title: Anomalous Network Packet Detection Using Data Stream Mining

abstract: In recent years, significant research has been devoted to the development of Intrusion Detection Systems (IDS) able to detect anomalous computer network traffic indicative of malicious activity. While signature-based IDS have proven effective in discovering known attacks, anomaly-based IDS hold the even greater promise of being able to automatically detect previously undocumented threats. Traditional IDS are generally trained in batch mode, and therefore cannot adapt to evolving network data streams in real time. To resolve this limitation, data stream mining techniques can be utilized to create a new type of IDS able to dynamically model a stream of network traffic. In this paper, we present two methods for anomalous network packet detection based on the data stream mining paradigm. The first of these is an adapted version of the DenStream algorithm for stream clustering specifically tailored to evaluate network traffic. In this algorithm, individual packets are treated as points and are flagged as normal or abnormal based on their belonging to either normal or outlier clusters. The second algorithm utilizes a histogram to create a model of the evolving network traffic to which incoming traffic can be compared using Pearson correlation. Both of these algorithms were tested using the first week of data from the DARPA ’99 dataset with Generic HTTP, Shell-code and Polymorphic attacks inserted. We were able to achieve reasonably high detection rates with moderately low false positive percentages for different types of attacks, though detection rates varied between the two algorithms. Overall, the histogram-based detection algorithm achieved slightly superior results, but required more parameters than the clustering-based algorithm. As a result of its fewer parameter requirements, the clustering approach can be more easily generalized to different types of network traffic streams.


title: Natural Language Access to Enterprise Data

abstract: This paper describes USI Answers — a natural language question answering system for enterprise data. We report on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system's interpretation of the query, and allows easy query adjustment and reformulation. The application is in use by more than 1500 users from Siemens Energy. We evaluate our approach on a data set consisting of fleet data.


title: Addressing the problems of data-centric physiology-affect relations modeling

abstract: Data-centric affect modeling may render itself restrictive in practical applications for three reasons, namely, it falls short of feature optimization, infers discrete affect classes, and deals with relatively small to average sized datasets. Though it seems practical to use the feature combinations already associated to commonly investigated sensors, there may be other potentially optimal features that can lead to new relations. Secondly, although it seems more realistic to view affect as continuous, it requires using continuous labels that will increase the difficulty of modeling. Lastly, although a large scale dataset reflects a more precise range of values for any given feature, it severely hinders computational efficiency. We address these problems when inferring physiology-affect relations from datasets that contain 2-3 million feature vectors, each with 49 features and labelled with continuous affect values. We employ automatic feature selection to acquire near optimal feature subsets and a fast approximate kNN algorithm to solve the regression problem and cope with the challenge of a large scale dataset. Our results show that high estimation accuracy may be achieved even when the selected feature subset is only about 7% of the original features. May the results here motivate the HCI community to pursue affect modeling without being deterred by large datasets and further the discussions on acquiring optimal features for accurate continuous affect approximation.


title: Aerial Lidar Data Classification using AdaBoost

abstract: We use the AdaBoost algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees. To do so we use five features: height, height variation, normal variation, lidar return intensity, and image intensity. We also use only lidar-derived features to organize the data into three classes (the road and grass classes are merged). We apply and test our results using ten regions taken from lidar data collected over an area of approximately eight square miles, obtaining higher than 92% accuracy. We also apply our classifier to our entire dataset, and present visual classification results both with and without uncertainty. We implement and experiment with several variations within the AdaBoost family of algorithms. We observe that our results are robust and stable over all the various tests and algorithmic variations. We also investigate features and values that are most critical in distinguishing between the classes. This insight is important in extending the results from one geographic region to another.


title: The feasibility of launching and detecting jamming attacks in wireless networks

abstract: Wireless networks are built upon a shared medium that makes it easy for adversaries to launch jamming-style attacks. These attacks can be easily accomplished by an adversary emitting radio frequency signals that do not follow an underlying MAC protocol. Jamming attacks can severely interfere with the normal operation of wireless networks and, consequently, mechanisms are needed that can cope with jamming attacks. In this paper, we examine radio interference attacks from both sides of the issue: first, we study the problem of conducting radio interference attacks on wireless networks, and second we examine the critical issue of diagnosing the presence of jamming attacks. Specifically, we propose four different jamming attack models that can be used by an adversary to disable the operation of a wireless network, and evaluate their effectiveness in terms of how each method affects the ability of a wireless node to send and receive packets. We then discuss different measurements that serve as the basis for detecting a jamming attack, and explore scenarios where each measurement by itself is not enough to reliably classify the presence of a jamming attack. In particular, we observe that signal strength and carrier sensing time are unable to conclusively detect the presence of a jammer. Further, we observe that although by using packet delivery ratio we may differentiate between congested and jammed scenarios, we are nonetheless unable to conclude whether poor link utility is due to jamming or the mobility of nodes. The fact that no single measurement is sufficient for reliably classifying the presence of a jammer is an important observation, and necessitates the development of enhanced detection schemes that can remove ambiguity when detecting a jammer. To address this need, we propose two enhanced detection protocols that employ consistency checking. The first scheme employs signal strength measurements as a reactive consistency check for poor packet delivery ratios, while the second scheme employs location information to serve as the consistency check. Throughout our discussions, we examine the feasibility and effectiveness of jamming attacks and detection schemes using the MICA2 Mote platform.


title: Word2vec semantic representation in multilabel classification for Indonesian news article

abstract: Mutilabel text classification is task to categorize a text into one or more categories. Like other supervised learning, performance of multilabel classification is limited when there are small labeled data and it leads to the difficulty of capturing semantic relationship. The previous research of multilabel classification for Indonesian news article focused on implementing multilabel classification using lexical feature that employed bag of words and TF-IDF term weighting, and there is no work yet that uses semantic features. The purpose of this paper is to present an implementation of multilabel classification using semantic feature based on Word2vec. Word2vec is an unsupervised task that is capable of utilizing unlabeled data to convert a word into its vector representation that can also find the semantic relationship between words by counting their distance. The experiment shows that the result using this semantic feature improves the previous result that used traditional bag of words and TF-IDF method. It escalates the testing F-measure value from 76.73% to 80.17%.


title: A Temperature-Compensated Digitally-Controlled Crystal Pierce Oscillator for Wireless Applications

abstract: In this paper, the DCXO is based on a Pierce oscillator with two MIM capacitor arrays for tuning the anti-resonant frequency of a 19.2MHz crystal. Each array of MIM capacitors is thermometer-coded and formatted in a matrix shape to facilitate layout. Although a segmented architecture is an area-efficient method for implementing a SC array, a thermometer-coded array provides the best linearity and guarantees a monotonic frequency tuning characteristic, which is of utmost importance in an AFC system.


title: Treatment of Internet gaming disorder: An international systematic review and CONSORT evaluation.

abstract: Abstract Treatment services for Internet gaming disorder are becoming increasingly prevalent worldwide, particularly in East Asia. This international systematic review was designed to appraise the quality standards of the gaming disorder treatment literature, a task previously undertaken by King et al. (2011) prior to the inclusion of Internet gaming disorder in Section III of the DSM-5 and ‘Gaming disorder’ in the draft ICD-11. The reporting quality of 30 treatment studies conducted from 2007 to 2016 was assessed. Reporting quality was defined according to the 2010 Consolidating Standards of Reporting Trials (CONSORT) statement. The results reaffirmed previous criticisms of these trials, namely: (a) inconsistencies in the definition, diagnosis, and measurement of disordered use; (b) lack of randomization and blinding; (c) lack of controls; and (d) insufficient information on recruitment dates, sample characteristics, and effect sizes. Although cognitive-behavioral therapy has a larger evidence base than other therapies, it remains difficult to make definitive statements on its benefits. Study design quality has not improved over the last decade, indicating a need for greater consistency and standardization in this area. Continuing international efforts to understand the core psychopathology of gaming disorder are vital to developing a model of best practice in treatment.


title: Discovering key moments in social media streams

abstract: This paper introduces a general technique, called LABurst, for identifying key moments, or moments of high impact, in social media streams without the need for domain-specific information or seed keywords. We leverage machine learning to model temporal patterns around bursts in Twitter's unfiltered public sample stream and build a classifier to identify tokens experiencing these bursts. We show LABurst performs competitively with existing burst detection techniques while simultaneously providing insight into and detection of unanticipated moments. To demonstrate our approach's potential, we compare two baseline event-detection algorithms with our language-agnostic algorithm to detect key moments across three major sporting competitions: 2013 World Series, 2014 Super Bowl, and 2014 World Cup. Our results show LABurst outperforms a time series analysis baseline and is competitive with a domain-specific baseline even though we operate without any domain knowledge. We then go further by transferring LABurst's models learned in the sports domain to the task of identifying earthquakes in Japan and show our method detects large spikes in earthquake-related tokens within two minutes of the actual event.


