title: The Role of Manifold Learning in Human Motion Analysis

abstract: Human body is an articulated object with high degrees of freedom. Despite the high dimensionality of the configuration space, many human motion activities lie intrinsically on low dimensional manifolds. Although the intrinsic body configuration manifolds might be very low in dimensionality, the resulting appearance manifolds are challenging to model given various aspects that affects the appearance such as the shape and appearance of the person performing the motion, or variation in the view point, or illumination. Our objective is to learn representations for the shape and the appearance of moving (dynamic) objects that support tasks such as synthesis, pose recovery, reconstruction, and tracking. We studied various approaches for representing global deformation manifolds that preserve their geometric structure. Given such representations, we can learn generative models for dynamic shape and appearance. We also address the fundamental question of separating style and content on nonlinear manifolds representing dynamic objects. We learn factorized generative models that explicitly decompose the intrinsic body configuration (content) as a function of time from the appearance/shape (style factors) of the person performing the action as time-invariant parameters. We show results on pose recovery, body tracking, gait recognition, as well as facial expression tracking and recognition.


title: Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds

abstract: Relationships between entities in datasets are often of multiple nature, like geographical distance, social relationships, or common interests among people in a social network, for example. This information can naturally be modeled by a set of weighted and undirected graphs that form a global multilayer graph, where the common vertex set represents the entities and the edges on different layers capture the similarities of the entities in term of the different modalities. In this paper, we address the problem of analyzing multi-layer graphs and propose methods for clustering the vertices by efficiently merging the information provided by the multiple modalities. To this end, we propose to combine the characteristics of individual graph layers using tools from subspace analysis on a Grassmann manifold. The resulting combination can then be viewed as a low dimensional representation of the original data which preserves the most important information from diverse relationships between entities. We use this information in new clustering methods and test our algorithm on several synthetic and real world datasets where we demonstrate superior or competitive performances compared to baseline and state-of-the-art techniques. Our generic framework further extends to numerous analysis and learning problems that involve different types of information on graphs.


title: Independent Vector Analysis for Source Separation Using a Mixture of Gaussians Prior

abstract: Convolutive mixtures of signals, which are common in acoustic environments, can be difficult to separate into their component sources. Here we present a uniform probabilistic framework to separate convolutive mixtures of acoustic signals using independent vector analysis (IVA), which is based on a joint distribution for the frequency components originating from the same source and is capable of preventing permutation disorder. Different gaussian mixture models (GMM) served as source priors, in contrast to the original IVA model, where all sources were modeled by identical multivariate Laplacian distributions. This flexible source prior enabled the IVA model to separate different type of signals. Three classes of models were derived and tested: noiseless IVA, online IVA, and noisy IVA. In the IVA model without sensor noise, the unmixing matrices were efficiently estimated by the expectation maximization (EM) algorithm. An online EM algorithm was derived for the online IVA algorithm to track the movement of the sources and separate them under nonstationary conditions. The noisy IVA model included the sensor noise and combined denoising with separation. An EM algorithm was developed that found the model parameters and separated the sources simultaneously. These algorithms were applied to separate mixtures of speech and music. Performance as measured by the signal-to-interference ratio (SIR) was substantial for all three models.


title: Evaluation of health information systems - problems and challenges

abstract: Abstract Objectives: Information technology (IT) is emerging in health care. A rigorous evaluation of this technology is recommended and of high importance for decision makers and users. However, many authors report problems during the evaluation of information technology in health care. In this paper, we discuss some of these problems, and propose possible solutions for these problems. Methods: Based on own experience and backed up by a literature review, some important problems during IT evaluation in health care together with their reasons, consequences and possible solutions are presented and structured. Results and conclusions: We define three main problem areas—the complexity of the evaluation object, the complexity of an evaluation project, and the motivation for evaluation. Many evaluation problems can be subsumed under those three problem areas. A broadly accepted framework for evaluation of IT in healthcare seems desirable to address those problems. Such a framework should help to formulate relevant questions, to find adequate methods and tools, and to apply them in a sensible way.


Multi-Task Deep Reinforcement Learning for Continuous Action Control


title: Effects of racial diversity on complex thinking in college students

abstract: Are experiment varying the racial (Black, White) and opinion composition in small-group discussions was conducted with college students (N = 357) at three universities to test for effects on the perceived novelty of group members' contributions to discussion and on participants' integrative complexity. Results showed that racial and opinion minorities were both perceived as contributing to novelty. Generally positive effects on integrative complexity were found when the groups had racial-and opinion-minority members and when members reported having racially diverse friends and classmates. The findings are discussed in the context of social psychological theories of minority influence and social policy implications for affirmative action. The research supports claims about the educational significance of race in higher education, as well as the complexity of the interaction of racial diversity with contextual and individual factors.


title: An Ultrasonic Navigation System for Blind People

abstract: The aim of this paper is to investigate the development of a navigation aid for blind and visually impaired people. It is based on a microcontroller with synthetic speech output. This aid is portable and gives information to the user about urban walking routes to point out what decisions to make. On the other hand, and in order to reduce navigation difficulties of the blind, an obstacle detection system using ultrasounds and vibrators is added to this device. The proposed system detects the nearest obstacle via stereoscopic sonar system and sends back vibro-tactile feedback to inform the blind about its localization.


title: Feature-weighted k-Nearest Neighbor Classifier

abstract: This paper proposes a feature weighting method based on X2 statistical test, to be used in conjunction with a k-NN classifier. Results of empirical experiments conducted using data from several knowledge domains are presented and discussed. Forty four out of forty five conducted experiments favoured the feature weighted approach and are empirical evidence that the proposed weighting process based on X2 is a good weighting strategy


title: Predicting Users' Personality from Instagram Pictures: Using Visual and/or Content Features?

abstract: Instagram is a popular social networking application that allows users to express themselves through the uploaded content and the different filters they can apply. In this study we look at personality prediction from Instagram picture features. We explore two different features that can be extracted from pictures: 1) visual features (e.g., hue, valence, saturation), and 2) content features (i.e., the content of the pictures). To collect data, we conducted an online survey where we asked participants to fill in a personality questionnaire and grant us access to their Instagram account through the Instagram API. We gathered 54,962 pictures of 193 Instagram users. With our results we show that visual and content features can be used to predict personality from and perform in general equally well. Combining the two however does not result in an increased predictive power. Seemingly, they are not adding more value than they already consist of independently.


Techniques to Identify Themes in Qualitative Data


title: Neural network regularization and ensembling using multi-objective evolutionary algorithms

abstract: Regularization is an essential technique to improve generalization of neural networks. Traditionally, regularization is conducted by including an additional term in the cost function of a learning algorithm. One main drawback of these regularization techniques is that a hyperparameter that determines to which extension the regularization influences the learning algorithm must be determined beforehand. This paper addresses the neural network regularization problem from a multi-objective optimization point of view. During the optimization, both structure and parameters of the neural network will be optimized. A slightly modified version of two multi-objective optimization algorithms, the dynamic weighted aggregation (DWA) method and the elitist non-dominated sorting genetic algorithm (NSGA-II) are used and compared. An evolutionary multi-objective approach to neural network regularization has a number of advantages compared to the traditional methods. First, a number of models with a spectrum of model complexity can be obtained in one optimization run instead of only one single solution. Second, an efficient new regularization term can be introduced, which is not applicable to gradient-based learning algorithms. As a natural by-product of the multi-objective optimization approach to neural network regularization, neural network ensembles can be easily constructed using the obtained networks with different levels of model complexity. Thus, the model complexity of the ensemble can be adjusted by adjusting the weight of each member network in the ensemble. Simulations are carried out on a test function to illustrate the feasibility of the proposed ideas.


title: Canonical Microcircuits for Predictive Coding

abstract: This Perspective considers the influential notion of a canonical (cortical) microcircuit in light of recent theories about neuronal processing. Specifically, we conciliate quantitative studies of microcircuitry and the functional logic of neuronal computations. We revisit the established idea that message passing among hierarchical cortical areas implements a form of Bayesian inference—paying careful attention to the implications for intrinsic connections among neuronal populations. By deriving canonical forms for these computations, one can associate specific neuronal populations with specific computational roles. This analysis discloses a remarkable correspondence between the microcircuitry of the cortical column and the connectivity implied by predictive coding. Furthermore, it provides some intuitive insights into the functional asymmetries between feedforward and feedback connections and the characteristic frequencies over which they operate.


title: Findings from GitHub: methods, datasets and limitations

abstract: GitHub, one of the most popular social coding platforms, is the platform of reference when mining Open Source repositories to learn from past experiences. In the last years, a number of research papers have been published reporting findings based on data mined from GitHub. As the community continues to deepen in its understanding of software engineering thanks to the analysis performed on this platform, we believe it is worthwhile to reflect how research papers have addressed the task of mining GitHub repositories over the last years. In this regard, we present a meta-analysis of 93 research papers which addresses three main dimensions of those papers: i) the empirical methods employed, ii) the datasets they used and iii) the limitations reported. Results of our meta-analysis show some concerns regarding the dataset collection process and size, the low level of replicability, poor sampling techniques, lack of longitudinal studies and scarce variety of methodologies.


