title: The structure of psychological well-being revisited

abstract: A theoretical model of psychological well-being that encompasses 6 distinct dimensions of wellness (Autonomy, Environmental Mastery, Personal Growth, Positive Relations with Others, Purpose in Life, Self-Acceptance) was tested with data from a nationally representative sample of adults (N = 1,108), aged 25 and older, who participated in telephone interviews. Confirmatory factor analyses provided support for the proposed 6-factor model, with a single second-order super factor. The model was superior in fit over single-factor and other artifactual models. Age and sex differences on the various well-being dimensions replicated prior findings. Comparisons with other frequently used indicators (positive and negative affect, life satisfaction) demonstrated that the latter neglect key aspects of positive functioning emphasized in theories of health and well-being.


title: A Survey on Structure from Motion

abstract: The structure from motion (SfM) problem in computer vision is the problem of recovering the three-dimensional ($3$D) structure of a stationary scene from a set of projective measurements, represented as a collection of two-dimensional ($2$D) images, via estimation of motion of the cameras corresponding to these images. In essence, SfM involves the three main stages of (1) extraction of features in images (e.g., points of interest, lines, etc.) and matching these features between images, (2) camera motion estimation (e.g., using relative pairwise camera positions estimated from the extracted features), and (3) recovery of the $3$D structure using the estimated motion and features (e.g., by minimizing the so-called reprojection error). This survey mainly focuses on relatively recent developments in the literature pertaining to stages (2) and (3). More specifically, after touching upon the early factorization-based techniques for motion and structure estimation, we provide a detailed account of some of the recent camera location estimation methods in the literature, followed by discussion of notable techniques for $3$D structure recovery. We also cover the basics of the simultaneous localization and mapping (SLAM) problem, which can be viewed as a specific case of the SfM problem. Further, our survey includes a review of the fundamentals of feature extraction and matching (i.e., stage (1) above), various recent methods for handling ambiguities in $3$D scenes, SfM techniques involving relatively uncommon camera models and image features, and popular sources of data and SfM software.


title: Bug-fix time prediction models: can we do better?

abstract: Predicting bug-fix time is useful in several areas of software evolution, such as predicting software quality or coordinating development effort during bug triaging. Prior work has proposed bug-fix time prediction models that use various bug report attributes (e.g., number of developers who participated in fixing the bug, bug severity, number of patches, bug-opener's reputation) for estimating the time it will take to fix a newly-reported bug. In this paper we take a step towards constructing more accurate and more general bug-fix time prediction models by showing how existing models fail to validate on large projects widely-used in bug studies. In particular, we used multivariate and univariate regression testing to test the prediction significance of existing models on 512,474 bug reports from five open source projects: Eclipse, Chrome and three products from the Mozilla project (Firefox, Seamonkey and Thunderbird). The results of our regression testing indicate that the predictive power of existing models is between 30% and 49% and that there is a need for more independent variables (attributes) when constructing a prediction model. Additionally, we found that, unlike in prior recent studies on commercial software, in the projects we examined there is no correlation between bug-fix likelihood, bug-opener's reputation and the time it takes to fix a bug. These findings indicate three open research problems: (1) assessing whether prioritizing bugs using bug-opener's reputation is beneficial, (2) identifying attributes which are effective in predicting bug-fix time, and (3) constructing bug-fix time prediction models which can be validated on multiple projects.


The Role of Interest Rate Swaps in Corporate Finance


title: The impact of critical success factors across the stages of enterprise resource planning implementations

abstract: The paper describes the impact of critical success factors (CSFs) across the stages of enterprise resource planning (ERP) implementations using the responses from 86 organizations that completed or are in the process of completing an ERP implementation. Our results provide advice to management on how best to utilize their limited resources to choose those CSFs that are most likely to have an impact upon the implementation of the ERP system.


title: The Cross-section of Expected Stock Returns

abstract: This paper studies the cross-sectional properties of return forecasts derived from Fama-MacBeth regressions. These forecasts mimic how an investor could, in real time, combine many firm characteristics to obtain a composite estimate of a stockâ€™s expected return. Empirically, the forecasts vary substantially across stocks and have strong predictive power for actual returns. For example, using ten-year rolling estimates of Fama- MacBeth slopes and a cross-sectional model with 15 firm characteristics (all based on low-frequency data), the expected-return estimates have a cross-sectional standard deviation of 0.87% monthly and a predictive slope for future monthly returns of 0.74, with a standard error of 0.07.


title: Differential tomography: a new framework for SAR interferometry

abstract: A new interferometric mode crossing the differential synthetic aperture radar (SAR) interferometry and multibaseline SAR tomography concepts, that can be termed differential SAR tomography, is proposed. Its potentials, coming from the joint elevation-velocity resolution capability of multiple scatterers, are discussed. Processing is cast in a bidimensional baseline-time spectral analysis framework, with sparse sampling. The use of a modern data-dependent bidimensional spectral estimator is proposed for joint baseline-time processing. Simulated results are reported for different baseline-time acquisition patterns and two motion conditions of layover scatterers, showing that this new challenging interferometric technique is promising.


title: ManyVis: Multiple Applications in an Integrated Visualization Environment

abstract: As the visualization field matures, an increasing number of general toolkits are developed to cover a broad range of applications. However, no general tool can incorporate the latest capabilities for all possible applications, nor can the user interfaces and workflows be easily adjusted to accommodate all user communities. As a result, users will often chose either substandard solutions presented in familiar, customized tools or assemble a patchwork of individual applications glued through ad-hoc scripts and extensive, manual intervention. Instead, we need the ability to easily and rapidly assemble the best-in-task tools into custom interfaces and workflows to optimally serve any given application community. Unfortunately, creating such meta-applications at the API or SDK level is difficult, time consuming, and often infeasible due to the sheer variety of data models, design philosophies, limits in functionality, and the use of closed commercial systems. In this paper, we present the ManyVis framework which enables custom solutions to be built both rapidly and simply by allowing coordination and communication across existing unrelated applications. ManyVis allows users to combine software tools with complementary characteristics into one virtual application driven by a single, custom-designed interface.


title: Why people hate your app: making sense of user feedback in a mobile app store

abstract: User review is a crucial component of open mobile app markets such as the Google Play Store. How do we automatically summarize millions of user reviews and make sense out of them? Unfortunately, beyond simple summaries such as histograms of user ratings, there are few analytic tools that can provide insights into user reviews. In this paper, we propose Wiscom, a system that can analyze tens of millions user ratings and comments in mobile app markets at three different levels of detail. Our system is able to (a) discover inconsistencies in reviews; (b) identify reasons why users like or dislike a given app, and provide an interactive, zoomable view of how users' reviews evolve over time; and (c) provide valuable insights into the entire app market, identifying users' major concerns and preferences of different types of apps. Results using our techniques are reported on a 32GB dataset consisting of over 13 million user reviews of 171,493 Android apps in the Google Play Store. We discuss how the techniques presented herein can be deployed to help a mobile app market operator such as Google as well as individual app developers and end-users.


title: A Brain Controlled Wheelchair to Navigate in Familiar Environments

abstract: While brain-computer interfaces (BCIs) can provide communication to people who are locked-in, they suffer from a very low information transfer rate. Further, using a BCI requires a concentration effort and using it continuously can be tiring. The brain controlled wheelchair (BCW) described in this paper aims at providing mobility to BCI users despite these limitations, in a safe and efficient way. Using a slow but reliable P300 based BCI, the user selects a destination amongst a list of predefined locations. While the wheelchair moves on virtual guiding paths ensuring smooth, safe, and predictable trajectories, the user can stop the wheelchair by using a faster BCI. Experiments with nondisabled subjects demonstrated the efficiency of this strategy. Brain control was not affected when the wheelchair was in motion, and the BCW enabled the users to move to various locations in less time and with significantly less control effort than other control strategies proposed in the literature.


title: A Hybrid ACO/PSO Control Algorithm for Distributed Swarm Robots

abstract: In this paper, we present a hybrid ant colony optimization/particle swarm optimization (ACO/PSO) control algorithm for distributed swarm robots, where each robot can only communicate with its neighbors within its communication range. A virtual pheromone mechanism is proposed as the message passing coordination scheme among the robots. This hybrid ACO/PSO architecture adopts the feedback mechanism from environment of ACO and the adaptive interplay among agents of PSO to create a dynamic optimization system, and it is well-suited for a large scale distributed multi-agent system under dynamic environments. Furthermore, a pheromone-edge pair propagation funneling method is developed to reduce the communication overhead among robots. The simulation results concretely demonstrate the robustness, scalability, and individual simplicity of the proposed control architecture in a swarm robot system with real-world constraints


title: Universal power management IC for small-scale energy harvesting with adaptive impedance matching

abstract: In this paper, we present a universal power management IC for small-scale energy harvesting with adaptive impedance matching. A non-inverting buck-boost converter is adopted to accommodate a wide input voltage range from different energy sources and varying environmental conditions. The circuit dynamically performs the resistive matching to harvest the maximum power. The circuit is fabricated in 0.18-µm CMOS. Simulation and measurement results of the IC verify the operation of the power stage and the function of controller circuit.


title: Mindfulness-based approaches: are they all the same?

