title: Cross-modal Moment Localization in Videos

abstract: In this paper, we address the temporal moment localization issue, namely, localizing a video moment described by a natural language query in an untrimmed video. This is a general yet challenging vision-language task since it requires not only the localization of moments, but also the multimodal comprehension of textual-temporal information (e.g., "first" and "leaving") that helps to distinguish the desired moment from the others, especially those with the similar visual content. While existing studies treat a given language query as a single unit, we propose to decompose it into two components: the relevant cue related to the desired moment localization and the irrelevant one meaningless to the localization. This allows us to flexibly adapt to arbitrary queries in an end-to-end framework. In our proposed model, a language-temporal attention network is utilized to learn the word attention based on the temporal context information in the video. Therefore, our model can automatically select "what words to listen to" for localizing the desired moment. We evaluate the proposed model on two public benchmark datasets: DiDeMo and Charades-STA. The experimental results verify its superiority over several state-of-the-art methods.


title: Deep Learning in Neural Networks: An Overview

abstract: In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.


title: Support Vector Machine Soft Margin Classifiers: Error Analysis

abstract: The purpose of this paper is to provide a PAC error analysis for the q-norm soft margin classifier, a support vector machine classification algorithm. It consists of two parts: regularization error and sample error. While many techniques are available for treating the sample error, much less is known for the regularization error and the corresponding approximation error for reproducing kernel Hilbert spaces. We are mainly concerned about the regularization error. It is estimated for general distributions by a K-functional in weighted Lq spaces. For weakly separable distributions (i.e., the margin may be zero) satisfactory convergence rates are provided by means of separating functions. A projection operator is introduced, which leads to better sample error estimates especially for small complexity kernels. The misclassification error is bounded by the V-risk associated with a general class of loss functions V. The difficulty of bounding the offset is overcome. Polynomial kernels and Gaussian kernels are used to demonstrate the main results. The choice of the regularization parameter plays an important role in our analysis.


title: Balancing Accuracy and Fun: Designing Camera Based Mobile Games for Implicit Heart Rate Monitoring

abstract: Heart rate monitoring is widely used in clinical care, fitness training, and stress management. However, tracking individuals' heart rates faces two major challenges, namely equipment availability and user motivation. In this paper, we present a novel technique, LivePulse Games (LPG), to measure users' heart rates in real time by having them play games on unmodified mobile phones. With LPG, the heart rate is calculated by detecting changes in transparency of users' fingertips via the built-in camera of a mobile device. More importantly, LPG integrate users' camera lens covering actions as an essential control mechanism in game play, and detect heart rates implicitly from intermittent lens covering actions. We explore the design space and trade-offs of LPG through three rounds of iterative design. In a 12-subject user study, we found that LPG are fun to play and can measure heart rates accurately. We also report the insights for balancing measurement speed, accuracy, and entertainment value in LPG.


title: A Deep Learning Approach to Link Prediction in Dynamic Networks

abstract: Time varying problems usually have complex underlying structures represented as dynamic networks where entities and relationships appear and disappear over time. The problem of efficiently performing dynamic link inference is extremely challenging due to the dynamic nature in massive evolving networks especially when there exist sparse connectivities and nonlinear transitional patterns. In this paper, we propose a novel deep learning framework, i.e., Conditional Temporal Restricted Boltzmann Machine (ctRBM), which predicts links based on individual transition variance as well as influence introduced by local neighbors. The proposed model is robust to noise and have the exponential capability to capture nonlinear variance. We tackle the computational challenges by developing an efficient algorithm for learning and inference of the proposed model. To improve the efficiency of the approach, we give a faster approximated implementation based on a proposed Neighbor Influence Clustering algorithm. Extensive experiments on simulated as well as real-world dynamic networks show that the proposed method outperforms existing algorithms in link inference on dynamic networks.


