title: Retrospective Encoders for Video Summarization

abstract: Supervised learning techniques have shown substantial progress on video summarization. State-of-the-art approaches mostly regard the predicted summary and the human summary as two sequences (sets), and minimize discriminative losses that measure element-wise discrepancy. Such training objectives do not explicitly model how well the predicted summary preserves semantic information in the video. Moreover, those methods often demand a large amount of human generated summaries. In this paper, we propose a novel sequence-to-sequence learning model to address these deficiencies. The key idea is to complement the discriminative losses with another loss which measures if the predicted summary preserves the same information as in the original video. To this end, we propose to augment standard sequence learning models with an additional “retrospective encoder” that embeds the predicted summary into an abstract semantic space. The embedding is then compared to the embedding of the original video in the same space. The intuition is that both embeddings ought to be close to each other for a video and its corresponding summary. Thus our approach adds to the discriminative loss a metric learning loss that minimizes the distance between such pairs while maximizing the distances between unmatched ones. One important advantage is that the metric learning loss readily allows learning from videos without human generated summaries. Extensive experimental results show that our model outperforms existing ones by a large margin in both supervised and semi-supervised settings.


title: Characterizing performance of load-aware network selection in multi-radio (WiFi/LTE) heterogeneous networks

abstract: In this paper, we consider the problem of network selection between different radio access technologies (RATs) deployed as part of an operator managed multi-RAT heterogeneous network. An urban deployment scenario is studied where WiFi small cells are overlaid on top of the 3GPP LTE network. We assume limited cooperation across the multi-RAT network and emphasize user-centric network selection algorithms to minimize feedback overhead and to better account for user preferences. Specifically, we investigate schemes that rely on network loading information with suitable adaptation of hysteresis mechanisms and compare them with WiFi-preferred schemes that only account for signal strength measurements. We also benchmark the performance of load-aware schemes against conventional cellrange extension methods that use network-wide optimization to offload users to small cells. The results of our systemlevel performance evaluation show that load-aware user-centric schemes can provide improved performance compared to the WiFi-preferred schemes and may even outperform network-based cell-range extension schemes under some conditions.


title: The Penn Treebank: An Overview

abstract: The Penn Treebank, in its eight years of operation (1989–1996), produced approximately 7 million words of part-of-speech tagged text, 3 million words of skeletally parsed text, over 2 million words of text parsed for predicateargument structure, and 1.6 million words of transcribed spoken text annotated for speech disfluencies. This paper describes the design of the three annotation schemes used by the Treebank: POS tagging, syntactic bracketing, and disfluency annotation and the methodology employed in production. All available Penn Treebank materials are distributed by the Linguistic Data Consortium http://www.ldc.upenn.edu.


title: Modeling the energy systems in Matlab Simulink - problematics of energy system control

abstract: The paper presents the models of the individual elements in the energy system created in Matlab Simulink. The reason for modeling energy system is the prediction of the behavior of individual subsystems. In cooperation with the validated models are solved the operational problems and power quality issues that are further discussed.


HARDNESS VERSUS TIME DEPENDENCY DURING ARTIFICIAL AGEING OF AlMgSi0.5 ALUMINIUM ALLOY


Factors Influencing Employee’s Job Satisfaction: An Empirical Study among Employees of Automobile Service Workshops in Assam


SIVSHM : Secure Inter-VM Shared Memory Technical Report UCSC-SSRC-1601 May 2016


title: PGQL: a property graph query language

abstract: Graph-based approaches to data analysis have become more widespread, which has given need for a query language for graphs. Such a graph query language needs not only SQL-like functionality for querying structured data, but also intrinsic support for typical graph-style applications: reachability analysis, path finding and graph construction. We propose a new query language for the popular Property Graph (PG) data model: the Property Graph Query Language (PGQL). PGQL is based on the paradigm of graph pattern matching, closely follows syntactic structures of SQL, and provides regular path queries with conditions on labels and properties to allow for reachability and path finding queries. Besides intrinsic vertex, edge and path types, PGQL also has the graph as intrinsic type and allows for graph construction and query composition.


Real Options for a Wind Farm in Wapakoneta, Ohio: Incorporating Uncertainty into Economic Feasibility Studies for Community Wind Authored by:


title: Robot manipulation through inverse kinematics

abstract: Inverse kinematics of a robot is very essential to find the joint variables that satisfy the desired pose of the robot during its manipulation. This is used in controlling the robot position, animation of the robot, etc. In this paper, step-by-step explanation and comparison of two widely used methods, namely, inverse kinematics and Jacobian inverse methods, for robot manipulation are presented. For this purpose a six degrees-of-freedom wrist-partitioned industrial robot KUKA KR5 Arc was used to illustrate the methods. A novel approach has been proposed for selecting the appropriate set of joint angles among the several inverse kinematic solutions. It is based on weight of each link and manipulability. The comparison of these approaches for linear and circular trajectory is presented. Their advantages, limitations, applications, and computations involved are also highlighted.


title: ThingStore: a platform for internet-of-things application development and deployment

abstract: An advanced app-store concept, called ThingStore, is introduced in this paper. It provides a "market place" environment to facilitate collaboration on Internet-of-Things (IoT) applications development, and a platform to host their deployment. ThingStore services three categories of users: (1) Thing Provider - "Things" (such as online cameras and sensors) can be made more intelligent through event detection software routines called smart services. A thing provider may deploy "things" and advertise their smart services at ThingStore market place. (2) Software Developer - Software developers can develop apps that query relevant smart services using EQL (Event Query Language) much like the way traditional database applications are conveniently developed atop a standard database management system today. (3) End User - An end user may subscribe to a particular app for event notification and management. In this IoT architecture, ThingStore is a computation hub that links together human, "things," and computer software in a cyber-physical lifecycle to enable fusion of human and machine intelligence to accomplish some common goal. Not only human, but also "things," may adjust the physical world. New changes in the physical world may, in turn, incur new event detections and therefore initiate another cycle of this ecology-inspired computational lifecycle.


title: Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions

abstract: Modern natural language processing systems have been touted as approaching human performance. However, existing datasets are imperfect tests. Examples are written with humans in mind, not computers, and often do not properly expose model limitations. We address this by developing a new process for crowdsourced annotation, adversarial writing, where humans interact with trained models and try to break them. Applying this annotation process to Trivia question answering yields a challenge set, which despite being easy for human players to answer, systematically stumps automated question answering systems. Diagnosing model errors on the evaluation data provides actionable insights to explore in developing more robust and generalizable question answering systems.


title: Unit Conflict Resolution for Automatic Math Word Problem Solving

abstract: Among the statistical approaches for math word problem solving, template based approaches have shown to be more robust against a wide spectrum of math word problems, while other approaches target simple arithmetic problems that compose of only one operation or equation. However, even template based systems are poor in performance for questions that contain different units to describe the same measurement. This paper presents a unit conflict resolution system to improve the performance and accuracy of template based systems under minimal supervision. To illustrate the importance of unit conflict resolution for math word problems, we have annotated a new dataset of 385 algebra word problems. We evaluate the performance of our approach both on a benchmark dataset and this new dataset. Experimental results show that integration of our system to an existing automatic math word problem solver outperforms state-of-the-art results when the dataset contains different units to describe the same measurement.


title: The emotional brain

abstract: The discipline of affective neuroscience is concerned with the neural bases of emotion and mood. The past 30 years have witnessed an explosion of research in affective neuroscience that has addressed questions such as: which brain systems underlie emotions? How do differences in these systems relate to differences in the emotional experience of individuals? Do different regions underlie different emotions, or are all emotions a function of the same basic brain circuitry? How does emotion processing in the brain relate to bodily changes associated with emotion? And, how does emotion processing in the brain interact with cognition, motor behaviour, language and motivation?


title: Manual material handling advice and assistive devices for preventing and treating back pain in workers

abstract: BACKGROUND: Training and assistive devices are considered major interventions to prevent back pain among workers exposed to manual material handling (MMH). OBJECTIVES: To determine the effectiveness of MMH advice and training and the provision of assistive devices in preventing and treating back pain. SEARCH STRATEGY: We searched MEDLINE to November 2005, EMBASE to August 2005, and CENTRAL, the Back Group's Trials Register, CINAHL, Nioshtic, CISdoc, Science Citation Index, and PsychLIT to September 2005. SELECTION CRITERIA: We included randomized controlled trials (RCT) and cohort studies with a concurrent control group, aimed at changing human behaviour in MMH and measuring back pain, back pain-related disability or sickness absence. DATA COLLECTION AND ANALYSIS: Two authors independently extracted the data and assessed the methodological quality using the criteria recommended by the Back Review Group for RCTs and MINORS for the cohort studies. One author of an original study supplied additional data for the review.The results and conclusions are based on the primary analysis of RCTs. We conducted a secondary analysis with cohort studies. We compared and contrasted the conclusions from the primary and secondary analyses. MAIN RESULTS: We included six RCTs (17,720 employees) and five cohort studies (772 employees). All studies focused on prevention of back pain. Two RCTs and all cohort studies met the majority of the quality criteria and were labeled high quality.We summarized the strength of the evidence with a qualitative analysis since the lack of data precluded a statistical analysis.There is moderate evidence that MMH advice and training are no more effective at preventing back pain or back pain-related disability than no intervention (four studies) or minor advice (one study). There is limited evidence that MMH advice and training are no more effective than physical exercise or back belt use in preventing back pain (three studies), and that MMH advice plus assistive devices are not more effective than MMH advice alone (one study) or no intervention (one study) in preventing back pain or related disability.The results of the cohort studies were similar to the randomised studies. AUTHORS' CONCLUSIONS: There is limited to moderate evidence that MMH advice and training with or without assistive devices do not prevent back pain, back pain-related disability or reduce sick leave when compared to no intervention or alternative interventions. There is no evidence available for the effectiveness of MMH advice and training or MMH assistive devices for treating back pain