title: Understanding the Spatial and Temporal Activity Patterns of Subway Mobility Flows

abstract: In urban transportation systems, mobility flows in the subway system reflect the spatial and temporal dynamics of working days. To investigate the variability of mobility flows, we analyse the spatial community through a series of snapshots of subway stations over sequential periods. Using Shanghai as a case study, we find that the spatial community snapshots reveal dynamic passenger activities. Adopting a dual-perspective, we apply spatial and temporal models separately to explore where and when individuals travel for entertainment. In the two models, microblog topics and spatial facilities such as food venues and entertainment businesses are used to characterise the spatial popularity of each station and people's travelling perceptions. In the studied case, the city centre is characterised by greater social influence, and it is better described by the spatial model. In the temporal model, shorter travel distances motivate individuals to start their trips earlier. Interestingly, as the number of food-related facilities near the starting station increases, until it exceeds 1563, the speed of people's journeys slows down. This study provides a method for modelling the effects of social features on mobility flows and for predicting the spatial-temporal mobility flows of newly built subway stations.


title: Heading Estimation for Indoor Pedestrian Navigation Using a Smartphone in the Pocket

abstract: Heading estimation is a central problem for indoor pedestrian navigation using the pervasively available smartphone. For smartphones placed in a pocket, one of the most popular device positions, the essential challenges in heading estimation are the changing device coordinate system and the severe indoor magnetic perturbations. To address these challenges, we propose a novel heading estimation approach based on a rotation matrix and principal component analysis (PCA). Firstly, through a related rotation matrix, we project the acceleration signals into a reference coordinate system (RCS), where a more accurate estimation of the horizontal plane of the acceleration signal is obtained. Then, we utilize PCA over the horizontal plane of acceleration signals for local walking direction extraction. Finally, in order to translate the local walking direction into the global one, we develop a calibration process without requiring noisy compass readings. Besides, a turn detection algorithm is proposed to improve the heading estimation accuracy. Experimental results show that our approach outperforms the traditional uDirect and PCA-based approaches in terms of accuracy and feasibility.


title: Parsec: A Parallel Simulation Environment for Complex Systems

abstract: Design and development costs for extremely large systems could be significantly reduced if only there were efficient techniques for evaluating design alternatives and predicting their impact on overall system performance metrics. Due to the systems' analytical intractability, simulation is the most common performance evaluation technique for such systems. However, the long execution times needed for sequential simulation models often hampers evaluation. The slow speeds of sequential model execution have led to growing interest in the use of parallel execution for simulating large-scale systems. Widespread use of parallel simulation, however; has been significantly hindered by a lack of tools for integrating parallel model execution into the overall framework of system simulation. Another drawback to widespread use of simulations is the cost of model design and maintenance. The simulation environment the authors developed at UCLA attempts to address some of these issues. It consists of three primary components: a parallel simulation language called Parsec (parallel simulation environment for complex systems), its GUI, called Pave, and the portable runtime system that implements the simulation algorithms.


COMPUTER NETWORKS JOURNAL (ELSEVIER SCIENCE) 1 –Draft– (DO NOT DISTRIBUTE) Wireless Sensor Networks: A Survey Revisited


title: Business analysis techniques in business reengineering

abstract: Purpose – Using 12 case studies, the purpose of this paper is to investigate the use of business analysis techniques in BPR. Some techniques are used more than others depending on the fit between the technique and the problem. Other techniques are preferred due to their versatility, easy to use, and flexibility. Some are difficult to use requiring skills that analysts do not possess. Problem analysis, and business process analysis and activity elimination techniques are preferred for process improvement projects, and technology analysis for technology problems. Root cause analysis (RCA) and activity-based costing (ABC) are seldom used. RCA requires specific skills and ABC is only applicable for discrete business activities. Design/methodology/approach – This is an exploratory case study analysis. The author analyzed 12 existing business reengineering (BR) case studies from the MIS literature. Cases include, but not limited to IBM Credit Union, Chase Manhattan Bank, Honeywell Corporation, and Cigna. Findin...


title: The Organizational Critical Success Factors for Adopting Cloud Computing in SMEs

abstract: Cloud computing has emerged as one of the most discussed topics among enterprise IT professionals. This paper is giving list of cloud computing advantages and disadvantages and some reasons will cause company adopt cloud based services or avoiding. The small and medium size enterprise does not possess sufficient financial and human recourse, in comparison to the large size factories, hence they are not capable of upgrading their IT requirements efficiently. It will not allow them to have a sufficient chance to compete with powerful competitors, in a business environment. Evaluating the impact of organizational factors on cloud computing adoption in SMEs was determined as the main purpose of this study and led to focus on the TOE framework as a famous theory on evaluating IT adoption. For this purpose, organizational context divided into three main factors in base of TOE framework include organizational, environmental and technical context. Three set of critical success factor criteria chosen by review of previous studies for each three level of organizational factors. It can be helpful for small and medium size enterprises which want to evaluate their organizational condition for adopting cloud based services.


title: Bag of Visual Words and Fusion Methods for Action Recognition: Comprehensive Study and Good Practice

abstract: A Comprehensive study on the BoVW pipeline for action recognition task.An evaluation and a generic analysis on 13 encoding methods.Intra-normalization for supervector based encoding methods.An evaluation on three fusion methods.Several good practices of the BoVW pipeline for action recognition task. Video based action recognition is one of the important and challenging problems in computer vision research. Bag of visual words model (BoVW) with local features has been very popular for a long time and obtained the state-of-the-art performance on several realistic datasets, such as the HMDB51, UCF50, and UCF101. BoVW is a general pipeline to construct a global representation from local features, which is mainly composed of five steps; (i) feature extraction, (ii) feature pre-processing, (iii) codebook generation, (iv) feature encoding, and (v) pooling and normalization. Although many efforts have been made in each step independently in different scenarios, their effects on action recognition are still unknown. Meanwhile, video data exhibits different views of visual patterns , such as static appearance and motion dynamics. Multiple descriptors are usually extracted to represent these different views. Fusing these descriptors is crucial for boosting the final performance of an action recognition system. This paper aims to provide a comprehensive study of all steps in BoVW and different fusion methods, and uncover some good practices to produce a state-of-the-art action recognition system. Specifically, we explore two kinds of local features, ten kinds of encoding methods, eight kinds of pooling and normalization strategies, and three kinds of fusion methods. We conclude that every step is crucial for contributing to the final recognition rate and improper choice in one of the steps may counteract the performance improvement of other steps. Furthermore, based on our comprehensive study, we propose a simple yet effective representation, called hybrid supervector, by exploring the complementarity of different BoVW frameworks with improved dense trajectories. Using this representation, we obtain impressive results on the three challenging datasets; HMDB51 (61.9%), UCF50 (92.3%), and UCF101 (87.9%).


title: On Data Clustering Analysis: Scalability, Constraints, and Validation

abstract: Clustering is the problem of grouping data based on similarity. While this problem has attracted the attention of many researchers for many years, we are witnessing a resurgence of interest in new clustering techniques. In this paper we discuss some very recent clustering approaches and recount our experience with some of these algorithms. We also present the problem of clustering in the presence of constraints and discuss the issue of clustering validation.


title: Coarse-to-fine trained multi-scale Convolutional Neural Networks for image classification

abstract: Convolutional Neural Networks (CNNs) have become forceful models in feature learning and image classification. They achieve translation invariance by spatial convolution and pooling mechanisms, while their ability in scale invariance is limited. To tackle the problem of scale variation in image classification, this work proposed a multi-scale CNN model with depth-decreasing multi-column structure. Input images were decomposed into multiple scales and at each scale image, a CNN column was instantiated with its depth decreasing from fine to coarse scale for model simplification. Scale-invariant features were learned by weights shared across all scales and pooled among adjacent scales. Particularly, a coarse-to-fine pre-training method imitating the human's development of spatial frequency perception was proposed to train this multi-scale CNN, which accelerated the training process and reduced the classification error. In addition, model averaging technique was used to combine models obtained during pre-training and further improve the performance. With these methods, our model achieved classification errors of 15.38% on CIFAR-10 dataset and 41.29% on CIFAR-100 dataset, i.e. 1.05% and 2.97% reduction compared with single-scale CNN model.


title: Toward negotiable reinforcement learning: shifting priorities in Pareto optimal sequential decision-making

abstract: Existing multi-objective reinforcement learning (MORL) algorithms do not account for objectives that arise from players with differing beliefs. Concretely, consider two players with different beliefs and utility functions who may cooperate to build a machine that takes actions on their behalf. A representation is needed for how much the machine's policy will prioritize each player's interests over time. Assuming the players have reached common knowledge of their situation, this paper derives a recursion that any Pareto optimal policy must satisfy. Two qualitative observations can be made from the recursion: the machine must (1) use each player's own beliefs in evaluating how well an action will serve that player's utility function, and (2) shift the relative priority it assigns to each player's expected utilities over time, by a factor proportional to how well that player's beliefs predict the machine's inputs. Observation (2) represents a substantial divergence from na\"{i}ve linear utility aggregation (as in Harsanyi's utilitarian theorem, and existing MORL algorithms), which is shown here to be inadequate for Pareto optimal sequential decision-making on behalf of players with different beliefs.


title: A Smooth Transition Multivariate GARCH Approach to Contagion.

abstract: This paper proposes a multivariate GARCH model with nonlinear conditional variances as a new approach to modeling contagion and interdependence between international financial markets. The conditional variances are allowed to change smoothly between two extreme regimes and the transition is governed by past volatility. Changes in volatility interactions are thus indicative of structural changes in market linkages with implications to contagion and interdependence. The model is derived both under normal and student-t distributional assumptions. A LM test of linearity in conditional variances is also derived. Simulation studies point to model robustness to different variance and correlation specifications. An empirical example is provided.


