by predicting an optimal, context-dependent set of LM interpolation weights. We show that this framework for contextual adaptation provides accuracy improvements under different possible mixture LM partitions that are relevant for both (1) Goal-oriented conversational agents where it's natural to partition the data by the requested application and for (2) Non-goal oriented conversational agents where the data can be partitioned using topic labels that come from predictions of a topic classifier. We obtain a relative WER improvement of 3% with a 1-pass decoding strategy and 6% in a 2-pass decoding framework, over an unadapted model. We also show up to a 15% relative improvement in recognizing named entities which is of significant value for conversational ASR systems.


title: Intercrossed Access Controls for Secure Financial Services on Multimedia Big Data in Cloud Systems

abstract: The dramatically growing demand of Cyber Physical and Social Computing (CPSC) has enabled a variety of novel channels to reach services in the financial industry. Combining cloud systems with multimedia big data is a novel approach for Financial Service Institutions (FSIs) to diversify service offerings in an efficient manner. However, the security issue is still a great issue in which the service availability often conflicts with the security constraints when the service media channels are varied. This paper focuses on this problem and proposes a novel approach using the Semantic-Based Access Control (SBAC) techniques for acquiring secure financial services on multimedia big data in cloud computing. The proposed approach is entitled IntercroSsed Secure Big Multimedia Model (2SBM), which is designed to secure accesses between various media through the multiple cloud platforms. The main algorithms supporting the proposed model include the Ontology-Based Access Recognition (OBAR) Algorithm and the Semantic Information Matching (SIM) Algorithm. We implement an experimental evaluation to prove the correctness and adoptability of our proposed scheme.


title: Domain-Adversarial Neural Networks

abstract: We introduce a new neural network learning algorithm suited to the context of domain adaptation, in which data at training and test time come from similar but different distributions. Our algorithm is inspired by theo ry on domain adaptation suggesting that, for effective domain transfer to be achiev ed, predictions must be made based on a data representation that cannot discriminate between the training (source) and test (target) domains. We propose a training objective that implements this idea in the context of a neural network, whose hidden layer is trained to be predictive of the classification target, but uninformati ve as to the domain of the input. Our experiments on a sentiment analysis classificati on benchmark, where the target data available at the training time is unlabeled, show that our neural network for domain adaption algorithm has better performance than either a standard neural networks and a SVM, trained on input features extracted with the state-ofthe-art marginalized stacked denoising autoencoders of Chen et al. (2012).


title: Performance analysis of latent fingerprint enhancement techniques

abstract: Fingerprint Enhancement is an important step used to improve the accuracy and rate of automated detection of fingerprints. Enhancing the latent fingerprints facilitates the extraction of minutiae from the fingerprint image. In this paper, two methods of latent fingerprint enhancement are carried out. The first uses Histogram Equalization for enhancing the latent fingerprint. The second method uses a novel approach of Binarization followed by Wiener filtering and an additional Median filter. The improvements in the image are then analyzed based on their entropy, contrast and moment.


title: Population-independent subsidy and lump-sum tax based control of multipopulation replicator dynamics

abstract: Many social systems consist of many selfish players. All players may not have the same sense of values, and players who have different senses of values can be considered to belong different populations. As a model of such systems, multipopulation replicator dynamics has been proposed. In general, the desirable state may not be realized in the systems of many selfish players. In order to realize the desirable state, the government that offers subsidies to players regardless of their populations has been introduced. In previous work, however, it is assumed that there exists an equilibrium point that corresponds to the desirable state. In this paper, in addition to the subsidy, we introduce a lump-sum tax that makes a desirable non-equilibrium target state an equilibrium point and formulate replicator dynamics with the lump-sum tax and the subsidy. We also derive the stabilization conditions of the target state in two-population two-strategy games.


title: Coronary heart disease in residents of Rochester, Minnesota, 1950-1975. I. Background and study design.

abstract: The unique medical data resource for the population of Rochester, Minnesota, is centered on the records of the Mayo Clinic and the Olmsted Medical and Surgical Group, which for several decades have provided nearly all medical care in this community. This resource has been utilized in a study of the incidence rates and secular trend in coronary heart disease for the period 1950-1975 among residents of Rochester. A total of 3,080 patients fulfilled the clinical and other criteria for inclusion in this study. The patients, classified by initial manifestation of coronary heart disease, consisted of 1,321 with myocardial infarction, 1,215 with angina pectoris, and 544 with sudden unexpected death. In this paper the background, clinical definitions, and study design are presented.