title: A Mathematical Programming Approach for Server Consolidation Problems in Virtualized Data Centers

abstract: Today's data centers offer IT services mostly hosted on dedicated physical servers. Server virtualization provides a technical means for server consolidation. Thus, multiple virtual servers can be hosted on a single server. Server consolidation describes the process of combining the workloads of several different servers on a set of target servers. We focus on server consolidation with dozens or hundreds of servers, which can be regularly found in enterprise data centers. Cost saving is among the key drivers for such projects. This paper presents decision models to optimally allocate source servers to physical target servers while considering real-world constraints. Our central model is proven to be an NP-hard problem. Therefore, besides an exact solution method, a heuristic is presented to address large-scale server consolidation projects. In addition, a preprocessing method for server load data is introduced allowing for the consideration of quality-of-service levels. Extensive experiments were conducted based on a large set of server load data from a data center provider focusing on managerial concerns over what types of problems can be solved. Results show that, on average, server savings of 31 percent can be achieved only by taking cycles in the server workload into account.


title: Small-Signal Discrete-Time Modeling of Digitally Controlled PWM Converters

abstract: The letter presents an exact small-signal discrete-time model for digitally controlled pulsewidth modulated (PWM) dc-dc converters operating in constant frequency continuous conduction mode (CCM) with a single effective A/D sampling instant per switching period. The model, which is based on well-known approaches to discrete-time modeling and the standard Z-transform, takes into account sampling, modulator effects and delays in the control loop, and is well suited for direct digital design of digital compensators. The letter presents general results valid for any CCM converter with leading or trailing edge PWM. Specific examples, including approximate closed-form expressions for control-to-output transfer functions are given for buck and boost converters. The model is verified in simulation using an independent system identification approach.


title: Epidemic processes in complex networks

abstract: In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and socio-technical systems. The complex properties of real world networks have a profound impact on the behavior of equilibrium and non-equilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. Here we present a coherent and comprehensive review of the vast research activity concerning epidemic processes, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, epidemiologists, computer and social scientists share a common interest in studying epidemic spreading and rely on very similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while we focus on the main results and the paradigmatic models in infectious disease modeling, we also present the major results concerning generalized social contagion processes. Finally we outline the research activity at the forefront in the study of epidemic spreading in co-evolving and time-varying networks.


title: A Bi-clustering Framework for Consensus Problems

abstract: We consider grouping as a general characterization for problems such as clustering, community detection in networks, and multiple parametric model estimation. We are interested in merging solutions from different grouping algorithms, distilling all their good qualities into a consensus solution. In this paper, we propose a biclustering framework and perspective for reaching consensus in such grouping problems. In particular, this is the first time that the task of finding/fitting multiple parametric models to a dataset is formally posed as a consensus problem. We highlight the equivalence of these tasks and establish the connection with the computational Gestalt program, which seeks to provide a psychologically inspired detection theory for visual events. We also present a simple but powerful biclustering algorithm, specially tuned to the nature of the problem we address, though general enough to handle many different instances inscribed within our characterization. The presentation is accompanied with di...


The Cycles of Theory Building in Management Research


title: Speeding up k-means Clustering by Bootstrap Averaging

abstract: K-means clustering is one of the most popular clustering algorithms used in data mining. However, clustering is a time consuming task, particularly with the large data sets found in data mining. In this paper we show how bootstrap averaging with k-means can produce results comparable to clustering all of the data but in much less time. The approach of bootstrap (sampling with replacement) averaging consists of running k-means clustering to convergence on small bootstrap samples of the training data and averaging similar cluster centroids to obtain a single model. We show why our approach should take less computation time and empirically illustrate its benefits. We show that the performance of our approach is a monotonic function of the size of the bootstrap sample. However, knowing the size of the bootstrap sample that yields as good results as clustering the entire data set remains an open and important question.


title: Cryptanalysis and improvement of an elliptic curve Diffie-Hellman key agreement protocol

abstract: In SAC'05, Strangio proposed protocol ECKE- 1 as an efficient elliptic curve Diffie-Hellman two-party key agreement protocol using public key authentication. In this letter, we show that protocol ECKE-1 is vulnerable to key-compromise impersonation attacks. We also present an improved protocol - ECKE-1N, which can withstand such attacks. The new protocol's performance is comparable to the well-known MQV protocol and maintains the same remarkable list of security properties.


