title: 6-DoF object pose from semantic keypoints

abstract: This paper presents a novel approach to estimating the continuous six degree of freedom (6-DoF) pose (3D translation and rotation) of an object from a single RGB image. The approach combines semantic keypoints predicted by a convolutional network (convnet) with a deformable shape model. Unlike prior work, we are agnostic to whether the object is textured or textureless, as the convnet learns the optimal representation from the available training image data. Furthermore, the approach can be applied to instance- and class-based pose recovery. Empirically, we show that the proposed approach can accurately recover the 6-DoF object pose for both instance- and class-based scenarios with a cluttered background. For class-based object pose estimation, state-of-the-art accuracy is shown on the large-scale PASCAL3D+ dataset.


title: Learning to generalize 3D spatial relationships

abstract: This paper presents an approach to learn meaningful spatial relationships in an unsupervised fashion from the distribution of 3D object poses in the real world. Our approach begins by extracting an over-complete set of features to describe the relative geometry of two objects. Each relationship type is modeled using a relevance-weighted distance over this feature space. This effectively ignores irrelevant feature dimensions. Our algorithm RANSEM for determining subsets of data that share a relationship as well as the model to describe each relationship is based on robust sample-based clustering. This approach combines the search for consistent groups of data with the extraction of models that precisely capture the geometry of those groups. An iterative refinement scheme has shown to be an effective approach for finding concepts of differing degrees of geometric specificity. Our results show that the models learned by our approach correlate strongly with the English labels that have been given by a human annotator to a set of validation data drawn from the NYUv2 real-world Kinect dataset, demonstrating that these concepts can be automatically acquired given sufficient experience. Additionally, the results of our method significantly out-perform K-means, a standard baseline for unsupervised cluster extraction.


title: A Hybrid Wired/Wireless Networking Infrastructure for Greenhouse Management

abstract: In this paper, the problems related to the management of a farm made up of several greenhouses are discussed. The management of this kind of farms requires data acquisition in each greenhouse and their transfer to a control unit which is usually located in a control room, separated from the production area. At present, the data transfer between the greenhouses and the control system is mainly provided by a suitable wired communication system, such as a fieldbus. In such contexts, even though the replacement of the wired system with a fully wireless one can appear very attractive, a fully wireless system can introduce some disadvantages. A solution based on a hybrid wired/wireless network, where Controller Area Network and ZigBee protocols are used, is presented along with all the related problems that this integration involves. In particular, in order to integrate at the Data Link Layer the wireless section with the wired one, a suitable multiprotocol bridge has been implemented. Moreover, at the Application Layer, porting of Smart Distributed System services on ZigBee, called ZSDS, allows one to access the network resources independently from the network segment they are connected to.


title: Moving Right Along: A Computational Model of Metaphoric Reasoning about Events

abstract: This paper describes the results of an implemented computational model that cashes out the belief that reasoning about abstract events and actions relies on metaphoric projections of embodied primitives. The specific task addressed is the interpretation of simple causal narratives taken from newspaper articles in the domains of Politics and Economics. When presented with a surface-parsed version of these narratives as input, the system described is able to generate commonsense inferences consistent with the input.


title: Tuning of PID controller based on Fruit Fly Optimization Algorithm

abstract: The Proportional — Integral — Derivative (PID) controllers are one of the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. PID tuning is the key issue in the design of PID controllers and most of the tuning processes are implemented manually resulting in difficulty and time consuming. To enhance the capabilities of traditional PID parameters tuning techniques, modern heuristics approaches, such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are employed recent years. In this paper, a novel tuning method based on Fruit Fly Optimization Algorithm (FOA) is proposed to optimize PID controller parameters. Each fruit fly's position represents a candidate solution for PID parameters. When the fruit fly swarm flies towards one location, it is treated as the evolution of each iterative swarm. After hundreds of iteration, the tuning results — the best PID controller parameters can be obtained. The main advantages of the proposed method include ease of implementation, stable convergence characteristic, large searching range, ease of transformation of such concept into program code and ease of understanding. Simulation results demonstrate that the FOA-Based optimized PID (FOA — PID) controller is with the capability of providing satisfactory closed — loop performance.


title: Non-Projective Dependency Parsing Using Spanning Tree Algorithms

abstract: We formalize weighted dependency parsing as searching for maximum spanning trees (MSTs) in directed graphs. Using this representation, the parsing algorithm of Eisner (1996) is sufficient for searching over all projective trees in O(n3) time. More surprisingly, the representation is extended naturally to non-projective parsing using Chu-Liu-Edmonds (Chu and Liu, 1965; Edmonds, 1967) MST algorithm, yielding an O(n2) parsing algorithm. We evaluate these methods on the Prague Dependency Treebank using online large-margin learning techniques (Crammer et al., 2003; McDonald et al., 2005) and show that MST parsing increases efficiency and accuracy for languages with non-projective dependencies.


title: Three tensions in participatory design for inclusion

abstract: One ideal of Participatory Design (PD) is active involvement by all stakeholders as co-designers. However, when PD is applied to real projects, certain compromises are unavoidable, no matter what stakeholders are involved. With this paper we want to shed light on some of the challenges in implementing "true" PD in the case of designing with children, in particular children with severe disabilities. We do this work to better understand challenges in an ongoing project, RHYME, and by doing so we hope to provide insight and inspiration for others.


title: Automated Verification of Neural Networks: Advances, Challenges and Perspectives

abstract: Neural networks are one of the most investigated and widely used techniques in Machine Learning. In spite of their success, they still find limited application in safety- and security-related contexts, wherein assurance about networks' performances must be provided. In the recent past, automated reasoning techniques have been proposed by several researchers to close the gap between neural networks and applications requiring formal guarantees about their behavior. In this work, we propose a primer of such techniques and a comprehensive categorization of existing approaches for the automated verification of neural networks. A discussion about current limitations and directions for future investigation is provided to foster research on this topic at the crossroads of Machine Learning and Automated Reasoning.


title: Modeling and simulation of cyberattacks for resilient cyber-physical systems

abstract: Securing cyber-physical systems (CPS) is an active area of research. For example, manufacturing and military CPS have been traditionally designed without an emphasis on cybersecurity. This paper presents a model-based secure-by-design approach for modeling and simulating the cybersecurity aspects of CPS. We demonstrate our systematic approach by modeling several classes of cyberattacks that may affect the normal operations of CPS, and evaluate the impact of these attacks on the system through the use of simulation. We follow a functional modeling approach that may reduce the engineering effort and increase the quality of the developed system, while also increasing the resilience of the system when exposed to cyberattacks.


title: Multi-Hop LoRa Networks Enabled by Concurrent Transmission

abstract: In this paper, we strive to construct an efficient multi-hop network based on the sub-GHz low-power wide-area technology. Specifically, we investigate the combination of LoRa, a physical-layer standard that can provide several-kilometer outdoor coverage, and concurrent transmission (CT), a recently proposed multi-hop protocol that can significantly improve the network efficiency. The main contributions of this paper are threefold. 1) Since the CT enhances the network efficiency by allowing synchronized packet collisions, the performance of the physical-layer receiver under such packet collisions needs to be carefully examined to ensure the network reliability. We first extensively evaluate the LoRa receiver performance under CT to verify that LoRa is compatible to CT. Specifically, we find that, due to the time-domain and frequency-domain energy spreading effect, LoRa is robust to the packet collisions resulting from CT. 2) We further find the receiver performance under CT can be further improved by introducing timing offsets between the relaying packets. In view of this, we propose a timing delay insertion method, the offset-CT method, that adds random timing delay before the packets while preventing the timing offset from diverging over the multi-hop network. 3) We conduct proof-of-concept experiments to demonstrate the feasibility of CT-based LoRa multi-hop network and the performance improvement brought by the proposed offset-CT method.


title: Representation Learning for Grounded Spatial Reasoning

abstract: The interpretation of spatial references is highly contextual, requiring joint inference over both language and the environment. We consider the task of spatial reasoning in a simulated environment, where an agent can act and receive rewards. The proposed model learns a representation of the world steered by instruction text. This design allows for precise alignment of local neighborhoods with corresponding verbalizations, while also handling global references in the instructions. We train our model with reinforcement learning using a variant of generalized value iteration. The model outperforms state-of-the-art approaches on several metrics, yielding a 45% reduction in goal localization error.


title: Do rats have orgasms?

abstract: Background : Although humans experience orgasms with a degree of statistical regularity, they remain among the most enigmatic of sexual responses; difficult to define and even more difficult to study empirically. The question of whether animals experience orgasms is hampered by similar lack of definition and the additional necessity of making inferences from behavioral responses. Method : Here we define three behavioral criteria, based on dimensions of the subjective experience of human orgasms described by Mah and Binik, to infer orgasm-like responses (OLRs) in other species: 1) physiological criteria that include pelvic floor and anal muscle contractions that stimulate seminal emission and/or ejaculation in the male, or that stimulate uterine and cervical contractions in the female; 2) short-term behavioral changes that reflect immediate awareness of a pleasurable hedonic reward state during copulation; and 3) long-term behavioral changes that depend on the reward state induced by the OLR, including sexual satiety, the strengthening of patterns of sexual arousal and desire in subsequent copulations, and the generation of conditioned place and partner preferences for contextual and partner-related cues associated with the reward state. We then examine whether physiological and behavioral data from observations of male and female rats during copulation, and in sexually-conditioned place- and partner-preference paradigms, are consistent with these criteria. Results : Both male and female rats display behavioral patterns consistent with OLRs. Conclusions : The ability to infer OLRs in rats offers new possibilities to study the phenomenon in neurobiological and molecular detail, and to provide both comparative and translational perspectives that would be useful for both basic and clinical research. Keywords: sex; pleasure; animals; male; female; genitosensory; ultrasonic vocalizations; conditioned preference; opioids; dopamine (Published: 25 October 2016) Citation: Socioaffective Neuroscience & Psychology 2016, 6 : 31883 - http://dx.doi.org/10.3402/snp.v6.31883 This paper is part of the Special Issue: Orgasm: Neurophysiological, psychological, and evolutionary perspectives . More papers from this issue can be found at www.socioaffectiveneuroscipsychol.net


title: Experimental Design for Learning Causal Graphs with Latent Variables.