abstract: Mindfulness-based approaches are increasingly employed as interventions for treating a variety of psychological, psychiatric and physical problems. Such approaches include ancient Buddhist mindfulness meditations such as Vipassana and Zen meditations, modern group-based standardized meditations, such as mindfulness-based stress reduction and mindfulness-based cognitive therapy, and further psychological interventions, such as dialectical behavioral therapy and acceptance and commitment therapy. We review commonalities and differences of these interventions regarding philosophical background, main techniques, aims, outcomes, neurobiology and psychological mechanisms. In sum, the currently applied mindfulness-based interventions show large differences in the way mindfulness is conceptualized and practiced. The decision to consider such practices as unitary or as distinct phenomena will probably influence the direction of future research. & 2011 Wiley Periodicals, Inc. J Clin Psychol 67:404‐424, 2011.


title: Efficacy of Mirror Therapy Containing Functional Tasks in Poststroke Patients

abstract: Objective ::: To investigate the effect of mirror therapy containing functional tasks on upper extremity function and activities of daily living in patients with subacute stroke.


title: A Potential-Based Characterization of the Transfer Gate in CMOS Image Sensors

abstract: A method to characterize the transfer gate (TG)- related parameters in a 4 T pixel is presented. The method is based on the pinning voltage measurement, which is proposed by Tan et al. [1] Using this method, the TG ON and OFF surface potential can be characterized. Based on the TG ON potential characterization, and according to the MOSFET model, the TG channel doping and the oxide thickness can be extracted from the measurements. Based on the TG OFF potential characterization, the TG surface potential dependence on the TG design parameters and the pixel biasing condition are analyzed. Using this method, whether the device is suffering from the drain-introduced barrier lowering effect or the short channel effect can be easily determined.


title: A Harmonic Motif Modularity Approach for Multi-layer Network Community Detection

abstract: During the past several years, multi-layer network community detection has drawn an increasing amount of attention and many approaches have been developed from different perspectives. Despite the success, they mainly rely on the lower-order connectivity structure at the level of individual nodes and edges. However, the higher-order connectivity structure plays the essential role as the building block for multiplex networks, which may contain better signature of community than edge. The main challenge in utilizing higher-order structure for multi-layer network community detection is that the most representative higher-order structure may vary from one layer to another. In this paper, we propose a higher-order structural approach for multi-layer network community detection, termed harmonic motif modularity (HM-Modularity). The key idea is to design a novel higher-order structure, termed harmonic motif, which is able to integrate higher-order structural information from multiple layers to construct a primary layer. The higher-order structural information of each individual layer is also extracted, which is taken as the auxiliary information for discovering the multi-layer community structure. A coupling is established between the primary layer and each auxiliary layer. Finally, a harmonic motif modularity is designed to generate the community structure. By solving the optimization problem of the harmonic motif modularity, the community labels of the primary layer can be obtained to reveal the community structure of the original multi-layer network. Experiments have been conducted to show the effectiveness of the proposed method.


title: Open domain question answering using Wikipedia-based knowledge model

abstract: This paper describes the use of Wikipedia as a rich knowledge source for a question answering (QA) system. We suggest multiple answer matching modules based on different types of semi-structured knowledge sources of Wikipedia, including article content, infoboxes, article structure, category structure, and definitions. These semi-structured knowledge sources each have their unique strengths in finding answers for specific question types, such as infoboxes for factoid questions, category structure for list questions, and definitions for descriptive questions. The answers extracted from multiple modules are merged using an answer merging strategy that reflects the specialized nature of the answer matching modules. Through an experiment, our system showed promising results, with a precision of 87.1%, a recall of 52.7%, and an F-measure of 65.6%, all of which are much higher than the results of a simple text analysis based system.


Spatial variation in physical and chemical parameters and benthic macro-invertebrate fauna of river Ogunpa, Ibadan


title: The assessment of Internet addiction among university students: some findings from a focus group

abstract: Original scientific paper The university student population has been regarded as one of the most susceptible to developing Internet addiction among all demographic groups of Internet users. This paper reports the findings obtained from a focus group study conducted among 8 undergraduate students on the issue of Internet addiction. Data and insights were sought from the undergraduates regarding their Internet use patterns as well as their response to the Generalized Problematic Internet Usage Scale 2 (GPIUS2). Findings revealed that Internet use patterns have changed in recent times and that the GPIUS2 is an appropriate measurement tool for Internet addiction among students. Other implications from the findings are duly discussed in this paper.


title: Anaphora and Discourse Structure

abstract: We argue in this article that many common adverbial phrases generally taken to signal a discourse relation between syntactically connected units within discourse structure instead work anaphorically to contribute relational meaning, with only indirect dependence on discourse structure. This allows a simpler discourse structure to provide scaffolding for compositional semantics and reveals multiple ways in which the relational meaning conveyed by adverbial connectives can interact with that associated with discourse structure. We conclude by sketching out a lexicalized grammar for discourse that facilitates discourse interpretation as a product of compositional rules, anaphor resolution, and inference.


title: Guidelines for using multiple views in information visualization

abstract: A multiple view system uses two or more distinct views to support the investigation of a single conceptual entity. Many such systems exist, ranging from computer-aided design (CAD) systems for chip design that display both the logical structure and the actual geometry of the integrated circuit to overview-plus-detail systems that show both an overview for context and a zoomed-in-view for detail. Designers of these systems must make a variety of design decisions, ranging from determining layout to constructing sophisticated coordination mechanisms. Surprisingly, little work has been done to characterize these systems or to express guidelines for their design. Based on a workshop discussion of multiple views, and based on our own design and implementation experience with these systems, we present eight guidelines for the design of multiple view systems.


title: BreakSense: Combining Physiological and Location Sensing to Promote Mobility during Work-Breaks

abstract: Work breaks can play an important role in the mental and physical well-being of workers and contribute positively to productivity. In this paper we explore the use of activity-, physiological-, and indoor-location sensing to promote mobility during work-breaks. While the popularity of devices and applications to promote physical activity is growing, prior research highlights important constraints when designing for the workplace. With these constraints in mind, we developed BreakSense, a mobile application that uses a Bluetooth beacon infrastructure, a smartphone and a smartwatch to encourage mobility during breaks with a game-like design. We discuss constraints imposed by design for work and the workplace, and highlight challenges associated with the use of noisy sensors and methods to overcome them. We then describe a short deployment of BreakSense within our lab that examined bound vs. unbound augmented breaks and how they affect users' sense of completion and readiness to work.


Developing and evaluating a mobile driver fatigue detection network based on electroencephalograph signals


title: Antioxidant properties and phenolic profile of the most widely appreciated cultivated mushrooms: a comparative study between in vivo and in vitro samples.

abstract: The present study reports a comparison of the antioxidant properties and phenolic profile of the most consumed species as fresh cultivated mushrooms and their mycelia produced in vitro: Agaricus bisporus (white and brown), Pleurotus ostreatus (oyster), Pleurotus eryngii (king oyster) and Lentinula edodes (shiitake). The antioxidant activity was evaluated through reducing power (Folin-Ciocalteu and Ferricyanide/Prussian blue assays), free radical scavenging activity (DPPH assay) and lipid peroxidation inhibition (β-carotene/linoleate and TBARS assays). The analysis of phenolic compounds was performed by HPLC/PAD. The mushroom species with the highest antioxidant potential was Agaricus bispous (brown). However, concerning to the species obtained in vitro, it was L. edodes that demonstrate the highest reducing power. Generally, in vivo samples revealed higher antioxidant properties than their mycelia obtained by in vitro techniques. About the phenolic compounds researched, they were detected both in mushrooms and mycelia without any particular abundance. Results showed that there is no correlation between the studied commercial mushrooms and the corresponding mycelia obtained in vitro. Nevertheless, this study contributes to the rise of data relatively to the species consumed as fresh mushrooms and the possibility of their in vitro production as a source of bioactive compounds.


Point Precisely: Towards Ensuring the Precision of Data in Generated Texts Using Delayed Copy Mechanism


title: A Graph-Based Algorithm for Inducing Lexical Taxonomies from Scratch

abstract: In this paper we present a graph-based approach aimed at learning a lexical taxonomy automatically starting from a domain corpus and the Web. Unlike many taxonomy learning approaches in the literature, our novel algorithm learns both concepts and relations entirely from scratch via the automated extraction of terms, definitions and hypernyms. This results in a very dense, cyclic and possibly disconnected hypernym graph. The algorithm then induces a taxonomy from the graph. Our experiments show that we obtain high-quality results, both when building brand-new taxonomies and when reconstructing WordNet sub-hierarchies.


title: Building facade detection, segmentation, and parameter estimation for mobile robot stereo vision

abstract: Building facade detection is an important problem in computer vision, with applications in mobile robotics and semantic scene understanding. In particular, mobile platform localization and guidance in urban environments can be enabled with accurate models of the various building facades in a scene. Toward that end, we present a system for detection, segmentation, and parameter estimation of building facades in stereo imagery. The proposed method incorporates multilevel appearance and disparity features in a binary discriminative model, and generates a set of candidate planes by sampling and clustering points from the image with Random Sample Consensus (RANSAC), using local normal estimates derived from Principal Component Analysis (PCA) to inform the planar models. These two models are incorporated into a two-layer Markov Random Field (MRF): an appearance- and disparity-based discriminative classifier at the mid-level, and a geometric model to segment the building pixels into facades at the high-level. By using object-specific stereo features, our discriminative classifier is able to achieve substantially higher accuracy than standard boosting or modeling with only appearance-based features. Furthermore, the results of our MRF classification indicate a strong improvement in accuracy for the binary building detection problem and the labeled planar surface models provide a good approximation to the ground truth planes.