title: Application of Convolutional Neural Network to Prediction of Temperature Distribution in Data Centers

abstract: We propose a model for predicting the temperature distribution in data centers by using a convolutional neural network (CNN). Changes in the temperature distribution depend on the local structure of the data center, such as equipment locations and server types. Although the various physical relations in a data center were modeled as a network in our previous work, there were no mechanisms for automatically extracting the structure of the data center. The use of a CNN is a technique for learning local structure adaptively, which allows learning complicated features, such as the various physical relations in a data center. We evaluate the performance of the proposed model by using actual data from an experimental data center. The evaluation indicates that the proposed model can predict 20-minute future temperature distributions over 48 locations in 0.42 ms, with a root mean square error (RMSE) of 0.96 degrees. This accuracy is a dramatic improvement over simple linear prediction models, and the accuracy is sufficient to allow for control of air conditioners on the basis of these temperature predictions.


title: Network Monitoring as a Streaming Analytics Problem

abstract: Programmable switches potentially make it easier to perform flexible network monitoring queries at line rate, and scalable stream processors make it possible to fuse data streams to answer more sophisticated queries about the network in real-time. However, processing such network monitoring queries at high traffic rates requires both the switches and the stream processors to filter the traffic iteratively and adaptively so as to extract only that traffic that is of interest to the query at hand. While the realization that network monitoring is a streaming analytics problem has been made earlier, our main contribution in this paper is the design and implementation of Sonata, a closed-loop system that enables network operators to perform streaming analytics for network monitoring applications at scale. To achieve this objective, Sonata allows operators to express a network monitoring query by considering each packet as a tuple. More importantly, Sonata allows them to partition the query across both the switches and the stream processor, and through iterative refinement, Sonata's runtime attempts to extract only the traffic that pertains to the query, thus ensuring that the stream processor can scale to satisfy a large number of queries for traffic at very high rates. We show with a simple example query involving DNS reflection attacks and traffic traces from one of the world's largest IXPs that Sonata can capture 95% of all traffic pertaining to the query, while reducing the overall data rate by a factor of about 400 and the number of required counters by four orders of magnitude.


title: Body and mind: a study of avatar personalization in three virtual worlds

abstract: An increasingly large number of users connect to virtual worlds on a regular basis to conduct activities ranging from gaming to business meetings. In all these worlds, users project themselves into the environment via an avatar: a 3D body which they control and whose appearance is often customizable. However, considering the prevalence of this form of embodiment, there is a surprising lack of data about how and why users customize their avatar, as well as how easy and satisfying the existing avatar creation tools are. In this paper, we report on a study investigating these issues through a questionnaire administered to more than a hundred users of three virtual worlds offering widely different avatar creation and customization systems (Maple Story, World of Warcraft, and Second Life). We illustrate the often-surprising choices users make when creating their digital representation and discuss the impact of our findings for the design of future avatar creation systems.


Harris ’ s Hawk Multi-Objective Optimizer for Reference Point Problems


title: Automatic Acquisition Of Hyponyms From Large Text Corpora

abstract: We describe a method for the automatic acquisition of the hyponymy lexical relation from unrestricted text. Two goals motivate the approach: (i) avoidance of the need for pre-encoded knowledge and (ii) applicability across a wide range of text. We identify a set of lexico-syntactic patterns that are easily recognizable, that occur frequently and across text genre boundaries, and that indisputably indicate the lexical relation of interest. We describe a method for discovering these patterns and suggest that other lexical relations will also be acquirable in this way. A subset of the acquisition algorithm is implemented and the results are used to augment and critique the structure of a large hand-built thesaurus. Extensions and applications to areas such as information retrieval are suggested.


title: The SmartHand transradial prosthesis

abstract: Background ::: Prosthetic components and control interfaces for upper limb amputees have barely changed in the past 40 years. Many transradial prostheses have been developed in the past, nonetheless most of them would be inappropriate if/when a large bandwidth human-machine interface for control and perception would be available, due to either their limited (or inexistent) sensorization or limited dexterity. SmartHand tackles this issue as is meant to be clinically experimented in amputees employing different neuro-interfaces, in order to investigate their effectiveness. This paper presents the design and on bench evaluation of the SmartHand.


title: Affective forecasting about hedonic loss and adaptation: Implications for damage awards.

abstract: In tort lawsuits, plaintiffs may seek damages for loss of enjoyment of life, so-called hedonic loss, which occurred as a result of an accident or injury. In 2 studies, we examined how people judge others' adaptation and hedonic loss after an injury. Laypeople's forecasts of hedonic loss are relevant to concerns about whether jurors appropriately compensate plaintiffs. Longitudinal data of subjective well-being (e.g., Binder & Coad, 2013) show that hedonic loss is domain-specific: Many physical impairments (e.g., strokes) inflict less hedonic loss than many persistent yet invisible ailments (e.g., mental illness and conditions that cause chronic pain). We used vignette methodology to determine whether laypeople (n = 68 community members and 65 students in Study 1; 87 community members and 93 students in Study 2) and rehabilitation professionals (n = 47 in Study 2) were aware of this fact. In Study 1, participants' ratings of hedonic loss subsequent to a physical injury and a comparably severe psychological impairment did not differ. In Study 2, ratings of short- and long-term hedonic loss stemming from paraplegia and chronic back pain showed that neither laypeople nor professionals understood that hedonic loss is domain-specific. These findings imply that observers may forecast a future for people who suffered serious physical injuries as grimmer than it is likely to be, and a future for people who experience chronic pain and psychological disorders as rosier than is likely. (PsycINFO Database Record


title: FrameNet CNL: a Knowledge Representation and Information Extraction Language

abstract: The paper presents a FrameNet-based information extraction and knowledge representation framework, called FrameNet-CNL. The framework is used on natural language documents and represents the extracted knowledge in a tailor-made Frame-ontology from which unambiguous FrameNet-CNL paraphrase text can be generated automatically in multiple languages. This approach brings together the fields of information extraction and CNL, because a source text can be considered belonging to FrameNet-CNL, if information extraction parser produces the correct knowledge representation as a result. We describe a state-of-the-art information extraction parser used by a national news agency and speculate that FrameNet-CNL eventually could shape the natural language subset used for writing the newswire articles.


title: Consequences of Connectivity: Characterizing Account Hijacking on Twitter

abstract: In this study we expose the serious large-scale threat of criminal account hijacking and the resulting damage incurred by users and web services. We develop a system for detecting large-scale attacks on Twitter that identifies 14 million victims of compromise. We examine these accounts to track how attacks spread within social networks and to determine how criminals ultimately realize a profit from hijacked credentials. We find that compromise is a systemic threat, with victims spanning nascent, casual, and core users. Even brief compromises correlate with 21% of victims never returning to Twitter after the service wrests control of a victim's account from criminals. Infections are dominated by social contagions---phishing and malware campaigns that spread along the social graph. These contagions mirror information diffusion and biological diseases, growing in virulence with the number of neighboring infections. Based on the severity of our findings, we argue that early outbreak detection that stems the spread of compromise in 24 hours can spare 70% of victims.


title: Phase-Oriented Treatment of Structural Dissociation in Complex Traumatization: Overcoming Trauma-Related Phobias

abstract: The theory of structural dissociation of the personality proposes that patients with complex trauma-related disorders are charac- terized by a division of their personality into different prototypical parts, each with its own psychobiological underpinnings. As one or more "ap- parently normal" parts (ANPs), patients have a propensity toward en- gaging in evolutionary prepared action systems for adaptation to daily living to guide their actions. Two or more "emotional" parts (EPs) are fixated in traumatic experience. As EPs, patients predominantly engage


Towards Editorial Transparency in Computational Journalism


title: Survey on Cryptographic Obfuscation

abstract: The recent result of Garg et al. (FOCS 2013) changed the previously pes- simistic attitude towards general purpose cryptographic obfuscation. Since their first candidate construction, several authors proposed newer and newer schemes with more persuasive security arguments and better eciency. At the same time, indistinguishabil- ity obfuscation proved its extreme usefulness by becoming the basis of many solutions for long-standing open problems in cryptography e.g. functional or witness encryption and others. In this survey, we give an overview of recent research, focusing on the theoretical results on general purpose obfuscation, particularly, indistinguishability obfuscation. IMPORTANT NOTE: This version of the manuscript not yet includes some new re- sults! Until the next update the reader is advised to check these as well (see page 24)!