title: A comparison of neustonic plastic and zooplankton abundance in southern California's coastal waters

abstract: Abstract The density of neustonic plastic particles was compared to that of zooplankton in the coastal ocean near Long Beach, California. Two trawl surveys were conducted, one after an extended dry period when there was little land-based runoff, the second shortly after a storm when runoff was extensive. On each survey, neuston samples were collected at five sites along a transect parallel to shore using a manta trawl lined with 333 μ mesh. Average plastic density during the study was 8 pieces per cubic meter, though density after the storm was seven times that prior to the storm. The mass of plastics was also higher after the storm, though the storm effect on mass was less than it was for density, reflecting a smaller average size of plastic particles after the storm. The average mass of plastic was two and a half times greater than that of plankton, and even greater after the storm. The spatial pattern of the ratio also differed before and after a storm. Before the storm, greatest plastic to plankton ratios were observed at two stations closest to shore, whereas after the storm these had the lowest ratios.


title: Display-relative calibration for optical see-through head-mounted displays

abstract: Optical see-through head-mounted displays (OSTHMDs) have many advantages in augmented reality application, but their utility in practical applications has been limited by the complexity of calibration. Because the human subject is an inseparable part of the eye-display system, previous methods for OSTHMD calibration have required extensive manual data collection using either instrumentation or manual point correspondences and are highly dependent on operator skill. This paper describes display-relative calibration (DRC) for OSTHMDs, a new two phase calibration method that minimizes the human element in the calibration process and ensures reliable calibration. Phase I of the calibration captures the parameters of the display system relative to a normalized reference frame and is performed in a jig with no human factors issues. The second phase optimizes the display for a specific user and the placement of the display on the head. Several phase II alternatives provide flexibility in a variety of applications including applications involving untrained users.


title: Bootstrapping K-means for big data analysis

abstract: In recent years, “Big data” has become a popular word in industrial field. Distributed data processing middleware such as Hadoop makes companies to be able to extract useful information from their big data. However, information retrieval from newly available big data is difficult even with the aid of distributed data processing because the task needs many cycles of hypothesis establishment and test due to lack of prior knowledge about the data. K-means algorithm is one of popular algorithms which can be used in earlier stages of data mining because of the algorithm's speed and unsupervised characteristics. However, with big data, even k-means algorithm is not fast enough to get a desired result in an expected time period. In the paper, we propose a fast k-means method based on statistical bootstrapping technique. Our proposed method achieves roughly 100 times speedup and similar accuracy compared to Lloyd algorithm which is the most popular k-means algorithm in industrial field.


title: Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods

abstract: The estimators most widely used to evaluate the prediction error of a non-linear regression model are examined. An extensive simulation approach allowed the comparison of the performance of these estimators for different non-parametric methods, and with varying signal-to-noise ratio and sample size. Estimators based on resampling methods such as Leave-one-out, parametric and non-parametric Bootstrap, as well as repeated Cross Validation methods and Hold-out, were considered. The methods used are Regression Trees, Projection Pursuit Regression and Neural Networks. The repeated-corrected 10-fold Cross-Validation estimator and the Parametric Bootstrap estimator obtained the best performance in the simulations.


title: Automated extraction and parameterization of motions in large data sets

abstract: Large motion data sets often contain many variants of the same kind of motion, but without appropriate tools it is difficult to fully exploit this fact. This paper provides automated methods for identifying logically similar motions in a data set and using them to build a continuous and intuitively parameterized space of motions. To find logically similar motions that are numerically dissimilar, our search method employs a novel distance metric to find "close" motions and then uses them as intermediaries to find more distant motions. Search queries are answered at interactive speeds through a precomputation that compactly represents all possibly similar motion segments. Once a set of related motions has been extracted, we automatically register them and apply blending techniques to create a continuous space of motions. Given a function that defines relevant motion parameters, we present a method for extracting motions from this space that accurately possess new parameters requested by the user. Our algorithm extends previous work by explicitly constraining blend weights to reasonable values and having a run-time cost that is nearly independent of the number of example motions. We present experimental results on a test data set of 37,000 frames, or about ten minutes of motion sampled at 60 Hz.


title: A Neurodynamic Approach for Real-Time Scheduling via Maximizing Piecewise Linear Utility

abstract: In this paper, we study a set of real-time scheduling problems whose objectives can be expressed as piecewise linear utility functions. This model has very wide applications in scheduling-related problems, such as mixed criticality, response time minimization, and tardiness analysis. Approximation schemes and matrix vectorization techniques are applied to transform scheduling problems into linear constraint optimization with a piecewise linear and concave objective; thus, a neural network-based optimization method can be adopted to solve such scheduling problems efficiently. This neural network model has a parallel structure, and can also be implemented on circuits, on which the converging time can be significantly limited to meet real-time requirements. Examples are provided to illustrate how to solve the optimization problem and to form a schedule. An approximation ratio bound of 0.5 is further provided. Experimental studies on a large number of randomly generated sets suggest that our algorithm is optimal when the set is nonoverloaded, and outperforms existing typical scheduling strategies when there is overload. Moreover, the number of steps for finding an approximate solution remains at the same level when the size of the problem (number of jobs within a set) increases.


title: THz beam forming and beam switching using lens-assisted quasi-optical THz transmitter

abstract: In this work, we present a lens-assisted quasi-optical THz transmitter using log-periodic toothed antenna (LPTA) integrated photomixer for beam forming and beam switching. The directivity of the proposed quasi-optical THz transmitter featuring one LPTA and highly-resistive silicon quasi-optics exceeds 26 dBi within the frequency range of 300–400 GHz. A steerable beam direction in the range of ±56° is achieved by a linear shift of the LPTA position on the extended hemispherical lens assembly. Further, a beam switching approach is realized with a 1×2 LPTA array and shows tilted main beam angles of ±33°. Finally, we study the influence of mutual coupling on the input antenna impedance of the linear antenna array.


title: Searching Personnel Relationship from Myanmar census data using Graph database and Deductive Reasoning prolog rules

abstract: Searching relationship among persons is more and more growing importance to acquire related information, genealogical relationship and personnel history. In this paper, the framework with three portions is proposed for exploring peoples' relationship from their personnel information. The first portion focuses on storage structure to store data in Graph database by representing persons as nodes and their attributes as properties. There are no predefined relationships or edges between person nodes. The second part focuses on searching relationships among persons. Graph database searching algorithm called Personnel Relationship Searching Algorithm is proposed. The last portion proposes Deductive Reasoning Algorithm to define two persons' relationship based on search results of match domain (or) attributes using predefined prolog rules. For example, if the match domain is relatives, the relation may be consanguine, such as GrandFather, Eldest_Son, Youngest_Son_In_Law, Khame_Khamet, etc. If the match domain is organization (or) hobby (or) religion, the relation may work in same organization, etc.


title: Compiling pattern matching

abstract: Introduction Pattern matching is a very powerful and useful device in programming. In functional languages it emerged in SASL [Turn76] and Hope [BursS0], and has also found its way into SML [Miln84]. The pattern mathing described here is that of LML which is a lazy ([Frie76] and [Henri76]) variant of ML. The pattern matching in LML evolved independently of that in SML so they are not (yet) the same, although very similar. The compilation of pattern matching in SML has been addressed in [Card84]. The LML compiler project began as an attempt to produce efficient code for a typed functional language with lazy evaluation. Since we regard pattern matching as an important language feature it should also yield efficient code. Only pattern matching in case expressions is described here, since we regard this as the basic pattern matching facility in the language. All other types of pattern mathing used in LML can be easily translated into case expressions, see [Augu84] for details. The compilation (of pattern matching) proceeds in several steps: • transform all pattern matching to case expressions. • transform complex case expressions into expressions that are easy to generate code for. • generate G-code for the case expressions, and from that machine code for the target machine.


title: Combined strength and endurance training in competitive swimmers.

abstract: A combined intervention of strength and endurance training is common practice in elite swimming training, but the scientific evidence is scarce. The influences between strength and endurance training have been investigated in other sports but the findings are scattered. Some state the interventions are negative to each other, some state there is no negative relationship and some find bisected and supplementary benefits from the combination when training is applied appropriately. The aim of this study was to investigate the impact of a combined intervention among competitive swimmers. 20 subjects assigned to a training intervention group (n = 11) or a control group (n = 9) from two different teams completed the study. Anthropometrical data, tethered swimming force, land strength, performance in 50m, 100m and 400m, work economy, peak oxygen uptake, stroke length and stroke rate were investigated in all subjects at pre- and post-test. A combined intervention of maximal strength and high aerobic intensity interval endurance training 2 sessions per week over 11 weeks in addition to regular training were used, while the control group continued regular practice with their respective teams. The intervention group improved land strength, tethered swimming force and 400m freestyle performance more than the control group. The improvement of the 400m was correlated with the improvement of tethered swimming force in the female part of the intervention group. No change occurred in stroke length, stroke rate, performance in 50m or 100m, swimming economy or peak oxygen uptake during swimming. Two weekly dry-land strength training sessions for 11 weeks increase tethered swimming force in competitive swimmers. This increment further improves middle distance swimming performance. 2 weekly sessions of high- intensity interval training does not improve peak oxygen uptake compared with other competitive swimmers. Key pointsTwo weekly sessions of dry land strength training improves the swimming force.Two weekly sessions of high-intensity endurance training did not cause improved endurance capacity.It may seem that dry land strength training can improve middle distance performance.