title: Sandwiched Graphene−Membrane Superstructures

abstract: We demonstrate by molecular dynamics simulations that graphene sheets could be hosted in the hydrophobic interior of biological membranes formed by amphiphilic phospholipid molecules. Our simulation shows that these hybrid graphene−membrane superstructures might be prepared by forming hydrated micelles of individual graphene flakes covered by phospholipids, which can be then fused with the membrane. Since the phospholipid layers of the membrane electrically isolate the embedded graphene from the external solution, the composite system might be used in the development of biosensors and bioelectronic materials.


title: The promise of Mechanical Turk: How online labor markets can help . . .

abstract: Combining evolutionary models with behavioral experiments can generate powerful insights into the evolution of human behavior. The emergence of online labor markets such as Amazon Mechanical Turk (AMT) allows theorists to conduct behavioral experiments very quickly and cheaply. The process occurs entirely over the computer, and the experience is quite similar to performing a set of computer simulations. Thus AMT opens the world of experimentation to evolutionary theorists. In this paper, I review previous work combining theory and experiments, and I introduce online labor markets as a tool for behavioral experimentation. I review numerous replication studies indicating that AMT data is reliable. I also present two new experiments on the reliability of self-reported demographics. In the first, I use IP address logging to verify AMT subjects’ self-reported country of residence, and find that 97% of responses are accurate. In the second, I compare the consistency of a range of demographic variables reported by the same subjects across two different studies, and find between 81% and 98% agreement, depending on the variable. Finally, I discuss limitations of AMT and point out potential pitfalls. I hope this paper will encourage evolutionary modelers to enter the world of experimentation, and help to strengthen the bond between theoretical and empirical analyses of the evolution of human behavior.


title: Internet Addiction and Excessive Social Networks Use: What About Facebook?

abstract: Facebook is notably the most widely known and used social network worldwide. It has been described as a valuable tool for leisure and communication between people all over the world. However, healthy and conscience Facebook use is contrasted by excessive use and lack of control, creating an addiction with severely impacts the everyday life of many users, mainly youths. If Facebook use seems to be related to the need to belong, affiliate with others and for self-presentation, the beginning of excessive Facebook use and addiction could be associated to reward and gratification mechanisms as well as some personality traits. Studies from several countries indicate different Facebook addiction prevalence rates, mainly due to the use of a wide-range of evaluation instruments and to the lack of a clear and valid definition of this construct. Further investigations are needed to establish if excessive Facebook use can be considered as a specific online addiction disorder or an Internet addiction subtype.


title: Ceramic interconnect bridge for heterogeneous integration and multiple chip packaging

abstract: In this paper, we describe the architecture and performance of a fine pitch multiple chip heterogeneous integration solution using ceramic interconnect bridge on organic substrate package. We present the increased IO density and improvement of electrical high-speed performance on signal integrity are achievable through this novel integration scheme, where dense copper routings on small ceramic elements are served as interconnect bridges. The cost and signal attenuation of using ceramic bridge is far better than that of silicon bridge or wafer interposer on substrate.


Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures


title: Anchored neighborhood regression based single image super-resolution from self-examples

abstract: In this paper, we present a novel self-learning single image super-resolution (SR) method, which restores a highresolution (HR) image from self-examples extracted from the low-resolution (LR) input image itself without relying on extra external training images. In the proposed method, we directly use sampled image patches as the anchor points, and then learn multiple linear mapping functions based on anchored neighborhood regression to transform LR space into HR space. Moreover, we utilize the flipped and rotated versions of the self-examples to expand the internal patch space. Experimental comparison on standard benchmarks with state-of-the-art methods validates the effectiveness of the proposed approach.


title: Ensembling Neural Networks for Digital Pathology Images Classification and Segmentation

abstract: In the last years, neural networks have proven to be a powerful framework for various image analysis problems. However, some application domains have specific limitations. Notably, digital pathology is an example of such fields due to tremendous image sizes and quite limited number of training examples available. In this paper, we adopt state-of-the-art convolutional neural networks (CNN) architectures for digital pathology images analysis. We propose to classify image patches to increase effective sample size and then to apply an ensembling technique to build prediction for the original images. To validate the developed approaches, we conducted experiments with Breast Cancer Histology Challenge dataset and obtained 90% accuracy for the 4-class tissue classification task.