abstract: We consider the problem of learning causal structures with latent variables using interventions. Our objective is not only to learn the causal graph between the observed variables, but to locate unobserved variables that could confound the relationship between observables. Our approach is stage-wise: We first learn the observable graph, i.e., the induced graph between observable variables. Next we learn the existence and location of the latent variables given the observable graph. We propose an efficient randomized algorithm that can learn the observable graph using O(d\log^2 n) interventions where d is the degree of the graph. We further propose an efficient deterministic variant which uses O(log n + l) interventions, where l is the longest directed path in the graph. Next, we propose an algorithm that uses only O(d^2 log n) interventions that can learn the latents between both non-adjacent and adjacent variables. While a naive baseline approach would require O(n^2) interventions, our combined algorithm can learn the causal graph with latents using O(d log^2 n + d^2 log (n)) interventions.


title: Inline DGA Detection with Deep Networks

abstract: Domain generation algorithms (DGAs) automatically generate large numbers of domain names in DNS domain fluxing for the purpose of command-and-control (C&C) communication. DGAs are immune to static prevention methods like blacklisting and sinkholing. Detection of DGAs in a live stream of queries in a DNS server is referred to as inline detection. Most of the previous approaches in the literature on DGA detection either: (i) are based on small synthetic data sets for training, rather than data collected from real traffic or (ii) require contextual information and therefore cannot be used for inline detection. In this work, we overcome these limitations by proposing a novel way to label a large volume of data collected from real traffic as DGA/non-DGA and by using deep learning techniques. Our classifiers can be trained with large amounts of real traffic, rather than small synthetic data sets, and therefore have better performance.


title: Automatic Labelling of Topic Models Using Word Vectors and Letter Trigram Vectors

abstract: The native representation of LDA-style topics is a multinomial distributions over words, which can be time-consuming to interpret directly. As an alternative representation, automatic labelling has been shown to help readers interpret the topics more efficiently. We propose a novel framework for topic labelling using word vectors and letter trigram vectors. We generate labels automatically and propose automatic and human evaluations of our method. First, we use a chunk parser to generate candidate labels, then map topics and candidate labels to word vectors and letter trigram vectors in order to find which candidate label is more semantically related to that topic. A label can be found by calculating the similarity between a topic and its candidate label vectors. Experiments on three common datasets show that not only the labelling method, but also out approach to automatic evaluation is effective.


title: A Novel Switchable Single- and Multifrequency Triple-Slot Antenna for 2.4-GHz Bluetooth, 3.5-GHz WiMax, and 5.8-GHz WLAN

abstract: A novel reconfigurable slot antenna with three switchable frequencies is presented. The uniqueness of this design is that it allows various groups of its frequency resonances to be selected electronically using three switches. Therefore, seven different states are achieved, and so three, two, or one resonances are possible at the same time. By using three sickle-shaped slots in the ground plane and three pairs of p-i-n diodes that are soldered between three metal strips inside the slots and the ground plane, the antenna can support 2.4-GHz Bluetooth and/or 3.5-GHz WiMAX and/or 5.8-GHz WLAN systems. The advantage of this design is that it overcomes the problem of conventional reconfigurable antennas of serving one frequency band at a time. The antenna design, simulation, and measurement results are presented. The antenna shows similar radiation patterns with low cross-polarization levels at all its operating frequencies. The simulated and measured results are in good agreement.


title: Molecular mechanisms of the intracranial aneurysms and their association with the long noncoding ribonucleic acid ANRIL – A review of literature

abstract: Long noncoding ribonucleic acids (RNAs) are important regulators of gene expression. Antisense noncoding RNA in the INK4 locus (ANRIL), which was coded on the Chr9p21.3 loci, participates in the pathogenesis of tumor, coronary artery disease, type 2 diabetes mellitus, and other diseases. A genome-wide association study indicated ANRIL to be a candidate gene that may lead to the development of an intracranial aneurysm (IA) formation. However, the detailed molecular mechanisms are unknown and have not been studied. Through reviewing the molecular mechanisms responsible for the development of IA and the regulation pathway of ANRIL, this paper presents four possible molecular mechanisms that may be responsible for the influence of ANRIL on the development of IAs, that is, cell cycling, Kruppel-like factor 2 (KLF2), caspase recruitment domain family member 8, and retinoid metabolism. ANRIL may become a molecular marker or therapeutic target of IA in the future. To the best of our knowledge, this is the first paper elucidating the molecular linkage between ANRIL and IAs.


title: Exposure to cyberbullying as a bystander: An investigation of desensitization effects among early adolescents

abstract: The purpose of the present study was to examine whether (repeated) exposure to cyberbullying as a bystander has an impact on early adolescents' moral evaluations in terms of a decrease in empathy and a shift towards a more tolerant attitude towards cyberbullying. A two-wave panel study with a 6-month time interval was conducted among a sample of 1412 adolescents aged 10-13. Cross-lagged panel analysis was used to investigate relationships over time between being a bystander of cyberbullying, empathic responsiveness towards distressed others, and the attitude towards cyberbullying, while taking into account involvement in cyberbullying as a victim or a perpetrator. The results indicate a negative relationship between standing by at Time 1 and empathic responsiveness at Time 2. In other words, exposure to cyberbullying as a bystander at Time 1 predicted subsequent lower levels of empathic responsiveness at Time 2. The attitude towards cyberbullying at Time 2 was not influenced by seeing more cyberbullying acts at Time 1. Further implications of the results for prevention and intervention, and for future research are discussed. One out of four adolescents witness cyberbullying at least once in a half year.Exposure to these acts predicts lower levels of empathic responsiveness over time.Exposure does not lead to a more tolerant attitude towards cyberbullying over time.


title: Playable experiences at AIIDE 2014

abstract: AIIDE 2014 is the second AIIDE event that has featured a playable experience track. This paper describes the seven entries that were accepted in the 2014 track, as well as the motivation behind the track and the criteria used to evaluate and accept entries.


Parkinsonism: onset, progression, and mortality


title: The Smart Home Concept : our immediate future

abstract: This general paper aims at presenting the Smart Home concept. In this paper, we will detail a) The Smart Home concept b) The various networks infrastructures specific to the habitat c) Our concepts to model the habitat and to provide the most adapted services to the inhabitants. Contrary to the other projects, we direct our work towards a sensors approach and an ontology modelling of the Smart Home. Our work has the originality to take into account the real heterogeneity of information present in an habitat and use a Service Oriented Approach (SOA). We can say that our paper is a good overview to present what is a Smart Home and which are the necessary hardware and software components to make a Smart Home.


LSTM-Based NeuroCRFs for Named Entity Recognition


title: Editing Description Logic Ontologies with the Protégé OWL Plugin

abstract: The growing interest in the Semantic Web and the Web Ontology Language (OWL) will reveal the potential of Description Logics in industrial projects. The rich semantics of OWL provide powerful reasoning capabilities that help build, maintain and query domain models for many purposes. However, before OWL can unfold its full potential, user-friendly tools with a scalable architecture are required. We present the OWL Plugin, an extension of the Protege ontology development environment, which can be used to define classes and properties, to edit logical class expressions, to invoke reasoners, and to link ontologies into the Semantic Web. We analyze some of the challenges for developers of Description Logic editors, and discuss some of our user interface design decisions.


title: Paper electronics and electronic paper

abstract: The manufacturing, converting and ennobling processes of paper are truly large area and reel-to-reel processes. Here, we describe a project focusing on using the converting and ennobling processes of paper in order to introduce electronic functions onto the paper surface. As key active electronic materials we are using organic molecules and polymers. We develop sensor, communication and display devices on paper and the main application areas are packaging and paper display applications.


title: Mobile Augmented Reality Authoring Tool

abstract: This paper propose a mobile Augmented Reality authoring tool interface that allows users to easily create Augmented Reality(AR) content with marker based and touch based interactions. It is possible to generate a simplified form of marker based AR content with the user interface. As for marker based interaction methods, there is one employing two markers and another using marker occlusion. This suggesting AR authoring tool system is providing another interaction method which is touch based interaction. This mobile AR authoring tool system recognizes user rotate, transfer, zoom-in and zoom-out gestures for virtual object. We seek to provide approaches for the user to actively interact with augmented reality content and ascertain the positive results of this study from the user evaluation of an authoring tool system and interaction.