title: Combining Network Access Control (NAC) and SIEM functionality based on open source

abstract: Security Information and Event Management (SIEM) solutions are expensive, difficult to handle, and still a further security system without interface to existing Network Access Control (NAC) or other security components of an enterprise. Thus, the research project CLEARER [2] aims at developing a security system, which extends existing NAC systems with SIEM functionality, additional analyzing methods and dynamical compliance support. This goal will only be reached by using Open Source Software (OSS). The expected result, a common security platform for attack prevention will be affordable for small and medium-sized enterprises (SME) in contrast to other vendor-based SIEM systems.


title: Population Based Training of Neural Networks

abstract: Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm. In this work we present \emph{Population Based Training (PBT)}, a simple asynchronous optimisation algorithm which effectively utilises a fixed computational budget to jointly optimise a population of models and their hyperparameters to maximise performance. Importantly, PBT discovers a schedule of hyperparameter settings rather than following the generally sub-optimal strategy of trying to find a single fixed set to use for the whole course of training. With just a small modification to a typical distributed hyperparameter training framework, our method allows robust and reliable training of models. We demonstrate the effectiveness of PBT on deep reinforcement learning problems, showing faster wall-clock convergence and higher final performance of agents by optimising over a suite of hyperparameters. In addition, we show the same method can be applied to supervised learning for machine translation, where PBT is used to maximise the BLEU score directly, and also to training of Generative Adversarial Networks to maximise the Inception score of generated images. In all cases PBT results in the automatic discovery of hyperparameter schedules and model selection which results in stable training and better final performance.


title: Educational Leadership: Modeling, mentoring, making and re-making a learning community

abstract: SUMMARY The paper presents a holistic view of educational leadership that is based on relational learning, collaboration, connectedness and commitment to self, to others and to community. The context of the study is a community high school where teacher-leaders are responsible for the governance, the daily administration and the teaching in the school. The interconnectedness of teaching, learning and leading are acknowledged by teacher-leaders whose work is based in an understanding that the intellectual, emotional, social, moral, spiritual and aesthetic dimensions of human beings are inextricably interconnected. The focus for school leaders is on working to co-create a learning environment which emphasizes authentic self-expression, the development of relationships, and the development of the whole person within the school, the local and global communities. Two major themes are used to explicate and discuss the enactment of these issues and to outline the boundaries and obstacles encountered in the reali...


title: Swoosh: a generic approach to entity resolution

abstract: We consider the entity resolution (ER) problem (also known as deduplication, or merge---purge), in which records determined to represent the same real-world entity are successively located and merged. We formalize the generic ER problem, treating the functions for comparing and merging records as black-boxes, which permits expressive and extensible ER solutions. We identify four important properties that, if satisfied by the match and merge functions, enable much more efficient ER algorithms. We develop three efficient ER algorithms: G-Swoosh for the case where the four properties do not hold, and R-Swoosh and F-Swoosh that exploit the four properties. F-Swoosh in addition assumes knowledge of the "features" (e.g., attributes) used by the match function. We experimentally evaluate the algorithms using comparison shopping data from Yahoo! Shopping and hotel information data from Yahoo! Travel. We also show that R-Swoosh (and F-Swoosh) can be used even when the four match and merge properties do not hold, if an "approximate" result is acceptable.


title: Transradial prostheses: Current status and future directions

abstract: Transradial prosthesis is an artificial wearable limb that provides the functionality and/or appearance of human wrist and hand. Over the recent years, both hardware and control systems for active transradial prosthesis show significant advancement. In that context, this paper reviews the modern transradial prostheses developed for performing activities of daily living. Focus is to provide an insight on the latest technologies, including mechanical designs and control algorithms developed to mimic the biological wrist and hand. The biomechanics of below-elbow of upper-limb and functional requirements such as grasping motion are initially presented. The design difficulties are discussed in the following section. Next, each of the modern transradial prosthesis are briefly reviewed based on design and/or control of fingers, wrists and body attachments. Finally, latest trends and future directions for developing transradial prostheses are explored and unveiled.


title: Nonlinear Embedding Transform for Unsupervised Domain Adaptation

abstract: The problem of domain adaptation (DA) deals with adapting classifier models trained on one data distribution to different data distributions. In this paper, we introduce the Nonlinear Embedding Transform (NET) for unsupervised DA by combining domain alignment along with similarity-based embedding. We also introduce a validation procedure to estimate the model parameters for the NET algorithm using the source data. Comprehensive evaluations on multiple vision datasets demonstrate that the NET algorithm outperforms existing competitive procedures for unsupervised DA.


title: mano: A Wearable Hand Exoskeleton for Activities of Daily Living and Neurorehabilitation

abstract: Hand sensorimotor impairments are among the most common consequences of injuries affecting the central and peripheral nervous systems, leading to a drastic reduction in the quality of life for affected individuals. Combining wearable robotic exoskeletons and human–machine interfaces is a promising avenue for the restoration and substitution of lost and impaired functions for these users. In this study, we present a novel hand exoskeleton, mano , designed to assist and restore hand functions of people with motor disabilities during activities of daily living (ADL) and in neurorehabilitative scenarios. Compared to state-of-the-art devices, our system is fully wearable, portable, and minimally obtrusive on the hand. The exoskeleton can actively control flexion and extension of all fingers, while allowing natural somatosensorial interactions with the environment surrounding the users. We evaluated the device from four different perspectives. A mechanical characterization, showing that the exoskeleton can cover more than 70% of healthy hand workspace and it can achieve forces at the fingertips sufficient for ADL. A functional characterization, where we showed how two users who suffered from spinal cord injuries were able to perform several ADL for the first time since their accidents. Third, we evaluated the system from a neuroimaging perspective, showing that the device can elicit EEG brain patterns typical of natural hand motions. We finally exemplified the control of the hand exoskeleton within an exemplar framework, a brain–machine interface scenario, showing that motor intention can be decoded for a continuous control of the device. Overall, our results showed that the device represents an ecological solution for use both in ADL and in scenarios aimed at promoting sensorimotor recovery.


title: Hidden Factor Analysis for Age Invariant Face Recognition

abstract: Age invariant face recognition has received increasing attention due to its great potential in real world applications. In spite of the great progress in face recognition techniques, reliably recognizing faces across ages remains a difficult task. The facial appearance of a person changes substantially over time, resulting in significant intra-class variations. Hence, the key to tackle this problem is to separate the variation caused by aging from the person-specific features that are stable. Specifically, we propose a new method, called Hidden Factor Analysis (HFA). This method captures the intuition above through a probabilistic model with two latent factors: an identity factor that is age-invariant and an age factor affected by the aging process. Then, the observed appearance can be modeled as a combination of the components generated based on these factors. We also develop a learning algorithm that jointly estimates the latent factors and the model parameters using an EM procedure. Extensive experiments on two well-known public domain face aging datasets: MORPH (the largest public face aging database) and FGNET, clearly show that the proposed method achieves notable improvement over state-of-the-art algorithms.


title: Energy Profiling Using IgProf

abstract: Energy efficiency has become a primary concern for data centers in recent years. Understanding where the energy has been spent within a software is fundamental for energy-efficiency study as a whole. In this paper, we take the first step towards this direction by building an energy profiling module on top of IgProf. IgProf is an application profiler developed at CERN for scientific computing workloads. The energy profiling module is based on sampling and obtains energy measurements from the Running Average Power Limit (RAPL) interface present on the latest Intel processors. The initial profiling results of a single-threaded program demonstrates potential, showing a close correlation between the execution time and the energy spent within a function.


A picture’s worth: Partner photographs reduce experimentally induced pain


title: Cyber resilience for mission assurance

abstract: Our national security and critical infrastructure sectors have become increasingly dependent on commercial information systems and technologies whose pedigree is uncertain given the globalization of the supply chain. Furthermore, these system architectures are brittle and fail or are compromised when subjected to ever-increasingly advanced and adaptive cyber attacks, resulting in failed, disrupted or compromised mission operations. While we must continue to raise the bar to protect mission critical systems from these threats by implementing best security practices, the current philosophy of trying to keep the adversaries out, or the assumption that they will be detected if they get through the first line of defense, is no longer valid. Given the sophistication, adaptiveness, and persistence of cyber threats, we can no longer assume that we can completely defend against intruders and must change our mindset to assume some degree of adversary success and be prepared to “fight through” cyber attacks to ensure mission success even in a degraded or contested environment. This paper will focus on actionable architectural and operational recommendations to address the advanced cyber threat and to enable mission assurance for critical operations. These recommendations can create transformational improvements by helping to reverse adversary advantage, minimize exploit impact to essential operations, increase adversary cost and uncertainty, and act as a deterrent. These approaches go well beyond traditional information assurance, disaster recovery and survivability techniques. The approaches and strategies to be discussed include creative applications of trust technologies and advanced detection capabilities in conjunction with combination of techniques using diversity, redundancy, isolation and containment, least privilege, moving target defense, randomization and unpredictability, deception, and adaptive management and response.