title: MISE-PIPE : Magnetic induction-based wireless sensor networks for underground pipeline monitoring

abstract: Underground pipelines constitute one of the most important ways to transport large amounts of fluid (e.g. oil and water) through long distances. However, existing leakage detection techniques do not work well in monitoring the underground pipelines due to the harsh underground environmental conditions. In this paper, a new solution, the magnetic induction (MI)-based wireless sensor network for underground pipeline monitoring (MISE-PIPE), is introduced to provide low-cost and real-time leakage detection and localization for underground pipelines. MISE-PIPE detects and localizes leakage by jointly utilizing the measurements of different types of sensors that are located both inside and around the underground pipelines. By adopting an MI waveguide technique, the measurements of different types of the sensors throughout the pipeline network can be reported to the administration center in real-time. The system architecture and operational framework of MISE-PIPE is first developed. Based on the operational framework, research challenges and open research issues are then discussed.


title: A Prototype Study of an Autonomous Robot Platform for Sewerage System Maintenance

abstract: The paper describes KURT, an autonomous robot platform prototypethat is able to navigate through a network of sewage pipes. KURT is asix-wheeled vehicle; it has modular, layered hardware and controlarchitectures. Its sensor configuration consists mainly of stationary andone flexible ultrasound transducers and of two inclinometers. In experimentsrun in a test sewerage system consisting of concrete pipes of 600 mmdiameter, it has proven its abilities to travel safely through straightpipes, to recognize different types of pipe crossings, to turn at crossings,and to navigate from a given start point to arbitrary specified goalpoints.


title: Analyzing In-Game Movements of Soccer Players at Scale

abstract: It is challenging to get access to datasets related to the physical performance of soccer players. The teams consider such information highly confidential, especially if it covers in-game performance.Hence, most of the analysis and evaluation of the players' performance do not contain much information on the physical aspect of the game, creating a blindspot in performance analysis. We propose a novel method to solve this issue by deriving movement characteristics of soccer players. We use event-based datasets from data provider companies covering 50+ soccer leagues allowing us to analyze the movement profiles of potentially tens of thousands of players without any major investment. Our methodology does not require expensive, dedicated player tracking system deployed in the stadium. We also compute the similarity of the players based on their movement characteristics and as such identify potential candidates who may be able to replace a given player. Finally, we quantify the uniqueness and consistency of players in terms of their in-game movements. Our study is the first of its kind that focuses on the movements of soccer players at scale, while it derives novel, actionable insights for the soccer industry from event-based datasets.


title: Severe menopausal symptoms in middle-aged women are associated to female and male factors

abstract: BACKGROUND ::: The frequency and intensity of menopausal symptoms within a given population, as assessed by several tools, may vary and depend on several factors, such as age, menopausal status, chronic conditions and personal and partner socio-demographic profile. ::: ::: ::: OBJECTIVE ::: To determine the frequency and intensity of menopausal symptoms and related risk factors among middle-aged women. ::: ::: ::: METHODS ::: In this cross-sectional study a total of 404 women aged 40 to 59 years, visiting inpatients at the Enrique C. Sotomayor Gynecology and Obstetrics Hospital, Guayaquil, Ecuador, were requested to fill out the menopause rating scale (MRS) and a questionnaire containing personal and partner data. ::: ::: ::: RESULTS ::: Mean age of surveyed women (n=404) was 48.2+/-5.7 years, 85.1% had 12 or less years of schooling and 44.8% were postmenopausal. None was on hormonal therapy (HT) for the menopause or psychotropic drugs. Regarding their partner, erectile dysfunction was present in 23.8%, premature ejaculation in 21.2% and 43.5% abused alcohol. The four most frequently found symptoms of those composing the MRS were muscle and joint problems (80%), depressive mood (73.5%), physical and mental exhaustion (71.3%) and irritability (68%). Mean total MRS score was 18+/-10.6 (median 17) and for subscales: 7.2+/-4.5 (somatic); 6.9+/-4.8 (psychological) and 3.9+/-3.4 (urogenital). Women presented severe scores in 53, 36.1, 48.3 and 49.8% for total MRS and somatic, psychological and urogenital subscales, respectively. After adjusting for confounding factors, logistic regression analysis determined that female higher parity and partner premature ejaculation increased the risk for presenting severe total MRS scores (impaired female quality of life), whereas women who had a positive perception of their health status were at decreased risk. ::: ::: ::: CONCLUSION ::: In this middle-aged series psychological menopausal symptoms were the most frequent in which severity was associated to parity and partner sexual dysfunction.


title: Automatic performance evaluation of Web search engines

abstract: Measuring the information retrieval effectiveness of World Wide Web search engines is costly because of human relevance judgments involved. However, both for business enterprises and people it is important to know the most effective Web search engines, since such search engines help their users find higher number of relevant Web pages with less effort. Furthermore, this information can be used for several practical purposes. In this study we introduce automatic Web search engine evaluation method as an efficient and effective assessment tool of such systems. The experiments based on eight Web search engines, 25 queries, and binary user relevance judgments show that our method provides results consistent with human-based evaluations. It is shown that the observed consistencies are statistically significant. This indicates that the new method can be successfully used in the evaluation of Web search engines.


title: Components of executive control with advantages for bilingual children in two cultures

abstract: A growing body of research has demonstrated that executive control develops earlier in bilingual children than in monolinguals (Bialystok, 2001; Carlson & Meltzoff, 2008; Kovacs, 2009), is more efficient in young adults than in their monolingual peers (Bialystok, 2006; Costa, Hernandez, & Sebastian-Galles, 2008; Colzato et al., 2008), and is maintained better by bilinguals in older age (Bialystok, Craik, Klein, & Viswanathan, 2004; Kave, Eyal, Shorek, & Cohen-Mansfield, 2008). However, there are two gaps in this literature. The first is an identification of the components of executive control that are affected by bilingualism upon which explanations can be based, and the second is a clear attribution of these effects to bilingualism rather than to some other factor that might correlate with bilingualism. ::: ::: ‘Executive control’ refers to a set of interrelated processes in the frontal lobes but there is debate over the precise components of this broad set of abilities and the degree to which the components develop and function independently of each other. A widely accepted view proposed by Miyake et al. (2000) is that the executive control system consists of three components: inhibition, updating (also called working memory), and shifting (also called cognitive flexibility). Moving beyond descriptive evidence towards an explanation for how bilingualism affects the development and function of executive control depends on identifying the relevant components. ::: ::: Although there is evidence for a facilitative effect of bilingualism on executive control, there is less agreement about the components of executive control that are affected by bilingualism. Green (1998) has argued that inhibitory control is the relevant process, but inhibition is not a unitary construct. Distinguishing between active and reactive inhibition, Colzato et al. (2008) showed bilingual advantages only for the latter. A further distinction is between inhibition of an interfering cue and inhibition of a habitual response, which Bunge and colleagues have labeled “interference suppression” and “response inhibition”, respectively (Bunge, Dudukovic, Thomason, Vaidya, & Gabrieli, 2002). In the first case, conflict is resolved by selectively attending to a relevant stimulus cue and ignoring a competing cue, and in the second, by refraining from executing an automatically-cued response. Studies with children have shown that bilinguals outperform monolinguals on conflict tasks that require interference suppression, what we call “inhibitory control” (Bialystok & Senman, 2004; Bialystok & Shapero, 2005) but not on those that require response inhibition, what we call “response suppression” (Bialystok, Craik, & Ryan, 2006; Carlson & Meltzoff, 2008; Martin-Rhee & Bialystok, 2008). In contrast, Meuter and Allport (1999) claim that the relevant component is cognitive flexibility, for example, the ability to switch between tasks. Studies with adults have supported this position (Bialystok, Craik, & Ryan, 2006; Costa et al., 2008), although the involvement of cognitive flexibility has not been demonstrated for children. Thus, to understand the effect of bilingualism on cognition it is necessary to identify the components of executive control modified by bilingualism in the same population using a task that can reveal all relevant processes and integrate with lifespan research. ::: ::: It is especially important to establish the source of the bilingual advantage for children because the components of executive control emerge at different times (Carlson, 2003; Diamond, 2002) and bilingualism may have different developmental influences at different ages. No study to date has compared the effect bilingualism on each of these components in children. Moreover, evidence for an effect of bilingualism on the development of some components but not others will inform theoretical models of executive control by distinguishing among component processes. The present study addresses this issue by using an executive control task, called the ‘faces task’, that allows for the calculation of three discrete component processes. ::: ::: The second issue is one that applies to all between-groups designs, namely, the comparability of the groups and the generalizability of the results to designated populations. In addition to the obvious difference in language background, monolingual and bilingual children may differ as well in terms of social experience, such as immigration or social class. Nonetheless, research examining bilinguals with different language pairs, different immigration histories, or different social classes has produced comparable results. For example, in the research by Martin-Rhee & Bialystok (2008), the bilingual children in Study 1 spoke English and French and none were immigrants; the bilingual children in Study 2 spoke different non-English languages with mixed immigration patterns; and the bilingual children in Study 3 primarily spoke Hebrew and were largely immigrants. Carlson and Meltzoff (2008) compared lower-class Spanish-English bilingual children to middle-class monolingual English-speaking children and still found advantages for bilingual children on tasks assessing executive control, especially when initial differences in English proficiency had been controlled. Thus it seems unlikely that some hidden factor is responsible for the results. However, in cases where the children were born in their current country, their parents were likely immigrants so the effect of immigration cannot be ruled out. The present study addresses this issue by including a sample of bilingual children who live in the same community and attend the same schools as the monolingual children and a sample of bilingual children who have equivalent experience in using two languages and similar socioeconomic background but live in a different country and have no history of immigration.