title: Lucid dreaming: Correspondence between dreamed and actual events in one subject during rem sleep

abstract: Abstract During lucid dreaming, a subject willed movements of his fingers, toes and feet, remembered tasks, and counted sensory stimuli. Dreamed speech was related to respiration. EMG activity corresponding to dreamed actions was greater in flexor than in extensor limb muscles and was never present in axial muscles.


title: Research on a Novel Power Inductor-Based Bidirectional Lossless Equalization Circuit for Series-Connected Battery Packs

abstract: Cell balancing plays an important role in preserving the life of series-connected battery packs; without a suitable balancing system, the individual cell voltages will differ over time, and the battery pack capacity will decrease quickly. This paper presents a novel power inductor-based bidirectional lossless equalization circuit. This circuit consists of several balancing sub-circuits, which allow the dynamic adjustment of the equalization path and equalization threshold. The simulation and experiment results demonstrate that the proposed circuit, which features a simple control method, fast balancing, and a large equalization current, exhibits outstanding equalization performance.


title: Harvesting Parallel News Streams to Generate Paraphrases of Event Relations

abstract: The distributional hypothesis, which states that words that occur in similar contexts tend to have similar meanings, has inspired several Web mining algorithms for paraphrasing semantically equivalent phrases. Unfortunately, these methods have several drawbacks, such as confusing synonyms with antonyms and causes with effects. This paper introduces three Temporal Correspondence Heuristics, that characterize regularities in parallel news streams, and shows how they may be used to generate high precision paraphrases for event relations. We encode the heuristics in a probabilistic graphical model to create the NEWSSPIKE algorithm for mining news streams. We present experiments demonstrating that NEWSSPIKE significantly outperforms several competitive baselines. In order to spur further research, we provide a large annotated corpus of timestamped news articles as well as the paraphrases produced by NEWSSPIKE.


title: Learning Compatibility Across Categories for Heterogeneous Item Recommendation

abstract: Identifying relationships between items is a key task of an online recommender system, in order to help users discover items that are functionally complementary or visually compatible. In domains like clothing recommendation, this task is particularly challenging since a successful system should be capable of handling a large corpus of items, a huge amount of relationships among them, as well as the high-dimensional and semantically complicated features involved. Furthermore, the human notion of "compatibility" to capture goes beyond mere similarity: For two items to be compatible---whether jeans and a t-shirt, or a laptop and a charger---they should be similar in some ways, but systematically different in others. ::: In this paper we propose a novel method, Monomer, to learn complicated and heterogeneous relationships between items in product recommendation settings. Recently, scalable methods have been developed that address this task by learning similarity metrics on top of the content of the products involved. Here our method relaxes the metricity assumption inherent in previous work and models multiple localized notions of 'relatedness,' so as to uncover ways in which related items should be systematically similar, and systematically different. Quantitatively, we show that our system achieves state-of-the-art performance on large-scale compatibility prediction tasks, especially in cases where there is substantial heterogeneity between related items. Qualitatively, we demonstrate that richer notions of compatibility can be learned that go beyond similarity, and that our model can make effective recommendations of heterogeneous content.


title: Beyond caption to narrative: Video captioning with multiple sentences

abstract: Recent advances in image captioning task have led to increasing interests in video captioning task. However, most works on video captioning are focused on generating single input of aggregated features, which hardly deviates from image captioning process and does not fully take advantage of dynamic contents present in videos. We attempt to generate video captions that convey richer contents by temporally segmenting the video with action localization, generating multiple captions from multiple frames, and connecting them with natural language processing techniques, in order to generate a story-like caption. We show that our proposed method can generate captions that are richer in contents and can compete with state-of-the-art method without explicitly using video-level features as input.