title: Black Pepper and its Pungent Principle-Piperine: A Review of Diverse Physiological Effects

abstract: Black pepper (Piper nigrum) is one of the most widely used among spices. It is valued for its distinct biting quality attributed to the alkaloid, piperine. Black pepper is used not only in human dietaries but also for a variety of other purposes such as medicinal, as a preservative, and in perfumery. Many physiological effects of black pepper, its extracts, or its major active principle, piperine, have been reported in recent decades. Dietary piperine, by favorably stimulating the digestive enzymes of pancreas, enhances the digestive capacity and significantly reduces the gastrointestinal food transit time. Piperine has been demonstrated in in vitro studies to protect against oxidative damage by inhibiting or quenching free radicals and reactive oxygen species. Black pepper or piperine treatment has also been evidenced to lower lipid peroxidation in vivo and beneficially influence cellular thiol status, antioxidant molecules and antioxidant enzymes in a number of experimental situations of oxidative stress. The most far-reaching attribute of piperine has been its inhibitory influence on enzymatic drug biotransforming reactions in the liver. It strongly inhibits hepatic and intestinal aryl hydrocarbon hydroxylase and UDP-glucuronyl transferase. Piperine has been documented to enhance the bioavailability of a number of therapeutic drugs as well as phytochemicals by this very property. Piperine's bioavailability enhancing property is also partly attributed to increased absorption as a result of its effect on the ultrastructure of intestinal brush border. Although initially there were a few controversial reports regarding its safety as a food additive, such evidence has been questionable, and later studies have established the safety of black pepper or its active principle, piperine, in several animal studies. Piperine, while it is non-genotoxic, has in fact been found to possess anti-mutagenic and anti-tumor influences.


title: AutoCog: Measuring the Description-to-permission Fidelity in Android Applications

abstract: The booming popularity of smartphones is partly a result of application markets where users can easily download wide range of third-party applications. However, due to the open nature of markets, especially on Android, there have been several privacy and security concerns with these applications. On Google Play, as with most other markets, users have direct access to natural-language descriptions of those applications, which give an intuitive idea of the functionality including the security-related information of those applications. Google Play also provides the permissions requested by applications to access security and privacy-sensitive APIs on the devices. Users may use such a list to evaluate the risks of using these applications. To best assist the end users, the descriptions should reflect the need for permissions, which we term description-to-permission fidelity. In this paper, we present a system AutoCog to automatically assess description-to-permission fidelity of applications. AutoCog employs state-of-the-art techniques in natural language processing and our own learning-based algorithm to relate description with permissions. In our evaluation, AutoCog outperforms other related work on both performance of detection and ability of generalization over various permissions by a large extent. On an evaluation of eleven permissions, we achieve an average precision of 92.6% and an average recall of 92.0%. Our large-scale measurements over 45,811 applications demonstrate the severity of the problem of low description-to-permission fidelity. AutoCog helps bridge the long-lasting usability gap between security techniques and average users.


title: Contextual LSTM (CLSTM) models for Large scale NLP tasks

abstract: Documents exhibit sequential structure at multiple levels of abstraction (e.g., sentences, paragraphs, sections). These abstractions constitute a natural hierarchy for representing the context in which to infer the meaning of words and larger fragments of text. In this paper, we present CLSTM (Contextual LSTM), an extension of the recurrent neural network LSTM (Long-Short Term Memory) model, where we incorporate contextual features (e.g., topics) into the model. We evaluate CLSTM on three specific NLP tasks: word prediction, next sentence selection, and sentence topic prediction. Results from experiments run on two corpora, English documents in Wikipedia and a subset of articles from a recent snapshot of English Google News, indicate that using both words and topics as features improves performance of the CLSTM models over baseline LSTM models for these tasks. For example on the next sentence selection task, we get relative accuracy improvements of 21% for the Wikipedia dataset and 18% for the Google News dataset. This clearly demonstrates the significant benefit of using context appropriately in natural language (NL) tasks. This has implications for a wide variety of NL applications like question answering, sentence completion, paraphrase generation, and next utterance prediction in dialog systems.


title: Persistent RNNs: stashing recurrent weights on-chip

abstract: This paper introduces a new technique for mapping Deep Recurrent Neural Networks (RNN) efficiently onto GPUs. We show how it is possible to achieve substantially higher computational throughput at low mini-batch sizes than direct implementations of RNNs based on matrix multiplications. The key to our approach is the use of persistent computational kernels that exploit the GPU's inverted memory hierarchy to reuse network weights over multiple timesteps. Our initial implementation sustains 2.8 TFLOP/s at a mini-batch size of 4 on an NVIDIA TitanX GPU. This provides a 16× reduction in activation memory footprint, enables model training with 12× more parameters on the same hardware, allows us to strongly scale RNN training to 128 GPUs, and allows us to efficiently explore end-to-end speech recognition models with over 100 layers.


title: Complexity Leadership Theory: Shifting leadership from the industrial age to the knowledge era

abstract: According to Hitt (1998), “we are on the precipice of an epoch,” in the midst of a new economic age, in which twenty-first century organizations are facing a complex competitive landscape driven largely by globalization and the technological revolution. This new age is about an economy where knowledge is a core commodity and the rapid production of knowledge and innovation is critical to organizational survival (Bettis and Hitt, 1995; Boisot, 1998). Consistent with these changes, much discussion is taking place in the management literature regarding challenges facing organizations in a transitioning world (Barkema et al., 2002; Bettis and Hitt, 1995; Child and McGrath, 2001).


title: Motivation and emotion as mediators in multimedia learning

abstract: Abstract Against the background of Moreno's “cognitive-affective theory of learning with media” (CATLM) ( Moreno, 2006 ), three papers on cognitive and affective processes in learning with multimedia are discussed in this commentary. The papers provide valuable insights in how cognitive processing and learning results can be affected by constructs such as “situational interest”, “positive emotions”, or “confusion”, and they suggest questions for further research in this field.


title: Ten Steps of EM Suffice for Mixtures of Two Gaussians

abstract: The Expectation-Maximization (EM) algorithm is a widely used method for maximum likelihood estimation in models with latent variables. For estimating mixtures of Gaussians, its iteration can be viewed as a soft version of the k-means clustering algorithm. Despite its wide use and applications, there are essentially no known convergence guarantees for this method. We provide global convergence guarantees for mixtures of two Gaussians with known covariance matrices. We show that the population version of EM, where the algorithm is given access to infinitely many samples from the mixture, converges geometrically to the correct mean vectors, and provide simple, closed-form expressions for the convergence rate. As a simple illustration, we show that, in one dimension, ten steps of the EM algorithm initialized at infinity result in less than 1\% error estimation of the means. In the finite sample regime, we show that, under a random initialization, $\tilde{O}(d/\epsilon^2)$ samples suffice to compute the unknown vectors to within $\epsilon$ in Mahalanobis distance, where $d$ is the dimension. In particular, the error rate of the EM based estimator is $\tilde{O}\left(\sqrt{d \over n}\right)$ where $n$ is the number of samples, which is optimal up to logarithmic factors.


title: The OPTLS Protocol and TLS 1.3

abstract: We present the OPTLS key-exchange protocol, its design, rationale and cryptographic analysis. OPTLS design has been motivated by the ongoing work in the TLS working group of the IETF for specifying TLS 1.3, the next-generation TLS protocol. The latter effort is intended to revamp the security of TLS that has been shown inadequate in many instances as well as to add new security and functional features. The main additions that influence the cryptographic design of TLS 1.3 (hence also of OPTLS) are a new "0-RTT requirement" (0-RTT stands for "zero round trip time") to allow clients that have previously retrieved or cached the public key of the server to send protected data already in the first flow of the protocol, making perfect forward secrecy (PFS) a mandatory requirement, and moving to elliptic curves as the main cryptographic basis for the protocol (for performance and security reasons). Accommodating these requirements calls for moving away from the RSA-centric design of TLS in favor of a protocol based on Diffie-Hellman techniques. OPTLS offers a simple design framework that supports all the above requirements from the protocol with a uniform and modular logic that helps in the specification, analysis, performance optimization, and future maintenance of the protocol. The current (draft) specification of TLS 1.3 builds upon the OPTLS framework as a basis for the cryptographic core of the handshake protocol adapting the different modes of OPTLS to the TLS 1.3 context.