title: Avoiding confusing features in place recognition

abstract: We seek to recognize the place depicted in a query image using a database of "street side" images annotated with geolocation information. This is a challenging task due to changes in scale, viewpoint and lighting between the query and the images in the database. One of the key problems in place recognition is the presence of objects such as trees or road markings, which frequently occur in the database and hence cause significant confusion between different places. As the main contribution, we show how to avoid features leading to confusion of particular places by using geotags attached to database images as a form of supervision. We develop a method for automatic detection of image-specific and spatially-localized groups of confusing features, and demonstrate that suppressing them significantly improves place recognition performance while reducing the database size. We show the method combines well with the state of the art bag-of-features model including query expansion, and demonstrate place recognition that generalizes over wide range of viewpoints and lighting conditions. Results are shown on a geotagged database of over 17K images of Paris downloaded from Google Street View.


title: Elephant herding optimization algorithm for support vector machine parameters tuning

abstract: Classification is part of various applications and it is an important problem that represents active research topic. Support vector machine is one of the widely used and very powerful classifier. The accuracy of support vector machine highly depends on learning parameters. Optimal parameters can be efficiently determined by using swarm intelligence algorithms. In this paper, we proposed recent elephant herding optimization algorithm for support vector machine parameter tuning. The proposed approach is tested on standard datasets and it was compared to other approaches from literature. The results of computational experiments show that our proposed algorithm outperformed genetic algorithms and grid search considering accuracy of classification.


title: Co-localization of mesotocin and opsin immunoreactivity in the hypothalamic preoptic nucleus of Xenopus laevis

abstract: The purpose of the present investigation was to provide a detailed description of the encephalic photoreceptors of Xenopus laevis at the light microscopic level and to determine their relationship with the neurosecretory cells of the hypothalamus in order to further our understanding of photoperiodic regulation of seasonal rhythms. Numerous opsin-positive neurons were found in the hypothalamic magnocellular preoptic nucleus and their axonal processes were seen to run laterally towards the basal regions of the brain, reaching the neural lobe of the hypophysis. Analysis of labelling with different antisera in adjacent sections, as well as double-immunolabelling carried out on the same section, revealed that mesotocin immunoreactivity was present in most of the opsin-positive neurons; however, no evidence for opsin and vasotocin coexpression was found in any of the sections analysed. The close localization of LHRH and opsin/mesotocin fibers in some regions of the brain, such as the median eminence, suggests that some interaction between these two systems might exist. In conclusion, in this study we provide the first strong evidence that the hypothalamic mesotocinergic neurons, which have been proved to be connected to the GnRH system in other species, are directly involved in photoreception in Xenopus laevis. These findings represent a novel contribution to our understanding of how light influences the seasonal reproductive cycles in lower vertebrates.


title: DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency

abstract: We present an unsupervised learning framework for simultaneously training single-view depth prediction and optical flow estimation models using unlabeled video sequences. Existing unsupervised methods often exploit brightness constancy and spatial smoothness priors to train depth or flow models. In this paper, we propose to leverage geometric consistency as additional supervisory signals. Our core idea is that for rigid regions we can use the predicted scene depth and camera motion to synthesize 2D optical flow by backprojecting the induced 3D scene flow. The discrepancy between the rigid flow (from depth prediction and camera motion) and the estimated flow (from optical flow model) allows us to impose a cross-task consistency loss. While all the networks are jointly optimized during training, they can be applied independently at test time. Extensive experiments demonstrate that our depth and flow models compare favorably with state-of-the-art unsupervised methods.


AndroTherm application for Peyronie disease (Phase I/II study)


Social Cognitive Theory and Mass Media Effects


title: Zero-Point Field, QED Coherence, Living Systems and Biophotons Emission

abstract: Living ::: organisms are high ordered and organized systems accumulating and successively ::: using low entropy energy to support all the processes needed for life. This low ::: level of entropy is a required condition in order to make possible the use of ::: endogenous energy for producing, for example, mechanical work. The commonly ::: accepted picture of condensed matter physics, exclusively considering the ::: perturbative coupling between QED Zero-Point-Field also known as “Quantum ::: Vacuum” and the matter system, is unable to thoroughly explain the true origin ::: of this low entropy energy reservoir and its dynamics. Recent researches ::: instead suggested that energy and mass of every particle or body could be ::: actually considered as arising from Quantum Vacuum dynamics which, in turn, can ::: exhibit, under suitable conditions always occurring in the case of living ::: systems, a coherent behavior characterized by a strong phase correlation ::: between matter and an electromagnetic field trapped inside this ensemble. In ::: this paper the preliminary model of Quantum Vacuum already proposed by author ::: is reformulated in terms of QED coherence in condensed matter showing it is able ::: to explain the origin of internal energy stock of living organisms. Within this ::: theoretical framework, an interpretation of some important experimental results ::: about biophotons emission by living systems under the influence of external ::: stimuli is also proposed, suggesting their origin could also arise from Quantum ::: Vacuum dynamics. This model, as shown, opens very interesting and exciting ::: scenarios of further developments in the understanding of the birth and ::: dynamics of life.


title: Determination of reference limits: statistical concepts and tools for sample size calculation

abstract: Reference limits are estimators for 'extreme' percentiles of the distribution of a quantitative diagnostic marker in the healthy population. In most cases, interest will be in the 90% or 95% reference intervals. The standard parametric method of determining reference limits consists of computing quantities of the form X̅±c·S. The proportion of covered values in the underlying population coincides with the specificity obtained when a measurement value falling outside the corresponding reference region is classified as diagnostically suspect. Nonparametrically, reference limits are estimated by means of so-called order statistics. In both approaches, the precision of the estimate depends on the sample size. We present computational procedures for calculating minimally required numbers of subjects to be enrolled in a reference study. The much more sophisticated concept of reference bands replacing statistical reference intervals in case of age-dependent diagnostic markers is also discussed.


title: Data sparseness in linear SVM

abstract: Large sparse datasets are common in many real-world applications. Linear SVM has been shown to be very efficient for classifying such datasets. However, it is still unknown how data sparseness would affect its convergence behavior. To study this problem in a systematic manner, we propose a novel approach to generate large and sparse data from real-world datasets, using statistical inference and the data sampling process in the PAC framework. We first study the convergence behavior of linear SVM experimentally, and make several observations, useful for real-world applications. We then offer theoretical proofs for our observations by studying the Bayes risk and PAC bound. Our experiment and theoretic results are valuable for learning large sparse datasets with linear SVM.


title: Consul: a communication substrate for fault-tolerant distributed programs

abstract: As human dependence on computing technology increases, so does the need for computer system dependability. This dissertation introduces Consul, a communication substrate designed to help improve system dependability by providing a platform for building fault-tolerant, distributed systems based on the replicated state machine approach. The key issues in this approach--ensuring replica consistency and reintegrating recovering replicas--are addressed in Consul by providing abstractions called fault-tolerant services. These include a broadcast service to deliver messages to a collection of processes reliably and in some consistent order, a membership service to maintain a consistent system-wide view of which processes are functioning and which have failed, and a recovery service to recover a failed process. ::: Fault-tolerant services are implemented in Consul by a unified collection of protocols that provide support for managing communication, redundancy, failures, and recovery in a distributed system. At the heart of Consul is Psync, a protocol that provides for multicast communication based on a context graph that explicitly records the partial (or causal) order of messages. This graph also serves as the basis for novel algorithms used in the ordering, membership, and recovery protocols. The ordering protocol combines the semantics of the operations encoded in messages with the partial order provided by Psync to increase the concurrency of the application. Similarly, the membership protocol exploits the partial ordering to allow different processes to conclude that a failure has occurred at different times relative to the sequence of messages received, thereby reducing the amount of synchronization required. The recovery protocol combines checkpointing with the replay of messages stored in the context graph to recover the state of a failed process. Moreover, this collection of protocols is implemented in a highly-configurable manner, thus allowing a system builder to easily tailor an instance of Consul from this collection of building-block protocols. ::: Consul is built in the x-Kernel and executes standalone on a collection of Sun 3 work-stations. Initial testing and performance studies have been done using two applications: a replicated directory and a distributed wordgame. These studies show that the semantic based order is more efficient than a total order in many situations, and that the overhead imposed by the checkpointing, membership, and recovery protocols is insignificant.


title: Methods for Evaluating the Content, Usability, and Efficacy of Commercial Mobile Health Apps

abstract: Commercial mobile apps for health behavior change are flourishing in the marketplace, but little evidence exists to support their use. This paper summarizes methods for evaluating the content, usability, and efficacy of commercially available health apps. Content analyses can be used to compare app features with clinical guidelines, evidence-based protocols, and behavior change techniques. Usability testing can establish how well an app functions and serves its intended purpose for a target population. Observational studies can explore the association between use and clinical and behavioral outcomes. Finally, efficacy testing can establish whether a commercial app impacts an outcome of interest via a variety of study designs, including randomized trials, multiphase optimization studies, and N-of-1 studies. Evidence in all these forms would increase adoption of commercial apps in clinical practice, inform the development of the next generation of apps, and ultimately increase the impact of commercial apps.


title: Robust Feature Selection by Mutual Information Distributions

abstract: Mutual information is widely used in artificial intelligence, in a descriptive way, to measure the stochastic dependence of discrete random variables. In order to address questions such as the reliability of the empirical value, one must consider sample-to-population inferential approaches. This paper deals with the distribution of mutual information, as obtained in a Bayesian framework by a second-order Dirichlet prior distribution. The exact analytical expression for the mean and an analytical approximation of the variance are reported. Asymptotic approximations of the distribution are proposed. The results are applied to the problem of selecting features for incremental learning and classification of the naive Bayes classifier. A fast, newly defined method is shown to outperform the traditional approach based on empirical mutual information on a number of real data sets. Finally, a theoretical development is reported that allows one to efficiently extend the above methods to incomplete samples in an easy and effective way.