title: Table of contents recognition for converting PDF documents in e-book formats

abstract: We describe one tool for Table of Content (ToC) identification and recognition from PDF books. This task is part of ongoing research on the development of tools for the semi-automatic conversion of PDF documents in the Epub format that can be read on several E-book devices. Among various sub-tasks, the ToC extraction and recognition is particularly useful for an easy navigation of book contents. The proposed tool first identifies the ToC pages. The bounding boxes of ToC titles in the book body are subsequently found in order to add suitable links in the Epub ToC. The proposed approach is tolerant to discrepancies between the ToC text and the corresponding titles. We evaluated the tool on several open access books edited by University Presses that are partner of the OAPEN EcontentPlus project


title: Oversight mechanisms in local government : a case of Ekurhuleni Metropolitan Municipality in South Africa

abstract: This article aims to enhance the understanding of the separation of powers governance framework in local government. The key objective of this article is to highlight how the Ekurhuleni Metropolitan Municipality established legislative mechanisms to foster greater oversight and separation of powers within their governance structures. It demonstrates that the relevance of the available mechanisms in current legislation can lead to the practical separation of executive and legislative powers as well as sound oversight within the municipality. The article acknowledges the conflation of executive and legislative functions in the municipal council as stipulated in the Constitution of the Republic of South Africa, 1996 section 151(2) which resulted in a lack of clarity of the roles of office bearers including an oversight deficiency. The exploration establishes that the use of mechanisms stipulated by the current legislation can enhance accountability and oversight to ensure checks and balances in the municipality. The article illustrates the importance of the section 79 committees as an oversight mechanism (established in respect of Section 43 of the Local Government: Municipal Structures Act, 1998 of the municipal council in relation to the separation of powers governance framework. The separation of powers governance model was utilised to obviate the concentration of abundant supremacy of the executive.


title: Generalized Methods for the Design of Quasi-Ideal Symmetric and Asymmetric Coupled-Line Sections and Directional Couplers

abstract: Generalized methods for the design of quasi-ideal symmetric and asymmetric coupled-line sections have been proposed. Two cases of coupled-line sections, in which the inductive coupling coefficient is either greater or smaller than the capacitive coupling coefficient, have been separately considered. To compensate for the coupling coefficients' inequality, a combination of lumped capacitors and short coupled-line sections has been introduced into the coupled-line section. The proposed methods allow for designing quasi-ideal coupled-line sections in nearly arbitrarily chosen dielectric media and for arbitrarily chosen coupling. The theoretical analyses have been verified by the design and measurement of three compensated coupled-line directional couplers illustrating application of the proposed methods in different dielectric structures.


title: Compact Routing on Power Law Graphs with Additive Stretch ∗

abstract: We present a universal routing scheme for unweighted, undirected networks that always routes a packet along a path whose length is at most an additive factor of d more than OPT (where OPT is the length of an optimal path), using O(e log2 n)-bit local routing tables and packet addresses, with d and e parameters of the network topology. For power-law random graphs, we demonstrate experimentally that d and e take on small values. The Thorup-Zwick universal multiplicative stretch 3 scheme has recently been suggested for routing on the Internet inter-AS graph; we argue, based on the results in this paper, that it is possible to improve worst-case performance on this graph by directly exploiting its power-law topology.


title: Self-Censorship on Facebook

abstract: We report results from an exploratory analysis examining “last-minute” self-censorship, or content that is filtered after being written, on Facebook. We collected data from 3.9 million users over 17 days and associate self-censorship behavior with features describing users, their social graph, and the interactions between them. Our results indicate that 71% of users exhibited some level of last-minute self-censorship in the time period, and provide specific evidence supporting the theory that a user’s “perceived audience” lies at the heart of the issue: posts are censored more frequently than comments, with status updates and posts directed at groups censored most frequently of all sharing use cases investigated. Furthermore, we find that: people with more boundaries to regulate censor more; males censor more posts than females and censor even more posts with mostly male friends than do females, but censor no more comments than females; people who exercise more control over their audience censor more content; and, users with more politically and age diverse friends censor less, in general.


ntology matching with semantic verification ves


title: Investigating the Effects of Process Variations and System Workloads on Reliability of STT-RAM Caches

abstract: In recent years, STT-RAMs have been proposed as a promising replacement for SRAMs in on-chip caches. Although STT-RAMs benefit from high-density, non-volatility, and low-power characteristics, high rates of read disturbances and write failures are the major reliability problems in STTRAM caches. These disturbance/failure rates are directly affected not only by workload behaviors, but also by process variations. Several studies characterized the reliability of STTRAM caches just for one cell, but vulnerability of STT-RAM caches cannot be directly derived from these models. This paper extrapolates the reliability characteristics of one STTRAM cell presented in previous studies to the vulnerability analysis of STT-RAM caches. To this end, we propose a highlevel framework to investigate the vulnerability of STT-RAM caches affected by the per-cell disturbance/failure rates as well as the workloads behaviors and process variations. This framework is an augmentation of gem5 simulator. The investigation reveals that: 1) the read disturbance rate in a cache varies by 6 orders of magnitude for different workloads, 2) the write failure rate varies by 4 orders of magnitude for different workloads, and 3) the process variations increase the read disturbance and write failure rates by up to 5.8x and 8.9x, respectively.


title: Maps of random walks on complex networks reveal community structure

abstract: To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network—including physics, chemistry, molecular biology, and medicine—information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.


title: Insecurity and Self-Acceptance Portrayed in Samsung Galaxy A8 Advertisement “Lets You Be You”: A Multimodal Analysis

abstract: This study analyzed the issue of insecurity and self-acceptance portrayed in Samsung Galaxy A8 advertisement entitled “Lets You Be You”. The main theory for this study is the theory of multimodal discourse analysis by Paltridge (2012) and the theory of body language by Pease (2004) as the supporting theory. Qualitative approach is applied because this study involves the interpretation of both verbal and nonverbal data. The findings showed how the issue of insecurity is reflected through the reaction of the participants in the video before and after receiving a compliment. Furthermore, the construction of the changing process from insecurity to self-acceptance can be seen from the genuine reaction of the compliment, as well as the change of facial expression throughout the video. Key words : Insecurity, Self-acceptance, Multimodal Discourse Analysis, Advertisement, Samsung


title: 'To play or not to play': A cross-temporal investigation using hedonic and instrumental perspectives to explain user intentions to explore a technology

abstract: The present research extends prior work on the relationship between users and technology by examining users' intention to explore a technology. Drawing on exploration and individual motivation theories, we developed and tested a model examining the effects of hedonic (i.e., personal innovativeness and cognitive absorption) and instrumental (i.e., performance expectancy and image enhancement) factors on individuals' intentions to explore a technology over time. Based on a study of 94 users exposed to a new technology, with measurements taken at two points in time, we found that both instrumental and hedonic factors affect individuals' intentions to explore, but their effects change over time such that as time goes by, the effect of personal innovativeness decreases and performance expectancy increases. In addition to our contributions and implications for research on technology acceptance, we present practical implications both for developers and managers, with a view toward helping the development and deployment of technologies that satisfy the evolution of users' needs over time.


title: Photovoltaic energy production forecast using support vector regression

abstract: Forecasting models for photovoltaic energy production are important tools for managing energy flows. The aim of this study was to accurately predict the energy production of a PV plant in Italy, using a methodology based on support vector machines . The model uses historical data of solar irradiance, environmental temperature and past energy production to predict the PV energy production for the next day with an interval of 15 min. The technique used is based on $$\nu $$?-SVR, a support vector regression model where you can choose the number of support vectors. The forecasts of energy production obtained with the proposed methodology are very accurate, with the $$R^{2}$$R2 coefficient exceeding 90 % . The quality of the predicted values strongly depends on the goodness of the weather forecast, and the $$R^{2}$$R2 value decreases if the predictions of irradiance and temperature are not very accurate.


title: Mixed Precision Training of Convolutional Neural Networks using Integer Operations

abstract: The state-of-the-art (SOTA) for mixed precision training is dominated by variants of low precision floating point operations, and in particular, FP16 accumulating into FP32 Micikevicius et al. (2017). On the other hand, while a lot of research has also happened in the domain of low and mixed-precision Integer training, these works either present results for non-SOTA networks (for instance only AlexNet for ImageNet-1K), or relatively small datasets (like CIFAR-10). In this work, we train state-of-the-art visual understanding neural networks on the ImageNet-1K dataset, with Integer operations on General Purpose (GP) hardware. In particular, we focus on Integer Fused-Multiply-and-Accumulate (FMA) operations which take two pairs of INT16 operands and accumulate results into an INT32 output.We propose a shared exponent representation of tensors and develop a Dynamic Fixed Point (DFP) scheme suitable for common neural network operations. The nuances of developing an efficient integer convolution kernel is examined, including methods to handle overflow of the INT32 accumulator. We implement CNN training for ResNet-50, GoogLeNet-v1, VGG-16 and AlexNet; and these networks achieve or exceed SOTA accuracy within the same number of iterations as their FP32 counterparts without any change in hyper-parameters and with a 1.8X improvement in end-to-end training throughput. To the best of our knowledge these results represent the first INT16 training results on GP hardware for ImageNet-1K dataset using SOTA CNNs and achieve highest reported accuracy using half-precision


title: A Comprehensive Review on Privacy-Preserving Metering in Smart Grid for Billing; Operation; and Rewarding Schemes, with Two Novel Solutions