The Pittsburgh Sleep Quality Index (PSQI)


title: Non-negative Matrix Factorization for Overlapping Clustering of Customer Inquiry and Review Data

abstract: Considering the complexity of clustering text datasets in terms of informal user generated content and the fact that there are multiple labels for each data point in many informal user generated content datasets, this paper focuses on Non-negative Matrix Factorization (NMF) algorithms for Overlapping Clustering of customer inquiry and review data, which has seldom been discussed in previous literature. We extend the use of Semi-NMF and Convex-NMF to Overlapping Clustering and develop a procedure of applying SemiNMF and Convex-NMF on Overlapping Clustering of text data. The developed procedure is tested based on customer review and inquiry datasets. The results of comparing SemiNMF and Convex-NMF with a baseline model demonstrate that they have advantages over the baseline model, since they do not need to adjust parameters to obtain similarly strong clustering performances. Moreover, we compare different methods of picking labels for generating Overlapping Clustering results from Soft Clustering algorithms, and it is concluded that thresholding by mean method is a simpler and relatively more reliable method compared to maximum n method.


title: Artificial grapheme-color synesthesia for wearable task support

abstract: This paper presents the benefits of generating an artificial visual synesthesia through a wearable computer. Following a short introduction to remind the need for seamless human-wearable computer interactions, this paper makes the case for drawing upon synesthesia, a combination of the senses naturally occurring in a small portion of the population, to augment everyday entities and more precisely to enrich written graphemes. We present the rationale behind our research and summarize the functionality, architecture and implementation of our current prototype. Preliminary results suggest that this kind of artificial synesthesia improves short term memory recall and visual information search times.


title: Handling Big Data Using NoSQL

abstract: With the emergence of Big Data, the use of NoSQL (Not only SQL) technology is rising rapidly among internet companies and other enterprises. Benefits include simplicity of design, horizontal scaling and finer control over availability. NoSQL databases are increasingly considered a viable alternative to relational databases, as more organizations recognize that its schema less data model is a better method for handling the large volumes of structured, semi structured and unstructured data, being captured and processed today. For example NoSQL databases are often used to collect and store social media data. This paper aims to introduce the concepts behind NoSQL, provides a review of relevant literature, highlights the different NoSQL database types, and provide arguments for and against adopting NoSQL. A small prototype application has been developed to assess the stated NoSQL benefits and illustrate the differences between the SQL and NoSQL approaches. The last section of the paper offers some conclusions and recommendations for further research to expand upon our research work.


title: Flexible sixteen monopole antenna array for microwave breast cancer detection

abstract: Radar based microwave imaging (MI) has been widely studied for breast cancer detection in recent times. Sensing dielectric property differences of tissues over a wide frequency band has been made possible by ultra-wideband (UWB) techniques. In this paper, a flexible, compact monopole antenna on a 100 μm Kapton polyimide is designed, using a high frequency structure simulator (HFSS), to be in contact with biological breast tissues over the 2-5GHz frequency range. The antenna parameters are optimized to obtain a good impedance match over the required frequency range. The designed antenna size is 18mm × 18mm. Further, a flexible conformal 4×4 ultra-wideband antenna array, in a format similar to that of a bra, was developed for a radar-based breast cancer detection system.


title: A Preliminary Study of Tweet Summarization using Information Extraction

abstract: Although the ideal length of summaries differs greatly from topic to topic on Twitter, previous work has only generated summaries of a pre-fixed length. In this paper, we propose an event-graph based method using information extraction techniques that is able to create summaries of variable length for different topics. In particular, we extend the Pageranklike ranking algorithm from previous work to partition event graphs and thereby detect finegrained aspects of the event to be summarized. Our preliminary results show that summaries created by our method are more concise and news-worthy than SumBasic according to human judges. We also provide a brief survey of datasets and evaluation design used in previous work to highlight the need of developing a standard evaluation for automatic tweet summarization task.


title: Random Subsets of Structured Deterministic Frames have MANOVA Spectra

abstract: We draw a random subset of [Formula: see text] rows from a frame with [Formula: see text] rows (vectors) and [Formula: see text] columns (dimensions), where [Formula: see text] and [Formula: see text] are proportional to [Formula: see text] For a variety of important deterministic equiangular tight frames (ETFs) and tight non-ETFs, we consider the distribution of singular values of the [Formula: see text]-subset matrix. We observe that, for large [Formula: see text], they can be precisely described by a known probability distribution-Wachter's MANOVA (multivariate ANOVA) spectral distribution, a phenomenon that was previously known only for two types of random frames. In terms of convergence to this limit, the [Formula: see text]-subset matrix from all of these frames is shown to be empirically indistinguishable from the classical MANOVA (Jacobi) random matrix ensemble. Thus, empirically, the MANOVA ensemble offers a universal description of the spectra of randomly selected [Formula: see text] subframes, even those taken from deterministic frames. The same universality phenomena is shown to hold for notable random frames as well. This description enables exact calculations of properties of solutions for systems of linear equations based on a random choice of [Formula: see text] frame vectors of [Formula: see text] possible vectors and has a variety of implications for erasure coding, compressed sensing, and sparse recovery. When the aspect ratio [Formula: see text] is small, the MANOVA spectrum tends to the well-known Marčenko-Pastur distribution of the singular values of a Gaussian matrix, in agreement with previous work on highly redundant frames. Our results are empirical, but they are exhaustive, precise, and fully reproducible.


title: Synthetic Ground Truth for Validation of Brain Tumor MRI Segmentation

abstract: Validation and method of comparison for segmentation of magnetic resonance images (MRI) presenting pathology is a challenging task due to the lack of reliable ground truth. We propose a new method for generating synthetic multi-modal 3D brain MRI with tumor and edema, along with the ground truth. Tumor mass effect is modeled using a biomechanical model, while tumor and edema infiltration is modeled as a reaction-diffusion process that is guided by a modified diffusion tensor MRI. We propose the use of warping and geodesic interpolation on the diffusion tensors to simulate the displacement and the destruction of the white matter fibers. We also model the process where the contrast agent tends to accumulate in cortical csf regions and active tumor regions to obtain contrast enhanced T1w MR image that appear realistic. The result is simulated multi-modal MRI with ground truth available as sets of probability maps. The system will be able to generate large sets of simulation images with tumors of varying size, shape and location, and will additionally generate infiltrated and deformed healthy tissue probabilities.


title: Fast linear iterations for distributed averaging

abstract: Abstract We consider the problem of finding a linear iteration that yields distributed averaging consensus over a network, i.e., that asymptotically computes the average of some initial values given at the nodes. When the iteration is assumed symmetric, the problem of finding the fastest converging linear iteration can be cast as a semidefinite program, and therefore efficiently and globally solved. These optimal linear iterations are often substantially faster than several common heuristics that are based on the Laplacian of the associated graph. We show how problem structure can be exploited to speed up interior-point methods for solving the fastest distributed linear iteration problem, for networks with up to a thousand or so edges. We also describe a simple subgradient method that handles far larger problems, with up to 100 000 edges. We give several extensions and variations on the basic problem.


title: Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization

abstract: Big Data is a research field involving a large number of collaborating disciplines. Based on bibliometric data downloaded from the Web of Science, this study applies various social network analysis and visualization tools to examine the structure and patterns of interdisciplinary collaborations, as well as the recently evolving overall pattern. This study presents the descriptive statistics of disciplines involved in publishing Big Data research; and network indicators of the interdisciplinary collaborations among disciplines, interdisciplinary communities, interdisciplinary networks, and changes in discipline communities over time. The findings indicate that the scope of disciplines involved in Big Data research is broad, but that the disciplinary distribution is unbalanced. The overall collaboration among disciplines tends to be concentrated in several key fields. According to the network indicators, Computer Science, Engineering, and Business and Economics are the most important contributors to Big Data research, given their position and role in the research collaboration network. Centering around a few important disciplines, all fields related to Big Data research are aggregated into communities, suggesting some related research areas, and directions for Big Data research. An ever-changing roster of related disciplines provides support, as illustrated by the evolving graph of communities.


title: A Co-training Approach for Multi-view Spectral Clustering

abstract: We propose a spectral clustering algorithm for the multi-view setting where we have access to multiple views of the data, each of which can be independently used for clustering. Our spectral clustering algorithm has a flavor of co-training, which is already a widely used idea in semi-supervised learning. We work on the assumption that the true underlying clustering would assign a point to the same cluster irrespective of the view. Hence, we constrain our approach to only search for the clusterings that agree across the views. Our algorithm does not have any hyperparameters to set, which is a major advantage in unsupervised learning. We empirically compare with a number of baseline methods on synthetic and real-world datasets to show the efficacy of the proposed algorithm.


title: Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach

abstract: The identification of some important retinal anatomical regions is a prerequisite for the computer aided diagnosis of several retinal diseases. In this paper, we propose a new adaptive method for the automatic segmentation of the optic disk in digital color fundus images, using mathematical morphology. The proposed method has been designed to be robust under varying illumination and image acquisition conditions, common in eye fundus imaging. Our experimental results based on two publicly available eye fundus image databases are encouraging, and indicate that our approach potentially can achieve a better performance than other known methods proposed in the literature. Using the DRIVE database (which consists of 40 retinal images), our method achieves a success rate of 100% in the correct location of the optic disk, with 41.47% of mean overlap. In the DIARETDB1 database (which consists of 89 retinal images), the optic disk is correctly located in 97.75% of the images, with a mean overlap of 43.65%.


title: Physiological measures of presence in stressful virtual environments

abstract: A common measure of the quality or effectiveness of a virtual environment (VE) is the mount of presence it evokes in users. Presence is often defined as the sense of being there in a VE. There has been much debate about the best way to measure presence, and presence researchers need, and have sought, a measure that is reliable, valid, sensitive, and objective.We hypothesized that to the degree that a VE seems real, it would evoke physiological responses similar to those evoked by the corresponding real environment, and that greater presence would evoke a greater response. To examine this, we conducted three experiments, the results of which support the use of physiological reaction as a reliable, valid, sensitive, and objective presence measure. The experiments compared participants' physiological reactions to a non-threatening virtual room and their reactions to a stressful virtual height situation. We found that change in heart rate satisfied our requirements for a measure of presence, change in skin conductance did to a lesser extent, and that change in skin temperature did not. Moreover, the results showed that inclusion of a passive haptic element in the VE significantly increased presence and that for presence evoked: 30FPS > 20FPS > 15FPS.