title: SVR-based music mood classification and context-based music recommendation

abstract: With the advent of the ubiquitous era, context-based music recommendation has become one of rapidly emerging applications. Context-based music recommendation requires multidisciplinary efforts including low level feature extraction, music mood classification and human emotion prediction. Especially, in this paper, we focus on the implementation issues of context-based mood classification and music recommendation. For mood classification, we reformulate it into a regression problem based on support vector regression (SVR). Through the use of the SVR-based mood classifier, we achieved 87.8% accuracy. For music recommendation, we reason about the user's mood and situation using both collaborative filtering and ontology technology. We implement a prototype music recommendation system based on this scheme and report some of the results that we obtained.


title: A penalty PALM method for sparse portfolio selection problems

abstract: In this paper, we propose a penalty proximal alternating linearized minimization method for the large-scale sparse portfolio problems in which a sequence of penalty subproblems are solved by utilizing the proximal alternating linearized minimization framework and sparse projection techniques. For exploiting the structure of the problems and reducing the computation complexity, each penalty subproblem is solved by alternately solving two projection problems. The global convergence of the method to a Karush-Kuhn-Tucker point or a local minimizer of the problem can be proved under the characteristic of the problem. The computational results with practical problems demonstrate that our method can find the suboptimal solutions of the problems efficiently and is competitive with some other local solution methods.


Multilingual Connotation Frames: A Case Study on Social Media for Targeted Sentiment Analysis and Forecast


title: An efficient divide-and-conquer approach for big data analytics in machine-to-machine communication

abstract: Machine-to-Machine (M2M) communication relies on the physical objects (e.g., satellites, sensors, and so forth) interconnected with each other, creating mesh of machines producing massive volume of data about large geographical area (e.g., living and non-living environment). Thus, the M2M is an ideal example of Big Data. On the contrary, the M2M platforms that handle Big Data might perform poorly or not according to the goals of their operator (in term of cost, database utilization, data quality, processing and computational efficiency, analysis and feature extraction applications). Therefore, to address the aforementioned needs, we propose a new effective, memory and processing efficient system architecture for Big Data in M2M, which, unlike other previous proposals, does not require whole set of data to be processed (including raw data sets), and to be kept in the main memory. Our designed system architecture exploits divide-and-conquer approach and data block-wise vertical representation of the database follows a particular petitionary strategy, which formalizes the problem of feature extraction applications. The architecture goes from physical objects to the processing servers, where Big Data set is first transformed into a several data blocks that can be quickly processed, then it classifies and reorganizes these data blocks from the same source. In addition, the data blocks are aggregated in a sequential manner based on a machine ID, and equally partitions the data using fusion algorithm. Finally, the results are stored in a server that helps the users in making decision. The feasibility and efficiency of the proposed system architecture are implemented on Hadoop single node setup on UBUNTU 14.04 LTS core?i5 machine with 3.2GHz processor and 4GB memory. The results show that the proposed system architecture efficiently extract various features (such as River) from the massive volume of satellite data.


title: Smart public transport system

abstract: Time is precious in today's fast moving life. The wastage of time by waiting long time for getting bus can be avoided by Smart Public Transportation System. Here a methodology based on combination of GPS and GSM/GPRS modem is discussed to help the people who utilize the public transportation for traveling. The user is provided with the information about the current location of nearest buses approaching the stop. Based on this the user may change his schedule thus saving the long waiting hours at the bus stop. Also the system will definitely control the accidents due to over speed, by alerting the driver and passengers. Also the information is recorded for future reference whenever the vehicle over speeds. A next stop indicator is installed in the bus to facilitate the passengers. The most important sections of the system are: Bus module, Bus stop module and Base station or Server module. Also people can access the nearest bus information through SMS service by sending the bus stop id to the server. In case of loss of GPS, a digital speedometer is used to check for over speeding. The project will help to reduce the long waiting hours for buses at the bus stops and detect the over speed of the buses thereby reducing the road accidents.


title: Measuring the Impact of Spelling Errors on the Quality of Machine Translation

abstract: In this paper we show how different types of spelling errors influence the quality of machine translation. We also propose a method to evaluate the impact of spelling errors correction on translation quality without expensive manual work of providing reference translations.


title: Intrusion detection in computer networks using hybrid machine learning techniques

abstract: The emergence of new networking paradigms like Cloud Computing and the Internet of Things has introduced new security challenges that deserve new mechanisms to guarantee the integrity, availability and confidentiality of information and services to the users. One of the currently most studied strategies to satisfy these necessities is the use of hybrid Machine Learning techniques to automatize the process of intrusion detection in computer networks. This paper presents the design, implementation and performance analysis of multiple hybrid Machine Learning models for the task of intrusion detection in computer networks. Our results show that the combination of supervised and unsupervised learning algorithms complement each other to the task of creating a model capable of adapting to the detection of known and unknown attacks.


title: The defense substance allicin from garlic permeabilizes membranes of Beta vulgaris, Rhoeo discolor, Chara corallina and artificial lipid bilayers

abstract: Abstract Background Allicin (diallylthiosulfinate) is the major volatile- and antimicrobial substance produced by garlic cells upon wounding. We tested the hypothesis that allicin affects membrane function and investigated 1) betanine pigment leakage from beetroot ( Beta vulgaris ) tissue, 2) the semipermeability of the vacuolar membrane of Rhoeo discolor cells, 3) the electrophysiology of plasmalemma and tonoplast of Chara corallina and 4) electrical conductivity of artificial lipid bilayers. Methods Garlic juice and chemically synthesized allicin were used and betanine loss into the medium was monitored spectrophotometrically. Rhoeo cells were studied microscopically and Chara- and artificial membranes were patch clamped. Results Beet cell membranes were approximately 200-fold more sensitive to allicin on a mol-for-mol basis than to dimethyl sulfoxide (DMSO) and approximately 400-fold more sensitive to allicin than to ethanol. Allicin-treated Rhoeo discolor cells lost the ability to plasmolyse in an osmoticum, confirming that their membranes had lost semipermeability after allicin treatment. Furthermore, allicin and garlic juice diluted in artificial pond water caused an immediate strong depolarization, and a decrease in membrane resistance at the plasmalemma of Chara , and caused pore formation in the tonoplast and artificial lipid bilayers. Conclusions Allicin increases the permeability of membranes. General significance Since garlic is a common foodstuff the physiological effects of its constituents are important. Allicin's ability to permeabilize cell membranes may contribute to its antimicrobial activity independently of its activity as a thiol reagent.


title: An empirical study on applying data mining techniques for the analysis and prediction of heart disease

abstract: The health care environment is found to be rich in information, but poor in extracting knowledge from the information. This is because of the lack of effective analysis tool to discover hidden relationships and trends in them. By applying the data mining techniques, valuable knowledge can be extracted from the health care system. Heart disease is a group of condition affecting the structure and functions of heart and has many root causes. Heart disease is the leading cause of death in the world over past ten years. Researches have been made with many hybrid techniques for diagnosing heart disease. This paper deals with an overall review of application of data mining in heart disease prediction.


title: An FPGA-based processor for training convolutional neural networks

abstract: Convolutional neural networks (CNNs) have gained great success in various computer vision applications. However, training a CNN model is computation-intensive and time-consuming. Hence training is mainly processed on large clusters of high-performance processors like server CPUs and GPUs. In this paper, we propose an FPGA-based processor design to accelerate the training process of CNNs. We first analyze the operations in all types of CNN layers in the training process. A uniform computation engine design is proposed to efficiently carry out all kinds of operations based on the analysis. Then a scalable accelerator framework is presented that exploits the parallelism further by unrolling the loops in two levels. The proposed accelerator design is demonstrated by implementing a processor on the Xilinx ZU19EG FPGA working at 200 MHz. The evaluation results on a group of CNN models show that our processor is 5.7 to 10.7-fold faster than the software implementations on the Intel Core i5-4440 CPU(@3.10GHz).


title: Target's range migration compensation in passive radar

abstract: Ideally the processing gain in passive radar is PG=BT. Where B is the bandwidth and T is the integration time. So by increasing T the target detectability will also increase. But practically, when we deal with moving targets, increasing T will result in a problem: target migration from its range cell that in turn results in decreased processing gain. In this paper we have suggested a method, by which we can compensate target's migration, while keeping the computational load low.


title: An algorithm for minimizing the Mumford-Shah functional

abstract: In this work we revisit the Mumford-Shah functional, one of the most studied variational approaches to image segmentation. The contribution of this paper is to propose an algorithm which allows to minimize a convex relaxation of the Mumford-Shah functional obtained by functional lifting. The algorithm is an efficient primal-dual projection algorithm for which we prove convergence. In contrast to existing algorithms for minimizing the full Mumford-Shah this is the first one which is based on a convex relaxation. As a consequence the computed solutions are independent of the initialization. Experimental results confirm that the proposed algorithm determines smooth approximations while preserving discontinuities of the underlying signal.


title: Integration of Machine Learning Techniques to Evaluate Dynamic Customer Segmentation Analysis for Mobile Customers

abstract: The telecommunications industry is highly competitive, which means that the mobile providers need a business intelligence model that can be used to achieve an optimal level of churners, as well as a minimal level of cost in marketing activities. Machine learning applications can be used to provide guidance on marketing strategies. Furthermore, data mining techniques can be used in the process of customer segmentation. The purpose of this paper is to provide a detailed analysis of the C.5 algorithm, within naive Bayesian modelling for the task of segmenting telecommunication customers behavioural profiling according to their billing and socio-demographic aspects. Results have been experimentally implemented.


title: Resource-based theories of competitive advantage: A ten-year retrospective on the resource-based view

abstract: The resource-based view can be positioned relative to at least three theoretical traditions: SCP-based theories of industry determinants of firm performance, neo-classical microeconomics, and evolutionary economics. In the 1991 article, only the first of these ways of positioning the resourcebased view is explored. This article briefly discusses some of the implications of positioning the resource-based view relative to these other two literatures; it also discusses some of the empirical implications of each of these different resource-based theories.