abstract: In smart grid, the Utility Provider (UP) collects users power measurements' for two main reasons: billing and operation. Billing needs coarse-grained measurements where there are no, or minimal, privacy concerns. On the other hand, operation needs fine-grained measurements which can highly affect users' privacy. Hence, users might be reluctant to participate in operational metering to protect their privacy. To overcome this issue, UP might offer rewards to allure users to report their measurements which endangers their privacy. In this paper, we survey the literature on privacy-preserving metering in smart grid. We propose a new taxonomy to categorize the literature based on whether measurements need to be attributable to users or not. This categorization is highly effective because attribution is one of the main causes of privacy issues. Our survey shows that rewarding schemes and incentive-based demand response are new research topics with limited studies on it. Hence, we propose two privacy-preserving rewarding schemes for operational metering. First, a light-weight solution that utilizes an already established link of trust i.e. Banks. It is based on the assumption that banks will not leak a client account details. This solution is based on hash chains and blind signatures to assure users anonymity with minimal overhead for the UP and the smart meter. The bank in this approach is state-less in which is not required the bank to keep communications states of users and the UP. Second, we proposed another approach that does not rely on or banks or trusted third parties. It is based on Paillier homomorphic encryption and Pedersen commitments. It assumes that the smart meters are organized in a tree structure; which is widely used implementation in the literature. Security analysis for the proposed approaches is presented including unlinkability proof for measurements to users.


title: ROAD AND ROADSIDE FEATURE EXTRACTION USING IMAGERY AND LIDAR DATA FOR TRANSPORTATION OPERATION

abstract: Transportation agencies require up-to-date, reliable, and feasibly acquired information on road geometry and features within proximity to the roads as input for evaluating and prioritizing new or improvement road projects. The information needed for a robust evaluation of road projects includes road centerline, width, and extent together with the average grade, cross-sections, and obstructions near the travelled way. Remote sensing is equipped with a large collection of data and well-established tools for acquiring the information and extracting aforementioned various road features at various levels and scopes. Even with many remote sensing data and methods available for road extraction, transportation operation requires more than the centerlines. Acquiring information that is spatially coherent at the operational level for the entire road system is challenging and needs multiple data sources to be integrated. In the presented study, we established a framework that used data from multiple sources, including one-foot resolution color infrared orthophotos, airborne LiDAR point clouds, and existing spatially non-accurate ancillary road networks. We were able to extract 90.25% of a total of 23.6 miles of road networks together with estimated road width, average grade along the road, and cross sections at specified intervals. Also, we have extracted buildings and vegetation within a predetermined proximity to the extracted road extent. 90.6% of 107 existing buildings were correctly identified with 31% false detection rate.


title: Combining a harmonic-based NMF decomposition with transient analysis for instantaneous percussion separation

abstract: Many recent approaches on musical source separation rely on model-based inference methods that take into account the signal's harmonic structure. To address the particular case of instantaneous percussion separation, we propose a method that combines a harmonic-based decomposition using a Non-negative Matrix Factorization (NMF) algorithm, with the transient analysis of spectral peaks from a single audio frame. The signal model allows the estimation of harmonic and non-harmonic sources. Later, as shown in the evaluation, adding transient peak information improves the Signal-to-Distortion Ratio (SDR). Compared to other existing methods, this approach achieves a comparable performance, being suitable at the same time for low-latency conditions.


title: Vehicle type classification and attribute prediction using multi-task RCNN

abstract: Vehicle classification is an important subject of study due to its significance in a number of areas including law enforcement, traffic surveillance, autonomous navigation, and transportation management. While numerous approaches have been proposed, few studies have been published with regard to the multi-view classification of vehicles captured in real surveillance. In this paper, we consider the multi-view classification of vehicles as an attribute prediction problem with views (rear, front, and side) as attributes. The corresponding multi-task learning is implemented in the Region-based Convolutional Neural Network (RCNN) framework, which classifies vehicle categories (car, truck, bus, and van) and predicts the attributes simultaneously. Experiments on a field-captured vehicle dataset provide satisfactory results, with approximate 83% accuracy for vehicle type classification and over 90% accuracy for attribute prediction.


title: Multimodal Gesture Recognition Using 3-D Convolution and Convolutional LSTM

abstract: Gesture recognition aims to recognize meaningful movements of human bodies, and is of utmost importance in intelligent human–computer/robot interactions. In this paper, we present a multimodal gesture recognition method based on 3-D convolution and convolutional long-short-term-memory (LSTM) networks. The proposed method first learns short-term spatiotemporal features of gestures through the 3-D convolutional neural network, and then learns long-term spatiotemporal features by convolutional LSTM networks based on the extracted short-term spatiotemporal features. In addition, fine-tuning among multimodal data is evaluated, and we find that it can be considered as an optional skill to prevent overfitting when no pre-trained models exist. The proposed method is verified on the ChaLearn LAP large-scale isolated gesture data set (IsoGD) and the Sheffield Kinect gesture (SKIG) data set. The results show that our proposed method can obtain the state-of-the-art recognition accuracy (51.02% on the validation set of IsoGD and 98.89% on SKIG).


title: Self-Integrating Rogowski Coil for High-Impulse Current Measurement

abstract: This paper presents the operating principle, the performance under impulse condition, and the design of a self-integrating Rogowski coil for measuring high-impulse currents. Oscillatory and overdamped unidirectional impulse currents are generated up to 10 kA and measured by different methods, namely, two commercial impulse current transformers, a resistive shunt, and the newly designed self-integrating Rogowski coil. For the resistive method, the voltage drop across the shunt affects the total voltage measured across the test object and causes distortion in the case of simultaneous measurement by an impulse current transformer having a small aperture. The Rogowski coil theory under impulse current conditions is presented. The concept of transmission lines is used, and the line parameters are optimized to achieve a self-integrating mode and avoid spurious effects of reflections and stray capacitances. The output voltage linearity of the designed self-integrating Rogowski coil is checked using different linear and nonlinear loads and coil termination resistances. It is found that at a termination resistance of 1 ?, satisfactory impulse current waveforms are measured by taking the commercial impulse current transformer as a reference signal. Results reveal that the measurement errors for the current peak and front and tail times are ±2%, ?6%, and ? 12%, respectively. Overdamped impulse currents are generated by different generator capacitances, where comparisons between the measured and calculated current waveform parameters have shown good agreement.


title: Recognition without awareness: Encoding and retrieval factors.

abstract: The article reports 4 experiments that explore the notion of recognition without awareness using words as the material. Previous work by Voss and associates has shown that complex visual patterns were correctly selected as targets in a 2-alternative forced-choice (2-AFC) recognition test although participants reported that they were guessing. The present experiments sought to extend this earlier work by having participants study words in different ways and then attempt to recognize the words later in a series of 4-alternative forced-choice (4-AFC) tests, some of which contained no target word. The data of interest are cases in which a target was present and participants stated that they were guessing, yet chose the correct item. This value was greater than p .25 in all conditions of the 4 experiments, demonstrating the phenomenon of recognition without awareness. Whereas Voss and colleagues attributed their findings with kaleidoscope patterns to enhanced processing fluency of perceptual attributes, the main factor associated with different levels of recognition without awareness in the present studies was a variable criterion for the subjective state accompanying selection of the “guess” option, depending on the overall difficulty of the recognition test. We conclude by discussing some implications of the results for the distinction between implicit and explicit memory.


MORE SENSE: MOvie REviews SENtiment analysis boosted with SEmantics


title: Fast 3D recognition and pose using the Viewpoint Feature Histogram

abstract: We present the Viewpoint Feature Histogram (VFH), a descriptor for 3D point cloud data that encodes geometry and viewpoint. We demonstrate experimentally on a set of 60 objects captured with stereo cameras that VFH can be used as a distinctive signature, allowing simultaneous recognition of the object and its pose. The pose is accurate enough for robot manipulation, and the computational cost is low enough for real time operation. VFH was designed to be robust to large surface noise and missing depth information in order to work reliably on stereo data.


title: Audio Recording Location Identification Using Acoustic Environment Signature

abstract: An audio recording is subject to a number of possible distortions and artifacts. Consider, for example, artifacts due to acoustic reverberation and background noise. The acoustic reverberation depends on the shape and the composition of the room, and it causes temporal and spectral smearing of the recorded sound. The background noise, on the other hand, depends on the secondary audio source activities present in the evidentiary recording. Extraction of acoustic cues from an audio recording is an important but challenging task. Temporal changes in the estimated reverberation and background noise can be used for dynamic acoustic environment identification (AEI), audio forensics, and ballistic settings. We describe a statistical technique based on spectral subtraction to estimate the amount of reverberation and nonlinear filtering based on particle filtering to estimate the background noise. The effectiveness of the proposed method is tested using a data set consisting of speech recordings of two human speakers (one male and one female) made in eight acoustic environments using four commercial grade microphones. Performance of the proposed method is evaluated for various experimental settings such as microphone independent, semi- and full-blind AEI, and robustness to MP3 compression. Performance of the proposed framework is also evaluated using Temporal Derivative-based Spectrum and Mel-Cepstrum (TDSM)-based features. Experimental results show that the proposed method improves AEI performance compared with the direct method (i.e., feature vector is extracted from the audio recording directly). In addition, experimental results also show that the proposed scheme is robust to MP3 compression attack.