title: Exploring Phylogenetic Relationships within Myriapoda and the Effects of Matrix Composition and Occupancy on Phylogenomic Reconstruction

abstract: Myriapods, including the diverse and familiar centipedes and millipedes, are one of the dominant terrestrial arthropod groups. Although molecular evidence has shown that Myriapoda is monophyletic, its internal phylogeny remains contentious and understudied, especially when compared to those of Chelicerata and Hexapoda. Until now, efforts have focused on taxon sampling (e.g., by including a handful of genes from many species) or on maximizing matrix size (e.g., by including hundreds or thousands of genes in just a few species), but a phylogeny maximizing sampling at both levels remains elusive. In this study, we analyzed 40 Illumina transcriptomes representing 3 of the 4 myriapod classes (Diplopoda, Chilopoda, and Symphyla); 25 transcriptomes were newly sequenced to maximize representation at the ordinal level in Diplopoda and at the family level in Chilopoda. Ten supermatrices were constructed to explore the effect of several potential phylogenetic biases (e.g., rate of evolution, heterotachy) at 3 levels of gene occupancy per taxon (50%, 75%, and 90%). Analyses based on maximum likelihood and Bayesian mixture models retrieved monophyly of each myriapod class, and resulted in 2 alternative phylogenetic positions for Symphyla, as sister group to Diplopoda + Chilopoda, or closer to Diplopoda, the latter hypothesis having been traditionally supported by morphology. Within centipedes, all orders were well supported, but 2 deep nodes remained in conflict in the different analyses despite dense taxon sampling at the family level. Relationships among centipede orders in all analyses conducted with the most complete matrix (90% occupancy) are at odds not only with the sparser but more gene-rich supermatrices (75% and 50% supermatrices) and with the matrices optimizing phylogenetic informativeness or most conserved genes, but also with previous hypotheses based on morphology, development, or other molecular data sets. Our results indicate that a high percentage of ribosomal proteins in the most complete matrices, in conjunction with distance from the root, can act in concert to compromise the estimated relationships within the ingroup. We discuss the implications of these findings in the context of the ever more prevalent quest for completeness in phylogenomic studies.


title: Definition of descriptors for semantic image interpretation

abstract: A lot of effort has been put into researching image interpretation, but there is still no universally accepted approach to map low-level feature into high level image semantic interpretation [1]. In this paper, a method for continuous low-level features vector quantization is presented so as to define appropriate values for descriptive variables. The similarity among different concepts of the domain is examined and compared by using the measure of similarity which is based on the probabilistic model and the measure of distance. Also, an abstract image description vector suitable for image analysis is given.


title: Practical Designs of Brain–Computer Interfaces Based on the Modulation of EEG Rhythms

abstract: A brain–computer interface (BCI) is a communication channel which does not depend on the brain’s normal output pathways of peripheral nerves and muscles [1–3]. It supplies paralyzed patients with a new approach to communicate with the environment. Among various brain monitoring methods employed in current BCI research, electroencephalogram (EEG) is the main interest due to its advantages of low cost, convenient operation and non-invasiveness. In present-day EEG-based BCIs, the following signals have been paid much attention: visual evoked potential (VEP), sensorimotor mu/beta rhythms, P300 evoked potential, slow cortical potential (SCP), and movement-related cortical potential (MRCP). Details about these signals can be found in chapter “Brain Signals for Brain–Computer Interfaces”. These systems offer some practical solutions (e.g., cursor movement and word processing) for patients with motor disabilities.


title: A 0.6-V/1.2-V low power single ended CMOS LNA for multi-standard RF front-ends

abstract: This paper presents a design of a low power single ended CMOS LNA for reconfigurable applications including GPS, GSM (DCS1800, PCS1900), 3G (UMTS), WLAN b/g and LTE. Based on a wideband input matching, the LNA stages cover all band of interest while achieving a good trade-off between high gain, low noise figure and low power consumption. For multi-standard aim, the LNA selects the desired bands using a high output resonant impedance with a switching technique. Implemented in 0.13-μm CMOS technology, the simulated LNA results at Vdd = 1.2 V perform a power gain higher than 30 dB, a noise figure below 2.3 dB, an input return loss less than -12 dB, and an IIP3 better than -19 dBm in frequency band 1.5-2.6 GHz. Besides, for the low mode at Vdd = 0.6 V, the power gain is better than 19 dB and the noise figure is about 2.9 dB. For all mentioned standards, the proposed LNA consumes 7.5 mW and 2.7 mW from supply voltage of 1.2 V and 0.6 V, respectively.