title: Effectiveness of vocabulary learning via mobile phone

abstract: Whereas the penetration of mobile phones in Asian countries keeps climbing, little research has explored the application of the short message service (SMS) in second language learning. This study aims to examine the effectiveness of SMS vocabulary lessons of limited lexical information on the small screens of mobile phones. Thirty high school students were randomly distributed into two groups and given two sets of English words either on paper or through SMS messages during two weeks. Students recognized more vocabulary during the post-test after reading the regular and brief SMS lessons than they did after reading the relatively more detailed print material. Qualitative data from interviews offer information about the learning process as well as the benefits and limitations of m-learning. Results of the questionnaires show that students in general hold positive attitudes towards learning vocabulary via mobile phone. On the other hand, technological limitations, unfamiliar presentations and learning activities may prevent students from reading SMS lessons.


title: Non Overlapping Clock generator for switched capacitor circuits in Bio-Medical applications

abstract: Switched capacitor circuits have become a popular method for implementing mixed signal blocks in standard CMOS technologies. Non-Overlapping Clock (NOC) generator is a key building block of switched capacitor circuits. Standard NOC circuits use simple inverters to realize delays. For high to moderate frequencies, the number of inverters required is nominal. But for low frequency applications like Bio-Medical Signal Processing, the number of inverters increase drastically affecting the area and power budget. In this work it is proposed to use an inverter in inverted form along with a simple inverter as an inverting circuit to realize significant delay with lesser number of transistors. Simulation results suggest that the proposed inverter is area and power efficient.


title: Structural Equation Modeling And Regression: Guidelines For Research Practice

abstract: The growing interest in Structured Equation Modeling (SEM) techniques and recognition of their importance in IS research suggests the need to compare and contrast different types of SEM techniques so that research designs can be selected appropriately. After assessing the extent to which these techniques are currently being used in IS research, the article presents a running example which analyzes the same dataset via three very different statistical techniques. It then compares two classes of SEM: covariance-based SEM and partial-least-squaresbased SEM. Finally, the article discusses linear regression models and offers guidelines as to when SEM techniques and when regression techniques should be used. The article concludes with heuristics and rule of thumb thresholds to guide practice, and a discussion of the extent to which practice is in accord with these guidelines.


title: End-to-End Deep Knowledge Tracing by Learning Binary Question-Embedding

abstract: Recent advancements in computer-assisted learning systems have increased research in the are of knowledge tracing, which estimates student proficiency based on their past interaction with the learning systems. In this context, the method called Deep Knowledge Tracing (DKT), which leverages recurrent neural networks, shows remarkable performance; however, existing knowledge tracing methods, including DKT, require human-predefined skill-tags that show what skill is required to solve a question. This limits the optimization of modeling student proficiency and the application to real-world data, which are often not well-organized by skill-tags. In this paper, we propose the first end-to-end DKT model (E2E-DKT), which does not depend on any human-predefined skill-tags. Regarding the correspondence between questions and tags as a binary embedding matrix, we introduce a new Q-Embedding Model, which learns the embedding matrix to help predict student proficiency from the student question-answer logs only. We provided two techniques for learning a better matrix, one is the reconstruction regularization of question-space and tag-space, and the other is the sparse regularization of the question-embedding matrix. Using two open datasets, we empirically validated that the learned tags show the same or better performance on DKT and have an information-efficient structure and more hierarchical relations among each tag than the human-predefined tags. These results show the potential of our proposed method to enhance the applicable scope and effectiveness of DKT, which could help improve the learning experience of students in more diverse environments.


title: Robust articulated-ICP for real-time hand tracking

abstract: We present a robust method for capturing articulated hand motions in realtime using a single depth camera. Our system is based on a realtime registration process that accurately reconstructs hand poses by fitting a 3D articulated hand model to depth images. We register the hand model using depth, silhouette, and temporal information. To effectively map low-quality depth maps to realistic hand poses, we regularize the registration with kinematic and temporal priors, as well as a data-driven prior built from a database of realistic hand poses. We present a principled way of integrating such priors into our registration optimization to enable robust tracking without severely restricting the freedom of motion. A core technical contribution is a new method for computing tracking correspondences that directly models occlusions typical of single-camera setups. To ensure reproducibility of our results and facilitate future research, we fully disclose the source code of our implementation.


title: Query-by-example Spoken Term Detection For OOV terms

abstract: The goal of Spoken Term Detection (STD) technology is to allow open vocabulary search over large collections of speech content. In this paper, we address cases where search term(s) of interest (queries) are acoustic examples. This is provided either by identifying a region of interest in a speech stream or by speaking the query term. Queries often relate to named-entities and foreign words, which typically have poor coverage in the vocabulary of Large Vocabulary Continuous Speech Recognition (LVCSR) systems. Throughout this paper, we focus on query-by-example search for such out-of-vocabulary (OOV) query terms. We build upon a finite state transducer (FST) based search and indexing system [1] to address the query by example search for OOV terms by representing both the query and the index as phonetic lattices from the output of an LVCSR system. We provide results comparing different representations and generation mechanisms for both queries and indexes built with word and combined word and subword units [2]. We also present a two-pass method which uses query-by-example search using the best hit identified in an initial pass to augment the STD search results. The results demonstrate that query-by-example search can yield a significantly better performance, measured using Actual Term-Weighted Value (ATWV), of 0.479 when compared to a baseline ATWV of 0.325 that uses reference pronunciations for OOVs. Further improvements can be obtained with the proposed two pass approach and filtering using the expected unigram counts from the LVCSR system's lexicon.


title: Learning Emotion Indicators from Tweets: Hashtags, Hashtag Patterns, and Phrases

abstract: We present a weakly supervised approach for learning hashtags, hashtag patterns, and phrases associated with five emotions: AFFECTION, ANGER/RAGE, FEAR/ANXIETY, JOY, and SADNESS/DISAPPOINTMENT. Starting with seed hashtags to label an initial set of tweets, we train emotion classifiers and use them to learn new emotion hashtags and hashtag patterns. This process then repeats in a bootstrapping framework. Emotion phrases are also extracted from the learned hashtags and used to create phrase-based emotion classifiers. We show that the learned set of emotion indicators yields a substantial improvement in F-scores, ranging from +%5 to +%18 over baseline classifiers.


title: A Big Data system supporting Bosch Braga Industry 4.0 strategy

abstract: Abstract People, devices, infrastructures and sensors can constantly communicate exchanging data and generating new data that trace many of these exchanges. This leads to vast volumes of data collected at ever increasing velocities and of different variety, a phenomenon currently known as Big Data. In particular, recent developments in Information and Communications Technologies are pushing the fourth industrial revolution, Industry 4.0, being data generated by several sources like machine controllers, sensors, manufacturing systems, among others. Joining volume, variety and velocity of data, with Industry 4.0, makes the opportunity to enhance sustainable innovation in the Factories of the Future. In this, the collection, integration, storage, processing and analysis of data is a key challenge, being Big Data systems needed to link all the entities and data needs of the factory. Thereby, this paper addresses this key challenge, proposing and implementing a Big Data Analytics architecture, using a multinational organisation (Bosch Car Multimedia – Braga) as a case study. In this work, all the data lifecycle, from collection to analysis, is handled, taking into consideration the different data processing speeds that can exist in the real environment of a factory (batch or stream).


title: Feature Selection with Data Balancing for Prediction of Bank Telemarketing

abstract: Nowadays, Telemarketing is an interactive technique of direct marketing that many banks apply to present a long term deposit to bank customers via the phone. Although the offering like this manner is powerful, it may make the customers annoyed. The data prediction is a popular task in data mining because it can be applied to solve this problem. However, the predictive performance may be decreased in case of the input data have many features like the bank customer information. In this paper, we focus on how to reduce the feature of input data and balance the training set for the predictive model to help the bank to increase the prediction rate. In the system performance evaluation, all accuracy rates of each predictive model based on the proposed approach compared with the original predictive model based on the truth positive and receiver operating characteristic measurement show the high performance in which the smaller number of features.


title: Embedding a Semantic Network in a Word Space

abstract: We present a framework for using continuousspace vector representations of word meaning to derive new vectors representing the meaning of senses listed in a semantic network. It is a post-processing approach that can be applied to several types of word vector representations. It uses two ideas: first, that vectors for polysemous words can be decomposed into a convex combination of sense vectors; secondly, that the vector for a sense is kept similar to those of its neighbors in the network. This leads to a constrained optimization problem, and we present an approximation for the case when the distance function is the squared Euclidean. We applied this algorithm on a Swedish semantic network, and we evaluate the quality of the resulting sense representations extrinsically by showing that they give large improvements when used in a classifier that creates lexical units for FrameNet frames.


title: Fusion-Based Background-Subtraction using Contour Saliency

abstract: We present a new contour-based background-subtraction technique using thermal and visible imagery for persistent object detection in urban settings. Statistical backgroundsubtraction in the thermal domain is used to identify the initial regions-of-interest. Color and intensity information are used within these areas to obtain the corresponding regionsof- interest in the visible domain. Within each region, input and background gradient information are combined to form a Contour Saliency Map. The binary contour fragments, obtained from corresponding Contour Saliency Maps, are then combined. An A path-constrained search along watershed boundaries is used to complete and close any broken contour segments. Lastly, the contour image is flood- filled to produce silhouettes. Results of our approach are presented and compared against manually segmented data.