title: Can We Trust Trust?

abstract: In this concluding essay I shall try to reconstruct what seem to me the central questions about trust that the individual contributions presented in this volume raise and partly answer. In the first section, I briefly qualify the claim that there is a degree of rational cooperation that should but does not exist, and I shall give a preliminary indication of the importance of the beliefs we hold about others, over and above the importance of the motives we may have for cooperation. In the second section, I define trust and the general conditions under which it becomes relevant for cooperation. In the third, I discuss the extent to which cooperation can come about independently of trust, and also whether trust can be seen as a result rather than a precondition of cooperation. In the final section, I address the question of whether there are rational reasons for people to trust and especially whether there are reasons to trust trust and, correspondingly, distrust distrust.


title: Neuroscience of Internet Pornography Addiction: A Review and Update

abstract: Many recognize that several behaviors potentially affecting the reward circuitry in human brains lead to a loss of control and other symptoms of addiction in at least some individuals. Regarding Internet addiction, neuroscientific research supports the assumption that underlying neural processes are similar to substance addiction. The American Psychiatric Association (APA) has recognized one such Internet related behavior, Internet gaming, as a potential addictive disorder warranting further study, in the 2013 revision of their Diagnostic and Statistical Manual. Other Internet related behaviors, e.g., Internet pornography use, were not covered. Within this review, we give a summary of the concepts proposed underlying addiction and give an overview about neuroscientific studies on Internet addiction and Internet gaming disorder. Moreover, we reviewed available neuroscientific literature on Internet pornography addiction and connect the results to the addiction model. The review leads to the conclusion that Internet pornography addiction fits into the addiction framework and shares similar basic mechanisms with substance addiction. Together with studies on Internet addiction and Internet Gaming Disorder we see strong evidence for considering addictive Internet behaviors as behavioral addiction. Future research needs to address whether or not there are specific differences between substance and behavioral addiction.


A Publication of the Association of Management SOCIAL MEDIA ANALYTICS FRAMEWORK : THE CASE OF TWITTER AND SUPER BOWL ADS


title: Skin color modeling for face detection and segmentation: a review and a new approach

abstract: Detecting skin-colored pixels, although seems a straightforward easy task, has proven to be quite a challenging task in images that are captured under complex unconstrained imaging conditions. Color segmentation is an important method of segmenting the body in images. In this paper, we first provides a review of skin color modeling research works for face detection. In particular, we focus on discussing the challenges of skin color modeling, detection, and segmentation, and then we present the skin color modeling and region-based skin segmentation. We also propose a new approach to analyze the hue characteristics of monitoring personnel in coal mine. We determine the skin color boundaries of the miners using the boundary detection method based on the response of visual hue. And then, we apply to determining hue area method using dual hue detector to segment skin color for miners. Experiments show that the method has excellent ability to segment skin regions of miners. We also summarize the most widely used methods for skin detection approaches and collecting their numerical evaluation results.


title: The effects of caffeine ingestion on the reaction time and short-term maximal performance after 36h of sleep deprivation

abstract: Abstract The aim of the present study was to investigate the effects of caffeine ingestion on cognitive and physical performances after 36 h of sleep deprivation. In randomized order, thirteen healthy male physical education students (age: 21.1 ± 1.1 years, body mass: 77.1 ± 7.2 kg, height: 1.77 ± 0.06 m) completed four test sessions at 18:00 h: after placebo or 5 mg·kg − 1 of caffeine ingestion during a baseline night (RN) (bed time: from 22:30 h to 07:00 h) or a night of 36 h of sleep deprivation (TSD). During each test session, participants performed the squat jump (SJ), the reaction time, and the 30-s Wingate tests (i.e., for the measurement of the peak (PP) and mean (MP) powers and the fatigue index (FI)). The results showed that PP and MP decreased and FI increased during the TSD compared to RN in the placebo condition (p


What do we need to do to tackle antimicrobial resistance?


title: Birds of a feather: students’ preferences for lecturers’ personalities as predicted by their own personality and learning approaches

abstract: Four-hundred and twenty-four students completed the Big Five (NEO-FFI: Costa & McCrae, 1992) and approaches to learning (Study Process Questionnaire: Biggs, 1987) scales, and rated the personality facets they desired in a good lecturer/professor. In general, students tended most to prefer lecturers who were emotionally stable (low in Neuroticism) and conscientious. However, correlations between students’ and their preferred lecturers’ personality characteristics revealed that students tended to prefer lecturers similar to themselves in all personality traits except Neuroticism, and particularly for Openness and Conscientiousness. Personality variables showed consistent incremental validity over age and gender in predicting students’ preferences, whereas learning approaches provided very modest additional information. Implications and recommendations for future studies are discussed.


title: Design considerations for an active soft orthotic system for shoulder rehabilitation

abstract: Strokes affect over 750,000 people annually in the United States. This significant and disabling condition can result in paralysis that must be treated by regular sessions with a dedicated physical therapist in order to regain motor function. However, the use of therapists is expensive, in high demand, and requires patient travel to a rehabilitation clinic. We propose an inexpensive and wearable upper body orthotics system that can be used at home to provide the same level of rehabilitation as the current physical therapy standard of care. The system is composed of a soft orthotic device with an integrated cable actuation system that is worn over the upper body, a limb position sensing system, and an actuator package. This paper presents initial design considerations and the evaluation of a proof of concept system for shoulder joint rehabilitation. Through simulations and experimental evaluation, the system is shown to be adjustable, easily wearable, and adaptable to misalignment and anatomical variations. Insights provided by these initial studies will inform the development of a complete upper body orthotic system.


title: Accurate Localization of In-Body Medical Implants Based on Spatial Sparsity

abstract: Wearable and implantable wireless communication devices have in recent years gained increasing attention for medical diagnostics and therapeutics. In particular, wireless capsule endoscopy has become a popular method to visualize and diagnose the human gastrointestinal tract. Estimating the exact position of the capsule when each image is taken is a very critical issue in capsule endoscopy. Several approaches have been developed by researchers to estimate the capsule location. However, some unique challenges exist for in-body localization, such as the severe multipath issue caused by the boundaries of different organs, inconsistency of signal propagation velocity and path loss parameters inside the human body, and the regulatory restrictions on using high-bandwidth or high-power signals. In this paper, we propose a novel localization method based on spatial sparsity. We directly estimate the location of the capsule without going through the usual intermediate stage of first estimating time-of-arrival or received-signal strength, and then a second stage of estimating the location. We demonstrate the accuracy of the proposed method through extensive Monte Carlo simulations for radio frequency emission signals within the required power and bandwidth range. The results show that the proposed method is effective and accurate, even in massive multipath conditions.


title: Wirelessly Automated Reconfigurable Antenna With Directional Selectivity

abstract: In this paper, a frequency reconfigurable antenna system with a directional selectivity is presented. The reconfiguration of the antenna’s radiation characteristics is wirelessly controlled through a mobile phone and a microcontroller. The antenna system is a multi-element radiator composed of four monopoles on top of a hexagonal ground plane, in addition to four rectangular microstrip two-elements antenna arrays. The monopoles and the microstrip arrays are designed to contribute to the communication requirements of the complete system. The position, orientation, and topology of the various antennas are optimized to achieve the required frequency reconfigurability in addition to the variable selective directivity performance. The whole system is fabricated and integrated in a single compact unit controlled by a microcontroller, four servo motors, and a Bluetooth module. The antenna system is tested and the measured results confirm the validity of the proposed design.


title: A case study in applying security design patterns for IoT software system

abstract: The Internet of Things (IoT) involves the increasing prevalence of objects and entities with the ability to transfer data over the network automatically. The main problem of IoT software system is that the security has not always been considered. This study applies five security design patterns for the development of an IoT software system. The security issue of unsecure application data is addressed by the security design patterns of secure directory, secure logger and exception manager. The security issues of unsecure wireless communication and unsecure user information are addressed by secure adapter pattern and input validation pattern, respectively.


title: Real-time traffic light recognition on mobile devices with geometry-based filtering

abstract: Understanding the status of the traffic signal at an intersection is crucial to many vehicle applications. For example, the information can be utilized to estimate the optimal speed for passing the intersection to increase the fuel efficiency, or to provide additional context information for predicting whether a vehicle would run the red light. In this paper, we propose a new real-time traffic light recognition system with very low computational requirements, suitable for use in mobile devices, such as smartphones and tablets, and video event data recorders. Our system does not rely on complex image processing techniques for detection; instead, we utilize a simple geometry-based technique to eliminate most false detections. Moreover, the proposed system performs well in general and realistic conditions, i.e., vibration caused by rough roads. Evaluation of our proposed system is performed with data collected from a smartphone onboard a scooter, including video footage recorded from the camera and data collected by GPS. It is shown that our system can accurately recognize the traffic light status in real-time as a vehicle carrying the device approaching the intersection.