title: Robust Matrix Elastic Net based Canonical Correlation Analysis: An Effective Algorithm for Multi-View Unsupervised Learning

abstract: This paper presents a robust matrix elastic net based canonical correlation analysis (RMEN-CCA) for multiple view unsupervised learning problems, which emphasizes the combination of CCA and the robust matrix elastic net (RMEN) used as coupled feature selection. The RMEN-CCA leverages the strength of the RMEN to distill naturally meaningful features without any prior assumption and to measure effectively correlations between different 'views'. We can further employ directly the kernel trick to extend the RMEN-CCA to the kernel scenario with theoretical guarantees, which takes advantage of the kernel trick for highly complicated nonlinear feature learning. Rather than simply incorporating existing regularization minimization terms into CCA, this paper provides a new learning paradigm for CCA and is the first to derive a coupled feature selection based CCA algorithm that guarantees convergence. More significantly, for CCA, the newly-derived RMEN-CCA bridges the gap between measurement of relevance and coupled feature selection. Moreover, it is nontrivial to tackle directly the RMEN-CCA by previous optimization approaches derived from its sophisticated model architecture. Therefore, this paper further offers a bridge between a new optimization problem and an existing efficient iterative approach. As a consequence, the RMEN-CCA can overcome the limitation of CCA and address large-scale and streaming data problems. Experimental results on four popular competing datasets illustrate that the RMEN-CCA performs more effectively and efficiently than do state-of-the-art approaches.


title: Graph based E-Government web service composition

abstract: Nowadays, e-government has emerged as a government policy to improve the quality and efficiency of public administrations. By exploiting the potential of new information and communication technologies, government agencies are providing a wide spectrum of online services. These services are composed of several web services that comply with well defined processes. One of the big challenges is the need to optimize the composition of the elementary web services. In this paper, we present a solution for optimizing the computation effort in web service composition. Our method is based on Graph Theory. We model the semantic relationship between the involved web services through a directed graph. Then, we compute all shortest paths using for the first time, an extended version of the FloydWarshall algorithm.


title: SOML: sparse online metric learning with application to image retrieval

abstract: Image similarity search plays a key role in many multimedia applications, where multimedia data (such as images and videos) are usually represented in highdimensional feature space. In this paper, we propose a novel Sparse Online Metric Learning (SOML) scheme for learning sparse distance functions from large-scale high-dimensional data and explore its application to image retrieval. In contrast to many existing distance metric learning algorithms that are often designed for low-dimensional data, the proposed algorithms are able to learn sparse distance metrics from high-dimensional data in an efficient and scalable manner. Our experimental results show that the proposed method achieves better or at least comparable accuracy performance than the state-of-the-art non-sparse distance metric learning approaches, but enjoys a significant advantage in computational efficiency and sparsity, making it more practical for real-world applications.


title: Event Recommendation in Event-based Social Networks

abstract: With the large number of events published all the time in event-based social networks (EBSN), it has become increasingly difficult for users to find the events that best match their preferences. Recommender systems appear as a natural solution to this problem. However, the event recommendation scenario is quite different from typical recommendation domains (e.g. movies), since there is an intrinsic new item problem involved (i.e. events can not be ”consumed” before their occurrence) and scarce collaborative information. Although some few works have appeared in this area, there is still lacking in the literature an extensive analysis of the different characteristics of EBSN data that can affect the design of event recommenders. In this paper we provide a contribution in this direction, where we investigate and discuss important features of EBSN such as sparsity, events life time, co-participation of users in events and geographic features. We also shed some light on the performance and limitations of several well known recommendation algorithms and combinations of them on real data collected from meetup.com.


title: An Architecture for Fast and General Data Processing on Large Clusters

abstract: Today, a myriad data sources, from the Internet to business operations to scientific instruments, produce large and valuable data streams. However, the processing capabilities of single machines have not kept up with the size of data. As a result, organizations increasingly need to scale out these computations to clusters of hundreds of machines. ::: ::: At the same time, the speed and sophistication required of data processing have grown. In addition to simple queries, complex algorithms like machine learning and graph analysis are becoming common. And in addition to batch processing, streaming analysis of real-time data is required to let organizations take timely action. Future computing platforms will need to not only scale out traditional workloads, but support these new applications too. ::: ::: This book, a revised version of the 2014 ACM Dissertation Award winning dissertation, proposes an architecture for cluster computing systems that can tackle emerging data processing workloads at scale. Whereas early cluster computing systems, like MapReduce, handled batch processing, our architecture also enables streaming and interactive queries, while keeping MapReduce's scalability and fault tolerance. And whereas most deployed systems only support simple one-pass computations (e.g., SQL queries), ours also extends to the multi-pass algorithms required for complex analytics like machine learning. Finally, unlike the specialized systems proposed for some of these workloads, our architecture allows these computations to be combined, enabling rich new applications that intermix, for example, streaming and batch processing. ::: ::: We achieve these results through a simple extension to MapReduce that adds primitives for data sharing, called Resilient Distributed Datasets (RDDs). We show that this is enough to capture a wide range of workloads. We implement RDDs in the open source Spark system, which we evaluate using synthetic and real workloads. Spark matches or exceeds the performance of specialized systems in many domains, while offering stronger fault tolerance properties and allowing these workloads to be combined. Finally, we examine the generality of RDDs from both a theoretical modeling perspective and a systems perspective. ::: ::: This version of the dissertation makes corrections throughout the text and adds a new section on the evolution of Apache Spark in industry since 2014. In addition, editing, formatting, drawing of illustrations, and links for the references have been added.


title: Curiosity, Information Gaps, and the Utility of Knowledge

abstract: We use an information-gap framework to capture the diverse motives driving the preference to obtain or avoid information. Beyond the conventional desire for information as an input to decision making, people are driven by curiosity, which is a desire for knowledge for its own sake, even in the absence of material benefits, and people are additionally motivated to seek out information about issues they like thinking about and avoid information about issues they do not like thinking about (an “ostrich effect”). The standard economic framework is enriched with the insights that knowledge has valence, that ceteris paribus people want to fill in information gaps, and that, beyond contributing to knowledge, information affects the focus of attention.


title: Constrained Hierarchical Clustering for News Events

abstract: Knowledge discovery from web news events has received great attention in recent years. In practice, this knowledge is a digital representation (virtual world) of various phenomena that occur in our physical world. Hierarchical clustering algorithms are used to organize related events into groups and subgroups according to some similarity measure. The main motivation for this organization is based on the hypothesis that if the user is interested in a specific event of a certain cluster, then the user may also be interested in other related events of this same cluster. However, existing event clustering methods do not effectively use the different types of information about events, such as temporal information, geographical data, name of people and organizations. In this paper, we propose the COH-KMeans algorithm (Constrained Hierarchical K-Means) that obtains a hierarchical clustering structure considering certain conditions imposed by the users, for example, events of similar content that occurred in nearby geographic locations or that occurred within a predefined time window. A statistical analysis of the experimental results reveals that the incorporation of constraints performed by COH-KMeans allows to obtain higher quality clusters when compared to a state-of-the-art unsupervised hierarchical clustering method. Moreover, we present our tool for exploratory analysis of events and we discuss how event clustering can be used to support the decision-making process from the perspective of a Data Analytics System.


title: Data augmentation for low resource languages.

abstract: Recently there has been interest in the approaches for training speech recognition systems for languages with limited resources. Under the IARPA Babel program such resources have been provided for a range of languages to support this research area. This paper examines a particular form of approach, data augmentation, that can be applied to these situations. Data augmentation schemes aim to increase the quantity of data available to train the system, for example semi-supervised training, multilingual processing, acoustic data perturbation and speech synthesis. To date the majority of work has considered individual data augmentation schemes, with few consistent performance contrasts or examination of whether the schemes are complementary. In this work two data augmentation schemes, semisupervised training and vocal tract length perturbation, are examined and combined on the Babel limited language pack configuration. Here only about 10 hours of transcribed acoustic data are available. Two languages are examined, Assamese and Zulu, which were found to be the most challenging of the Babel languages released for the 2014 Evaluation. For both languages consistent speech recognition performance gains can be obtained using these augmentation schemes. Furthermore the impact of these performance gains on a down-stream keyword spotting task are also described. Index Terms: data augmentation, speech recognition, babel


title: Layout and Content Extraction for PDF Documents

abstract: Portable document format (PDF) is a common output format for electronic documents. Most PDF documents are untagged and do not have basic high-level document logical structural information, which makes the reuse or modification of the documents difficult. We developed techniques that identified logical components on a PDF document page. The outlines, style attributes and the contents of the logical components were extracted and expressed in an XML format. These techniques could facilitate the reuse and modification of the layout and the content of a PDF document page.


title: Incremental updating approximations in probabilistic rough sets under the variation of attributes

abstract: The attribute set in an information system evolves in time when new information arrives. Both lower and upper approximations of a concept will change dynamically when attributes vary. Inspired by the former incremental algorithm in Pawlak rough sets, this paper focuses on new strategies of dynamically updating approximations in probabilistic rough sets and investigates four propositions of updating approximations under probabilistic rough sets. Two incremental algorithms based on adding attributes and deleting attributes under probabilistic rough sets are proposed, respectively. The experiments on five data sets from UCI and a genome data with thousand attributes validate the feasibility of the proposed incremental approaches.