title: Inspiratory muscle training reduces diaphragm activation and dyspnea during exercise in COPD.

abstract: Among patients with chronic obstructive pulmonary disease (COPD), those with the lowest maximal inspiratory pressures experience greater breathing discomfort (dyspnea) during exercise. In such individuals, inspiratory muscle training (IMT) may be associated with improvement of dyspnea, but the mechanisms for this are poorly understood. Therefore, we aimed to identify physiological mechanisms of improvement in dyspnea and exercise endurance following inspiratory muscle training (IMT) in patients with COPD and low maximal inspiratory pressure (Pimax). The effects of 8 wk of controlled IMT on respiratory muscle function, dyspnea, respiratory mechanics, and diaphragm electromyography (EMGdi) during constant work rate cycle exercise were evaluated in patients with activity-related dyspnea (baseline dyspnea index <9). Subjects were randomized to either IMT or a sham training control group ( n = 10 each). Twenty subjects (FEV1 = 47 ± 19% predicted; Pimax = -59 ± 14 cmH2O; cycle ergometer peak work rate = 47 ± 21% predicted) completed the study; groups had comparable baseline lung function, respiratory muscle strength, activity-related dyspnea, and exercise capacity. IMT, compared with control, was associated with greater increases in inspiratory muscle strength and endurance, with attendant improvements in exertional dyspnea and exercise endurance time (all P < 0.05). After IMT, EMGdi expressed relative to its maximum (EMGdi/EMGdimax) decreased ( P < 0.05) with no significant change in ventilation, tidal inspiratory pressures, breathing pattern, or operating lung volumes during exercise. In conclusion, IMT improved inspiratory muscle strength and endurance in mechanically compromised patients with COPD and low Pimax. The attendant reduction in EMGdi/EMGdimax helped explain the decrease in perceived respiratory discomfort despite sustained high ventilation and intrinsic mechanical loading over a longer exercise duration. NEW & NOTEWORTHY In patients with COPD and low maximal inspiratory pressures, inspiratory muscle training (IMT) may be associated with improvement of dyspnea, but the mechanisms for this are poorly understood. This study showed that 8 wk of home-based, partially supervised IMT improved respiratory muscle strength and endurance, dyspnea, and exercise endurance. Dyspnea relief occurred in conjunction with a reduced activation of the diaphragm relative to maximum in the absence of significant changes in ventilation, breathing pattern, and operating lung volumes.


title: An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework

abstract: In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN) model is trained in the learning stage on the daily stock prices between 1997 and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used in the training stage. The trained model is then tested with data from 2007 to 2017. The results indicate that by choosing the most appropriate technical indicators, the neural network model can achieve comparable results against the Buy and Hold strategy in most of the cases. Furthermore, fine tuning the technical indicators and/or optimization strategy can enhance the overall trading performance.


title: Using Data Mining to Predict Hospital Admissions From the Emergency Department

abstract: Crowding within emergency departments (EDs) can have significant negative consequences for patients. EDs therefore need to explore the use of innovative methods to improve patient flow and prevent overcrowding. One potential method is the use of data mining using machine learning techniques to predict ED admissions. This paper uses routinely collected administrative data (120 600 records) from two major acute hospitals in Northern Ireland to compare contrasting machine learning algorithms in predicting the risk of admission from the ED. We use three algorithms to build the predictive models: 1) logistic regression; 2) decision trees; and 3) gradient boosted machines (GBM). The GBM performed better (accuracy = 80.31%, AUC-ROC = 0.859) than the decision tree (accuracy = 80.06%, AUC-ROC = 0.824) and the logistic regression model (accuracy = 79.94%, AUC-ROC = 0.849). Drawing on logistic regression, we identify several factors related to hospital admissions, including hospital site, age, arrival mode, triage category, care group, previous admission in the past month, and previous admission in the past year. This paper highlights the potential utility of three common machine learning algorithms in predicting patient admissions. Practical implementation of the models developed in this paper in decision support tools would provide a snapshot of predicted admissions from the ED at a given time, allowing for advance resource planning and the avoidance bottlenecks in patient flow, as well as comparison of predicted and actual admission rates. When interpretability is a key consideration, EDs should consider adopting logistic regression models, although GBM’s will be useful where accuracy is paramount.


title: Improving Germanium Recovering Technology in Zinc Hydrometallurgy

abstract: The influencing factors for the recovering technology of germanium in germanium-enriched oxide ore by hydrometallurgy are studied to decrease the loss of germanium and improve germanium recovery.According to the influencing factors of germanium recovering,such as the germanium leaching process,germanium precipitation with tannin,ignition process and so on,the pertinency measures are taken to control the influencing factors,the leaching rate of germanium can be 79.64%;germanium precipitation rate can be 99.08%;the main grade of germanium concentration can be 20.70%;the recovery can be 59.54%.


title: Image Steganography and Steganalysis: Concepts and Practice

abstract: In the last few years, we have seen many new and powerful steganography and steganalysis techniques reported in the literature. In the following paper we go over some general concepts and ideas that apply to steganography and steganalysis. Specifically we establish a framework and define notion of security for a steganographic system. We show how conventional definitions do not really adequately cover image steganography and an provide alternate definition. We also review some of the more recent image steganography and steganalysis techniques.


title: Chimera ‐ Three Heads for English-to-Czech Translation

abstract: This paper describes our WMT submissions CU-BOJAR and CU-DEPFIX, the latter dubbed “CHIMERA” because it combines on three diverse approaches: TectoMT, a system with transfer at the deep syntactic level of representation, factored phrase-based translation using Moses, and finally automatic rule-based correction of frequent grammatical and meaning errors. We do not use any off-the-shelf systemcombination method.


title: Science of Security: Combining Theory and Measurement to Reflect the Observable

abstract: What would a “Science of Security” look like? This question has received considerable attention over the past 10 years. No one argues against the desirability of making security research more “scientific.” But how would one would go about that? We argue that making progress on this requires clarifying what “scientific” means in the context of computer security, and that has received too little attention. We pursue this based on a review of literature in the history and Philosophy of Science and a belief that work under the theme “Science of Security” should align with and ideally, benefit from what has been learned over a few hundred years in science. We offer observations and insights, with a view that the security community can benefit from better leveraging past lessons and common practices well-accepted by consensus in the mainstream scientific community—but which appear little recognized in the security community.


title: Web of Science use in published research and review papers 1997–2017: a selective, dynamic, cross-domain, content-based analysis

abstract: Clarivate Analytics’s Web of Science (WoS) is the world’s leading scientific citation search and analytical information platform. It is used as both a research tool supporting a broad array of scientific tasks across diverse knowledge domains as well as a dataset for large-scale data-intensive studies. WoS has been used in thousands of published academic studies over the past 20 years. It is also the most enduring commercial legacy of Eugene Garfield. Despite the central position WoS holds in contemporary research, the quantitative impact of WoS has not been previously examined by rigorous scientific studies. To better understand how this key piece of Eugene Garfield’s heritage has contributed to science, we investigated the ways in which WoS (and associated products and features) is mentioned in a sample of 19,478 English-language research and review papers published between 1997 and 2017, as indexed in WoS databases. We offered descriptive analyses of the distribution of the papers across countries, institutions and knowledge domains. We also used natural language processingtechniques to identify the verbs and nouns in the abstracts of these papers that are grammatically connected to WoS-related phrases. This is the first study to empirically investigate the documentation of the use of the WoS platform in published academic papers in both scientometric and linguistic terms.


title: A neurocognitive approach to understanding the neurobiology of addiction

abstract: Recent concepts of addiction to drugs (e.g. cocaine) and non-drugs (e.g. gambling) have proposed that these behaviors are the product of an imbalance between three separate, but interacting, neural systems: an impulsive, largely amygdala–striatum dependent, neural system that promotes automatic, habitual and salient behaviors; a reflective, mainly prefrontal cortex dependent, neural system for decision-making, forecasting the future consequences of a behavior, and inhibitory control; and the insula that integrates interoception states into conscious feelings and into decision-making processes that are involved in uncertain risk and reward. These systems account for poor decision-making (i.e. prioritizing short-term consequences of a decisional option) leading to more elevated addiction risk and relapse. This article provides neural evidence for this three-systems neural model of addiction.


title: Brain Graphs: Graphical Models of the Human Brain Connectome

abstract: Brain graphs provide a relatively simple and increasingly popular way of modeling the human brain connectome, using graph theory to abstractly define a nervous system as a set of nodes (denoting anatomical regions or recording electrodes) and interconnecting edges (denoting structural or functional connections). Topological and geometrical properties of these graphs can be measured and compared to random graphs and to graphs derived from other neuroscience data or other (nonneural) complex systems. Both structural and functional human brain graphs have consistently demonstrated key topological properties such as small-worldness, modularity, and heterogeneous degree distributions. Brain graphs are also physically embedded so as to nearly minimize wiring cost, a key geometric property. Here we offer a conceptual review and methodological guide to graphical analysis of human neuroimaging data, with an emphasis on some of the key assumptions, issues, and trade-offs facing the investigator.