Following the path: Increasing trends of HIV-1 drug resistance in China


title: © 2003 University of Koblenz-Landau (Why) Should We Use SEM? Pros and Cons of Structural Equation Modeling

abstract: During the last two decades, Structural Equation Modeling (SEM) has evolved from a statistical technique for insiders to an established valuable tool for a broad scientific public. This class of analyses has much to offer, but at what price? This paper provides an overview on SEM, its underlying ideas, potential applications and current software. Furthermore, it discusses avoidable pitfalls as well as built-in drawbacks in order to lend support to researchers in deciding whether or not SEM should be integrated into their research tools. Commented findings of an internet survey give a “State of the Union Address” on SEM users and usage. Which kinds of models are preferred? Which software is favoured in current psychological research? In order to assist the reader on his first steps, a SEM first-aid kit is included. Typical problems and possible solutions are addressed, helping the reader to get the support he needs. Hence, the paper may assist the novice on the first steps and self-critically reminds the advanced reader of the limitations of Structural Equation Modeling


title: Trajectory planning for a quadrotor helicopter

abstract: A simple direct method able to generate time-optimal trajectories for a micro quadrotor helicopter is presented. It is based on modeling the quadrotor trajectory as a composition of a parametric function P(lambda) defining the quadrotor path, and a monotonically increasing function lambda(t), specifying the motion on this path. The optimal evolutions of P(lambda) and lambda(t), which are approximated using B-spline functions, are found using a nonlinear optimization technique. The proposed method accounts for the most important constraints inherent to the system behavior, such as underactuation, obstacles avoidance and limits on actuator torques and speeds.


title: Learning a CNN-based End-to-End Controller for a Formula SAE Racecar

abstract: We present a set of CNN-based end-to-end models for controls of a Formula SAE racecar, along with various benchmarking and visualization tools to understand model performance. We tackled three main problems in the context of cone-delineated racetrack driving: (1) discretized steering, which translates a first-person frame along to the track to a predicted steering direction. (2) real-value steering, which translates a frame view to a real-value steering angle, and (3) a network design for predicting brake and throttle. We demonstrate high accuracy on our discretization task, low theoretical testing errors with our model for real-value steering, and a starting point for future work regarding a controller for our vehicle's brake and throttle. Timing benchmarks suggests that the networks we propose have the latency and throughput required for real-time controllers, when run on GPU-enabled hardware.


title: Query Expansion with Enriched User Profiles for Personalized Search Utilizing Folksonomy Data

abstract: Query expansion has been widely adopted in Web search as a way of tackling the ambiguity of queries. Personalized search utilizing folksonomy data has demonstrated an extreme vocabulary mismatch problem that requires even more effective query expansion methods. Co-occurrence statistics, tag-tag relationships, and semantic matching approaches are among those favored by previous research. However, user profiles which only contain a user's past annotation information may not be enough to support the selection of expansion terms, especially for users with limited previous activity with the system. We propose a novel model to construct enriched user profiles with the help of an external corpus for personalized query expansion. Our model integrates the current state-of-the-art text representation learning framework, known as word embeddings, with topic models in two groups of pseudo-aligned documents. Based on user profiles, we build two novel query expansion techniques. These two techniques are based on topical weights-enhanced word embeddings, and the topical relevance between the query and the terms inside a user profile, respectively. The results of an in-depth experimental evaluation, performed on two real-world datasets using different external corpora, show that our approach outperforms traditional techniques, including existing non-personalized and personalized query expansion methods.


abstract: Attention-deficit hyperactivity disorder (ADHD) is a disorder of inattention, impulsivity, and hyperactivity that affects 8-12% of children worldwide. Although the rate of ADHD falls with age, at least half of children with the disorder will have impairing symptoms in adulthood. Twin, adoption, and molecular genetic studies show ADHD to be highly heritable, and other findings have recorded obstetric complications and psychosocial adversity as predisposing risk factors. Converging evidence from animal and human studies implicates the dysregulation of frontal-subcortical-cerebellar catecholaminergic circuits in the pathophysiology of ADHD, and molecular imaging studies suggest that abnormalities of the dopamine transporter lead to impaired neurotransmission. Studies during the past decade have shown the safety and effectiveness of new non-stimulant drugs and long-acting formulations of methylphenidate and amfetamine. Other investigations have also clarified the appropriate role of targeted psychosocial treatments in the context of ongoing pharmacotherapy.


title: Psychological Stressors, Overview*

abstract: A wide variety of circumstances and conditions can be subsumed within the general class of psychological stressors. Historical perspectives and conceptual considerations are reviewed, along with important and challenging issues for defining and assessing psychological stressors. Characteristics of psychological stressors and individual differences in susceptibility to psychological stressors are also discussed.


title: Core and Peripheral Criteria of Video Game Addiction in the Game Addiction Scale for Adolescents

abstract: Abstract Assessment of video game addiction often involves measurement of peripheral criteria that indicate high engagement with games, and core criteria that indicate problematic use of games. A survey of the Norwegian population aged 16–74 years (N=10,081, response rate 43.6%) was carried out in 2013, which included the Gaming Addiction Scale for Adolescents (GAS). Confirmatory factor analysis showed that a two-factor structure, which separated peripheral criteria from core criteria, fitted the data better (CFI=0.963; RMSEA=0.058) compared to the original one-factor solution where all items are determined to load only on one factor (CFI=0.905, RMSEA=0.089). This was also found when we analyzed men aged ≤33 years, men aged >33 years, women aged ≤33 years, and women aged >33 years separately. This indicates that the GAS measures both engagement and problems related to video games. Multi-group measurement invariance testing showed that the factor structure was valid in all four groups (configural invarianc...


title: Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning

abstract: This paper gives an overview of the majorization-minimization (MM) algorithmic framework, which can provide guidance in deriving problem-driven algorithms with low computational cost. A general introduction of MM is presented, including a description of the basic principle and its convergence results. The extensions, acceleration schemes, and connection to other algorithmic frameworks are also covered. To bridge the gap between theory and practice, upperbounds for a large number of basic functions, derived based on the Taylor expansion, convexity, and special inequalities, are provided as ingredients for constructing surrogate functions. With the pre-requisites established, the way of applying MM to solving specific problems is elaborated by a wide range of applications in signal processing, communications, and machine learning.


title: Is mitral E/E' ratio a reliable predictor of left ventricular diastolic pressures in patients without heart failure?

abstract: Conflicting evidence exists as to whether the mitral E/Eratio can be a reliable predictor of the left ventricular end- diastolic pressure (LVEDP). Our aim was to assess the value of the mitral E/Eratio for the estimation of left ven- tricular diastolic pressures (LVDP) in patients without heart failure (HF). Methods and results Echo-Doppler examination and left heart catheterization were carried out in 100 consecutive patients to assess the correlation between echo-Doppler parameters and the LVDP. The E/A ratio showed the best correlation with the pre-a LVDP and the LVEDP, whereas septal and mean E/Eratios were significantly correlated with pre-a LVDP but not with the LVEDP. No difference in the echo-Doppler parameters was found between patients with normal and elevated LVEDP. Mitral E/Eratio was significantly higher in patients with an ejection fraction (EF) ,50% com- pared with those with the EF ≥50% and in patients with a dilated left ventricular (LV) compared with those with a normal LV. No significant difference in mean LVEDP was found among the three groups with E/Eratios of ,8, 8-15, and .15. The best cut-off values identified by receiver operating characteristic curve analysis for septal, lateral, and mean E/Ehad sensitivities of 53, 68, and 54% and specificities of 66, 51, and 69% for identifying a .15 mmHg LVEDP. Conclusion In patients without HF mitral E/Eratio is influenced by EF and LV volumes and is better correlated with the pre-a LVDP than with the LVEDP. The suboptimal sensitivity and specificity of E/Efor predicting increased LVDP suggest that the mitral E/Eratio is of limited clinical value in patients without HF.


title: Data parallel algorithms

abstract: Parallel computers with tens of thousands of processors are typically programmed in a data parallel style, as opposed to the control parallel style used in multiprocessing. The success of data parallel algorithms—even on problems that at first glance seem inherently serial—suggests that this style of programming has much wider applicability than was previously thought.