DEWS 2008 E 1-2 Probabilistic Event Stream Processing with Lineage


title: A Protegé Plug-In for Ontology Extraction from Text Based on Linguistic Analysis

abstract: In this paper we describe a plug-in (OntoLT) for the widely used Protege ontology development tool that supports the interactive extraction and/or extension of ontologies from text. The OntoLT approach provides an environment for the integration of linguistic analysis in ontology engineering through the definition of mapping rules that map linguistic entities in annotated text collections to concept and attribute candidates (i.e. Protege classes and slots). The paper ex-plains this approach in more detail and discusses some initial experiments on deriving a shallow ontology for the neurology domain from a corresponding collection of neurological scientific abstracts.


title: High-performance multicast services in 5G big data network with massive MIMO

abstract: This paper investigates the application of 5G wireless multicast to the big data system. In particular, we propose a hybrid unicast/multicast transmission scheme using 3D massive multi-input multi-output (MIMO) technique. The proposed scheme employs multicast beamforming in the multicast group and multi-user MIMO (MU-MIMO) linear precoding in the unicast group. It also applies the null space based interference cancellation to eliminate the signal leakage generated by other groups. Simulation results show that the proposed system can significantly improve the system throughput with an antenna array configuration, precoding/beamforming schemes and user grouping strategies.


title: The Knowledge Awakens: Keeping Knowledge Bases Fresh with Emerging Entities

abstract: Entity search over news, social media and the Web allows users to precisely retrieve concise information about specific people, organizations, movies and their characters, and other kinds of entities. This expressive search mode builds on two major assets: 1) a knowledge base (KB) that contains the entities of interest and 2) entity markup in the documents of interest derived by automatic disambiguation of entity names (NED) and linking names to the KB. These prerequisites are not easily available, though, in the important case when a user is interested in a newly emerging entity (EE) such as new movies, new songs, etc. Automatic methods for detecting and canonicalizing EEs are not nearly at the same level as the NED methods for prominent entities that have rich descriptions in the KB. ::: ::: To overcome this major limitation, we have developed an approach and prototype system that allows searching for EEs in a user-friendly manner. The approach leverages the human in the loop by prompting for user feedback on candidate entities and on characteristic keyphrases for EEs. For convenience and low burden on users, this process is supported by the automatic harvesting oftentative keyphrases. Our demo system shows this interactive process and its high usability.


title: A Proposed Hybrid Agile Framework Model for Mobile Applications Development

abstract: With the increasing in mobile application systems and a high competition between companies, that led to increase in the number of mobile application projects. Mobile software development is a group of process for creating software for mobile devices with limited resources like small screen, low-power. The development of mobile applications is a big challenging because of rapidly changing business requirements and technical constraints for mobile systems. So, developers faced the challenge of a dynamic environment and the Changing of mobile application requirements. Moreover, Mobile applications should adapt appropriate software development methods that act in response efficiently to these challenges. However, at the moment, there is limited knowledge about the suitability of different software practices for the development of mobile applications. According to many researchers ,Agile methodologies was found to be most suitable for mobile development projects as they are short time, require flexibility, reduces waste and time to market. Finally, in this research we are looking for a suitable process model that conforms to the requirement of mobile application, we are going to investigate agile development methods to find a way, making the development of mobile application easy and compatible with mobile device features.


title: Comparison of problem-based learning and lecture-based learning in midwifery.

abstract: BACKGROUND ::: Lecturing is the most common teaching method in the medical education. Problem-based learning (PBL) is one of the active and student-oriented learning strategies which have gained attention in the recent decades. ::: ::: ::: OBJECTIVE ::: To compare the effect of PBL and lecture-based learning (LBL) on the satisfaction and learning progress of undergraduate midwifery students. ::: ::: ::: DESIGN ::: Quasi-experimental study. ::: ::: ::: SETTINGS ::: Hamadan University of Medical Sciences, Hamedan, Iran. ::: ::: ::: PARTICIPANTS ::: Fifty-six undergraduate midwifery students in two different classes participated in the study. ::: ::: ::: METHODS ::: Randomly, one class (experimental group) received PBL in addition to LBL and the other one (control group) received traditional LBL. "Pregnancy and childbirth", a 2-credit course, was selected for this purpose. Scientific pre-test and post-tests were performed and satisfaction questionnaire was filled in for both groups. A questionnaire to compare PBL and LBL was filled in only for the experimental group. The instruments used in the study were "Demographic data form", "Students' learning progress test", "Student's satisfaction of teaching method", and the "Teaching method evaluation". ::: ::: ::: RESULTS ::: Learning progress in PBL group was significantly more than control group. PBL improved application of theory lesson in clinical practice, increased learning motivation and enhanced educational activity in class. There was more satisfaction with PBL method. There was no relationship between the students' satisfaction and learning progress. 81.8% of students considered PBL the more suitable and better method. ::: ::: ::: CONCLUSION ::: PBL should be applied more in undergraduate midwifery courses. According to these advantages, the quality of midwifery education can be improved via this method.


title: Regenerative Therapies for Central Nervous System Diseases: a Biomaterials Approach

abstract: The central nervous system (CNS) has a limited capacity to spontaneously regenerate following traumatic injury or disease, requiring innovative strategies to promote tissue and functional repair. Tissue regeneration strategies, such as cell and/or drug delivery, have demonstrated promising results in experimental animal models, but have been difficult to translate clinically. The efficacy of cell therapy, which involves stem cell transplantation into the CNS to replace damaged tissue, has been limited due to low cell survival and integration upon transplantation, while delivery of therapeutic molecules to the CNS using conventional methods, such as oral and intravenous administration, have been limited by diffusion across the blood-brain/spinal cord-barrier. The use of biomaterials to promote graft survival and integration as well as localized and sustained delivery of biologics to CNS injury sites is actively being pursued. This review will highlight recent advances using biomaterials as cell- and drug-delivery vehicles for CNS repair.


title: Design and implementation of server cluster dynamic load balancing based on OpenFlow

abstract: Nowadays, the Internet is flooded with huge traffic, many applications have millions users, a single server is difficult to bear a large number of clients' access, so many application providers will put several servers as a computing unit to provide support for a specific application, usually people will use distributed computing, load balancing technology to complete the work. A typical load balancing technique is to use a dedicated load balancer to forward the client requests to different servers, this technique requires dedicated hardware support, the hardware is expensive, lacks of flexibility and is easy to become a single point failure. There will be a new solution for load balancing with OpenFlow proposed., this paper mainly studies dynamic load balancing technology in the OpenFlow environment, the Controller collected the server running status through the SNMP protocol, and calculated the aggregated load of the severs according to dynamic load balancing scheduling algorithm, the OpenFlow switch will forward the client's request to the server whose aggregated load is smallest, thus minimize the response time of the web server. In the OpenFlow network environment, using this method can brings high flexibility without additional equipment.


title: Joint training of convolutional and non-convolutional neural networks

abstract: We describe a simple modification of neural networks which consists in extending the commonly used linear layer structure to an arbitrary graph structure. This allows us to combine the benefits of convolutional neural networks with the benefits of regular networks. The joint model has only a small increase in parameter size and training and decoding time are virtually unaffected. We report significant improvements over very strong baselines on two LVCSR tasks and one speech activity detection task.


title: Retrieving attributes using web tables

abstract: In this paper we propose an attribute retrieval approach which extracts and ranks attributes from Web tables. We combine simple heuristics to filter out improbable attributes and we rank attributes based on frequencies and a table match score. Ranking is reinforced with external evidence from Web search, DBPedia and Wikipedia. Our approach can be applied to whatever instance (e.g. Canada) to retrieve its attributes (capital, GDP). It is shown it has a much higher recall than DBPedia and Wikipedia and that it works better than lexico-syntactic rules for the same purpose.


title: Theory, Measurement, and Controversy in Positive Psychology, Health Psychology, and Cancer: Basics and Next Steps

abstract: The aims of this commentary are two-fold: First, to amplify some of the points that Aspinwall, Tedeschi, Coyne, Tennen, and Ranchor have raised, noting the importance of a return to basics. Second, to posit next steps in theory development and methods at the intersection of health psychology, positive psychology, and cancer. Additional theory development, more applications of large prospective studies, and instrument refinements are warranted to understand the effects of positive constructs on health outcomes and adaptation to cancer. This area of research would be strengthened by studies that incorporate survival, health-related quality of life, and well-being outcome measures, using cancer registries and/or multiple raters. More observational studies are necessary. Attention to social justice questions is suggested in future studies at the intersection of these fields.