title: The Use of Thai Musical Instruments as a Tool in Music Therapy with Akaboshi's Musical Therapy Method

abstract: Differences between the two groups in terms of obtained muscle strength were measured. The Manual Muscle Test (MMT) of the Lovett, Medical Research Council was used before and after the experiment. The results showed that the majority of subjects showed greater improvement of muscle strength after using Thai musical instruments when compared to subjects in the traditional Akaboshi group. There were however, some drawbacks in the use of Thai instruments due to their size and weight. Some instruments were found to be unsuitable for certain muscle groups and some types of disabilities. The results of this small experiment support the hypothesis that Thai instruments could be used in lieu of those in the original Akaboshi methodology.


title: Teaching virtual reality with HTC vive and leap motion

abstract: Creating high quality virtual reality (VR) experience takes time and requires extensive practice. Although, multiple virtual and augmented reality courses existed for years all over the world, high costs of the equipment were always in the way of building up the up to date knowledge. Rapid development of the technology caused the new VR boom and exposed a serious lack of the experienced VR/AR developers. In this paper, we present our introductory VR courses for master students. The aim of the courses is to provide introduction to the basics of VR and AR and supporting technology. We move the teaching focus from the specifics of a particular VR system to the skill build-up and development of the VR user experience. For that we provide the access to the up to date consumer hardware, such as HTC Vive and Leap Motion. We discuss the structure of the courses and methodology, and provide the teaching materials. Moreover, we discuss in details our updated practical course that unites the development of a low-cost desktop and a high-quality immersive VR applications using only off-the-shelf consumer equipment. Furthermore, we discuss the overall course evaluation by the students and further opportunities for their professional growth, as well as consecutive changes that will be made next.


title: Graph-based Neural Multi-Document Summarization

abstract: We propose a neural multi-document summarization (MDS) system that incorporates sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural Networks as input node features. Through multiple layer-wise propagation, the GCN generates high-level hidden sentence features for salience estimation. We then use a greedy heuristic to extract salient sentences while avoiding redundancy. In our experiments on DUC 2004, we consider three types of sentence relation graphs and demonstrate the advantage of combining sentence relations in graphs with the representation power of deep neural networks. Our model improves upon traditional graph-based extractive approaches and the vanilla GRU sequence model with no graph, and it achieves competitive results against other state-of-the-art multi-document summarization systems.


title: From Rank-N to Rank-1 Face Recognition Based on Motion Similarity

abstract: In this paper, we present a sequential framework using facial motion information as a subsidiary to improve face recognition performance. As is generally known, reasonable static face recognition has been achieved based on subspace reduction techniques. In order to further improve performance, some extra cues, such as temporal variation, are investigated by building dynamic models. We propose a permuted similarity motion feature and integrate it into a sequential recognition system. This system can select the best candidate from the Rank-N candidates picked up in the recognition step based on static appearance parameters by using motion information. The recognition rate of the motion similarity is compared with the motion feature obtained from auto-regressive models to ::: prove its efficiency. In addition, the sequential system achieves better performance when the motion information is integrated with the static appearance information in a flexible manner.


Annotating the Little Prince with Chinese AMRs


title: In-air gestures around unmodified mobile devices

abstract: We present a novel machine learning based algorithm extending the interaction space around mobile devices. The technique uses only the RGB camera now commonplace on off-the-shelf mobile devices. Our algorithm robustly recognizes a wide range of in-air gestures, supporting user variation, and varying lighting conditions. We demonstrate that our algorithm runs in real-time on unmodified mobile devices, including resource-constrained smartphones and smartwatches. Our goal is not to replace the touchscreen as primary input device, but rather to augment and enrich the existing interaction vocabulary using gestures. While touch input works well for many scenarios, we demonstrate numerous interaction tasks such as mode switches, application and task management, menu selection and certain types of navigation, where such input can be either complemented or better served by in-air gestures. This removes screen real-estate issues on small touchscreens, and allows input to be expanded to the 3D space around the device. We present results for recognition accuracy (93% test and 98% train), impact of memory footprint and other model parameters. Finally, we report results from preliminary user evaluations, discuss advantages and limitations and conclude with directions for future work.