Design of Substrate Integrated Waveguide Components


title: Fast, Exact and Multi-Scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs

abstract: In this work we propose a structured prediction technique that combines the virtues of Gaussian Conditional Random Fields (G-CRF) with Deep Learning: (a) our structured prediction task has a unique global optimum that is obtained exactly from the solution of a linear system (b) the gradients of our model parameters are analytically computed using closed form expressions, in contrast to the memory-demanding contemporary deep structured prediction approaches that rely on back-propagation-through-time, (c) our pairwise terms do not have to be simple hand-crafted expressions, as in the line of works building on the DenseCRF, but can rather be `discovered' from data through deep architectures, and (d) out system can trained in an end-to-end manner. Building on standard tools from numerical analysis we develop very efficient algorithms for inference and learning, as well as a customized technique adapted to the semantic segmentation task. This efficiency allows us to explore more sophisticated architectures for structured prediction in deep learning: we introduce multi-resolution architectures to couple information across scales in a joint optimization framework, yielding systematic improvements. We demonstrate the utility of our approach on the challenging VOC PASCAL 2012 image segmentation benchmark, showing substantial improvements over strong baselines. We make all of our code and experiments available at {this https URL}


title: Coping with stress: divergent strategies of optimists and pessimists

abstract: Previous research has shown that dispositional optimism is a prospective predictor of successful adaptation to stressful encounters. In this research we attempted to identify possible mechanisms underlying these effects by examining how optimists differ from pessimists in the kinds of coping strategies that they use. The results of two separate studies revealed modest but reliable positive correlations between optimism and problem-focused coping, seeking of social support, and emphasizing positive aspects of the stressful situation. Pessimism was associated with denial and distancing (Study 1), with focusing on stressful feelings, and with disengagement from the goal with which the stressor was interfering (Study 2). Study 1 also found a positive association between optimism and acceptance/resignation, but only when the event was construed as uncontrollable. Discussion centers on the implications of these findings for understanding the meaning of people's coping efforts in stressful circumstances.


title: Challenges in 802.11 encryption algorithms: The need for an adaptive scheme for improved performance

abstract: Wireless LAN (WLAN), despite its popularity, is subject to various security threats. Encrypting the data being transmitted is one of the approaches to address such risks. However, encryption algorithms are known to be computationally intensive and the relation between the strength of encryption and computational intensity is inversely proportional. In this paper, we discuss the challenges in the implementation of encryption algorithm in WLAN. We then compare and analyze the results of experiments to compare these algorithms vis-a-vis their energy consumption. It will be seen that different encryption schemes are fit for different types of messages. We propose an intelligent encryption scheme for optimal security.


title: SGM: Sequence Generation Model for Multi-label Classification

abstract: Multi-label classification is an important yet challenging task in natural language processing. It is more complex than single-label classification in that the labels tend to be correlated. Existing methods tend to ignore the correlations between labels. Besides, different parts of the text can contribute differently for predicting different labels, which is not considered by existing models. In this paper, we propose to view the multi-label classification task as a sequence generation problem, and apply a sequence generation model with a novel decoder structure to solve it. Extensive experimental results show that our proposed methods outperform previous work by a substantial margin. Further analysis of experimental results demonstrates that the proposed methods not only capture the correlations between labels, but also select the most informative words automatically when predicting different labels.


title: Sentiment analysis of product reviews: A review

abstract: Now a day's internet is the most valuable source of learning, getting ideas, reviews for a product or a service. Everyday millions of reviews are generated in the internet about a product, person or a place. Because of their huge number and size it is very difficult to handle and understand such reviews. Sentiment analysis is such a research area which understands and extracts the opinion from the given review and the analysis process includes natural language processing (NLP), computational linguistics, text analytics and classifying the polarity of the opinion. In the field of sentiment analysis there are many algorithms exist to tackle NLP problems. Each algorithm is used by several applications. In this paper we have shown the taxonomy of various sentiment analysis methods. This paper also shows that Support vector machine (SVM) gives high accuracy compared to Naive bayes and maximum entropy methods.


title: A novel PMSM hybrid sensorless control strategy for EV applications based on PLL and HFI

abstract: In this paper, a novel hybrid sensorless control strategy for Permanent Magnet Synchronous Machine (PMSM) drives applied to Electric Vehicles (EV) is presented. This sensorless strategy covers the EV full speed range and also has speed reversal capability. It combines a High Frequency Injection (HFI) technique for low and zero speeds, and a Phase-Locked Loop (PLL) for the medium and high speed regions. A solution to achieve smooth transitions between the PLL and the HFI strategies is also proposed, allowing to correctly detect the rotor position polarity when HFI takes part. Wide speed and torque four-quadrant simulation results are provided, which validate the proposed sensorless strategy for being further implemented in EV.


title: Barrier Repair Therapy in Atopic Dermatitis: An Overview

abstract: Atopic eczema or dermatitis (AD) is a chronically relapsing dermatitis associated with pruritus, sleep disturbance, psychosocial symptoms, and impaired quality of life. It affects 10-20 % of school-aged children, and there is evidence to suggest that this prevalence is increasing. Filaggrin (filament-aggregating protein) has an important function in epidermal differentiation and barrier function. Null mutations within the filaggrin gene cause ichthyosis vulgaris and appear to be a major risk factor for developing AD. The affected skin of atopic individuals is deficient in filaggrin degradation products or ceramides. Avoidance of triggering factors, optimal skin care, topical corticosteroids, and calcineurin inhibitors are the mainstays of therapy for AD. Proper moisturizer therapy can reduce the frequency and intensity of flares, as well as the need for topical corticosteroids or topical calcineurin inhibitors. Recent advances in the understanding of the pathophysiological process of AD involving filaggrin and ceramides has led to the concept of barrier therapy and the production of new moisturizers and topical skin products targeted to correct reduced amounts of ceramides and natural moisturizing factors in the skin with natural moisturizing factors, ceramides, and pseudoceramide products. Emollients, both creams and ointments, improve the barrier function of the stratum corneum by providing it with water and lipids. Studies on AD and barrier repair treatment show that adequate lipid replacement therapy reduces the inflammation and restores epidermal function. We reviewed 12 randomized trials and 11 cohort studies and found some evidence that certain products had therapeutic efficacy in improving clinical and/or biophysical parameters of patients with AD. Nevertheless, study methods were often flawed and sample sizes were small. Additional research is warranted to better understand the optimal formulary compositions. Also, long-term studies would be important to evaluate whether lipid barrier replacement therapy reduces bacterial colonization or prevents progression of the atopic march.


title: Optimum pMOS-to-nMOS Width Ratio for Efficient Subthreshold CMOS Circuits

abstract: The subthreshold region of operation in digital CMOS circuits provides a suitable low-power solution for many applications that need tremendously low-energy operation. However, this advantage comes at the cost of speed, so enhancing the speed of subthreshold circuits can expand their application spectrum. This paper presents the optimum pMOS-to-nMOS width ratio that leads to the maximum frequency of operation in the subthreshold region with no extra energy cost. The optimum pMOS-to-nMOS width ratio is obtained and compared through three approaches: 1) finding the maximum current-over-capacitance ratio of a biased transistor; 2) deriving an analytical expression by minimizing the delay of an inverter; and 3) simulating different CMOS logic gates in the subthreshold region. Simulation results illustrate that in the subthreshold region, the frequency attains its maximum at the optimum pMOS-to-nMOS width ratio independent of the supply voltage. Using this optimum value in designing a carry-look-ahead adder improves the propagation delay, energy consumption, and energy-delay product by up to 33%, 36%, and 57%, receptively, compared with when the conventional pMOS-to-nMOS width ratio (in the superthreshold region) is used. We verified our analytical model by performing circuit simulations in four CMOS technologies (TSMC 180 nm, IBM 130 nm, TSMC 90 nm, and ST 65 nm).


Semantic Embedding for Sketch-Based 3D Shape Retrieval


title: A CSP\AM-BA-SVM Approach for Motor Imagery BCI System

abstract: Brain–computer interface (BCI) has become extremely popular in recent decades. It gained its significance from the intention of helping paralyzed people communicate with the external environment. One of the major challenges facing BCI systems is obtaining reliable classification accuracy of motor imagery (MI) mental tasks. In this paper, a novel CSP\AM-BA-SVM approach is proposed using bio-inspired algorithms for feature selection and classifier optimization to improve classification accuracy of the MI-BCI systems. The proposed approach applies optimum selection of time interval for each subject. The features are extracted from EEG signal using the common spatial pattern (CSP). Binary CSP is extended to multi-class problems by utilizing one-vs-one strategy. This paper introduces applying a hybrid attractor metagene (AM) algorithm along with the Bat optimization algorithm (BA) to select the most discriminant CSP features and optimize SVM parameters. The efficacy of the proposed approach was examined using three data sets. The proposed approach has achieved 78.55% accuracy and 0.71 mean kappa for BCI Competition IV data set 2a, 86.6% accuracy and 0.82 mean kappa for BCI Competition III data set IIIa, and 85% for the binary class BCI Competition III data set IVa. For multi-class data sets, the proposed approach outperforms winners of BCIC IV, 2a and BCIC III, IIIa with kappa 0.14 and 0.17, respectively. For binary class BCIC III, IVa, it performed slightly better than existing studies in the literature by $\approx ~0.5$ %. The proposed CSP\AM-BA-SVM transcends the traditional CSP\SVM approach and other existing studies.