title: GSET somi: a game-specific eye tracking dataset for somi

abstract: In this paper, we present an eye tracking dataset of computer game players who played the side-scrolling cloud game Somi. The game was streamed in the form of video from the cloud to the player. This dataset can be used for designing and testing game-specific visual attention models. The source code of the game is also available to facilitate further modifications and adjustments. For collecting this data, male and female candidates were asked to play the game in front of a remote eye-tracking device. For each player, we recorded gaze points, video frames of the gameplay, and mouse and keyboard commands. For each video frame, a list of its game objects with their locations and sizes was also recorded. This data, synchronized with eye-tracking data, allows one to calculate the amount of attention that each object or group of objects draw from each player. As a benchmark, we also show various attention patterns could be identified among players.


title: Using Multimedia Vocabulary Annotations in L2 Reading and Listening Activities

abstract: This paper reviews the role of multimedia vocabulary annotation (MVA) in facilitating second language (L2) reading and listening activities. It examines the multimedia learning and multimedia language learning theories that underlie the MVA research, synthesizes the findings on MVA in the last decade, and identifies three underresearched areas on the subject.


title: Care to Comment? Recommendations for Commenting on News Stories

abstract: Many websites provide commenting facilities for users to express their opinions or sentiments with regards to content items, such as, videos, news stories, blog posts, etc. Previous studies have shown that user comments contain valuable information that can provide insight on Web documents and may be utilized for various tasks. This work presents a model that predicts, for a given user, suitable news stories for commenting. The model achieves encouraging results regarding the ability to connect users with stories they are likely to comment on. This provides grounds for personalized recommendations of stories to users who may want to take part in their discussion. We combine a content-based approach with a collaborative-filtering approach (utilizing users' co-commenting patterns) in a latent factor modeling framework. We experiment with several variations of the model's loss function in order to adjust it to the problem domain. We evaluate the results on two datasets and show that employing co-commenting patterns improves upon using content features alone, even with as few as two available comments per story. Finally, we try to incorporate available social network data into the model. Interestingly, the social data does not lead to substantial performance gains, suggesting that the value of social data for this task is quite negligible.


title: Government-incentivized crowdfunding for one-belt, one-road enterprises: design and research issues

abstract: Abstract Background Local, state, and federal governments have started exploring the potential of crowdfunding in transforming conventional financing methods used previously to fund public projects and services. While crowdfunding has been applied to improve government financing methods in recent years, little is known about how this new model can be extended and applied in international collaboration among governmentincentivized projects. Methods In this paper, we explores what roles crowdfunding can play as a financial intermediary to support government-incentivized multinational projects in the context of the One-Belt, One-Road initiative. Results We conjecture that crowdfunding can offer an efficient mechanism to improve participatory budgeting and facilitate private–public collaboration while providing a high level of transparency in the budget decision process. Conclusions The paper also discusses research issues and challenges, including features of the crowdfunding platforms that can affect the adoption and use of the crowdfunding platforms in multinational initiatives.


title: Application study on induction heating using half bridge LLC resonant inverter

abstract: Nowadays, an induction heating has been a popular heating technique and used in various applications such as melting, heat treating, preheating for welding, brazing, soldering, curing, sealing and shrink fitting. It is the fast, efficient, precise, repeatable and non-contact technique for heating metals or other electrically-conductive materials. This paper aims to present the induction heating to half bridge LLC resonant inverter application. The analysis contains six components: transformer, full bridge rectifier, half bridge inverter, resonant tank, high frequency transformer, and coil. Furthermore, a PLL controller is used in the system. Basically, an induction power is supplied to the induction heating system in which an alternating current of a converter is delivered to a work piece. The convertor is LLC that is used in this analysis for improving its resonant. The work piece is 20-cm length and 9-mm diameter with 5-turn coil. In addition, it is heated for 60 seconds with temperature at 352 degree Celsius.


title: Community Cloud Computing

abstract: Cloud Computing is rising fast, with its data centres growing at an unprecedented rate. However, this has come with concerns over privacy, efficiency at the expense of resilience, and environmental sustainability, because of the dependence on Cloud vendors such as Google, Amazon and Microsoft. Our response is an alternative model for the Cloud conceptualisation, providing a paradigm for Clouds in the community, utilising networked personal computers for liberation from the centralised vendor model. Community Cloud Computing (C3) offers an alternative architecture, created by combing the Cloud with paradigms from Grid Computing, principles from Digital Ecosystems, and sustainability from Green Computing, while remaining true to the original vision of the Internet. It is more technically challenging than Cloud Computing, having to deal with distributed computing issues, including heterogeneous nodes, varying quality of service, and additional security constraints. However, these are not insurmountable challenges, and with the need to retain control over our digital lives and the potential environmental consequences, it is a challenge we must pursue.


Identification And Control Of Non-Linear Dynamical Systems Using Neural Networks


"Intelligent Heart Disease Prediction System Using Data Mining Techniques"


title: Omnidirectional Circularly Polarized Antenna Utilizing Zeroth-Order Resonance of Epsilon Negative Transmission Line

abstract: The omnidirectional circularly polarized (CP) antenna using a circular mushroom structure with curved branches is proposed. The antenna is based on the zeroth-order resonance (ZOR) mode of epsilon negative (ENG) transmission line (TL) to obtain a vertical polarization and an omnidirectional radiation pattern. Also, the horizontal polarization is obtained by the curved branches. The 90° phase difference between two orthogonal polarizations is inherently provided by the zeroth-order resonator. Therefore, the antenna has an omnidirectional CP radiation pattern in the azimuthal plane. In addition, this antenna is planar type and simply designed without a dual feeding structure and 90° phase shifter. The measured average axial ratio and left-hand (LH) CP gain are 2.03 dB and - 0.40 dBic, respectively, in the azimuthal plane.


title: Functional Dependencies for Graphs

abstract: We propose a class of functional dependencies for graphs, referred to as GFDs. GFDs capture both attribute-value dependencies and topological structures of entities, and subsume conditional functional dependencies (CFDs) as a special case. We show that the satisfiability and implication problems for GFDs are coNP-complete and NP-complete, respectively, no worse than their CFD counterparts. We also show that the validation problem for GFDs is coNP-complete. Despite the intractability, we develop parallel scalable algorithms for catching violations of GFDs in large-scale graphs. Using real-life and synthetic data, we experimentally verify that GFDs provide an effective approach to detecting inconsistencies in knowledge and social graphs.


title: Surgical Correction of Posttraumatic Scapulothoracic Bursitis, Rhomboid Major Muscle Injury, Ipsilateral Glenohumeral Instability, and Headaches Resulting from Circus Acrobatic Maneuvers

abstract: We report the case of a 28-year-old transgender (male-to-female) patient that had a partial tear of the rhomboid major tendon, scapulothoracic bursitis, and glenohumeral instability on the same side. These conditions resulted from traumatic events during circus acrobatic maneuvers. Additional aspects of this case that make it unique include (1) the main traumatic event occurred during a flagpole exercise, where the patient's trunk was suspended horizontally while a vertical pole was grasped with both hands, (2) headaches were associated with the periscapular injury and they improved after scapulothoracic bursectomy and rhomboid tendon repair, (3) surgical correction was done during the same operation with an open anterior capsular-labral reconstruction, open scapulothoracic bursectomy without bone resection, and rhomboid tendon repair, (4) a postoperative complication of tearing of the serratus anterior and rhomboid muscle attachments with recurrent scapulothoracic pain occurred from patient noncompliance, and (5) the postoperative complication was surgically corrected and ultimately resulted in an excellent outcome at the one-year final follow-up.


PlatPal: Detecting Malicious Documents with Platform Diversity


title: Combining Iris and Periocular Recognition Using Light Field Camera

abstract: Iris and Periocular biometrics has proved its effectiveness in accurately verifying the subject of interest. Recent improvements in visible spectrum Iris and Periocular verification have further boosted its application to unconstrained scenarios. However existing visible Iris verification systems suffer from low quality samples because of the limited depth-of-field exhibited by the conventional Iris capture systems. In this work, we propose a robust Iris and Periocular erification scheme in visible spectrum using Light Field Camera (LFC). Since the light field camera can provide multiple focus images in single capture, we are motivated to investigate its applicability for robust Iris and Periocular verification by exploring its all-in-focus property. Further, the use of all-in-focus property will extend the depth-of-focus and overcome the problem of focus that plays a predominant role in robust Iris and Periocular verification. We first collect a new Iris and Periocular biometric database using both light field and conventional camera by simulating real life scenarios. We then propose a new scheme for feature extraction and classification by exploring the combination of Local Binary Patterns (LBP) and Sparse Reconstruction Classifier (SRC). Extensive experiments are carried out on the newly collected database to bring out the merits and demerits on applicability of light field camera for Iris and Periocular verification. Finally, we also present the results on combining the information from Iris and Periocular biometrics using weighted sum rule.