title: SOT for MOT

abstract: In this paper we present a robust tracker to solve the multiple object tracking (MOT) problem, under the framework of tracking-by-detection. As the first contribution, we innovatively combine single object tracking (SOT) algorithms with multiple object tracking algorithms, and our results show that SOT is a general way to strongly reduce the number of false negatives, regardless of the quality of detection. Another contribution is that we show with a deep learning based appearance model, it is easy to associate detections of the same object efficiently and also with high accuracy. This appearance model plays an important role in our MOT algorithm to correctly associate detections into long trajectories, and also in our SOT algorithm to discover new detections mistakenly missed by the detector. The deep neural network based model ensures the robustness of our tracking algorithm, which can perform data association in a wide variety of scenes. We ran comprehensive experiments on a large-scale and challenging dataset, the MOT16 benchmark, and results showed that our tracker achieved state-of-the-art performance based on both public and private detections.


title: Polarization Reconfigurable Slot Antenna With a Novel Compact CPW-to-Slotline Transition for WLAN Application

abstract: A polarization reconfigurable slot antenna with a novel coplanar waveguide (CPW)-to-slotline transition for wireless local area networks (WLANs) is proposed and tested. The antenna consists of a square slot, a reconfigurable CPW-to-slotline transition, and two p-i-n diodes. No extra matching structure is needed for modes transiting, which makes it much more compact than all reference designs. The -10 dB bandwidths of an antenna with an implemented bias circuit are 610 (25.4%) and 680 MHz (28.3%) for vertical and horizontal polarizations, respectively. The radiation pattern and gain of the proposed antenna are also tested, and the radiation pattern data were compared to simulation results.


title: LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators

abstract: Layout is important for graphic design and scene generation. We propose a novel Generative Adversarial Network, called LayoutGAN, that synthesizes layouts by modeling geometric relations of different types of 2D elements. The generator of LayoutGAN takes as input a set of randomly-placed 2D graphic elements and uses self-attention modules to refine their labels and geometric parameters jointly to produce a realistic layout. Accurate alignment is critical for good layouts. We thus propose a novel differentiable wireframe rendering layer that maps the generated layout to a wireframe image, upon which a CNN-based discriminator is used to optimize the layouts in image space. We validate the effectiveness of LayoutGAN in various experiments including MNIST digit generation, document layout generation, clipart abstract scene generation and tangram graphic design.


title: A solution approach to valuation with unhedgeable risks

abstract: We study a class of stochastic optimization models of expected utility in markets with stochastically changing investment opportunities. The prices of the primitive assets are modelled as diffusion processes whose coefficients evolve according to correlated diffusion factors. Under certain assumptions on the individual preferences, we are able to produce reduced form solutions. Employing a power transformation, we express the value function in terms of the solution of a linear parabolic equation, with the power exponent depending only on the coefficients of correlation and risk aversion. This reduction facilitates considerably the study of the value function and the characterization of the optimal hedging demand. The new results demonstrate an interesting connection with valuation techniques using stochastic differential utilities and also, with distorted measures in a dynamic setting.


title: Basics of coalitional games with applications to communications and networking

abstract: Game theory is the study of decision making in an interactive environment. Coalitional games fulfill the promise of group efficient solutions to problems involving strategic actions. Formulation of optimal player behavior is a fundamental element in this theory. This paper comprises a self-instructive didactic means to study basics of coalitional games indicating how coalitional game theory tools can provide a framework to tackle different problems in communications and networking. We show that coalitional game approaches achieve an improved performance compare to non-cooperative game theoretical solutions.


title: End-to-End Neural Sentence Ordering Using Pointer Network

abstract: Sentence ordering is one of important tasks in NLP. Previous works mainly focused on improving its performance by using pair-wise strategy. However, it is nontrivial for pair-wise models to incorporate the contextual sentence information. In addition, error prorogation could be introduced by using the pipeline strategy in pair-wise models. In this paper, we propose an end-to-end neural approach to address the sentence ordering problem, which uses the pointer network (Ptr-Net) to alleviate the error propagation problem and utilize the whole contextual information. Experimental results show the effectiveness of the proposed model. Source codes and dataset of this paper are available.


title: RecTime: Real-Time recommender system for online broadcasting

abstract: Abstract Recommender systems for online broadcasting become important as the number of channels has been increasing. In online broadcasting, to provide accurate recommendation, recommender systems should take time factors as well as users’ condition into account, but the conventional systems don’t. This paper proposes a real-time recommender system for online broadcasting called RecTime which considers time factors and preferences simultaneously. Specifically, RecTime employs a 4-d tensor factorization, which considers two more dimensions regarding the time factors, while typical collaboriative filtering methods only consider two dimensions, users and items. By factorizing the 4-d tensor, the system naturally identifies the recommendation time and the items at the same time. In our experiments on real-world data, RecTime properly models users’ watching patterns and significantly outperforms previous methods in terms of the accuracy on the recommendation time as well as the items.


title: Half-Mode Substrate Integrated Waveguide (HMSIW) Leaky-Wave Antenna for Millimeter-Wave Applications

abstract: A novel leaky-wave antenna is demonstrated and developed at Ka-band in this work based on the newly proposed half-mode substrate integrated waveguide (HWSIW). This antenna is accurately simulated by using a full-wave electromagnetic simulator and then fabricated through a single-layer printed circuit board (PCB) process. Wide bandwidth and a quasi-omnidirectional radiation pattern are obtained. The proposed antenna is therefore a good candidate for millimeter-wave applications. Measured results are in good agreement with simulated results.


title: Automated data structure generation: refuting common wisdom

abstract: Common wisdom in the automated data structure generation community states that declarative techniques have better usability than imperative techniques, while imperative techniques have better performance. We show that this reasoning is fundamentally flawed: if we go to the declarative limit and employ constraint logic programming (CLP), the CLP data structure generation has orders of magnitude better performance than comparable imperative techniques. Conversely, we observe and argue that when it comes to realistically complex data structures and properties, the CLP specifications become more obscure, indirect, and difficult to implement and understand than their imperative counterparts. We empirically evaluate three competing generation techniques, CLP, Korat, and UDITA, to validate these observations on more complex and interesting data structures than any prior work in this area. We explain why these observations are true, and discuss possible techniques for attaining the best of both worlds.


title: Sleeping brain , learning brain . The role of sleep for memory systems

abstract: The hypothesis that sleep participates in the consolidation of recent memory traces has been investigated using four main paradigms: (1) effects of post-training sleep deprivation on memory consolidation, (2) effects of learning on post-training sleep, (3) effects of within sleep stimulation on the sleep pattern and on overnight memories, and (4) re-expression of behavior-specific neural patterns during post-training sleep. These studies convincingly support the idea that sleep is deeply involved in memory functions in humans and animals. However, the available data still remain too scarce to confirm or reject unequivocally the recently upheld hypothesis that consolidations of non-declarative and declarative memories are respectively dependent upon REM and NREM sleep processes. NeuroReport 12:A111-A124 (C) 2001 Lippincott Williams & Wilkins.


title: The BRICS component model: a model-based development paradigm for complex robotics software systems

abstract: Because robotic systems get more complex all the time, developers around the world have, during the last decade, created component-based software frameworks (Orocos, Open-RTM, ROS, OPRoS, SmartSoft) to support the development and reuse of "large grained" pieces of robotics software. This paper introduces the BRICS Component Model (BCM) to provide robotics developers with a set of guidelines, metamodels and tools for structuring as much as possible the development of, both, individual components and component-based architectures, using one or more of the aforementioned software frameworks at the same time, without introducing any framework- or application-specific details. The BCM is built upon two complementary paradigms: the "5Cs" (separation of concerns between the development aspects of Computation, Communication, Coordination, Configuration and Composition) and the meta-modeling approach from Model-Driven Engineering.


Ways of Asking and Replying in Duplicate Question Detection


Inner Attention based Recurrent Neural Networks for Answer Selection


title: Distributed Resource Allocation in 5G Cellular Networks

abstract: Future wireless networks are expected to be highly heterogeneous, with the co‐existence of macrocells and small cells as well as the provisioning for device‐to‐device communication. In such heterogeneous and multi‐tier systems, centralized radio resource allocation and interference management schemes will not be scalable. Therefore, distributed resource allocation schemes will need to be designed. However, designing such distributed schemes is one of the fundamental research challenges for 5G multi‐tier cellular wireless networks. After a brief overview of 5G cellular systems, this chapter highlights three novel approaches that can be used to solve the distributed‐resource allocation problems in future heterogeneous networks. Specifically, we utilize the concepts of stable matching, factor‐graph‐based message passing, and distributed auctions and show their effectiveness in obtaining distributed solutions to the resource allocation problem. To this end, a brief qualitative comparison in terms of performance metrics, such as complexity, convergence, and signaling overhead, is presented.


title: Lung nodule detection in CT using 3D convolutional neural networks

abstract: We propose a new computer-aided detection system that uses 3D convolutional neural networks (CNN) for detecting lung nodules in low dose computed tomography. The system leverages both a priori knowledge about lung nodules and confounding anatomical structures and data-driven machine-learned features and classifier. Specifically, we generate nodule candidates using a local geometric-model-based filter and further reduce the structure variability by estimating the local orientation. The nodule candidates in the form of 3D cubes are fed into a deep 3D convolutional neural network that is trained to differentiate nodule and non-nodule inputs. We use data augmentation techniques to generate a large number of training examples and apply regularization to avoid overfitting. On a set of 99 CT scans, the proposed system achieved state-of-the-art performance and significantly outperformed a similar hybrid system that uses conventional shallow learning. The experimental results showed benefits of using a priori models to reduce the problem space for data-driven machine learning of complex deep neural networks. The results also showed the advantages of 3D CNN over 2D CNN in volumetric medical image analysis.