Puzzle task ERP response: time-frequency and source localization analysis


title: Conjunctive representations in learning and memory: Principles or cortical and hippocampal functioning

abstract: The authors present a theoretical framework for understanding the roles of the hippocampus and neocortex in learning and memory. This framework incorporates a theme found in many theories of hippocampal function: that the hippocampus is responsible for developing conjunctive representations binding together stimulus elements into a unitary representation that can later be recalled from partial input cues. This idea is contradicted by the fact that hippocampally lesioned rats can learn nonlinear discrimination problems that require conjunctive representations. The authors' framework accommodates this finding by establishing a principled division of labor, where the cortex is responsible for slow learning that integrates over multiple experiences to extract generalities whereas the hippocampus performs rapid learning of the arbitrary contents of individual experiences. This framework suggests that tasks involving rapid, incidental conjunctive learning are better tests of hippocampal function. The authors implement this framework in a computational neural network model and show that it can account for a wide range of data in animal learning.


Ubiquitous Commerce : The New World of Technologies


title: Design, Synthesis and Biological evaluation of Pyrazole analogues of Natural

abstract: A series of pyrazole analogues of natural piperine were synthesized by removing the basic piperidine m oiety from the piperine nucleus. Piperine upon hydrolysis and oxidation, co nverted to piperonal and allowed to condense with sacetophenone gave chalcone derivative and cyclized finally with thiosemicarbazide to form pyrazole derivatives of p iperine. Docking studies were carried out against different targets like Cyc looxygenase, farnasyl transferase receptors. Majori ty of the synthesized chemical compounds showed good fit with the active site of all the docked targets.Compound 6a have sho wn significant anti inflammatory activity and 6d and 6c have shown sign ificant anticancer activity when compared with stan dard drugs.


title: Nonlinear dimensionality reduction for visualization

abstract: The visual interpretation of data is an essential step to guide any further processing or decision making. Dimensionality reduction (or manifold learning) tools may be used for visualization if the resulting dimension is constrained to be 2 or 3. The field of machine learning has developed numerous nonlinear dimensionality reduction tools in the last decades. However, the diversity of methods reflects the diversity of quality criteria used both for optimizing the algorithms, and for assessing their performances. In addition, these criteria are not always compatible with subjective visual quality. Finally, the dimensionality reduction methods themselves do not always possess computational properties that are compatible with interactive data visualization. This paper presents current and future developments to use dimensionality reduction methods for data visualization.


title: A Content-Adaptively Sparse Reconstruction Method for Abnormal Events Detection With Low-Rank Property

abstract: This paper presents a content-adaptively sparse reconstruction method for abnormal events detection by exploiting the low-rank property of video sequences. In dictionary learning phase, the bases which describe more important characteristics of the normal behavior patterns are assigned with lower reconstruction costs. Based on the low-rank property of the bases captured by the low-rank approximation, a weighted sparse reconstruction method is proposed to measure the abnormality of testing samples. Multiscale 3-D gradient features, which encode the spatiotemporal information, are adopted as the low level descriptors. The benefits of the proposed method are threefold: first, the low-rank property is utilized to learn the underlying normal dictionaries, which can represent groups of similar normal features effectively; second, the sparsity-based algorithm can adaptively determine the number of dictionary bases, which makes it a preferable choice for representing the dynamic scene semantics; and third, based on the weighted sparse reconstruction method, the proposed method is more efficient for detecting the abnormal events. Experimental results on the public datasets have shown that the proposed method yields competitive performance comparing with the state-of-the-art methods.


title: Memory: brain systems and behavior

abstract: Abstract Certain questions about memory address a relatively global, structural level of analysis. Is there one kind of memory or many? What brain structures or systems are involved in memory and what jobs do they do? One useful approach to such questions has focused on studies of neurological patients with memory impairment and parallel studies with animal models.


title: Quality improvement and optimization of test cases: a hybrid genetic algorithm based approach

abstract: Software development organizations spend considerable portion of their budget and time in testing related activities. The effectiveness of the verification and validation process depends upon the number of errors found and rectified before releasing the software to the customer side. This in turn depends upon the quality of test cases generated. The solution is to choose the most important and effective test cases and removing the redundant and unnecessary ones; which in turn leads to test case optimization. To achieve test case optimization, this paper proposed a heuristics guided population based search approach namely Hybrid Genetic Algorithm (HGA) which combines the features of Genetic Algorithm (GA) and Local Search (LS) techniques to reduce the number of test cases by improving the quality of test cases during the solution generation process. Also, to evaluate the performance of the proposed approach, a comparative study is conducted with Genetic Algorithm and Bacteriologic Algorithm (BA) and concluded that, the proposed HGA based approach produces better results.


title: Effects of Mindfulness-Based Stress Reduction on Medical and Premedical Students

abstract: The inability to cope successfully with the enormous stress of medical education may lead to a cascade of consequences at both a personal and professional level. The present study examined the short-term effects of an 8-week meditation-b ased stress reduction intervention on premedical and medical students using a well-controlled statistical design. Findings indicate that participation in the intervention can effectively (1) reduce self-reported state and trait anxiety, (2) reduce reports of overall psychological distress including depression, (3) increase scores on overall empathy levels, and (4) increase scores on a measure of spiritual experiences assessed at termination of intervention. These results (5) replicated in the wait-list control group, (6) held across different experiments, and (7) were observed during the exam period. Future research should address potential long-term effects of mindfulness training for medical and premedical students.


title: Joint Semantic Segmentation and 3D Reconstruction from Monocular Video

abstract: We present an approach for joint inference of 3D scene structure and semantic labeling for monocular video. Starting with monocular image stream, our framework produces a 3D volumetric semantic + occupancy map, which is much more useful than a series of 2D semantic label images or a sparse point cloud produced by traditional semantic segmentation and Structure from Motion(SfM) pipelines respectively. We derive a Conditional Random Field (CRF) model defined in the 3D space, that jointly infers the semantic category and occupancy for each voxel. Such a joint inference in the 3D CRF paves the way for more informed priors and constraints, which is otherwise not possible if solved separately in their traditional frameworks. We make use of class specific semantic cues that constrain the 3D structure in areas, where multiview constraints are weak. Our model comprises of higher order factors, which helps when the depth is unobservable.We also make use of class specific semantic cues to reduce either the degree of such higher order factors, or to approximately model them with unaries if possible. We demonstrate improved 3D structure and temporally consistent semantic segmentation for difficult, large scale, forward moving monocular image sequences.


Power Approaches for Implantable Medical Devices


title: On the convergence properties of GAN training

abstract: Recent work has shown local convergence of GAN training for absolutely continuous data and generator distributions. In this note we show that the requirement of absolute continuity is necessary: we describe a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is generally not convergent. Furthermore, we discuss recent regularization strategies that were proposed to stabilize GAN training. Our analysis shows that while GAN training with instance noise or gradient penalties converges, Wasserstein-GANs and Wasserstein-GANs-GP with a finite number of discriminator updates per generator update do in general not converge to the equilibrium point. We explain these results and show that both instance noise and gradient penalties constitute solutions to the problem of purely imaginary eigenvalues of the Jacobian of the gradient vector field. Based on our analysis, we also propose a simplified gradient penalty with the same effects on local convergence as more complicated penalties.


title: Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques

abstract: Four machine learning algorithms are used for prediction in stock markets.Focus is on data pre-processing to improve the prediction accuracy.Technical indicators are discretised by exploiting the inherent opinion.Prediction accuracy of algorithms increases when discrete data is used. This paper addresses problem of predicting direction of movement of stock and stock price index for Indian stock markets. The study compares four prediction models, Artificial Neural Network (ANN), Support Vector Machine (SVM), random forest and naive-Bayes with two approaches for input to these models. The first approach for input data involves computation of ten technical parameters using stock trading data (open, high, low & close prices) while the second approach focuses on representing these technical parameters as trend deterministic data. Accuracy of each of the prediction models for each of the two input approaches is evaluated. Evaluation is carried out on 10years of historical data from 2003 to 2012 of two stocks namely Reliance Industries and Infosys Ltd. and two stock price indices CNX Nifty and S&P Bombay Stock Exchange (BSE) Sensex. The experimental results suggest that for the first approach of input data where ten technical parameters are represented as continuous values, random forest outperforms other three prediction models on overall performance. Experimental results also show that the performance of all the prediction models improve when these technical parameters are represented as trend deterministic data.


title: Knowledge management enablers: a case study

abstract: Purpose – To analyze the crucial role that enablers play in carrying out knowledge management within the enterprise.Design/methodology/approach – This research uses the method of a case study and has directed the survey on Advanced Semiconductor Engineering, Inc. (ASE) and VIA Technologies, Inc. (VIA). It is anticipated through the case study of these two companies that it will be possible to verify the finding of enablers concluded by other papers, thus showing the inter‐relationship between theory and business.Findings – It is found that among the enablers: on the part of strategy and leadership; obtaining top managements' support is most important; among organization culture enablers is the forming of an atmosphere and culture of sharing is most important but needs to be supplemented by informational technology; among people enablers, other than training courses and channels that provide learning, employee incentive program is one of the executing key factor; and among informational technology enablers...