title: Honeycomb: creating intrusion detection signatures using honeypots

abstract: This paper describes a system for automated generation of attack signatures for network intrusion detection systems. Our system applies pattern-matching techniques and protocol conformance checks on multiple levels in the protocol hierarchy to network traffic captured a honeypot system. We present results of running the system on an unprotected cable modem connection for 24 hours. The system successfully created precise traffic signatures that otherwise would have required the skills and time of a security officer to inspect the traffic manually.


title: Deep Text Classification Can be Fooled

abstract: In this paper, we present an effective method to craft text adversarial samples, revealing one important yet underestimated fact that DNN-based text classifiers are also prone to adversarial sample attack. Specifically, confronted with different adversarial scenarios, the text items that are important for classification are identified by computing the cost gradients of the input (white-box attack) or generating a series of occluded test samples (black-box attack). Based on these items, we design three perturbation strategies, namely insertion, modification, and removal, to generate adversarial samples. The experiment results show that the adversarial samples generated by our method can successfully fool both state-of-the-art character-level and word-level DNN-based text classifiers. The adversarial samples can be perturbed to any desirable classes without compromising their utilities. At the same time, the introduced perturbation is difficult to be perceived.


title: A layout dependent full-chip copper electroplating topography model

abstract: In this paper, a layout dependent full-chip electroplating (ECP) topography model is developed based on the additive nature of the physics of the EP process. Two layout attributes: layout density, and feature perimeter sum are used to compute the post-ECP topography. Under a unified mechanism, two output variables representing the final topography: the array height and the step height are modeled simultaneously. Using the proposed model long-range effects of the ECP process can be incorporated easily as well. The simulation results of our model were verified with test structure experimental data published in the literature and are presented in this paper. The results show that the errors are less than 5%. This model is not limited to the regular test structures; it can also be used for any practical design. The results of such partial application are shown here as well. Our proposed ECP model can be used to model systematic variations caused by an ECP process or by a chemical mechanical planarization (CMP) process. The potential applications of this model include: layout design evaluation for catastrophic failure prevention; yield aware design (design for manufacturability), and variation- aware timing analysis.


title: Impact of sepsis on CD4 T cell immunity

abstract: Sepsis remains the primary cause of death from infection in hospital patients, despite improvements in antibiotics and intensive-care practices. Patients who survive severe sepsis can display suppressed immune function, often manifested as an increased susceptibility to (and mortality from) nosocomial infections. Not only is there a significant reduction in the number of various immune cell populations during sepsis, but there is also decreased function in the remaining lymphocytes. Within the immune system, CD4 T cells are important players in the proper development of numerous cellular and humoral immune responses. Despite sufficient clinical evidence of CD4 T cell loss in septic patients of all ages, the impact of sepsis on CD4 T cell responses is not well understood. Recent findings suggest that CD4 T cell impairment is a multipronged problem that results from initial sepsis-induced cell loss. However, the subsequent lymphopenia-induced numerical recovery of the CD4 T cell compartment leads to intrinsic alterations in phenotype and effector function, reduced repertoire diversity, changes in the composition of naive antigen-specific CD4 T cell pools, and changes in the representation of different CD4 T cell subpopulations (e.g., increases in Treg frequency). This review focuses on sepsis-induced alterations within the CD4 T cell compartment that influence the ability of the immune system to control secondary heterologous infections. The understanding of how sepsis affects CD4 T cells through their numerical loss and recovery, as well as function, is important in the development of future treatments designed to restore CD4 T cells to their presepsis state.


The effect of external sodium concentration on the sodium fluxes in frog skeletal muscle


title: Intrusion detection systems: A survey and taxonomy

abstract: This paper presents a taxonomy of intrusion detection systems that is then used to survey and classify a number of research prototypes. The taxonomy consists of a classification first of the detection principle, and second of certain operational aspects of the intrusion detection system as such. The systems are also grouped according to the increasing difficulty of the problem they attempt to address. These classifications are used predictively, pointing towards a number of areas of future research in the field of intrusion detection.


title: Machine Learning Algorithms and Their Application to Ore Reserve Estimation of Sparse and Imprecise Data

abstract: Traditional geostatistical estimation techniques have been used predominantly by the mining industry for ore reserve estimation. Determination of mineral reserve has posed considerable challenge to mining engineers due to the geological complexities of ore body formation. Extensive research over the years has resulted in the development of several state-of-the-art methods for predictive spatial mapping, which could be used for ore reserve estimation; and recent advances in the use of machine learning algorithms (MLA) have provided a new approach for solving the prob-lem of ore reserve estimation. The focus of the present study was on the use of two MLA for estimating ore reserve: namely, neural networks (NN) and support vector machines (SVM). Application of MLA and the various issues involved with using them for reserve estimation have been elaborated with the help of a complex drill-hole dataset that exhibits the typical properties of sparseness and impreciseness that might be associated with a mining dataset. To investigate the accuracy and applicability of MLA for ore reserve estimation, the generalization ability of NN and SVM was compared with the geostatistical ordinary kriging (OK) method.


title: Capability Driven Development: An Approach to Designing Digital Enterprises

abstract: The need for organizations to operate in changing environments is addressed by proposing an approach that integrates organizational development with information system (IS) development taking into account changes in the application context of the solution. This is referred to as Capability Driven Development (CDD). A meta-model representing business and IS designs consisting of goals, key performance indicators, capabilities, context and capability delivery patterns, is being proposed. The use of the meta-model is validated in three industrial case studies as part of an ongoing collaboration project, whereas one case is presented in the paper. Issues related to the use of the CDD approach, namely, CDD methodology and tool support are also discussed.


title: Connectors i processos de gramaticalització

abstract: In this article, we analyse the characteristics of the grammaticalization process that originates connectors, taking as analysis corpus the book «Costums de Tortosa». We have identified the temporal forms that became interordinate connectors (causal, consecutive, concessive, conditional and adversative), and have outlined the characteristics of the semantic change and of the sintactical reanalysis which can be ::: observed in this process. As an example of this general process, we have analysed the semantic change in the case «encara» / «encara que» and the sintactical reanalysis in the semifixed structure «jasia co que».


title: EEG Vigilance Profile as a Supplement of Psychiatric Data

abstract: A vigilance measurement based on EEG spectra was employed to supplement the clinical data. In an experimental situation when the patient was left undisturbed after a standard stimulation, four typical


title: A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis

abstract: Opinion mining from customer reviews has become pervasive in recent years. Sentences in reviews, however, are usually classified independently, even though they form part of a review's argumentative structure. Intuitively, sentences in a review build and elaborate upon each other; knowledge of the review structure and sentential context should thus inform the classification of each sentence. We demonstrate this hypothesis for the task of aspect-based sentiment analysis by modeling the interdependencies of sentences in a review with a hierarchical bidirectional LSTM. We show that the hierarchical model outperforms two non-hierarchical baselines, obtains results competitive with the state-of-the-art, and outperforms the state-of-the-art on five multilingual, multi-domain datasets without any hand-engineered features or external resources.


title: Analysis, evaluation and development of wheelchair-mounted robotic arms

abstract: This paper focuses on kinematic analysis and evaluation of wheelchair mounted robotic arms (WMRA). It addresses the kinematics of the WMRA with respect to its ability to reach common positions while performing activities of daily living (ADL). A procedure is developed for the kinematic analysis and evaluation of a WMRA. In an effort to evaluate two commercial WMRAs, the procedure for kinematic analysis is applied to each manipulator. Design recommendations and insights with regard to each device are obtained and used to design a new WMRA to overcome the limitations of these devices. This method benefits the researchers by providing a standardized procedure for kinematic analysis of WMRAs that is capable of evaluating independent designs.


title: Cyber-Physical Systems Security: Experimental Analysis of a Vinyl Acetate Monomer Plant

abstract: We describe an approach for analysing and attacking the physical part (a process) of a cyber-physical system. The stages of this approach are demonstrated in a case study, a simulation of a vinyl acetate monomer plant. We want to demonstrate in particular where security has to rely on expert knowledge in the domain of the physical components and processes of a system and that there are major challenges for converting cyber attacks into successful cyber-physical attacks.


title: Strenuous exercise increases late outgrowth endothelial cells in healthy subjects

abstract: Endothelial progenitor cells (EPCs) and late outgrowth endothelial cells (OECs) seem to play an important role in vessel formation. While EPCs seem to exert their function mainly through a paracrine effect, the OECs can develop into mature endothelial cells and form tubular structures. Exercise is known to increase angiogenic factors that can mobilize EPCs; however, the effect on OECs is not known. We investigated the response to a single session of strenuous exercise on OECs, vascular endothelial growth factor (VEGF) and inflammatory cell levels in the healthy. Eleven healthy subjects performed 1 h of spinning exercise. Blood samples were collected at 1, 6, 24 and 48 h post-exercise for cell culture and biochemical analysis. OEC colonies doubled one hour after the spinning session (baseline 4.5 +/- 4.3 vs. 9.0 +/- 3.7, P < 0.05). Serum VEGF increased from 194 +/- 107 pg/ml at baseline to 224 +/- 111 pg/ml after 1 h, p = ns and neutrophilic granulocytes increased from 3.73 +/- 1.38 at baseline to 9.08 +/- 10.5 at 1 h (P < 0.01). The increased levels of OECs, VEGF and neutrophilic granulocytes declined gradually at the following time points. VEGF levels and neutrophilic granulocytes were highly correlated to OEC levels, r = 0.903 (VEGF) and r = 0.85 (neutrophilic granulocytes), respectively. Strenuous physical activity increases OEC colonies and is correlated to serum VEGF and neutrophilic granulocytes levels. An acute exercise-induced inflammatory response might be responsible for the VEGF release and subsequent increase of OECs. The clinical importance of these findings remains to be elucidated.