Design-for-Assembly (DFA) by Reverse Engineering


title: Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

abstract: Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS) to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To address these challenges, we propose a new fault diagnosis scheme which is composed of multiple extreme learning machines (ELM) in a hierarchical structure, where a forwarding list of ELM layers is concatenated and each of them is processed independently for its corresponding role. The framework enables both representational feature learning and fault classification. The multi-layered ELM based representational learning covers functions including data preprocessing, feature extraction and dimension reduction. An ELM based autoencoder is trained to generate a hidden layer output weight matrix, which is then used to transform the input dataset into a new feature representation. Compared with the traditional feature extraction methods which may empirically wipe off some “insignificant’ feature information that in fact conveys certain undiscovered important knowledge, the introduced representational learning method could overcome the loss of information content. The computed output weight matrix projects the high dimensional input vector into a compressed and orthogonally weighted distribution. The last single layer of ELM is applied for fault classification. Unlike the greedy layer wise learning method adopted in back propagation based deep learning (DL), the proposed framework does not need iterative fine-tuning of parameters. To evaluate its experimental performance, comparison tests are carried out on a wind turbine generator simulator. The results show that the proposed diagnostic framework achieves the best performance among the compared approaches in terms of accuracy and efficiency in multiple faults detection of wind turbines.


title: Emerging attacks on VANET security based on GPS Time Spoofing

abstract: Car2X communication is about to enter the mass market in upcoming years. So far all realization proposals heavily depend on the global positioning system for providing location information and time synchronization. However, studies on security impact of this kind of data input have focused on the possibility to spoof location information. In contrast, attacks on time synchronization have not received much attention so far. Thus, an analysis of the attack potential on vehicular ad-hoc network (VANET) realizations in regard to spoofed time information is provided in this work. Thereby, we show that this kind of attack allows for severe denial of service attacks. Moreover, by such attacks one can violate the non-repudiation feature of the security system by offering the possibility to misuse authentication features. Additionally, a sybil attack can be performed and reliability of the basic data sets of time and position inside VANET messages is highly questionable considering the outlined attacks. Mechanisms to avoid or limit the impact of outlined security flaws are discussed. An evaluation of the possibility to carry out the described attacks in practice using a current Car2X hardware solution is provided.


title: Effect of glitches against masked AES S-box implementation and countermeasure

abstract: Masking of gates is one of the most popular techniques to prevent differential power analysis (DPA) of AES algorithm. It has been shown that the logic circuits used in the implementation of cryptographic algorithms leak side-channel information inspite of masking, which can be exploited, in differential power attacks. The phenomenon in CMOS circuits responsible for the leakage of masked circuits is known as glitching. Motivated by this fact, the authors analyse the effect of glitches in CMOS circuits against masked implementation of the AES S-box. The authors explicitly demonstrate that glitches do not affect always. There exists a relation between combinational path delay of the circuit and timing difference of input vectors to the circuit, which has a bearance on the amount of information leaked by the masked gates. A balanced masked S-box circuit is proposed where the inputs are synchronised by sequential components. Detailed SPICE results are shown to support the claim that the modifications indeed reduce the vulnerability of the masked AES S-box against DPA attacks.


title: Creating Human-like Autonomous Players in Real-time First Person Shooter Computer Games

abstract: This paper illustrates how we create a software agent by employing FALCON, a self-organizing neural network that performs reinforcement learning, to play a well-known first person shooter computer game known as Unreal Tournament 2004. Through interacting with the game environment and its opponents, our agent learns in real-time without any human intervention. Our agent bot participated in the 2K Bot Prize competition, similar to the Turing test for intelligent agents, wherein human judges were tasked to identify whether their opponents in the game were human players or virtual agents. To perform well in the competition, an agent must act like human and be able to adapt to some changes made to the game. Although our agent did not emerge top in terms of humanlike, the overall performance of our agent was encouraging as it acquired the highest game score while staying convincing to be human-like in some judges’ opinions.


title: Can hardware performance counters be trusted?

abstract: When creating architectural tools, it is essential to know whether the generated results make sense. Comparing a toolpsilas outputs against hardware performance counters on an actual machine is a common means of executing a quick sanity check. If the results do not match, this can indicate problems with the tool, unknown interactions with the benchmarks being investigated, or even unexpected behavior of the real hardware. To make future analyses of this type easier, we explore the behavior of the SPEC benchmarks with both dynamic binary instrumentation (DBI) tools and hardware counters. We collect retired instruction performance counter data from the full SPEC CPU 2000 and 2006 benchmark suites on nine different implementations of the times86 architecture. When run with no special preparation, hardware counters have a coefficient of variation of up to 1.07%. After analyzing results in depth, we find that minor changes to the experimental setup reduce observed errors to less than 0.002% for all benchmarks. The fact that subtle changes in how experiments are conducted can largely impact observed results is unexpected, and it is important that researchers using these counters be aware of the issues involved.


title: Examining the anatomy of last-mile distribution in e-commerce omnichannel retailing: A supply network configuration approach

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to investigate the interplay between configuration dimensions (network structure, network flow, relationship governance, and service architecture) of last-mile supply networks (LMSN) and the underlying mechanisms influencing omnichannel performance. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: Based on mixed-method design incorporating a multiple embedded case study, mapping, survey, and archival records, this research involved undertaking in-depth within- and cross-case analyses to examine seven LMSNs, employing a configuration approach. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The existing literature in the operations management (OM) field was shown to provide limited understanding of LMSNs within the emerging omnichannel context. Case results suggest that particular configurations have intrinsic capabilities, and that these directly influence omnichannel performance. The study further proposes a taxonomy of LMSNs comprising six forms, with two hybrids, supporting the notion of equifinality in configuration theory. Propositions are developed to further explore interdependencies between configurational attributes, refining the relationship between LMSN types, and factors influencing omnichannel performance. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: The findings provide retailers with a set of design parameters for the (re)configuration of LMSNs and facilitate performance evaluation using the concept of fit between configurational attributes. The developed model sheds light on the consequential effects when certain configurational attributes are altered, preempting managerial attention. Given the global trend in urbanization, improved LMSN performance would have positive societal impacts in terms of service and resource efficiency. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This is one of the first studies in the OM field to critically analyze LMSNs and their behaviors in omnichannel retailing. Additionally, the paper offers several important avenues for future research.


title: Intermittent fasting dissociates beneficial effects of dietary restriction on glucose metabolism and neuronal resistance to injury from calorie intake

abstract: Dietary restriction has been shown to have several health benefits including increased insulin sensitivity, stress resistance, reduced morbidity, and increased life span. The mechanism remains unknown, but the need for a long-term reduction in caloric intake to achieve these benefits has been assumed. We report that when C57BL/6 mice are maintained on an intermittent fasting (alternate-day fasting) dietary-restriction regimen their overall food intake is not decreased and their body weight is maintained. Nevertheless, intermittent fasting resulted in beneficial effects that met or exceeded those of caloric restriction including reduced serum glucose and insulin levels and increased resistance of neurons in the brain to excitotoxic stress. Intermittent fasting therefore has beneficial effects on glucose regulation and neuronal resistance to injury in these mice that are independent of caloric intake.


title: Classification of labour contractions using KNN classifier

abstract: Uterine contractions of desired strength and frequency are one of the important requirements for the smooth progress of cervical dilation and delivery of baby. Presently, Tocograph is used to monitor the strength, duration and frequency of uterine contractions. One of the major drawbacks of Tocography is subjectivity in interpretation. Therefore, there is a need for an objective method for classification of uterine contractions. In this paper, a K Nearest Neighbor based classification method is presented for automated classification of different types of uterine contractions during labour. For the study, CTG signals from Physionet database were used. The signals were annotated by an expert Doctor in three categories: mild (n=33), moderate (n=64) and strong (n=96). After processing of signals, eight features were extracted, followed by implementation of an appropriate classifier. K Nearest Neighbor and Rule based K Nearest Neighbor classifiers were used to classify the uterine contractions into mild, moderate and strong. We achieved an accuracy of 90.91%, 85% and 85.71% for classification using Rule based K Nearest Neighbor classification method.


title: SNAP‐tag Based Proteomics Approach for the Study of the Retrograde Route

abstract: Proteomics is a powerful technique for protein identification at large scales. A number of proteomics approaches have been developed to study the steady state composition of intracellular compartments. Here, we report a novel vectorial proteomics strategy to identify plasma membrane proteins that undergo retrograde transport to the trans-Golgi network (TGN). This strategy is based on the covalent modification of the plasma membrane proteome with a membrane impermeable benzylguanine derivative. Benzylguanine-tagged plasma membrane proteins that are subsequently targeted to the retrograde route are covalently captured by a TGN-localized SNAP-tagged fusion protein, which allows for their identification. The approach was validated step-by-step using a well explored retrograde cargo protein, the B-subunit of Shiga toxin. It was then extended to the proteomics format. Among other hits we found one of the historically first identified cargo proteins that undergo retrograde transport, which further validated our approach. Most of the other hits were kinases, receptors or transporters. In conclusion, we have pioneered a vectorial proteomics approach that complements traditional methods for the study of retrograde protein trafficking. This approach is of generic nature and could in principle be extended to other endocytic pathways.