title: The Determinants of Credit Spread Changes

abstract: Using straight industrial bonds with quoted prices, we investigate the determinants of credit spread changes. The variables that should in theory determine credit spread changes in fact have limited explanatory power. Further, the residuals from this first-pass regression are highly cross-correlated, and principal components analysis strongly suggests they are driven by a single common factor. We investigate several macro-economic and financial variables as candidate proxies for this factor. We cannot, however, find any set of variables which explain this common systematic factor. Our results suggest the corporate bond market is a segmented market driven by corporate bond specific supply/demand shocks.


title: Italy goes to Stanford: a collection of CoreNLP modules for Italian

abstract: In this we paper present Tint, an easy-to-use set of fast, accurate and extendable Natural Language Processing modules for Italian. It is based on Stanford CoreNLP and is freely available as a standalone software or a library that can be integrated in an existing project.


title: PCG classification using a neural network approach

abstract: Phonocardiography (PCG) is the one of noninvasive ways to diagnose condition of human heart. The mechanics of heart muscle contractions and closure of the heart valves generates vibrations audible as sounds and murmurs, which can be analysed by qualified cardiologists. Developing an accurate algorithm to determine whether patients' heart works properly or should be referred to an expert for further diagnosis would significantly improve the quality of healthcare system. It would allow to perform less unnecessary, expensive and time consuming examinations. The analysed data consisted of PCG recordings from the training set provided by the organizers of the PhysioNet Challenge 2016. Its length variedfrom several to 120 seconds. We propose the machine learning algorithm based on neural networks. The segmentation of the PCG signals is performed with algorithm based on Hidden Markov Model. Whereas, the features necessary to define whether the signal looks normal or should be further analysed were carefully chosen by our team and belonged to time domain, ordinate axis or frequency domain group. The great emphasis was put on the statistical features representing the characteristics of the signal. Their optimal values were found during the process of learning of our algorithm. The best overall score we achieved in the official phase of the PhysioNet Challenge 2016 is 0.79 with specificity 0.76 and sensitivity 0.81.


title: Integrating State Machine Analysis with System-Theoretic Process Analysis.

abstract: Safety becomes a critical aspect for software-intensive systems in different applications areas. Many hazard analysis techniques are proposed and used to investigate system design models to elicit hazards and design flaws. STPA (SystemTheoretic Process Analysis) is a modern hazard analysis technique, which is based on a new systems-theoretic model of accidents for large and complex systems. With STPA, the system is viewed as interacting control loops and the accidents are considered as results from inadequate enforcement of safety constraints in design, development and operation. STPA still needs appropriate diagrammatic notations to represent the relation between the process model variables, control actions and hazards. For this purpose, we propose to integrate state machine analysis with STPA to provide a suitable notation of arguments between the states of controllers, control actions and hazards.


title: A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

abstract: Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results.


title: 3D facial expression recognition using SIFT descriptors of automatically detected keypoints

abstract: Methods to recognize humans’ facial expressions have been proposed mainly focusing on 2D still images and videos. In this paper, the problem of person-independent facial expression recognition is addressed using the 3D geometry information extracted from the 3D shape of the face. To this end, a completely automatic approach is proposed that relies on identifying a set of facial keypoints, computing SIFT feature descriptors of depth images of the face around sample points defined starting from the facial keypoints, and selecting the subset of features with maximum relevance. Training a Support Vector Machine (SVM) for each facial expression to be recognized, and combining them to form a multi-class classifier, an average recognition rate of 78.43% on the BU-3DFE database has been obtained. Comparison with competitor approaches using a common experimental setting on the BU-3DFE database shows that our solution is capable of obtaining state of the art results. The same 3D face representation framework and testing database have been also used to perform 3D facial expression retrieval (i.e., retrieve 3D scans with the same facial expression as shown by a target subject), with results proving the viability of the proposed solution.


title: Autonomous tour guide robot by using ultrasonic range sensors and QR code recognition in indoor environment

abstract: This paper addresses the challenge of mobile robot navigation in indoor environments. There is a critical need for cost-effective, reliable, and fairly accurate solutions to meet the demands of indoor robotic applications. Currently, researchers are exploring various approaches for this problem. The one we are presenting in this paper is based on QR (Quick Response) codes to provide location references for mobile robots. The mobile robot is equipped with a Smartphone that is programmed to detect and read information on QR codes that are strategically placed in the operating environment of the robot. The mobile robot can perform the autonomous run throughout the guide route by using real-time QR code recognition. The lab information on QR code is played to the visitors using Text-to-Speech provided through Android device. Ultrasonic range sensors which can detect objects and measure distances with high accuracy are used to implement the wall-following and obstacle-avoidance behaviors. The collected sonar range information by ultrasonic range sensors is processed by a microcontroller that autonomously controls a tour guide robot. An algorithm based on a proportional-integral-derivative (PID) control is applied to the tour guide robot to perform more accurate robot motion control. A Bluetooth technology is used to send stored information on QR codes from the Smartphone to the tour guide robot wirelessly. The experimental setup of the tour guide robot along with the successful implementation of the efficient method for a navigation technique is presented.


title: Low-Power Hybrid Memory Cubes With Link Power Management and Two-Level Prefetching

abstract: The hybrid memory cube (HMC) is a 3-D-stacked DRAM architecture designed for substantially improved memory bandwidth. In particular, its I/O interface achieves up to 320 GB/s of external bandwidth through high-speed serial links. However, it comes at the cost of large static power of off-chip links, which dominates total power consumption of HMCs. In this paper, we propose an adaptive mechanism to partially disable off-chip links of HMCs to reduce the energy consumption of the off-chip links. In order to determine the number of the links to be disabled upon application loads, we develop a simple hardware module called link delay monitor to simulate all different link configurations at the same time and find the largest number of the links to be disabled while satisfying the given performance constraint. We also present two-level prefetching with in-HMC prefetch buffers to further improve the efficiency of our link power management scheme in the presence of prefetching. Evaluations show that our scheme reduces the energy consumption of HMCs by 52% on average with a negligible performance degradation.


title: An investigation into users’ considerations towards using password managers

abstract: Password managers, though commonly recommended by security experts, are still not used by many users. Understanding why some choose to use password managers while others do not is important towards generally understanding why some users do what they do and, by extension, designing motivational tools such as video tutorials to help motivate more to use password managers. To investigate differences between those who do and do not use a password manager, for this paper, we distributed an online survey to a total of 137 users and 111 non-users of the tool that asked about their opinions/experiences with password managers. Furthermore, since emotion has been identified by work in psychology and communications as influential in other risk-laden decision-making (e.g., safe-sex behavior such as condom use), we asked participants who use a password manager to rate how they feel for 45 different emotions, or, as the case for those who do not use a password manager, to rate how they imagine they would feel the 45 emotions if they did use the tool. Our results show that "users" of password managers noted convenience and usefulness as the main reasons behind using the tool, rather than security gains, underscoring the fact that even a large portion of users of the tool are not considering security as the primary benefit while making the decision. On the other hand, "non-users" noted security concerns as the main reason for not using a password manager, highlighting the prevalence of suspicion arising from lack of understanding of the technology itself. Finally, analysis of the differences in emotions between "users" and "non-users" reveals that participants who never use a password manager are more likely to feel suspicious compared to "users," which could be due to misunderstandings about the tool.


Influence and Dissemination Of Sentiments in Social Network Communication Patterns


title: Capturing correlations of multiple labels: A generative probabilistic model for multi-label learning

abstract: Recent years have witnessed a considerable surge of interest in the multi-label learning problem. It has been shown that a key factor for a successful multi-label learning algorithm is to effectively exploit relations between labels. However, most of the previous work exploiting label relations focuses on pairwise relations. To handle the situations where there are intrinsic correlations among multiple labels, in this paper, we propose a generative model, Labeled Four-Level Pachinko Allocation Model (L-F-L-PAM), to capture correlations among multiple labels. In our approach of multi-label learning on text data, we apply the proposed model for inferring the training data and the standard Four-Level Pachinko Allocation Model for the test data. Furthermore, we propose a pruned Gibbs Sampling algorithm in the test stage to reduce the inference time. Finally, extensive experiments have been performed to validate the effectiveness and efficiency of our new approach. The results demonstrate significant improvements of our model over Labeled LDA (L-LDA) and superiority in terms of both effectiveness and computational efficiency over other high-performing multi-label learning methods.