title: A comparison of event models for Naive Bayes text classification

abstract: Recent work in text classification has used two different first-order probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey and Croft 1996; Koller and Sahami 1997). Others use a multinomial model, that is, a uni-gram language model with integer word counts (e.g. Lewis and Gale 1994; Mitchell 1997). This paper aims to clarify the confusion by describing the differences and details of these two models, and by empirically comparing their classification performance on five text corpora. We find that the multi-variate Bernoulli performs well with small vocabulary sizes, but that the multinomial performs usually performs even better at larger vocabulary sizes--providing on average a 27% reduction in error over the multi-variate Bernoulli model at any vocabulary size.


title: A Growing Neural Gas Network Learns Topologies

abstract: An incremental network model is introduced which is able to learn the important topological relations in a given set of input vectors by means of a simple Hebb-like learning rule. In contrast to previous approaches like the "neural gas" method of Martinetz and Schulten (1991, 1994), this model has no parameters which change over time and is able to continue learning, adding units and connections, until a performance criterion has been met. Applications of the model include vector quantization, clustering, and interpolation.


title: HVAC system modeling for range prediction of electric vehicles

abstract: The HVAC system is considered as the largest auxiliary power load in electric vehicles (EV). Therefore, this paper presents a detailed modeling of an EV-based HVAC system to support a priori prediction of HVAC system energy consumption under consideration of the EV users thermal comfort. This prediction is integrated into a navigation system to allow the driver entering the preferred parameters of thermal comfort and advising the driver about the predicted overall energy consumption. The advice acceptance might increase the awareness of the driver regarding the potential saved energy and leads to an energy-efficient vehicle operation by extending the overall driving range.


title: A Systematic Approach to Define the Domain of Information System Security Risk Management

abstract: Today, security concerns are at the heart of information systems, both at technological and organizational levels. With over 200 practitioner-oriented risk management methods and several academic security modelling frameworks available, a major challenge is to select the most suitable approach. Choice is made even more difficult by the absence of a real understanding of the security risk management domain and its ontology of related concepts. This chapter contributes to the emergence of such an ontology. It proposes and applies a rigorous approach to build an ontology, or domain model, of information system security risk management. The proposed domain model can then be used to compare, select or otherwise improve security risk management methods.


title: Affine Parallelization Using Dependence and Cache Analysis in a Binary Rewriter

abstract: Today, nearly all general-purpose computers are parallel, but nearly all software running on them is serial. Bridging this disconnect by manually rewriting source code in parallel is prohibitively expensive. Hence, automatic parallelization is an attractive alternative. We present a method to perform automatic parallelization in a binary rewriter. The input to the binary rewriter is the serial binary executable program and the output is a parallel binary executable. The advantages of parallelization in a binary rewriter versus a compiler include: (i) applicability to legacy binaries whose source is not available; (ii) applicability to library code that is often distributed only as binary; (iii) usability by end-user of the software; and (iv) applicability to assembly-language programs. Adapting existing parallelizing compiler methods that work on source code to binaries is a significant challenge. This is primarily because symbolic and array index information used by existing parallelizing compilers to take parallelizing decisions is not available from a binary. We show how to adapt existing parallelization methods to binaries without using symbolic and array index information to achieve equivalent source parallelization from binaries. Results using our x86 binary rewriter called SecondWrite are presented in detail for the dense-matrix and regular Polybench benchmark suite. For these, the average speedup from our method for binaries is 27.95X versus 28.22X from source on 24 threads, compared to the input serial binary. Super-linear speedups are possible due to cache optimizations. In addition our results show that our method is robust and has correctly parallelized much larger binaries from the SPEC 2006 and OMP 2001 benchmark suites, totaling over 2 million source lines of code (SLOC). Good speedups result on the subset of those benchmarks that have affine parallelism in them; this subset exceeds 100,000 SLOC. This robustness is unusual even for the latest leading source parallelizers, many of which are famously fragile.


title: FACSCaps: Pose-Independent Facial Action Coding with Capsules

abstract: Most automated facial expression analysis methods treat the face as a 2D object, flat like a sheet of paper. That works well provided images are frontal or nearly so. In real-world conditions, moderate to large head rotation is common and system performance to recognize expression degrades. Multi-view Convolutional Neural Networks (CNNs) have been proposed to increase robustness to pose, but they require greater model sizes and may generalize poorly across views that are not included in the training set. We propose FACSCaps architecture to handle multi-view and multi-label facial action unit (AU) detection within a single model that can generalize to novel views. Additionally, FACSCaps's ability to synthesize faces enables insights into what is leaned by the model. FACSCaps models video frames using matrix capsules, where hierarchical pose relationships between face parts are built into internal representations. The model is trained by jointly optimizing a multi-label loss and the reconstruction accuracy. FACSCaps was evaluated using the FERA 2017 facial expression dataset that includes spontaneous facial expressions in a wide range of head orientations. FACSCaps outperformed both state-of-the-art CNNs and their temporal extensions.


Agenda detector: labeling tweets with political policy agenda


title: Rethinking e-inclusion

abstract: The first aim of this paper is to revisit the theoretical framework from which the notions of social inclusion/exclusion have developed. The second aim is to address four issues related to the notion of social e-inclusion, drawing on nine case studies carried out in Italy inside the European funded project SIGIS “Strategies of Inclusion: Gender and the Information Society”. The first of these e-inclusion issues is that it is impossible to discuss e- inclusion/exclusion in terms of digital divide without it becoming an a priori idea. The second issue is that e-inclusion is not a universal aspiration, in that all the societies automatically tend to realize in the same measure. The third is that the debate on e-inclusion/exclusion seems to lack not only a robust sociological and political vision, but additionally, an anthropological awareness which leaves wide gaps in our reading of cultural differences in this field. The fourth issue which will be addressed is that e-inclusion must be related to the notion of “administrative burden”, which measures time and costs required to citizens in order to use online information and services provided by public administration. An increase in e- inclusion implies in fact an increase of administrative burden for citizens.


title: Modular joint design for performance enhanced humanoid robot LOLA

abstract: The paper presents the performance enhanced humanoid robot LOLA which is currently being manufactured. The goal of the project is the realization of a fast, human-like walking motion. The robot is characterized by its lightweight construction, a modular, multi-sensory joint design with brushless motors and an electronics architecture using decentralized joint controllers. The fusion of motor, gear and sensors into a highly integrated, mechatronic joint module has several advantages for the whole system, including high power density, good dynamic performance and reliability. Additional degrees of freedom are introduced in elbow, waist and toes. Linear actuators are employed for the knee joints for a better mass distribution in the legs


title: Paradox of richness: a cognitive model of media choice

abstract: Researchers have long studied the effects of social presence and media richness on media choice and the effects of media use. This focus on social presence and social psychological theories has led to valuable research on communication. However, little research (either empirical or theoretical) has been done to understand the ways in which media choices influence the cognitive processes that underlie communication. In this paper, we present a cognitive-based view of media choice and media use, based on dual process theories of cognition, which argue that in order for individuals to systematically process messages, they must be motivated to process the message and have the ability to process it. We argue that the use of rich media high in social presence induces increased motivation but decreases the ability to process information, while the use of lean media low in social presence induces decreased motivation but increases the ability to process information. The paradox of richness lies in its duality of impact: from a cognitive perspective, rich media high in social presence simultaneously acts to both improve and impair performance.


title: What makes a best exemplar

abstract: This paper is part of a broader multilingual research project that addresses the relationships between affective phenomena (as units of personal experience), concepts (as units of cognitive processing) and the lexical items we use in talking about affective experience (as units of verbal communication. The focus will be here on data obtained in six Scandinavian and Romance languages (N=1093) with the help of a reasoning task that supplemented a free listing and a ranking task. The task was specifically designed for assessing whether informants’ choice of best (or most typical) exemplars of a superordinate category in the affective domain relies on a similarity-based strategy of attribute matching and family resemblance computation as suggested by prototype theory. As a consequence, the instructions were formulated so to encourage the use of a similarity-based comparison in justifying the choice of best exemplars. The responses provided by the informants show instead that participants may resort to a variety of alternative criteria (frequency, complexity, basicness, intensity, cultural or personal meaning, etc.) for assigning best-exemplar status. It is contended that such alternative criteria are consistent with a theory- (or knowledge-) based approach to concepts and categorization and that a hybrid, multi-strategy approach is more suitable in order to account for categorization in the affective domain.