title: A Review of the Single Phase Photovoltaic Module Integrated Converter Topologies With Three Different DC Link Configurations

abstract: The annual world photovoltaic (PV) cell/module production is growing at almost an exponential rate and has reached 1727 MW in 2005. Building integrated PV (BIPV) projects are emerging as the strongest part of the PV market and grid interactive inverters are a key component in determining the total system cost. Module integrated converter (MIC) technology has become a global trend in grid interactive PV applications and may assist in driving down the balance of system costs to secure an improved total system cost. This paper concentrates on the topology study of the PV MICs in the power range below 500 W and covers most topologies recently proposed for MIC applications. The MIC topologies are classified into three different arrangements based on the dc link configurations. A systematic discussion is also provided at the end of the paper that focuses on the major advantages and disadvantages of each MIC arrangement. These are considered in detail and will provide a useful framework and point of reference for the next generation MIC designs and applications.


title: Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human--like learning

abstract: Autonomous lifelong development and learning is a fundamental capability of humans, differentiating them from current deep learning systems. However, other branches of artificial intelligence have designed crucial ingredients towards autonomous learning: curiosity and intrinsic motivation, social learning and natural interaction with peers, and embodiment. These mechanisms guide exploration and autonomous choice of goals, and integrating them with deep learning opens stimulating perspectives. Deep learning (DL) approaches made great advances in artificial intelligence, but are still far away from human learning. As argued convincingly by Lake et al., differences include human capabilities to learn causal models of the world from very little data, leveraging compositional representations and priors like intuitive physics and psychology. However, there are other fundamental differences between current DL systems and human learning, as well as technical ingredients to fill this gap, that are either superficially, or not adequately, discussed by Lake et al. These fundamental mechanisms relate to autonomous development and learning. They are bound to play a central role in artificial intelligence in the future. Current DL systems require engineers to manually specify a task-specific objective function for every new task, and learn through off-line processing of large training databases. On the contrary, humans learn autonomously open-ended repertoires of skills, deciding for themselves which goals to pursue or value, and which skills to explore, driven by intrinsic motivation/curiosity and social learning through natural interaction with peers. Such learning processes are incremental, online, and progressive. Human child development involves a progressive increase of complexity in a curriculum of learning where skills are explored, acquired, and built on each other, through particular ordering and timing. Finally, human learning happens in the physical world, and through bodily and physical experimentation, under severe constraints on energy, time, and computational resources. In the two last decades, the field of Developmental and Cognitive Robotics (Cangelosi and Schlesinger, 2015; Asada et al., 2009), in strong interaction with developmental psychology and neuroscience, has achieved significant advances in computational


abstract: Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has only a limited number of labeled samples. To cope with the considerable change between feature distributions of different domains, we propose a new cross-domain kernel learning framework into which many existing kernel methods can be readily incorporated. Our framework, referred to as Domain Transfer Multiple Kernel Learning (DTMKL), simultaneously learns a kernel function and a robust classifier by minimizing both the structural risk functional and the distribution mismatch between the labeled and unlabeled samples from the auxiliary and target domains. Under the DTMKL framework, we also propose two novel methods by using SVM and prelearned classifiers, respectively. Comprehensive experiments on three domain adaptation data sets (i.e., TRECVID, 20 Newsgroups, and email spam data sets) demonstrate that DTMKL-based methods outperform existing cross-domain learning and multiple kernel learning methods.


title: Femur and humerus length in trisomy 21 fetuses at 11–14 weeks of gestation

abstract: Objective ::: ::: To determine the value of measuring fetal femur and humerus length at 11–14 weeks of gestation in screening for chromosomal defects. ::: ::: ::: ::: Methods ::: ::: Femur and humerus lengths were measured using transabdominal ultrasound in 1018 fetuses immediately before chorionic villus sampling for karyotyping at 11–14 weeks of gestation. In the group of chromosomally normal fetuses, regression analysis was used to determine the association between long bone length and crown–rump length (CRL). Femur and humerus lengths in fetuses with trisomy 21 were compared with those of normal fetuses. ::: ::: ::: ::: Results ::: ::: The median gestation was 12 (range, 11–14) weeks. The karyotype was normal in 920 fetuses and abnormal in 98, including 65 cases of trisomy 21. In the chromosomally normal group the fetal femur and humerus lengths increased significantly with CRL (femur length = − 6.330 + 0.215 × CRL in mm, r = 0.874, P < 0.0001; humerus length = − 6.240 + 0.220 × CRL in mm, r = 0.871, P < 0.0001). In the Bland–Altman plot the mean difference between paired measurements of femur length was 0.21 mm (95% limits of agreement − 0.52 to 0.48 mm) and of humerus length was 0.23 mm (95% limits of agreement − 0.57 to 0.55 mm). In the trisomy 21 fetuses the median femur and humerus lengths were significantly below the appropriate normal mean for CRL by 0.4 and 0.3 mm, respectively (P = 0.002), but they were below the respective 5th centile of the normal range in only six (9.2%) and three (4.6%) of the cases, respectively. ::: ::: ::: ::: Conclusion ::: ::: At 11–14 weeks of gestation the femur and humerus lengths in trisomy 21 fetuses are significantly reduced but the degree of deviation from normal is too small for these measurements to be useful in screening for trisomy 21. Copyright © 2004 ISUOG. Published by John Wiley & Sons, Ltd.