title: Load balancing in 5G cloud radio access networks supporting IoT communications for smart communities

abstract: Internet of Things (IoT) communication is vital for the developing of smart communities. The rapid growth of IoT depends on reliable wireless networks. The evolving 5G cellular system addresses this challenge by adopting cloud computing technology in Radio Access Network (RAN); namely Cloud RAN or CRAN. CRAN enables better scalability, flexibility, and performance that allows 5G to provide connectivity for the vast volume of IoT devices envisioned for smart cities. This work investigates the load balance (LB) problem in CRAN, with the goal of reducing latencies experienced by IoT communications. Eight practical LB algorithms are studied and evaluated in CRAN environment, based on real cellular network traffic characteristics provided by Nokia Research. Experiment results on queue-length analysis show that the simple, light-weight queue-based LB is almost as effectively as the much more complex waiting-time-based LB. We believe that this study is significant in enabling 5G networks for providing IoT communication backbone in the emerging smart communities; it also has wide applications in other distributed systems.


title: Seismic parameters’ combinations for the optimum prediction of the damage state of R/C buildings using neural networks

abstract: Abstract The aim of the present paper is to investigate the number and the combination of 14 seismic parameters through which an optimum prediction for the damage state of r/c buildings can be achieved using Artificial Neural Networks (ANNs). Multilayer perceptron networks are utilized. For the training of the ANNs a data set is created using results from Nonlinear Time History Analyses of 30 r/c buildings with different structural dynamic characteristics, which are subjected to 65 actual ground motions. The Maximum Interstorey Drift Ratio is used as the damage index. Two versions of the “Stepwise method”, i.e. the Forward Stepwise Method and the Backward Stepwise Method, as well as the “Weights Method”, are adopted as methods for the investigation of the most effective combinations of the examined seismic parameters. The most significant conclusion that turned out is that ANNs can predict adequately the seismic damage state of r/c buildings if at least 5 seismic parameters are used as inputs. The classification of the seismic parameters on the basis of their correlation with the damage state is not unique, since it depends to the configuration and the training algorithm of ANNs as well as the method which is utilized for the classification.


title: A Mechanism-Based Pharmacokinetic Enzyme Turnover Model for Dichloroacetic Acid Autoinhibition in Rats.

abstract: Dichloroacetic acid (DCA), a halogenated organic acid, is a pyruvate dehydrogenase kinase inhibitor that has been used to treat congenital or acquired lactic acidosis and is currently in early-phase clinical trials for cancer treatment. DCA was found to inhibit its own metabolism by irreversibly inactivating glutathione transferase zeta 1 (GSTZ1-1), resulting in nonlinear kinetics and abnormally high accumulation ratio after repeated dosing. In this analysis, a semi-mechanistic pharmacokinetic enzyme turnover model was developed for the first time to capture DCA autoinhibition, gastrointestinal region-dependent absorption, and time-dependent change in bioavailability in rats. The maximum rate constant for DCA-induced GSTZ1-1 inactivation is estimated to be 0.96/h, which is 110 times that of the rate constant for GSTZ1-1 natural degradation (0.00875/h). The model-predicted DCA concentration that corresponds to 50% of maximum enzyme inhibition (EC50) is 4.32 mg/L. The constructed pharmacokinetic enzyme turnover model, when applied to human data, could be used to predict the accumulation of DCA after repeated oral dosing, guide selection of dosing regimens in clinical studies, and facilitate clinical development of DCA.


title: Occupational engagement and cognitive functioning among persons with schizophrenia: an explorative study

abstract: AbstractBackground: Cognitive functioning may have implications for engagement in daily occupations among people with schizophrenia.Objectives: This cross-sectional study explores relationships between time use assessed occupational engagement and cognitive functioning among persons with schizophrenia.Methods: Thirty-nine participants from four mental health care services in Sweden participated. The Profile of Occupational Engagement among persons with Severe mental illness (POES) and a cognitive test battery was used.Results: Higher attention and psychomotor speed and higher scores in information processing speed, immediate and delayed verbal recall, and immediate and delayed visual recall were significantly correlated with higher scores in occupational engagement. Regression analyzes revealed that information processing speed and delayed visual recall best explained the variance in occupational engagement (R2 = 0.36).Conclusions: Cognitive functioning has implications for occupational engagement, and th...