title: Probabilistic relations between words: Evidence from reduction in lexical production

abstract: The ideas of frequency and predictability have played a fundamental role in models of human language processing for well over a hundred years (Schuchardt, 1885; Jespersen, 1922; Zipf, 1929; Martinet, 1960; Oldfield & Wingfield, 1965; Fidelholz, 1975; Jescheniak & Levelt, 1994; Bybee, 1996). While most psycholinguistic models have thus long included word frequency as a component, recent models have proposed more generally that probabilistic information about words, phrases, and other linguistic structure is represented in the minds of language users and plays a role in language comprehension (Jurafsky, 1996; MacDonald, 1993; McRae, Spivey-Knowlton, & Tanenhaus, 1998; Narayanan & Jurafsky, 1998; Trueswell & Tanenhaus, 1994) production (Gregory, Raymond, Bell, Fosler-Lussier, & Jurafsky, 1999; Roland & Jurafsky, 2000) and learning (Brent & Cartwright, 1996; Landauer & Dumais, 1997; Saffran, Aslin, & Newport, 1996; Seidenberg & MacDonald, 1999). In recent papers (Bell, Jurafsky, Fosler-Lussier, Girand, & Gildea, 1999; Gregory et al., 1999; Jurafsky, Bell, Fosler-Lussier, Girand, & Raymond, 1998), we have been studying the role of predictability and frequency in lexical production. Our goal is to understand the many factors that affect production variability as reflected in reduction processes such as vowel reduction, durational shortening, or final segmental deletion of words in spontaneous speech. One proposal that has resulted from this work is the Probabilistic Reduction Hypothesis: word forms are reduced when they have a higher probability. The probability of a word is conditioned on many aspects of its context, including neighboring words, syntactic and lexical structure, semantic expectations, and discourse factors. This proposal thus generalizes over earlier models which refer only to word frequency (Zipf, 1929; Fidelholz, 1975; Rhodes, 1992, 1996) or predictability (Fowler & Housum, 1987). In this paper we focus on a particular domain of probabilistic linguistic knowledge in lexical production: the role of local probabilistic relations between words.


title: An inductorless linear optical receiver for 20Gbaud/s (40Gb/s) PAM-4 modulation using 28nm CMOS

abstract: This paper ::: 1 ::: presents a linear optical receiver designed using a 28nm CMOS technology suitable for 20Gbaud/s (40Gb/s) PAM-4 modulation. The optical receiver consists of a transimpedance amplifier (gain adjustable from 40dBΩ to 56dBO) followed by a variable gain amplifier (gain adjustable from 6dB to 17dB). Capacitive peaking is used to achieve a bandwidth of ~10GHz, thus avoiding the use of on-chip inductors which require large die area. A robust automatic gain control loop is used to ensure a constant differential output voltage swing of ~100mV for an input dynamic range of 20μA to 500μA (peak current). Over this same range, high linearity (total harmonic distortion less than 5%, 250MHz sinewave, 10harmonics taken into account) is obtained. The rms input referred noise current (integrated from 10MHz to 20GHz) was 2.5μA ::: rms ::: . The linear optical receiver consumes 56mW from a 1.5V supply voltage.


title: Taking your own path: Individual differences in executive function and language processing skills in child learners

abstract: Abstract Children as old as 5 or 6 years display selective difficulties in revising initial interpretive commitments, as indicated by both online and offline measures of sentence comprehension. It is likely, however, that individual children differ in how well they can recover from misinterpretations and in the age at which they become adult-like in these abilities. To better understand the cognitive functions that support sentence processing and revision, the current work investigated how individual differences in children’s ability to interpret temporarily ambiguous sentences relate to individual differences in other linguistic and domain-general cognitive abilities. Children were tested over 2 days on a battery of executive function, working memory, and language comprehension tasks. Performance on these tasks was then used to predict online and offline measures of children’s ability to revise initial misinterpretations of temporarily ambiguous sentences. We found two measures of children’s cognitive flexibility to be related to their ambiguity resolution abilities. These results provide converging evidence for the hypothesis that the ability to revise initial interpretive commitments is supported by domain-general executive function abilities, which are highly variable and not fully developed in children.


title: Gaussian process style transfer mapping for historical Chinese character recognition

abstract: ABSTRACT Historical Chinese character recognition is very important to larger scale historical document digitalization,but is a very challenging problem due to lack of labeled training samples. This paper proposes a novel non-linear transfer learning method, namely Gaussian Process Style Transfer Mapping (GP-STM). The GP-STMextends traditional linear Style Transfer Mapping (STM) by using Gaussian process and kernel methods. WithGP-STM, existing printed Chinese character samples are used to help the recognition of historical Chinesecharacters. To demonstrate this framework, we compare feature extraction methods, train a modi ed quadraticdiscriminant function (MQDF) classi er on printed Chinese character samples, and implement the GP-STMmodel on Dunhuang historical documents. Various kernels and parameters are explored, and the impact ofthe number of training samples is evaluated. Experimental results show that accuracy increases by nearly 15percentage points (from 42.8% to 57.5%) using GP-STM, with an improvement of more than 8 percentage points(from 49.2% to 57.5%) compared to the STM approach.Keywords: Gaussian process, style transfer mapping, historical Chinese character recognition


title: How to prevent workplace incivility?: Nurses' perspective

abstract: Background: Many articles have studied workplace incivility and its influence on outcomes, but very few have been conducted to assess how to prevent this issue. In this study, we aimed to determine how to prevent workplace incivility from the nurses' perspective. Materials and Methods: This was a qualitative study which was based on a conventional content analysis approach. Thirty four nurses (25 to 52 years old) from seven training hospitals in Tabriz, Iran were selected through purposive sampling. Thirty six semi-structured interviews and eight field notes were analyzed. Results: The data analysis revealed 417 codes, ten categories, three subthemes and one theme, that is, A Need for a Comprehensive Attempt. Attempt of organization, nurses, and public as subthemes are needed to prevent workplace incivility. Conclusions: The findings of the study indicated that a comprehensive and systematic attempt was needed to prevent incivility. Nurses should try to improve their skills; officials should try to show the real image and position of nurses and hospitals to the community.


Hate Me, Hate Me Not: Hate Speech Detection on Facebook


title: Miller compensation using current buffers in fully differential CMOS two-stage operational amplifiers

abstract: Several Miller compensation schemes using a current buffer in series with the compensation capacitor to modify the right-half-plane zero in fully differential two-stage CMOS operational amplifiers are analyzed. One scheme uses a current mirror as a current buffer, while the rest use a common-gate transistor as a current buffer. The gain transfer functions are derived for each topology, and approximate transfer-function coefficients are found that allow accurate estimation of the zero(s) and poles.


DualIso : Scalable Subgraph Pattern Matching On Large Labeled Graphs SUPPLEMENT


title: Chinese text-line detection from web videos with fully convolutional networks

abstract: In recent years, video becomes the dominant resource of information on the Web, where the text within video usually carries significant semantic information. Video text extraction and recognition plays an essential role in web multimedia understanding and retrieval for big visual data analytics and applications. To deal with challenging backgrounds and embedding noises, most conventional approaches usually tend to design sophisticated pre-processing and post-progressing steps before and after text detection. In this paper, we present a simple yet powerful pipeline that directly and uniformly detects Chinese text lines for embedded captions from web videos. In this Chinese text-line detection system, a fully convolutional network with local context is adopted to localize via an end-to-end learning way. The produced caption predictions are with the word level that could be directly fed into the character classifier. Text-line construction is then performed by heuristic strategies. A variety of experiments are conducted on several real-world web video datasets and demonstrated the effectiveness and efficiency of our proposed method. The proposed system can directly detect the English word and Chinese characters in the caption text-lines without word or character segmentation with the high performance on real-world web video datasets.


title: An End-to-End Signal Strength Model for Underwater Optical Communications

abstract: In this paper, we present a generic model of signal strength in underwater optical communications. The model includes light sources, detectors, amplifier and detector circuitry, optics, as well as a simple extinction model of the water channel. The end-to-end model provides insights into optimization approaches for underwater optical modems and enables relative pose estimation between underwater optical transmitters and receivers. We instantiate our model to the AquaOptical model by determining its parameters and verifying the model prediction in a suite of pool experiments.


title: A design of power amplifier for CMOS crystal-less wireless connectivity

abstract: This paper describes power amplifier designs based on transformer matching architecture. The power amplifier have been designed in a 40nm CMOS technology, thus targeting automotive radar and millimeter-wave applications. Their operating ranges are 76–81 GHz and the gain is 20.04 dB at center frequency. It provides a saturated output power of 12.5 dBm with the power-added efficiency (PAE) of 15.06%, and the output 1-dB gain-compressed power (P1dB) is 8.28 dBm with the PAE of 7.8% at 79 GHz, respectively.


title: Position-Based Multi-Agent Dynamics for Real-Time Crowd Simulation (MiG paper)

abstract: Exploiting the efficiency and stability of Position-Based Dynamics (PBD), we introduce a novel crowd simulation method that runs at interactive rates for hundreds of thousands of agents. Our method enables the detailed modeling of per-agent behavior in a Lagrangian formulation. We model short-range and long-range collision avoidance to simulate both sparse and dense crowds. On the particles representing agents, we formulate a set of positional constraints that can be readily integrated into a standard PBD solver. We augment the tentative particle motions with planning velocities to determine the preferred velocities of agents, and project the positions onto the constraint manifold to eliminate colliding configurations. The local short-range interaction is represented with collision and frictional contact between agents, as in the discrete simulation of granular materials. We incorporate a cohesion model for modeling collective behaviors and propose a new constraint for dealing with potential future collisions. Our new method is suitable for use in interactive games.