High-definition fiber tractography and language.


title: Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization

abstract: Face frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful results and preserve texture details in a high-resolution. This paper proposes a High Fidelity Pose Invariant Model (HF-PIM) to produce photographic and identity-preserving results. HF-PIM frontalizes the profiles through a novel texture warping procedure and leverages a dense correspondence field to bind the 2D and 3D surface spaces. We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks. Different from those reconstruction methods relying on 3D data, we also propose Adversarial Residual Dictionary Learning (ARDL) to supervise facial texture map recovering with only monocular images. Exhaustive experiments on both controlled and uncontrolled environments demonstrate that the proposed method not only boosts the performance of pose-invariant face recognition but also dramatically improves high-resolution frontalization appearances.


title: Discovering Purchasing Pattern of Sport Items Using Market Basket Analysis

abstract: One of the oldest problem in data mining is the market basket problem, the search for meaningful associations in customer purchase data. Currently, the Sport Company has an issue on sport items arrangement in accordance with customer purchasing pattern. They noticed that, the sales of certain products become decrease when they made some arrangement to the shelves. The Sport Company do not have any available computerized mechanism to provide the best arrangement of item store at the retail store. Everything is done manually by the owner of the shop according their own style. This study intends to identify purchasing pattern of sport items by adopting data mining technique which is Market Basket Analysis. This data mining pattern will help the retailer to make a better arrangement of the products at the premise. Historical data is analyzed to identify associated items from purchasing data of customer that involved sales data, items data and order data. As a result from this research, the sports items will be arranged according to the best rules identified and propose a new pattern.


title: Active Ageing: An Empirical Approach to the WHO Model

abstract: Background. In the beginning of the 21st century, the world summit on population taking place in Madrid approved active ageing, WHO (2002) as the main objective of health and social policies for old people. Few studies have been done on the scientific validity of the construct. This study aims to validate the construct of active ageing and test empirically the WHO (2002) model of Active Ageing in a sample of community-dwelling seniors. Methods. 1322 old people living in the community were interviewed using an extensive assessment protocol to measure WHO's determinants of active ageing and performed an exploratory factor analysis followed by a confirmatory factor analyses. Results. We did not confirm the active ageing model, as most of the groups of determinants are either not independent or not significant. We got to a six-factor model (health, psychological component, cognitive performance, social relationships, biobehavioural component, and personality) explaining 54.6% of total variance. Conclusion. The present paper shows that there are objective as well as subjective variables contributing to active ageing and that psychological variables seem to give a very important contribute to the construct. The profile of active ageing is expected to vary between contexts and cultures and can be used to guide specific community and individually based interventions.


title: Designing Leisure Products for People With Dementia: Developing ‘‘the Chitchatters’’ Game

abstract: Providing leisure to people with dementia is a serious challenge, for health care professionals and designers and engineers of products used for activity sessions. This article describes the design process of ‘‘the Chitchatters,’’ a leisure game for a group of people with dementia in day care centers. The game aims to stimulate social interaction among people with dementia. Different stakeholders, such as older adults with dementia, their relatives and care professionals were involved in the design process via qualitative research methods as participant observation and the use of probes. These methods were applied to give the design team insight into the experiential world of people with dementia. This article presents how design insights from practice and literature can be translated into a real design for a leisure product for group use by older people with dementia, and shows designers how to work with, and design for, special groups.


title: Pricing and the psychology of consumption.

abstract: The way you set prices doesn't just influence demand. It also guides the way buyers use your product or service - and that can have a lasting impact on customer relationships.


title: Combining deep learning and language modeling for segmentation-free OCR from raw pixels

abstract: We present a simple yet effective LSTM-based approach for recognizing machine-print text from raw pixels. We use a fully-connected feed-forward neural network for feature extraction over a sliding window, the output of which is directly fed into a stacked bi-directional LSTM. We train the network using the CTC objective function and use a WFST language model during recognition. Experimental results show that this simple system outperforms extensively tuned state-of-the-art HMM models on the DARPA Arabic Machine Print corpus.


title: Skeletonization using SSM of the Distance Transform

abstract: This paper proposes a new approach for skeletonization based on the skeleton strength map (SSM) caculated by Euclidean distance transform of a binary image. After the distance transform and gradient are computed, isotropic diffusion is performed on the gradient vector field and the skeleton strength map is computed from the diffused vector field. A critical point set is then selected from local maxima of the SSM. The critical points are located on significant visual parts of the object. The skeleton is obtained by connecting the critical points with geodesic paths. This approach overcomes intrinsic drawbacks of distance transform based skeletons, since it yields stable and connected skeletons without losing significant visual parts.


title: Camera2Caption: A real-time image caption generator

abstract: The recent advances in Deep Learning based Machine Translation and Computer Vision have led to excellent Image Captioning models using advanced techniques like Deep Reinforcement Learning. While these models are very accurate, these often rely on the use of expensive computation hardware making it difficult to apply these models in realtime scenarios, where their actual applications can be realised. In this paper, we carefully follow some of the core concepts of Image Captioning and its common approaches and present our simplistic encoder and decoder based implementation with significant modifications and optimizations which enable us to run these models on low-end hardware of hand-held devices. We also compare our results evaluated using various metrics with state-of-the-art models and analyze why and where our model trained on MSCOCO dataset lacks due to the trade-off between computation speed and quality. Using the state-of-the-art TensorFlow framework by Google, we also implement a first of its kind Android application to demonstrate the realtime applicability and optimizations of our approach.


title: A Game Theoretic Approach for the Real-Life Multiple-Criterion Vehicle Routing Problem With Multiple Time Windows

abstract: This paper presents a new framework for the optimization of real-life multiple-objective vehicle routing problems with multiple time windows. This framework uses a hybrid variable neighborhood tabu search heuristic that chooses Pareto nondominated solutions from the search space of solutions that satisfy a set of Nash equilibrium conditions for a multiple-agent game theory model. Even though the framework is general and can tackle different classes of vehicle routing problems, it is herein tested on three objectives: minimizing the total travel cost (expressed in time units), maximizing the minimal customers’ utility, and maximizing the minimal drivers’ utility. The results on real-life instances provided by a Canadian transportation company highlight the benefits of the multiple-criteria model; an important motivation to the transportation industry for its real-life implementation.


title: A novel feature selection algorithm for text categorization

abstract: With the development of the web, large numbers of documents are available on the Internet. Digital libraries, news sources and inner data of companies surge more and more. Automatic text categorization becomes more and more important for dealing with massive data. However the major problem of text categorization is the high dimensionality of the feature space. At present there are many methods to deal with text feature selection. To improve the performance of text categorization, we present another method of dealing with text feature selection. Our study is based on Gini index theory and we design a novel Gini index algorithm to reduce the high dimensionality of the feature space. A new measure function of Gini index is constructed and made to fit text categorization. The results of experiments show that our improvements of Gini index behave better than other methods of feature selection.


title: Efficient Construction of Compact Shedding Filters for Data Stream Processing

abstract: High-volume source streams, coupled with fluctuating rates, necessitate adaptive load shedding in data stream processing. When ignored, a continual query (CQ) server may randomly drop items, when its capacity is inadequate to handle the arriving data, and degrade the quality of the query results. To alleviate this problem, filters can be used at the source nodes. However, regular source filtering in itself is not sufficient to prevent random dropping, because the amount of data passing through the filters can still surpass the server's capacity. In this case, intelligent load shedding can be applied by the source filters to minimize the degradation in result quality. In this paper, we introduce a novel type of load-shedding source filters, called non- uniformly regulated (NR) sifters. An NR sifter judiciously applies varying amounts of load shedding to different regions of the data space within the sifter. We formulate the problem of constructing NR sifters as an optimization one. NR sifters are compact and quickly configurable, allowing frequent adaptations, and provide fast lookup f.or deciding if a data item should be dropped. We structure NR sifters as a set of (sifter region, drop threshold) pairs to achieve compactness, develop query consolidation techniques to enable quick construction, and introduce flexible space partitioning mechanisms to realize fast lookup.


title: Deep Epitomic Convolutional Neural Networks

abstract: Deep convolutional neural networks have recently proven extremely competitive in challenging image recognition tasks. This paper proposes the epitomic convolution as a new building block for deep neural networks. An epitomic convolution layer replaces a pair of consecutive convolution and max-pooling layers found in standard deep convolutional neural networks. The main version of the proposed model uses mini-epitomes in place of filters and computes responses invariant to small translations by epitomic search instead of max-pooling over image positions. The topographic version of the proposed model uses large epitomes to learn filter maps organized in translational topographies. We show that error back-propagation can successfully learn multiple epitomic layers in a supervised fashion. The effectiveness of the proposed method is assessed in image classification tasks on standard benchmarks. Our experiments on Imagenet indicate improved recognition performance compared to standard convolutional neural networks of similar architecture. Our models pre-trained on Imagenet perform excellently on Caltech-101. We also obtain competitive image classification results on the small-image MNIST and CIFAR-10 datasets.


title: Numerical Coordinate Regression with Convolutional Neural Networks

abstract: We study deep learning approaches to inferring numerical coordinates for points of interest in an input image. Existing convolutional neural network-based solutions to this problem either take a heatmap matching approach or regress to coordinates with a fully connected output layer. Neither of these approaches is ideal, since the former is not entirely differentiable, and the latter lacks inherent spatial generalization. We propose our differentiable spatial to numerical transform (DSNT) to fill this gap. The DSNT layer adds no trainable parameters, is fully differentiable, and exhibits good spatial generalization. Unlike heatmap matching, DSNT works well with low heatmap resolutions, so it can be dropped in as an output layer for a wide range of existing fully convolutional architectures. Consequently, DSNT offers a better trade-off between inference speed and prediction accuracy compared to existing techniques. When used to replace the popular heatmap matching approach used in almost all state-of-the-art methods for pose estimation, DSNT gives better prediction accuracy for all model architectures tested.