title: An Injectable 64 nW ECG Mixed-Signal SoC in 65 nm for Arrhythmia Monitoring

abstract: A syringe-implantable electrocardiography (ECG) monitoring system is proposed. The noise optimization and circuit techniques in the analog front-end (AFE) enable 31 nA current consumption while a minimum energy computation approach in the digital back-end reduces digital energy consumption by 40%. The proposed SoC is fabricated in 65 nm CMOS and consumes 64 nW while successfully detecting atrial fibrillation arrhythmia and storing the irregular waveform in memory in experiments using an ECG simulator, a live sheep, and an isolated sheep heart.


title: Cut the Crap: Language — Risks and Relationships in Systemic Therapy and Supervision

abstract: This article concerns the use of language in both therapy and supervision, and how it contributes to the necessary risk-taking that will be a part of therapists' and supervisors' repertoires. The focus will be on my own examples in both roles, and will refer to both successes and failures, while questioning the usefulness of either concept. Within a postmodern paradigm, and influenced by thinkers including Foucault and Bakhtin as well as systemic clinicians, I will examine the use of language (including in cross-cultural settings where risks can sometimes be amplified) and of humour and self while exploring the meanings of identity. I contend that all relationships contain some element of risk and the ways in which we create a safe enough context, through the uses of language as well as nonlinguistic means, are the crucial elements in our endeavours as supervisors and therapists. I favour the idea that therapy is more of an art than a science and that this is most exemplified in the graceful use of language by which we strive to engage rather than alienate people. The article may be considered by some as being useful advice, and by others as a risk too far, such is the controversial nature of language, which highlights the importance of relationships and context.


title: A Self-Improving Convolution Neural Network for the Classification of Hyperspectral Data

abstract: In this letter, a self-improving convolutional neural network (CNN) based method is proposed for the classification of hyperspectral data. This approach solves the so-called curse of dimensionality and the lack of available training samples by iteratively selecting the most informative bands suitable for the designed network via fractional order Darwinian particle swarm optimization. The selected bands are then fed to the classification system to produce the final classification map. Experimental results have been conducted with two well-known hyperspectral data sets: Indian Pines and Pavia University. Results indicate that the proposed approach significantly improves a CNN-based classification method in terms of classification accuracy. In addition, this letter uses the concept of dither for the first time in the remote sensing community to tackle overfitting.


Hair Mechanical Properties Depending on Age and Origin


title: Recommendations in location-based social networks: a survey

abstract: Recent advances in localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related contents, such as geo-tagged photos and notes. We refer to these social networks as location-based social networks (LBSNs). Location data bridges the gap between the physical and digital worlds and enables a deeper understanding of users' preferences and behavior. This addition of vast geo-spatial datasets has stimulated research into novel recommender systems that seek to facilitate users' travels and social interactions. In this paper, we offer a systematic review of this research, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges that location brings to recommender systems for LBSNs. We present a comprehensive survey analyzing 1) the data source used, 2) the methodology employed to generate a recommendation, and 3) the objective of the recommendation. We propose three taxonomies that partition the recommender systems according to the properties listed above. First, we categorize the recommender systems by the objective of the recommendation, which can include locations, users, activities, or social media. Second, we categorize the recommender systems by the methodologies employed, including content-based, link analysis-based, and collaborative filtering-based methodologies. Third, we categorize the systems by the data sources used, including user profiles, user online histories, and user location histories. For each category, we summarize the goals and contributions of each system and highlight the representative research effort. Further, we provide comparative analysis of the recommender systems within each category. Finally, we discuss the available data-sets and the popular methods used to evaluate the performance of recommender systems. Finally, we point out promising research topics for future work. This article presents a panorama of the recommender systems in location-based social networks with a balanced depth, facilitating research into this important research theme.


title: Self-Substrate-Triggered Technique to Enhance Turn-On Uniformity of Multi-Finger ESD Protection Devices

abstract: A novel self-substrate-triggered (SST) technique is proposed to solve the nonuniform turn-on issue of the multi-finger GGNMOS for ESD protection. The first turned-on center finger is used to trigger on all fingers in the GGNMOS structure with self-substrate-triggered technique. So, the turn-on uniformity and ESD robustness of GGNMOS can be greatly improved by the new proposed self-substrate-triggered technique.


title: Flexible value structures in banking

abstract: How service-oriented architectures can help achieve the business objectives of the transformation process in banking.


title: Accurate, Dense, and Robust Multiview Stereopsis

abstract: This paper proposes a novel algorithm for multiview stereopsis that outputs a dense set of small rectangular patches covering the surfaces visible in the images. Stereopsis is implemented as a match, expand, and filter procedure, starting from a sparse set of matched keypoints, and repeatedly expanding these before using visibility constraints to filter away false matches. The keys to the performance of the proposed algorithm are effective techniques for enforcing local photometric consistency and global visibility constraints. Simple but effective methods are also proposed to turn the resulting patch model into a mesh which can be further refined by an algorithm that enforces both photometric consistency and regularization constraints. The proposed approach automatically detects and discards outliers and obstacles and does not require any initialization in the form of a visual hull, a bounding box, or valid depth ranges. We have tested our algorithm on various data sets including objects with fine surface details, deep concavities, and thin structures, outdoor scenes observed from a restricted set of viewpoints, and "crowded" scenes where moving obstacles appear in front of a static structure of interest. A quantitative evaluation on the Middlebury benchmark shows that the proposed method outperforms all others submitted so far for four out of the six data sets.


title: The palliative prognostic score and survival in patients with advanced solid tumors receiving chemotherapy

abstract: PURPOSE ::: To evaluate the accuracy of the Palliative Prognostic Score (PaP score) in selecting metastatic gastrointestinal or nonsmall-cell lung cancer patients candidate to palliative chemotherapy. ::: ::: ::: MATERIALS AND METHODS ::: The PaP score was calculated in 173 patients with advanced, pretreated gastrointestinal or nonsmall-cell lung cancer before starting a further line of chemotherapy with palliative aim. Symptom distress score was calculated using the Edmonton Symptom Assessment System (ESAS) before every course of chemotherapy. Univariate analysis of survival was performed using the logrank test; multivariate analysis was performed using the Cox regression model. Symptom distress scores were compared using multivariate analysis of variance test for repeated measures, and overall symptom distress score was compared using analysis of variance test for repeated measures. ::: ::: ::: RESULTS ::: Overall median survival was 26 weeks; in PaP score class A it was 32 weeks, and in class B 8 weeks (p < 0.0001). No patient was classified in class C. The two-class PaP score resulted in an independent prognostic factor (p = 0.022), as well as Karnofsky performance status (p = 0.002) and colorectal cancer (p = 0.017). A trend towards worsening of symptom distress was observed in the entire population and in class A. The high number of missed data did not permit an adequate analysis in class B. ::: ::: ::: CONCLUSIONS ::: The PaP score seems to discriminate patients who could benefit by palliative chemotherapy from those who could better benefit by supportive and palliative approach. However, the data are insufficient to validate the use of the PaP score in patients to be treated with palliative chemotherapy, and further trials should be planned to assess its ability to improve the quality of care in oncology and the appropriateness in the choice of palliative chemotherapy.


title: Sewing interest in E-textiles: analyzing making from a gendered perspective

abstract: In this paper we explore the appropriateness of e-textiles for teaching programming to mixed gender groups ages 8-12, allowing children to construct maker identities around technology. Our findings demonstrate the potential of e-textiles to promote girls' and boys' computational literacy, and the required craft and programming skills for making that can disrupt binary gender roles. We argue it allows both girls and boys to demonstrate technical mastery as well as to explore and construct a spectrum of gendered sociotechnical identities that might otherwise be obscured by conventional masculinist attitudes towards technology.


title: Freebase QA: Information Extraction or Semantic Parsing?

abstract: We contrast two seemingly distinct approaches to the task of question answering (QA) using Freebase: one based on information extraction techniques, the other on semantic parsing. Results over the same test-set were collected from two state-ofthe-art, open-source systems, then analyzed in consultation with those systems’ creators. We conclude that the differences between these technologies, both in task performance, and in how they get there, is not significant. This suggests that the semantic parsing community should target answering more compositional open-domain questions that are beyond the reach of more direct information extraction methods.


title: A low-cost, simple, and rapid fabrication method for paper-based microfluidics using wax screen-printing

abstract: Wax screen-printing as a low-cost, simple, and rapid method for fabricating paper-based microfluidic devices (µPADs) is reported here. Solid wax was rubbed through a screen onto paper filters. The printed wax was then melted into the paper to form hydrophobic barriers using only a hot plate. We first studied the relationship between the width of a hydrophobic barrier and the width of the original design line. We also optimized the heating temperature and time and determined the resolution of structures fabricated using this technique. The minimum width of hydrophilic channel and hydrophobic barrier is 650 and 1300 µm, respectively. Next, our fabrication method was compared to a photolithographic method using the reaction between bicinchoninic acid (BCA) and Cu1+ to demonstrate differences in background reactivity. Photolithographically defined channels exhibited a high background while wax printed channels showed a very low background. Finally, the utility of wax screen-printing was demonstrated for the simultaneous determination of glucose and total iron in control human serum samples using an electrochemical method with glucose oxidase and a colorimetric method with 1,10-phenanthroline. This study demonstrates that wax screen-printing is an easy-to-use and inexpensive alternative fabrication method for µPAD, which will be especially useful in developing countries.