title: A dual polarized aperture coupled microstrip patch antenna with high isolation for RFID applications

abstract: Design and development of a dual polarized aperture coupled circular microstrip patch antenna at C-band are presented. Two orthogonal feeds exciting the patch antenna yield greater than 28 dB isolation. The antenna has 7.8 dBi gain, 70/spl deg/ 3-dB beamwidth and 7 dB front-to-back ratio. These features make the antenna very suitable for medium range radio frequency automatic identification (RFID) applications.


title: Dataset Optimization for Real-Time Pedestrian Detection

abstract: This paper tackles the problem of data selection for training set generation in the context of near real-time pedestrian detection through the introduction of a training methodology: FairTrain. After highlighting the impact of poorly chosen data on detector performance, we introduce a new data selection technique utilizing the expectation-maximization algorithm for data weighting. FairTrain also features a version of the cascade-of-rejectors enhanced with data selection principles. Experiments on the INRIA and CALTECH data sets prove that, when finely trained, a simple HoG-based detector can outperform most of its near real-time competitors.


title: 5G C-RAN With Optical Fronthaul: An Analysis From a Deployment Perspective

abstract: The fifth generation (5G) wireless technology is designed to provide significantly faster Internet access, with lower latency, and ubiquitous mobile coverage compared to its predecessors. However, as there will be hundreds and thousands of wireless cells deployed in the 5G network, transporting the enormous volume of data instigated from high capacity 5G wireless cells to the core network in a cost-effective and greener manner with a low latency still remains one of the major hurdles. Therefore, in this paper, we exploit different optical transport network architectures and technologies that can be used to build an efficient fronthaul network for 5G, in order to carry data between wireless cells and the central office. Particularly, we equitably compare multiple fronthaul architectures in terms of major requirements of 5G fronthaul network such as bandwidth requirements, delay budgets, deployment costs, complexity of radio remote head, and the ability to support advanced wireless functions. In order to analyze the deployment cost of entire 5G transport and wireless networks, we develop a joint optimization framework to optimally deploy fiber-based fronthaul network and 5G wireless network simultaneously. Our framework is also capable of setting limits on the required network coverage and capacity. We analyze the optimal deployment costs under different deployment scenarios and different fronthaul technologies. Our analyses provide insights into modeling future-proof optical transport networks to realize 5G network deployment.


title: Sharp analysis of low-rank kernel matrix approximations

abstract: We consider supervised learning problems within the positive-definite kernel framework, such as kernel ridge regression, kernel logistic regression or the support vector machine. With kernels leading to infinite-dimensional feature spaces, a common practical limiting difficulty is the necessity of computing the kernel matrix, which most frequently leads to algorithms with running time at least quadratic in the number of observations n, i.e., O(n^2). Low-rank approximations of the kernel matrix are often considered as they allow the reduction of running time complexities to O(p^2 n), where p is the rank of the approximation. The practicality of such methods thus depends on the required rank p. In this paper, we show that in the context of kernel ridge regression, for approximations based on a random subset of columns of the original kernel matrix, the rank p may be chosen to be linear in the degrees of freedom associated with the problem, a quantity which is classically used in the statistical analysis of such methods, and is often seen as the implicit number of parameters of non-parametric estimators. This result enables simple algorithms that have sub-quadratic running time complexity, but provably exhibit the same predictive performance than existing algorithms, for any given problem instance, and not only for worst-case situations.


title: The microbiome of professional athletes differs from that of more sedentary subjects in composition and particularly at the functional metabolic level

abstract: Objective It is evident that the gut microbiota and factors that influence its composition and activity effect human metabolic, immunological and developmental processes. We previously reported that extreme physical activity with associated dietary adaptations, such as that pursued by professional athletes, is associated with changes in faecal microbial diversity and composition relative to that of individuals with a more sedentary lifestyle. Here we address the impact of these factors on the functionality/metabolic activity of the microbiota which reveals even greater separation between exercise and a more sedentary state. Design Metabolic phenotyping and functional metagenomic analysis of the gut microbiome of professional international rugby union players (n=40) and controls (n=46) was carried out and results were correlated with lifestyle parameters and clinical measurements (eg, dietary habit and serum creatine kinase, respectively). Results Athletes had relative increases in pathways (eg, amino acid and antibiotic biosynthesis and carbohydrate metabolism) and faecal metabolites (eg, microbial produced short-chain fatty acids (SCFAs) acetate, propionate and butyrate) associated with enhanced muscle turnover (fitness) and overall health when compared with control groups. Conclusions Differences in faecal microbiota between athletes and sedentary controls show even greater separation at the metagenomic and metabolomic than at compositional levels and provide added insight into the diet–exercise–gut microbiota paradigm.


title: Graphene-polymer nanocomposites for structural and functional applications

abstract: Abstract The introduction of graphene-based nanomaterials has prompted the development of flexible nanocomposites for emerging applications in need of superior mechanical, thermal, electrical, optical, and chemical performance. These nanocomposites exhibit outstanding structural performance and multifunctional properties by synergistically combining the characteristics of both components if proper structural and interfacial organization is achieved. Here, we briefly introduce the material designs and basic interfacial interactions in the graphene-polymer nanocomposites and the corresponding theoretical models for predicting the mechanical performances of such nanocomposites. Then, we discuss various assembly techniques available for effectively incorporating the strong and flexible graphene-based components into polymer matrices by utilization of weak and strong interfacial interactions available in functionalized graphene sheets. We discuss mechanical performance and briefly summarize other physical (thermal, electrical, barrier, and optical) properties, which are controlled by processing conditions and interfacial interactions. Finally, we present a brief outlook of the developments in graphene-based polymer nanocomposites by discussing the major progress, opportunities, and challenges.


title: Assertion guided symbolic execution of multithreaded programs

abstract: Symbolic execution is a powerful technique for systematic testing of sequential and multithreaded programs. However, its application is limited by the high cost of covering all feasible intra-thread paths and inter-thread interleavings. We propose a new assertion guided pruning framework that identifies executions guaranteed not to lead to an error and removes them during symbolic execution. By summarizing the reasons why previously explored executions cannot reach an error and using the information to prune redundant executions in the future, we can soundly reduce the search space. We also use static concurrent program slicing and heuristic minimization of symbolic constraints to further reduce the computational overhead. We have implemented our method in the Cloud9 symbolic execution tool and evaluated it on a large set of multithreaded C/C++ programs. Our experiments show that the new method can reduce the overall computational cost significantly.


title: Unsupervised Template Learning for Fine-Grained Object Recognition

abstract: Fine-grained recognition refers to a subordinate level of recognition, such as recognizing different species of animals and plants. It differs from recognition of basic categories, such as humans, tables, and computers, in that there are global similarities in shape and structure shared cross different categories, and the differences are in the details of object parts. We suggest that the key to identifying the fine-grained differences lies in finding the right alignment of image regions that contain the same object parts. We propose a template model for the purpose, which captures common shape patterns of object parts, as well as the cooccurrence relation of the shape patterns. Once the image regions are aligned, extracted features are used for classification. Learning of the template model is efficient, and the recognition results we achieve significantly outperform the state-of-the-art algorithms.


Coupled 3D Convolutional Neural Networks for Audio-Visual Recognition


title: Traffic Analysis with Deep Learning

abstract: Deep Neural Networks (DNN) has obtained enormous attention with its advantageous feature learning and its powerful prediction ability. In this paper, we broadly study the applicability of deep learning to traffic analysis and present its effectiveness on the feature extraction for state-of-the-art machine learning algorithms, website and keyword fingerprinting attacks, and the prediction on the fingerprintability of websites. To the best of our knowledge, this is the first extensive work to introduce various applications using DNN in traffic analysis. With great help of DNN, the quality of cutting edge website fingerprinting attacks is upgraded while the feature dimension becomes much lower. As the classifiers, DNN successfully detects which website the user visited among 100 websites with 91% TPR and 1% FPR against 100,000 background websites, and as the fingerprintability predictors, it almost perfectly determines the fingerprintability of 4,500 website traffic instances with 99% of accuracy.


title: Controlling data in the cloud: outsourcing computation without outsourcing control

abstract: Cloud computing is clearly one of today's most enticing technology areas due, at least in part, to its cost-efficiency and flexibility. However, despite the surge in activity and interest, there are significant, persistent concerns about cloud computing that are impeding momentum and will eventually compromise the vision of cloud computing as a new IT procurement model. In this paper, we characterize the problems and their impact on adoption. In addition, and equally importantly, we describe how the combination of existing research thrusts has the potential to alleviate many of the concerns impeding adoption. In particular, we argue that with continued research advances in trusted computing and computation-supporting encryption, life in the cloud can be advantageous from a business intelligence standpoint over the isolated alternative that is more common today.


title: New category of ultra-thin notchless 6T SRAM cell layout topologies for sub-22nm

abstract: The extent to which the 6T SRAM bit cell can be perpetuated through continued scaling is of enormous technological and economic importance. Understanding the growing limitations in lithography, design and process technology, coupled with the mechanisms which drive systematic mismatch, provides direction in identifying more optimum solutions. We propose an alternative, ultra-thin (UT) SRAM cell layout topology as a means to address many of the challenging bit cell design constraints facing the most advanced CMOS process technologies today. Compared to the industry standard 6T topology, the newly proposed cell offers: 1) a lower bit line capacitance, 2) reduced M1 complexity and 3) notchless design for improved resistance to alignment induced device mismatch.