title: A COMPARISON BETWEEN CIRCULAR AND HEXAGONAL ARRAY GEOMETRIES FOR SMART ANTENNA SYSTEMS USING PARTICLE SWARM OPTIMIZATION ALGORITHM

abstract: In this paper, circular and hexagonal array geometries for smart antenna applications are compared. Uniform circular (UCA) and hexagonal arrays (UHA) with 18 half-wave dipole elements are examined; also planar (2 concentric rings of radiators) uniform circular (PUCA) and hexagonal arrays (PUHA) are considered. The effect of rotating the outer ring of the PUCA is studied. In our analysis, the method of moments is used to compute the response of the uniform circular and hexagonal dipole arrays in a mutual coupling environment. The particle swarm optimization (PSO) algorithm is used to optimize the complex excitations, amplitudes and phases, of the adaptive arrays elements for beamforming.


title: What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data

abstract: The problem of job stress is generally recognized as one of the major factors leading to a spectrum of health problems. People with certain professions, like intensive care specialists or call-center operators, and people in certain phases of their lives, like working parents with young children, are at increased risk of getting overstressed. Stress management should start far before the stress start causing illnesses. The current state of sensor technology allows to develop systems measuring physical symptoms reflecting the stress level. In this paper we (1) formulate the problem of stress identification and categorization from the sensor data stream mining perspective, (2) consider a reductionist approach for arousal identification as a drift detection task, (3) highlight the major problems of dealing with GSR data, collected from a watch-style stress measurement device in normal (i.e. in non-lab) settings, and propose simple approaches how to deal with them, and (4) discuss the lessons learnt from the conducted experimental study on real GSR data collected during the recent field study.


title: Internet Addiction: Diagnosis and Treatment Considerations

abstract: Internet addiction is a new and often unrecognized clinical disorder that can cause relational, occupational, and social problems. Pathological gambling is compared to problematic internet use because of overlapping diagnostic criteria. As computers are used with great frequency, detection and diagnosis of internet addiction is often difficult. Symptoms of a possible problem may be masked by legitimate use of the internet. Clinicians may overlook asking questions about computer use. To help clinicians identify internet addiction in practice, this paper provides an overview of the problem and the various subtypes that have been identified. The paper reviews conceptualizations of internet addiction, various forms that the disorder takes, and treatment considerations for working with this emergent client population.


title: Cognitive Intelligence: Deep Learning, Thinking, and Reasoning by Brain-Inspired Systems

abstract: The theme of IEEE ICCI*CC'16 on Cognitive Informatics CI and Cognitive Computing CC was on cognitive computers, big data cognition, and machine learning. CI and CC are a contemporary field not only for basic studies on the brain, computational intelligence theories, and denotational mathematics, but also for engineering applications in cognitive systems towards deep learning, deep thinking, and deep reasoning. This paper reports a set of position statements presented in the plenary panel Part I in IEEE ICCI*CC'16 at Stanford University. The summary is contributed by invited panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and CC.


title: Development of a real-time three-dimensional spinal motion measurement system for clinical practice

abstract: This paper presents an inertial based sensing system for real-time three-dimensional measurement of human spinal motion, in a portable and non-invasive manner. Applications of the proposed system range from diagnosis of spine injury to postural monitoring, on-field as well as in the lab setting. The system is comprised of three inertial measurement sensors, respectively attached and calibrated to the head, torso and hips, based on the subject's anatomical planes. Sensor output is transformed into meaningful clinical parameters of rotation (twist), flexion-extension and lateral bending of each body segment, with respect to calibrated global reference space. Modeling the spine as a compound flexible pole model allows dynamic measurement of three-dimensional spine motion, which can be animated and monitored in real-time using our interactive GUI. The accuracy of the proposed sensing system has been verified with subject trials using a VICON optical motion measurement system. Experimental results indicate an error of less than 3.1 degrees in segment orientation tracking.


title: Quasi-online reinforcement learning for robots

abstract: This paper describes quasi-online reinforcement learning: while a robot is exploring its environment, in the background a probabilistic model of the environment is built on the fly as new experiences arrive; the policy is trained concurrently based on this model using an anytime algorithm. Prioritized sweeping, directed exploration, and transformed reward functions provide additional speed-ups. The robot quickly learns goal-directed policies from scratch, requiring few interactions with the environment and making efficient use of available computation time. From an outside perspective it learns the behavior online and in real time. We describe comparisons with standard methods and show the individual utility of each of the proposed techniques


title: E-government and organizational change: Reappraising the role of ICT and bureaucracy in public service delivery

abstract: Abstract There is a substantial literature on e-government that discusses information and communication technology (ICT) as an instrument for reducing the role of bureaucracy in government organizations. The purpose of this paper is to offer a critical discussion of this literature and to provide a complementary argument which favors the use of ICT in the public sector to support the operations of bureaucratic organizations. Based on the findings of a case study – of the Venice Municipality in Italy – the paper discusses how ICT can be used to support rather than eliminate bureaucracy. Using the concepts of e-bureaucracy and functional simplification and closure, the paper proposes evidence and support for the argument that bureaucracy should be preserved and enhanced where e-government policies are concerned. Functional simplification and closure are very valuable concepts for explaining why this should be a viable approach.


title: LSTM-based Deep Learning Models for Non-factoid Answer Selection

abstract: In this paper, we apply a general deep learning (DL) framework for the answer selection task, which does not depend on manually defined features or linguistic tools. The basic framework is to build the embeddings of questions and answers based on bidirectional long short-term memory (biLSTM) models, and measure their closeness by cosine similarity. We further extend this basic model in two directions. One direction is to define a more composite representation for questions and answers by combining convolutional neural network with the basic framework. The other direction is to utilize a simple but efficient attention mechanism in order to generate the answer representation according to the question context. Several variations of models are provided. The models are examined by two datasets, including TREC-QA and InsuranceQA. Experimental results demonstrate that the proposed models substantially outperform several strong baselines.


title: General-purpose fuzzy controller for DC-DC converters

abstract: In this paper, a general-purpose fuzzy controller for DC-DC converters is investigated. Based on a qualitative description of the system to be controlled, fuzzy controllers are capable of good performances, even for those systems where linear control techniques fail, e.g., when a mathematical description is not available or is in the presence of wide parameter variations. The presented approach is general and can be applied to any DC-DC converter topologies. Controller implementation is relatively simple and can guarantee a small-signal response as fast and stable as other standard regulators and an improved large-signal response. Simulation results of buck-boost and Sepic converters show control potentialities.


title: FOCS: Fast Overlapped Community Search

abstract: Discovery of natural groups of similarly functioning individuals is a key task in analysis of real world networks. Also, overlap between community pairs is commonplace in large social and biological graphs, in particular. In fact, overlaps between communities are known to be denser than the non-overlapped regions of the communities. However, most of the existing algorithms that detect overlapping communities assume that the communities are denser than their surrounding regions, and falsely identify overlaps as communities. Further, many of these algorithms are computationally demanding and thus, do not scale reasonably with varying network sizes. In this article, we propose Fast Overlapped Community Search (FOCS), an algorithm that accounts for local connectedness in order to identify overlapped communities. FOCS is shown to be linear in number of edges and nodes. It additionally gains in speed via simultaneous selection of multiple near-best communities rather than merely the best, at each iteration. FOCS outperforms some popular overlapped community finding algorithms in terms of computational time while not compromising with quality.


title: Cuff-Less Estimation of Blood Pressure Using Pulse Transit Time and Pre-ejection Period

abstract: Non-invasive and cuff-less measurement of arterial blood pressure (BP) is desirable for continuous patient monitoring. Among the various possible techniques pulse transit time (PTT) based approach for estimation of BP is the most promising one. But change in BP is reflected both in PTT and pre-ejection period (PEP). We propose to measure PTT using multiple PPG and PPG-ECG combination recorded at two different sites along the same artery and argue that this approach will eliminate a number of assumptions like constant PEP, involved in the most common method of measuring PTT using QRS complex as timing reference. This approach can help in finding an appropriate BP calibration methodology since true PTT can be measured by this technique.


title: Evolution of autonomic Database Management Systems

abstract: The evolution of computer is started from human dependent to self-management. With the development of technology, people want to manage the complex systems in an efficient and reliable manner. Database Management Systems (DBMSs) started from the single user with limited operations; and with the passage of time more functionality is added to meet the need of the users. Maximum number of operations, huge volume and multi-user environment also increased the complexity. Due to complexity and heterogeneity, job of DBAs become difficult and laborious. The solution to these DBMS's problems is self-managing or autonomic DBMS (ADBMS). An ADBMS have the ability to adopt changes according to the environment and available resources without or with less human intervention. This paper examines the three commercial leading DBMSs, IBM's DB2, Oracle by Oracle Corporation and Microsoft SQL Server with respect to autonomic characteristics. On the basis of analysis and other parameters autonomic computing (AC) maturity value is assigned to each autonomic component of three DBMSs. At the end, the evolving status of AC in DBMSs is presented.


title: Approximating the diameter of a set of points in the Euclidean space

abstract: Given a set P with n points in Rk, its diameter dp is the maximum of the Euclidean distances between its points. We describe an algorithm that in m⩽n iterations obtains r1<r2<⋯<rm⩽dp⩽min{3r1, 5−23rm}. For k fixed, the cost of each iteration is O(n). In particular, the first approximation r1 is within 3 of dp, independent of the dimension k.