title: Monocular Road Terrain Detection by Combining Visual and Spatial Information

abstract: For future driver assistance systems and autonomous vehicles, the road course, i.e., the width and shape of the driving path, is an important source of information. In this paper, we introduce a new hierarchical two-stage approach for learning the spatial layout of road scenes. In the first stage, base classifiers analyze the local visual properties of patches extracted from monocular camera images and provide metric confidence maps. We use classifiers for road appearance, boundary appearance, and lane-marking appearance. The core of the proposed approach is the computation of SPatial RAY (SPRAY) features from each metric confidence map in the second stage. A boosting classifier selecting discriminative SPRAY features can be trained for different types of road terrain and allows capturing the local visual properties together with their spatial layout in the scene. In this paper, the extraction of road area and ego-lane on inner-city video streams is demonstrated. In particular, the detection of the ego-lane is a challenging semantic segmentation task showing the power of SPRAY features, because on a local appearance level, the ego-lane is not distinguishable from other lanes. We have evaluated our approach operating at 20 Hz on a graphics processing unit on a publicly available data set, demonstrating the performance on a variety of road types and weather conditions.


title: QuickNet: Maximizing Efficiency and Efficacy in Deep Architectures

abstract: We present QuickNet, a fast and accurate network architecture that is both faster and significantly more accurate than other fast deep architectures like SqueezeNet. Furthermore, it uses less parameters than previous networks, making it more memory efficient. We do this by making two major modifications to the reference Darknet model (Redmon et al, 2015): 1) The use of depthwise separable convolutions and 2) The use of parametric rectified linear units. We make the observation that parametric rectified linear units are computationally equivalent to leaky rectified linear units at test time and the observation that separable convolutions can be interpreted as a compressed Inception network (Chollet, 2016). Using these observations, we derive a network architecture, which we call QuickNet, that is both faster and more accurate than previous models. Our architecture provides at least four major advantages: (1) A smaller model size, which is more tenable on memory constrained systems; (2) A significantly faster network which is more tenable on computationally constrained systems; (3) A high accuracy of 95.7 percent on the CIFAR-10 Dataset which outperforms all but one result published so far, although we note that our works are orthogonal approaches and can be combined (4) Orthogonality to previous model compression approaches allowing for further speed gains to be realized.


title: Compressing Neural Networks using the Variational Information Bottleneck

abstract: Neural networks can be compressed to reduce memory and computational requirements, or to increase accuracy by facilitating the use of a larger base architecture. In this paper we focus on pruning individual neurons, which can simultaneously trim model size, FLOPs, and run-time memory. To improve upon the performance of existing compression algorithms we utilize the information bottleneck principle instantiated via a tractable variational bound. Minimization of this information theoretic bound reduces the redundancy between adjacent layers by aggregating useful information into a subset of neurons that can be preserved. In contrast, the activations of disposable neurons are shut off via an attractive form of sparse regularization that emerges naturally from this framework, providing tangible advantages over traditional sparsity penalties without contributing additional tuning parameters to the energy landscape. We demonstrate state-of-the-art compression rates across an array of datasets and network architectures.


title: Pattern Discovery Techniques for the Text Mining and its Applications

abstract: Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This paper include different an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information.


title: Exploring Normalization in Deep Residual Networks with Concatenated Rectified Linear Units.

abstract: Deep Residual Networks (ResNets) have recently achieved state-of-the-art results on many challenging computer vision tasks. In this work we analyze the role of Batch Normalization (BatchNorm) layers on ResNets in the hope of improving the current architecture and better incorporating other normalization techniques, such as Normalization Propagation (NormProp), into ResNets. Firstly, we verify that BatchNorm helps distribute representation learning to residual blocks at all layers, as opposed to a plain ResNet without BatchNorm where learning happens mostly in the latter part of the network. We also observe that BatchNorm well regularizes Concatenated ReLU (CReLU) activation scheme on ResNets, whose magnitude of activation grows by preserving both positive and negative responses when going deeper into the network. Secondly, we investigate the use of NormProp as a replacement for BatchNorm in ResNets. Though NormProp theoretically attains the same effect as BatchNorm on generic convolutional neural networks, the identity mapping of ResNets invalidates its theoretical promise and NormProp exhibits a significant performance drop when naively applied. To bridge the gap between BatchNorm and NormProp in ResNets, we propose a simple modification to NormProp and employ the CReLU activation scheme. We experiment on visual object recognition benchmark datasets such as CIFAR-10/100 and ImageNet and demonstrate that 1) the modified NormProp performs better than the original NormProp but is still not comparable to BatchNorm and 2) CReLU improves the performance of ResNets with or without normalizations.


title: Improvement of pixel electrode circuit for active-matrix OLED by application of reversed-biased voltage

abstract: In this paper, an improved ac pixel electrode circuit for active-matrix organic light-emitting display (AMOLED) has been proposed by adding a thin-film transistor. This circuit can provide an ac driving mode for AMOLED and makes the OLED in a reversed-biased voltage during the reverse cycle. And a circuit design for understanding ac driving mode was presented. The circuit simulation results indicate that this circuit is feasible. The circuit structure is practical for the AMOLED pixel driver; it can improve the performance of OLED.


title: Collaborative Deep Reinforcement Learning

abstract: Besides independent learning, human learning process is highly improved by summarizing what has been learned, communicating it with peers, and subsequently fusing knowledge from different sources to assist the current learning goal. This collaborative learning procedure ensures that the knowledge is shared, continuously refined, and concluded from different perspectives to construct a more profound understanding. The idea of knowledge transfer has led to many advances in machine learning and data mining, but significant challenges remain, especially when it comes to reinforcement learning, heterogeneous model structures, and different learning tasks. Motivated by human collaborative learning, in this paper we propose a collaborative deep reinforcement learning (CDRL) framework that performs adaptive knowledge transfer among heterogeneous learning agents. Specifically, the proposed CDRL conducts a novel deep knowledge distillation method to address the heterogeneity among different learning tasks with a deep alignment network. Furthermore, we present an efficient collaborative Asynchronous Advantage Actor-Critic (cA3C) algorithm to incorporate deep knowledge distillation into the online training of agents, and demonstrate the effectiveness of the CDRL framework using extensive empirical evaluation on OpenAI gym.


title: T.: Designing relational agents as long term social companions for older adults. In: Intelligent Virtual Agents

abstract: Older adults with strong social connections are at a reduced risk for health problems and mortality. We describe two field studies to inform the development of a virtual agent designed to provide long-term, continuous social support to isolated older adults. Findings include the topics that older adults would like to discuss with a companion agent, in addition to overall reactions to interacting with a remote-controlled companion agent installed in their home for a week. Results indicate a generally positive attitude towards companion agents and a rich research agenda for virtual companion agents.


title: Validating Storage System Instrumentation

abstract: There is a large body of work-such as system administration and intrusion detection-that relies upon storage system logs and snapshots. These solutions rely on accurate system records, however, little effort has been made to verify the correctness of logging instrumentation and log reliability. We present a solution, called ExDiff, that uses expectation differencing to validate storage system logs. Our solution can identify development errors such as the omission of a logging point and runtime errors such as log crashes. ExDiff uses metadata snapshots and activity logs to predict the expected state of the system and compares that with the system's actual state. Mismatches between the expected and actual metadata states can then be used to highlight gaps in log coverage, as well as aid in identifying specific types of missing entries. We show that ExDiff provides valuable insight to system designers, administrators and researchers by accurately identifying gaps in log coverage, providing clues useful in isolating specific types of missing log entries, and highlighting potential misunderstandings in logged action.


title: A module for anomaly detection in ICS networks

abstract: Network security monitoring using machine learning algorithms is a topic that has been well researched and found to be difficult to use. We propose to use a specific approach in restricted IP network environments and leverage the network state information and information from individual connections for increased level of sensitivity. The approach is meant for use in restricted IP networks which exhibit a level of determinism that enables the use of machine learning approach. In this work we use algorithm called Self-Organizing Maps. We introduce an implementation of self-organizing maps engine built on top of the Bro network security monitor. An implemented selection of initial features for the Self-Organizing Maps is provided and a sample sub-set is used when training a SOM lattice for network data from an industrial control system environment. The anomaly detection prototype described in this paper is meant as a complementary mechanism, not a standalone solution for network security monitoring.


title: mlegp: statistical analysis for computer models of biological systems using R

abstract: Summary: Gaussian processes (GPs) are flexible statistical models commonly used for predicting output from complex computer codes. As such, GPs are well suited for the analysis of computer models of biological systems, which have been traditionally difficult to analyze due to their high-dimensional, non-linear and resource-intensive nature. We describe an R package, mlegp, that fits GPs to computer model outputs and performs sensitivity analysis to identify and characterize the effects of important model inputs. ::: ::: Availability: http://www.biomath.org/mlegp ::: ::: Contact: kdorman@iastate.edu ::: ::: Supplementary information: See http://www.biomath.org/mlegp for a user manual and examples.


title: Occupational Therapy With Children With Pervasive Developmental Disorders

abstract: OBJECTIVE ::: Although the prevalence of children with pervasive developmental disorders (PDD) has increased, empirical data about the role and practices of occupational therapists have not been reported in the literature. This descriptive study investigated the practice of occupational therapists with children with PDD. ::: ::: ::: METHOD ::: A survey was mailed to 500 occupational therapists in the Sensory Integration Special Interest Section or School System Special Interest Section of the American Occupational Therapy Association in eastern and midwestern United States. The valid return rate was 58% (292 respondents). The survey used Likert scale items to measure frequency of performance problems observed in children with PDD, performance areas addressed in intervention, perceived improvement in performance, and frequency of use of and competency in intervention approaches. ::: ::: ::: RESULTS ::: The respondents primarily worked in schools and reported that in the past 5 years they had served an increasing number of children with PDD. Most respondents provided direct services and appeared to use holistic approaches in which they addressed multiple performance domains. They applied sensory integration and environmental modification approaches most frequently and believed that they were most competent in using these approaches. Respondents who reported more frequent use of and more competence in sensory integration approaches perceived more improvement in children's sensory processing. Respondents who reported more frequent use of and more competence in child-centered play perceived more improvement in children's sensory integration and play skills.