title: Investigating Capsule Networks with Dynamic Routing for Text Classification

abstract: In this study, we explore capsule networks with dynamic routing for text classification. We propose three strategies to stabilize the dynamic routing process to alleviate the disturbance of some noise capsules which may contain"background"information or have not been successfully trained. A series of experiments are conducted with capsule networks on six text classification benchmarks. Capsule networks achieve state of the art on 4 out of 6 datasets, which shows the effectiveness of capsule networks for text classification. We additionally show that capsule networks exhibit significant improvement when transfer single-label to multi-label text classification over strong baseline methods. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for text modeling.


title: Anomaly detection from multivariate time-series with sparse representation

abstract: Anomaly detection from sensor data is an important data mining application for efficient and secure operation of complicated systems. In this study, we propose a novel anomaly detection method for multivariate time-series to capture relationships of variables and time-domain correlations simultaneously, without assuming any generative models of signals. The supposed framework in this study is a semi-supervised anomaly detection where we seek unusual parts of test data compared with reference data. The proposed method is based on feature extraction with sparse representation and relationship learning with dimensionality reduction. Our idea comes from the similarity between a sparse feature matrix extracted from multivariate time-series and a term-document matrix. We conducted experiments with synthetic and simulated data, and confirmed that the proposed method successfully detected anomalies in multivariate time-series signals. Especially, it demonstrated superior performance with anomalies in which only relationships of time-series patterns are changed from reference data (multivariate anomalies).


title: Why Do Students Have Difficulties Completing Homework? The Need for Homework Management

abstract: Homework is a common and widespread educational activity. Yet, as homework often takes place amidst the pull of more attractive and competing after-school activities, doing homework presents multiple challenges for many students, even for those students who find their assignments meaningful and interesting. In this article, I first examine five major homework challenges, including arranging a conducive homework environment, budgeting time and pacing themselves to meet homework deadlines, handling homework distractions, keeping themselves motivated during homework sessions, and coping with negative affect or mood swings encountered while doing homework. I then discuss a range of strategies that students may use to deal with these challenges as well as implications for teachers and families seeking to help students manage their homework more responsibly.


title: The Architecture of Mr. DLib's Scientific Recommender-System API

abstract: Recommender systems in academia are not widely available. This may be in part due to the difficulty and cost of developing and maintaining recommender systems. Many operators of academic products such as digital libraries and reference managers avoid this effort, although a recommender system could provide significant benefits to their users. In this paper, we introduce Mr. DLib's "Recommendations as-a-Service" (RaaS) API that allows operators of academic products to easily integrate a scientific recommender system into their products. Mr. DLib generates recommendations for research articles but in the future, recommendations may include call for papers, grants, etc. Operators of academic products can request recommendations from Mr. DLib and display these recommendations to their users. Mr. DLib can be integrated in just a few hours or days; creating an equivalent recommender system from scratch would require several months for an academic operator. Mr. DLib has been used by GESIS Sowiport and by the reference manager JabRef. Mr. DLib is open source and its goal is to facilitate the application of, and research on, scientific recommender systems. In this paper, we present the motivation for Mr. DLib, the architecture and details about the effectiveness. Mr. DLib has delivered 94m recommendations over a span of two years with an average click-through rate of 0.12%.


title: Searching Concepts and Keywords in the Holy Quran

abstract: The Arabic language is one of the oldest languages in the world that presents its own features and challenges while searching for Arabic-based content. The most search systems for the Holy Quran is organized around words (contained in the target verses) rather than the concepts which those words denote. As a word denote many concepts (polysemy) and a concept can be denoted by many words (synonyms). In this study, we present a methodology to search about concepts and keywords in the Holy Quran while improving the chances of finding all desired verses. Three approaches are applied; text-based, stembased, and synonyms-based system. These three approaches are compared with respect to precision. The text-based approach is based on a full word, the synonyms-based approach is based on the synonyms of the word, and the stem-based approach is based on the stem of the word. We apply the light stemming algorithm to find the stem of the word.


title: Drug-induced bronchospasm.