title: A randomized algorithm for principal component analysis

abstract: Principal component analysis (PCA) requires the computation of a low-rank approximation to a matrix containing the data being analyzed. In many applications of PCA, the best possible accuracy of any rank-deficient approximation is at most a few digits (measured in the spectral norm, relative to the spectral norm of the matrix being approximated). In such circumstances, efficient algorithms have not come with guarantees of good accuracy, unless one or both dimensions of the matrix being approximated are small. We describe an efficient algorithm for the low-rank approximation of matrices that produces accuracy very close to the best possible, for matrices of arbitrary sizes. We illustrate our theoretical results via several numerical examples.


title: Characteristics of Backspatter on the Firearm and Shooting Hand-An Experimental Analysis of Close-range Gunshots

abstract: Contact and close-range gunshot injuries can produce bloodstains on the shooting hand and weapon. In this study, backspatter was analyzed in experimental horizontal pistol shots into blood-filled sponges. The shooting distances were set between 0 and 45 cm. With increasing shooting distance, there was a significant decrease in the degree of backspatter. Microspatter could be detected on the weapon and shooting hand up to a shooting distance of 40 cm. They were predominantly located on thumb and index finger as well as the radial dorsal aspects of the hand. Fine spray of microspatter combined with elongated droplets in the shape of exclamation marks was found at contact and 2-cm distance shots. An angular calculation based on the shape of the bloodstains was possible up to a shooting distance of 2 cm. No bloodstains were detected beyond 45 cm. Language: en


Detecting Android Malware By Using A Machine Learning Ensemble Method


title: Single-image deraining using an adaptive nonlocal means filter

abstract: An adaptive rain streak removal algorithm for a single image is proposed in this work. We observe that a typical rain streak has an elongated elliptical shape with a vertical orientation. Thus, we first detect rain streak regions by analyzing the rotation angle and the aspect ratio of the elliptical kernel at each pixel location. We then perform the nonlocal means filtering on the detected rain streak regions by selecting nonlocal neighbor pixels and their weights adaptively. Experimental results demonstrate that the proposed algorithm removes rain streaks more efficiently and provides higher restored image qualities than conventional algorithms.


title: Turning telecommunications call details to churn prediction: a data mining approach

abstract: Abstract As deregulation, new technologies, and new competitors open up the mobile telecommunications industry, churn prediction and management has become of great concern to mobile service providers. A mobile service provider wishing to retain its subscribers needs to be able to predict which of them may be at-risk of changing services and will make those subscribers the focus of customer retention efforts. In response to the limitations of existing churn-prediction systems and the unavailability of customer demographics in the mobile telecommunications provider investigated, we propose, design, and experimentally evaluate a churn-prediction technique that predicts churning from subscriber contractual information and call pattern changes extracted from call details. This proposed technique is capable of identifying potential churners at the contract level for a specific prediction time-period. In addition, the proposed technique incorporates the multi-classifier class-combiner approach to address the challenge of a highly skewed class distribution between churners and non-churners. The empirical evaluation results suggest that the proposed call-behavior-based churn-prediction technique exhibits satisfactory predictive effectiveness when more recent call details are employed for the churn prediction model construction. Furthermore, the proposed technique is able to demonstrate satisfactory or reasonable predictive power within the one-month interval between model construction and churn prediction. Using a previous demographics-based churn-prediction system as a reference, the lift factors attained by our proposed technique appear largely satisfactory.


title: Poverty, development, and environment

abstract: This paper examines the complex relationship that exists between poverty and natural resource degradation in developing countries. The rural poor are often concentrated in fragile, or less favorable, environmental areas. Consequently, their livelihoods can be intimately dependent on natural resource use and ecosystem services. The relationship between poverty and natural resource degradation may depend on a complex range of choices and tradeoffs available to the poor, which in the absence of capital, labor, and land markets, is affected by their access to outside employment and any natural resource endowments. The paper develops a poverty–environment model to characterize some of these linkages, and concludes by discussing policy implications and avenues for further research.


title: Circularly polarized hemispherical antennas for telemetry and telecommand applications in satellite communication

abstract: This study describes about Ku-band antennas with circular polarization in hemispherical radiation pattern for telemetry and telecommand applications of satellite communication. The first section of antenna contains a septum polarizer structure, which converts linear-polarization to circular-polarizations (left-hand and right-hand). This structure feeds a circular aperture waveguide, which is the second part. The axial ratio performance of the antenna is improved by adding some metallic rings with optimized dimensions. Therefore, circular polarization is achieved in wider azimuth and elevation angles. Two different designs are carried out for 11.35–11.8 GHz and 14.3–14.65 GHz. The return losses are greater than 15 dB in the given frequency bands. Besides, the axial ratio of the antenna is under 3 dB throughout 170° at azimuth and elevation, which proves circular polarization property in hemispherical region. The gain at the broadside is almost 9.3 dB, and it is higher than −10 dB throughout 170° angle coverage.


title: In Situ Insights

abstract: When consuming content in applications such as e-readers, word processors, and Web browsers, users often see mentions to topics (or concepts) that attract their attention. In a scenario of significant practical interest, topics are explored in situ, without leaving the context of the application: The user selects a mention of a topic (in the form of continuous text), and the system subsequently recommends references (e.g., Wikipedia concepts) that are relevant in the context of the application. In order to realize this experience, it is necessary to tackle challenges that include: users may select any continuous text, even potentially noisy text for which there is no corresponding reference in the knowledge base; references must be relevant to both the user selection and the text around it; and the real estate available on the application may be constrained, thus limiting the number of results that can be shown. In this paper, we study this novel recommendation task, that we call in situ insights: recommending reference concepts in response to a text selection and its context in-situ of a document consumption application. We first propose a selection-centric context language model and a selection-centric context semantic model to capture user interest. Based on these models, we then measure the quality of a reference concept across three aspects: selection clarity, context coherence, and concept relevance. By leveraging all these aspects, we put forward a machine learning approach to simultaneously decide if a selection is noisy, and filter out low-quality candidate references. In order to quantitatively evaluate our proposed techniques, we construct a test collection based on the simulation of the in situ insights scenario using crowdsourcing in the context of a real-word e-reader application. Our experimental evaluation demonstrates the effectiveness of the proposed techniques.


title: Similarity clustering for data fusion in Wireless Sensor Networks using k-means

abstract: Wireless Sensor Networks consist of a powerful technology for monitoring the physical world. Particularly, in-network data fusion techniques are very important to applications such as target classification and tracking to reduce the communication burden in these constrained networks. However, the efficiency of the solution can be affected by the data correlation among several sensor nodes. Thus, the application of value fusion (for clusters of nodes with correlated measurements) and decision fusion (combining the local decisions of the clusters) is a common strategy. In this work, we propose an algorithm for properly selecting the groups of nodes with correlated measurements. Experiments show that our algorithm is 30% better than a solution that considers only the spatial coherence regions.


title: Embedded real-time multi-baseline stereo

abstract: Dense depth map estimation from stereo cameras has many applications in robotic vision, e.g., obstacle detection, especially when performed in real-time. The range in which depth values can be accurately estimated is usually limited for two-camera stereo setups due to the fixed baseline between the cameras. In addition, two-camera setups suffer from wrong depth estimates caused by local minima in the matching cost functions. Both problems can be alleviated by adding more cameras as this creates multiple baselines of different lengths and since multi-image matching leads to unique minima. However, using more cameras usually comes at an increase in run-time. In this paper, we present a novel embedded system for multi-baseline stereo. By exploiting the parallelization capabilities within FPGAs, we are able to estimate a depth map from multiple cameras in real-time. We show that our approach requires only little more power and weight compared to a two-camera stereo system. At the same time, we show that our system produces significantly better depth maps and is able to handle occlusion of some cameras, resulting in the redundancy typically desired for autonomous vehicles. Our system is small in size and leight-weight and can be employed even on a MAV platform with very strict power, weight, and size requirements.


title: Separating Topology and Geometry in Space Planning

abstract: We are dealing with the problem of space layout planning here. We present an architectural conceptual CAD approach. Starting with design specifications in terms of constraints over spaces, a specific enumeration heuristics leads to a complete set of consistent conceptual design solutions named topological solutions. These topological solutions which do not presume any precise definitive dimension correspond to the sketching step that an architect carries out from the Design specifications on a preliminary design phase in architecture.


title: Towards Evolutional Compression

abstract: Compressing convolutional neural networks (CNNs) is essential for transferring the success of CNNs to a wide variety of applications to mobile devices. In contrast to directly recognizing subtle weights or filters as redundant in a given CNN, this paper presents an evolutionary method to automatically eliminate redundant convolution filters. We represent each compressed network as a binary individual of specific fitness. Then, the population is upgraded at each evolutionary iteration using genetic operations. As a result, an extremely compact CNN is generated using the fittest individual. In this approach, either large or small convolution filters can be redundant, and filters in the compressed network are more distinct. In addition, since the number of filters in each convolutional layer is reduced, the number of filter channels and the size of feature maps are also decreased, naturally improving both the compression and speed-up ratios. Experiments on benchmark deep CNN models suggest the superiority of the proposed algorithm over the state-of-the-art compression methods.