title: Factors affecting consumers’ perception of electronic payment: an empirical analysis

abstract: Purpose – In view of the promising growth of e‐payment in Malaysia, this study aims to discover the factors influencing perception towards electronic payment (e‐payment) from the Malaysian consumers’ perspective.Design/methodology/approach – Literature indicates that factors such as benefits, trust, self‐efficacy, ease of use, and security influence consumers’ perception towards e‐payment. A self‐reporting questionnaire was developed and disseminated to 200 respondents, out of which 183 valid responses were considered for further statistical analysis.Findings – The multiple linear regression results reveal that benefits, self‐efficacy, and ease of use exert significant influences on consumers’ perception towards e‐payment. However, the insignificant results obtained for trust and security warrant further investigation.Research limitations/implications – This study proposes five factors for measuring consumers’ perception towards e‐payment which is replicable across different economies. However, the small ...


title: Skeleton-based image warping

abstract: Image warping refers to the 2D resampling of a source image onto a target image. Despite the variety of techniques proposed, a large class of image warping problems remains inadequately solved: mapping between two images which are delimited by arbitrary, closed, planar curves, e.g., handdrawn curves. This paper describes a novel algorithm to perform image warping among arbitrary planar shapes whose boundary correspondences are known. A generalized polar coordinate parameterization is introduced to facilitate an efficient mapping procedure. Images are treated as collections of interior layers, extracted via a thinning process. Mapping these layers between the source and target images generates the 2D resampling grid that defines the warping. The thinning operation extends the standard polar coordinate representation to deal with arbitrary shapes.


title: Shape Robust Text Detection With Progressive Scale Expansion Network

abstract: Scene text detection has witnessed rapid progress especially with the recent development of convolutional neural networks. However, there still exists two challenges which prevent the algorithm into industry applications. On the one hand, most of the state-of-art algorithms require quadrangle bounding box which is in-accurate to locate the texts with arbitrary shape. On the other hand, two text instances which are close to each other may lead to a false detection which covers both instances. Traditionally, the segmentation-based approach can relieve the first problem but usually fail to solve the second challenge. To address these two challenges, in this paper, we propose a novel Progressive Scale Expansion Network (PSENet), which can precisely detect text instances with arbitrary shapes. More specifically, PSENet generates the different scale of kernels for each text instance, and gradually expands the minimal scale kernel to the text instance with the complete shape. Due to the fact that there are large geometrical margins among the minimal scale kernels, our method is effective to split the close text instances, making it easier to use segmentation-based methods to detect arbitrary-shaped text instances. Extensive experiments on CTW1500, Total-Text, ICDAR 2015 and ICDAR 2017 MLT validate the effectiveness of PSENet. Notably, on CTW1500, a dataset full of long curve texts, PSENet achieves a F-measure of 74.3% at 27 FPS, and our best F-measure (82.2%) outperforms state-of-art algorithms by 6.6%. The code will be released in the future.


title: Reference Model of Multi-Entity Bayesian Networks for Predictive Situation Awareness

abstract: During the past quarter-century, situation awareness (SAW) has become a critical research theme, because of its importance. Since the concept of SAW was first introduced during World War I, various versions of SAW have been researched and introduced. Predictive Situation Awareness (PSAW) focuses on the ability to predict aspects of a temporally evolving situation over time. PSAW requires a formal representation and a reasoning method using such a representation. A Multi-Entity Bayesian Network (MEBN) is a knowledge representation formalism combining Bayesian Networks (BN) with First-Order Logic (FOL). MEBN can be used to represent uncertain situations (supported by BN) as well as complex situations (supported by FOL). Also, efficient reasoning algorithms for MEBN have been developed. MEBN can be a formal representation to support PSAW and has been used for several PSAW systems. Although several MEBN applications for PSAW exist, very little work can be found in the literature that attempts to generalize a MEBN model to support PSAW. In this research, we define a reference model for MEBN in PSAW, called a PSAW-MEBN reference model. The PSAW-MEBN reference model enables us to easily develop a MEBN model for PSAW by supporting the design of a MEBN model for PSAW. In this research, we introduce two example use cases using the PSAW-MEBN reference model to develop MEBN models to support PSAW: a Smart Manufacturing System and a Maritime Domain Awareness System.