abstract: The major categories of medications known to induce bronchospasm or cough, including beta-blockers, cholinesterase inhibitors, angiotensin-converting enzyme inhibitors, and inhalational agents are reviewed. The anatomy and physiology of the human airway relevant to this topic are discussed as well.


title: A Survey of Malware Detection Techniques

abstract: Abstract 31 Introduction 42 What is Malware? 4 2.1 Who are the Users and Creators of Malware? . . . . . . . . . . . . . . . 6 3 The Malware Detector 64 Malware Detection Techniques 7 4.1 Anomaly-based Detection . . . . . . . . . . . . . . . . . . . . . . . . . . 94.1.1 Dynamic Anomaly-based Detection . . . . . . . . . . . . . . . . 104.1.2 Static Anomaly-based Detection . . . . . . . . . . . . . . . . . . 154.1.3 Hybrid Anomaly-based Detection . . . . . . . . . . . . . . . . . . 164.2 Speciﬁcation-based Detection . . . . . . . . . . . . . . . . . . . . . . . . 184.2.1 Dynamic Speciﬁcation-based Detection . . . . . . . . . . . . . . 184.2.2 Static Speciﬁcation-based Detection . . . . . . . . . . . . . . . . 264.2.3 Hybrid Speciﬁcation-based Detection . . . . . . . . . . . . . . . 284.3 Signature-based detection . . . . . . . . . . . . . . . . . . . . . . . . . . 314.3.1 Dynamic Signature-based Detection . . . . . . . . . . . . . . . . 334.3.2 Static Signature-based Detection . . . . . . . . . . . . . . . . . . 344.3.3 Hybrid Signature-based Detection . . . . . . . . . . . . . . . . . 38


title: "Dr. Detective": combining gamification techniques and crowdsourcing to create a gold standard in medical text

abstract: This paper proposes a design for a gamified crowdsourcing workflow to extract annotation from medical text. Developed in the context of a general crowdsourcing platform, Dr. Detective is a game with a purpose that engages medical experts into solving annotation tasks on medical case reports, tailored to capture disagreement between annotators. It incorporates incentives such as learning features, to motivate a continuous involvement of the expert crowd. The game was designed to identify expressions valuable for training NLP tools, and interpret their relation in the context of medical diagnosing. In this way, we can resolve the main problem in gathering ground truth from experts - that the low inter-annotator agreement is typically caused by different interpretations of the text. We report on the results of a pilot study assessing the usefulness of this game. The results show that the quality of the annotations by the expert crowd are comparable to those of an NLP parser. Furthermore, we observed that allowing game users to access each others' answers increases agreement between annotators.


title: Investigating mobile payment acceptance using technological-personal-environmental (TPE) framework: A case of Indonesia

abstract: This study aims to investigate the influence of technological, personal, and environmental factors on users' intention to adopt mobile payment from the perspective of Indonesian consumers. We conducted an online survey, which obtained 1684 valid respondents. Those data were analyzed using SEM method with AMOS 21.0 application. This study showed that intrinsic motivation and facilitating condition significantly have a positive effect on consumer's perceived usefulness and perceived ease of use. Then, personal innovativeness only showed to have a significant effect on perceived ease of use, while social influence only showed to have a significant effect on perceived usefulness. In addition, perceived ease of use also showed to have a significant effect on perceived usefulness. Lastly, both perceived usefulness and perceived ease of use proved to have a significant impact on consumers' intention to use mobile payment thus confirming the applicability of the Technology Acceptance Model in the mobile payment acceptance context.


title: User Collusion Avoidance CP-ABE With Efficient Attribute Revocation for Cloud Storage

abstract: Attribute-based encryption (ABE) can guarantee confidentiality and achieve fine-grained data access control in a cloud storage system. Due to the fact that every attribute in ABE may be shared by multiple users and each user holds multiple attributes, any single-attribute revocation for some user may affect the other users with the same attribute in the system. Therefore, how to revoke attribute efficiently is an important and challenging problem in ABE schemes. In order to solve above problems, we first give a concrete attack to the existing ABE scheme with attribute revocation. Then, we formalize the definition and security model, which model collusion attack executed by the existing users cooperating with the revoked users. Finally, we present a user collusion avoidance ciphertext-policy ABE scheme with efficient attribute revocation for the cloud storage system. The problem of attribute revocation is solved efficiently by exploiting the concept of an attribute group. When an attribute is revoked from a user, the group manager updates other users’ secret keys. Furthermore, we prove that the proposed scheme is secure against collusion attack launched by the existing users and the revoked users. The security of the proposed scheme is reduced to the computational Diffie–Hellman assumption.