title: Promoting relaxation using virtual reality, olfactory interfaces and wearable EEG

abstract: The ability to relax is sometimes challenging to achieve, nevertheless it is extremely important for mental and physical health, particularly to effectively manage stress and anxiety. We propose a virtual reality experience that integrates a wearable, low-cost EEG headband and an olfactory necklace that passively promotes relaxation. The physiological response was measured from the EEG signal. Relaxation scores were computed from EEG frequency bands associated with a relaxed mental state using an entropy-based signal processing approach. The subjective perception of relaxation was determined using a questionnaire. A user study involving 12 subjects showed that the subjective perception of relaxation increased by 26.1 % when using a VR headset with the olfactory necklace, compared to not being exposed to any stimulus. Similarly, the physiological response also increased by 25.0 %. The presented work is the first Virtual Reality Therapy system that uses scent in a wearable manner and proves its effectiveness to increase relaxation in everyday life situations.


title: A novel multi-target detection algorithm for automotive FMCW radar

abstract: In automotive radar systems resolution of each target in multi-target environment is necessary. A conventional FMCW radar which transmits and receives triangular waves, does not effectively remove the ghost-targets which arise in the process of Doppler-range localization process. Using a sophisticated waveform design can help removing the ghost-targets, however, there remains many of them. In this paper, we propose a novel ghost-target removal algorithm using the inter-relationship between the upbeat and downbeat frequency after the Fast Fourier Transform(FFT).


title: Modified Epidemic Routing with Redundant Message Elimination in Delay Tolerant Network

abstract: This paper introduces a new scheme to control redundant message copies in Delay Tolerant mobile Ad-hoc networks (MANET) an d also proposes an efficient message buffering strategy. Delay tolerant networks (DTN) generally lack end-to-end connectivity. The researchers in this area had proposed to maintain multiple message copies in routing protocols to transmit messages in DTN. But it increases the network traffic due to a large number of message redundancies. In this work, to control the message redundancy in DTN, we have introduced a predetermined threshold value to the network, and a counter flag to each node for every message. The counter flags record the number of encounters with other nodes having that particular message copy. If the counter flag of a node for a particular message reaches the predefined threshold, the node removes that particular copy. With this proposed scheme message copies are decreased and finally they are removed completely. In addition to this, we have introduced an efficient message buffering strategy. In DTN, each node can contain a limited number of messages. When the message buffer is full, the node deletes the oldest message from its buffer. In this new scheme, the successful delivery efficiency is still the same as epidemic routing and the redundant copies are efficiently controlled to a relatively low level and the message buffer utilization is also high.


title: Extraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics

abstract: Abstract Electrocorticogram (ECoG) recordings for neuroprosthetics provide a mesoscopic level of abstraction of brain function between microwire single neuron recordings and the electroencephalogram (EEG). Single-trial ECoG neural interfaces require appropriate feature extraction and signal processing methods to identify and model in real-time signatures of motor events in spontaneous brain activity. Here, we develop the clinical experimental paradigm and analysis tools to record broadband (1 Hz to 6 kHz) ECoG from patients participating in a reaching and pointing task. Motivated by the significant role of amplitude modulated rate coding in extracellular spike based brain–machine interfaces (BMIs), we develop methods to quantify spatio-temporal intermittent increased ECoG voltages to determine if they provide viable control inputs for ECoG neural interfaces. This study seeks to explore preprocessing modalities that emphasize amplitude modulation across frequencies and channels in the ECoG above the level of noisy background fluctuations in order to derive the commands for complex, continuous control tasks. Preliminary experiments show that it is possible to derive online predictive models and spatially localize the generation of commands in the cortex for motor tasks using amplitude modulated ECoG.


RELATIONSHIP OF FAMILY HISTORY OF ALZHEIMER’S DISEASE AND APOE GENOTYPE WITH CEREBRAL AMYLOID DEPOSITION AND GLUCOSE METABOLISM IN COGNITIVELY NORMAL MIDDLE- AND OLD-AGED ADULTS


title: Development of Platform Independent Hand Gesture Controlled Wearable Mouse

abstract: Human-Computer Interaction (HCI) is an ever growing and a commercially developing technology. Smart devices and wearable electronics are becoming very popular, but as of now, there is no available alternative to practically replace the conventional mouse. This paper presents a design for a fully operational hand-gesture controlled wearable mouse. It is easy to wear and remove that makes it ideal for daily use. It is a platform-independent design using Human Interface Device (HID) technology. It is a simple plug and play type device without the need for a special driver and other software support. From motion detection point of view, what are the differences between accelerometer and gyroscope are also clearly explained here with help of graphs and results. The suitable sensor has been used to detect hand gestures and accordingly change the position of the mouse pointer.


title: Simultaneous Visual Analysis of Multiple Software Hierarchies

abstract: We propose a tree visualization technique for comparison of structures and attributes across multiple hierarchies. Many software systems are structured hierarchically by design. For example, developers subdivide source code into libraries, modules, and functions. This design propagates to software configuration and business processes, rendering software hierarchies even more important. Often these structural elements are attributed with reference counts, code quality metrics, and the like. Throughout the entire software life cycle, these hierarchies are reviewed, integrated, debugged, and changed many times by different people so that the identity of a structural element and its attributes is not clearly traceable. We argue that pairwise comparison of similar trees is a tedious task due to the lack of overview, especially when applied to a large number of hierarchies. Therefore, we strive to visualize multiple similar trees as a whole by merging them into one supertree. To merge structures and combine attributes from different trees, we leverage the Jaccard similarity and solve a matching problem while keeping track of the origin of a structure element and its attributes. Our visualization approach allows users to inspect these supertrees using node-link diagrams and indented tree plots. The nodes in these plots depict aggregated attributes and, using word-sized line plots, detailed data. We demonstrate the usefulness of our method by exploring the evolution of software repositories and debugging data processing pipelines using provenance data.


title: Effect of Explicit Emotional Adaptation on Prosocial Behavior of Humans towards Robots depends on Prior Robot Experience

abstract: Emotional adaptation increases pro-social behavior of humans towards robotic interaction partners. Social cues are an important factor in this context. This work investigates, if emotional adaptation still works under absence of human-like facial Action Units. A human-robot dialog scenario is chosen using NAO pretending to work for a supermarket and involving humans providing object names to the robot for training purposes. In a user study, two conditions are implemented with or without explicit emotional adaptation of NAO to the human user in a between-subjects design. Evaluations of user experience and acceptance are conducted based on evaluated measures of human-robot interaction (HRI). The results of the user study reveal a significant increase of helpfulness (number of named objects), anthropomorphism, and empathy in the explicit emotional adaptation condition even without social cues of facial Action Units, but only in case of prior robot contact of the test persons. Otherwise, an opposite effect is found. These findings suggest, that reduction of these social cues can be overcome by robot experience prior to the interaction task, e.g. realizable by an additional bonding phase, confirming the importance of such from previous work. Additionally, an interaction with academic background of the participants is found.


title: Classifying Different Emotional States by Means of EEG-Based Functional Connectivity Patterns

abstract: This study aimed to classify different emotional states by means of EEG-based functional connectivity patterns. Forty young participants viewed film clips that evoked the following emotional states: neutral, positive, or negative. Three connectivity indices, including correlation, coherence, and phase synchronization, were used to estimate brain functional connectivity in EEG signals. Following each film clip, participants were asked to report on their subjective affect. The results indicated that the EEG-based functional connectivity change was significantly different among emotional states. Furthermore, the connectivity pattern was detected by pattern classification analysis using Quadratic Discriminant Analysis. The results indicated that the classification rate was better than chance. We conclude that estimating EEG-based functional connectivity provides a useful tool for studying the relationship between brain activity and emotional states.


CONSUMER MOTIVATION AND CONCERN FACTORS FOR ONLINE SHOPPING IN TURKEY


title: Adaptive Burst-Writes (ABW): Memory Requests Scheduling to Reduce Write-Induced Interference

abstract: Main memory latencies have become a major performance bottleneck for chip-multiprocessors (CMPs). Since reads are on the critical path, existing memory controllers prioritize reads over writes. However, writes must be eventually processed when the write queue is full. These writes are serviced in a burst to reduce the bus turnaround delay and increase the row-buffer locality. Unfortunately, a large number of reads may suffer long queuing delay when the burst-writes are serviced. The long write latency of future nonvolatile memory will further exacerbate the long queuing delay of reads during burst-writes. In this article, we propose a run-time mechanism, Adaptive Burst-Writes (ABW), to reduce the queuing delay of reads. Based on the row-buffer hit rate of writes and the arrival rate of reads, we dynamically control the number of writes serviced in a burst to trade off the write service time and the queuing latency of reads. For prompt adjustment, our history-based mechanism further terminates the burst-writes earlier when the row-buffer hit rate of writes in the previous burst-writes is low. As a result, our policy improves system throughput by up to 28p (average 10p) and 43p (average 14p) in CMPs with DRAM-based and PCM-based main memory.