title: Evaluating Depth Perception of 3D Stereoscopic Videos

abstract: 3D video quality of experience (QoE) is a multidimensional problem; many factors contribute to the global rating like image quality, depth perception and visual discomfort. Due to this multidimensionality, it is proposed in this paper, that as a complement to assessing the quality degradation due to coding or transmission, the appropriateness of the non-distorted signal should be addressed. One important factor here is the depth information provided by the source sequences. From an application-perspective, the depth-characteristics of source content are of relevance for pre-validating whether the content is suitable for 3D video services. In addition, assessing the interplay between binocular and monocular depth features and depth perception are relevant topics for 3D video perception research. To achieve the evaluation of the suitability of 3D content, this paper describes both a subjective experiment and a new objective indicator to evaluate depth as one of the added values of 3D video.


title: Physiological measures of presence in virtual environments

abstract: Virtual environments (VEs) are the most advanced human-computer interfaces yet developed. Researchers, by the development of new methods, theories, and technologies, have endeavored to make effective VEs. The definition of effectiveness changes based on the application of the VE. For flight simulators, training transfer is important. For architectural walkthroughs, accurate perception of space is important. For treatment of phobias and post-traumatic stress disorders, presence – evoking in patients the feeling that they are near the source of their phobia or stress – is important [Hodges, 1994]. It is on this last concept, presence, that this paper focuses.


title: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence Generating Pictorial Storylines via Minimum-Weight Connected Dominating Set Approximation in Multi-View Graphs

abstract: This paper introduces a novel framework for generating pictorial storylines for given topics from text and image data on the Internet. Unlike traditional text summarization and timeline generation systems, the proposed framework combines text and image analysis and delivers a storyline containing textual, pictorial, and structural information to provide a sketch of the topic evolution. A key idea in the framework is the use of an approximate solution for the dominating set problem. Given a collection of topic-related objects consisting of images and their text descriptions, a weighted multi-view graph is first constructed to capture the contextual and temporal relationships among these objects. Then the objects are selected by solving the minimum-weighted connected dominating set problem defined on this graph. Comprehensive experiments on real-world data sets demonstrate the effectiveness of the proposed framework.


title: Nonconvex optimization by fast simulated annealing

abstract: Recent advances in the solution of nonconvex optimization problems use simulated annealing techniques that are considerably faster than exhaustive global search techniques. This letter presents a simulated annealing technique, which is t/log (t) times faster than conventional simulated annealing, and applies it to a multisensor location and tracking problem.


title: Choice decision of e-learning system: Implications from construal level theory

abstract: Abstract This study investigates user acceptance of a new e-learning system when users can choose between the old and the new systems. Drawing upon construal level theory and technology acceptance model, this study proposes that users’ construal level of an e-learning system interacts with their perceptions of the system (i.e., PEoU and PU) and affects their adoption intention. Data collected from 131 participants in a laboratory experiment show that a higher construal level strengthened the effect of PEoU but mitigated the effect of PU on participants’ attitude toward using the system, thus affecting adoption intention. Theoretical contributions and implications are discussed.


title: A Fast Technique for White Blood Cells Nuclei Automatic Segmentation Based on Gram-Schmidt Orthogonalization

abstract: Blood testing is one of the most important clinical examinations. Counting different blood cells is a significant process in a clinical laboratory. Manual microscopic evaluation is compulsory in case there is suspicious abnormality in the blood sample. Yet, the manual inspection is time-consuming and requires adequate technical knowledge. Therefore, automatic medical diagnosis systems are necessary to help physicians to diagnose diseases in a fast and nonetheless competent way. Cell automatic classification has wider interest especially for clinics and laboratories. Segmentation is the most important step for automatic classification success. This paper represents an efficient technique for automatic blood cell nuclei segmentation. This technique is relying on enhancing the color of the target object, nucleus, and filtering the image. Small objects are eliminated employing morphological operations. A set of 365 blood images was used to quantitatively evaluate this segmentation technique. Assessment of the proposed technique on the blood image set gives 85.4% accuracy. In comparison to other published technique that was implemented and executed on the same dataset, the proposed segmentation technique performance was found to be superior. A differential segmentation performance evaluation was performed on the five normal white blood cell types to compare isolated performance. Eosin Phil was found to have the highest segmentation accuracy with 90.1%. Lymphocyte and Basophil have the lowest accuracy with 78.3% and 78.6% respectively. The blood images dataset and the source code are published on MATLAB file exchange website for comparison and re-production.


title: Dialog-based Language Learning

abstract: A long-term goal of machine learning research is to build an intelligent dialog agent. Most research in natural language understanding has focused on learning from fixed training sets of labeled data, with supervision either at the word level (tagging, parsing tasks) or sentence level (question answering, machine translation). This kind of supervision is not realistic of how humans learn, where language is both learned by, and used for, communication. In this work, we study dialog-based language learning, where supervision is given naturally and implicitly in the response of the dialog partner during the conversation. We study this setup in two domains: the bAbI dataset of (Weston et al., 2015) and large-scale question answering from (Dodge et al., 2015). We evaluate a set of baseline learning strategies on these tasks, and show that a novel model incorporating predictive lookahead is a promising approach for learning from a teacher's response. In particular, a surprising result is that it can learn to answer questions correctly without any reward-based supervision at all.


title: Component Selection for Component Based Software Engineering

abstract: selection is not an easy task in Component Based Software Engineering .and it is very difficult to select component for CBSE. Component Based Software Engineering (CBSE) is a concerned with the assembly of pre-existing software components that leads to a software system that responds to client-specific requirements. This paper presents an approach for defining evaluation criteria for reusable software components. We introduce taxonomy of factors that influence selection, describe each of them, and present a hierarchical decomposition method for deriving reuse goals from factors and formulating the goals into an evaluation criteria hierarchy. It also presents a summary of the common problems in reusable off-the-shelf software selection, describes the method .It also indicates that the evaluated aspects of the method are feasible, improve the quality and efficiency of reusable software selection. In this paper the selection of component is done on the basis of the cost of the component which is calculated on the basis of the quality attributes of the component. The approach used for selecting the component is a part of OTSO method that has been developed for reusable component selection process. Keywords software reuse, COTS, multiple criteria decision making, OTSO stands for Off-The-Shelf Option.


title: Overview of ESP8266 Wi-Fi module based Smart Irrigation System using IOT

abstract: This paper demonstrates the efficient use of Internet of Things for the traditional agriculture. It shows the use of Arduino and ESP8266 based monitored and controlled smart irrigation systems, which is also cost-effective and simple. It is beneficial for farmers to irrigate there land conveniently by the application of automatic irrigation system. This smart irrigation system has pH sensor, water flow sensor, temperature sensor and soil moisture sensor that measure respectively and based on these sensors arduino microcontroller drives the servo motor and pump. Arduino received the information and transmitted with ESP8266 Wi-Fi module wirelessly to the website through internet. This transmitted information is monitor and control by using IOT. This enables the remote control mechanism through a secure internet web connection to the user. A website has been prepared which present the actual time values and reference values of various factors needed by crops. Users can control water pumps and sprinklers through the website and keep an eye on the reference values which will help the farmer increase production with quality crops.


title: Spotify -- Large Scale, Low Latency, P2P Music-on-Demand Streaming

abstract: Spotify is a music streaming service offering low-latency access to a library of over 8 million music tracks. Streaming is performed by a combination of client-server access and a peer-to-peer protocol. In this paper, we give an overview of the protocol and peer-to-peer architecture used and provide measurements of service performance and user behavior. The service currently has a user base of over 7 million and has been available in six European countries since October 2008. Data collected indicates that the combination of the client-server and peer-to-peer paradigms can be applied to music streaming with good results. In particular, 8.8% of music data played comes from Spotify's servers while the median playback latency is only 265 ms (including cached tracks). We also discuss the user access patterns observed and how the peer-to-peer network affects the access patterns as they reach the server.


title: Processes Meet Big Data: Connecting Data Science with Process Science

abstract: As more and more companies are embracing Big data, it has become apparent that the ultimate challenge is to relate massive amounts of event data to processes that are highly dynamic. To unleash the value of event data, events need to be tightly connected to the control and management of operational processes. However, the primary focus of Big data technologies is currently on storage, processing, and rather simple analytical tasks. Big data initiatives rarely focus on the improvement of end-to-end processes. To address this mismatch, we advocate a better integration of data science , data technology and process science . Data science approaches tend to be process agonistic whereas process science approaches tend to be model-driven without considering the “evidence” hidden in the data. Process mining aims to bridge this gap. This editorial discusses the interplay between data science and process science and relates process mining to Big data technologies, service orientation, and cloud computing.