title: Automatic Identification of Research Articles from Crawled Documents

abstract: Online digital libraries that store and index research articles not only make it easier for researchers to search for scientific information, but also have been proven as powerful resources in many data mining, machine learning and information retrieval applications that require high-quality data. The quality of the data available in digital libraries highly depends on the quality of a classifier that identifies research articles from a set of crawled documents, which in turn depends, among other things, on the choice of the feature representation. The commonly used “bag of words” representation for document classification can result in prohibitively high dimensional input spaces and may not capture the specifics of research articles. In this paper, we propose novel features that result in effective and efficient classification models for automatic identification of research articles. Experimental results on two datasets compiled from the CiteSeer digital library show that our models outperform strong baselines using a significantly smaller number of features.


title: Automated document segmentation

abstract: Abstract Automated text block extraction in documents with a complicated layout is discussed. The proposed segmentation algorithm is based on a growing procedure guided by local information. Lines, text and graphic blocks are detected. Image skew is corrected.


title: Ferroelasticity and domain physics in two-dimensional transition metal dichalcogenide monolayers

abstract: United States. Department of Energy. Office of Basic Energy Sciences (Massachusetts Institute of Technology. Energy Frontier Research Center for Excitonics (award no. DE-SC0001088))


title: A new subtle hand gestures recognition algorithm based on EMG and FSR

abstract: The paper proposes a new algorithm that combines the EMG (Electromyography) signals and pressure signals for detecting subtle hand gestures. First, the algorithm uses Myo armband to acquire the forearm EMG signals and the algorithm uses an array of five force sensitive resistors (FSRs) to acquire the muscle expansion pressure signals of the back of the hand. Then the algorithm has an activity detection on the collected signals. The algorithm has selected the top 50 best features of a gesture via the open source tool scikit-learn. After that the cross-validation is used to select the best parameter k of kNN(k-Nearest Neighbor) algorithm. Finally, the selected k of kNN algorithm is used for gesture classification in real time. The experiment shows that the algorithm presented in this paper has a classification accuracy of 96.05% for 21 pre-defined hand gestures, and the combination of EMG and FSR is significantly more accurate for subtle gesture recognition than the one using EMG only and using FSR only.


title: UrbanMatch - linking and improving Smart Cities Data

abstract: Urban-related data and geographic information are becoming mainstream in the Linked Data community due also to the popularity of Location-based Services. In this paper, we introduce the UrbanMatch game, a mobile gaming application that joins data linkage and data quality/trustworthiness assessment in an urban environment. By putting together Linked Data and Human Computation, we create a new interaction paradigm to consume and produce location-specic linked data by involving and engaging the nal user. The UrbanMatch game is also oered as an example of value proposition and business model of a new family of linked data applications based on gaming in Smart Cities.


title: Connecting in-body nano communication with body area networks: Challenges and opportunities of the Internet of Nano Things

abstract: Nano-communication is considered to become a major building block for many novel applications in the health care and fitness sector. Given the recent developments in the scope of nano machinery, coordination and control of these devices becomes the critical challenge to be solved. In-Body Nano-Communication based on either molecular, acoustic, or RF radio communication in the terahertz band supports the exchange of messages between these in-body devices. Yet, the control and communication with external units is not yet fully understood. In this paper, we investigate the challenges and opportunities of connecting Body Area Networks and other external gateways with in-body nano-devices, paving the road towards more scalable and efficient Internet of Nano Things (IoNT) systems. We derive a novel network architecture supporting the resulting requirements and, most importantly, investigate options for the simulation based performance evaluation of such novel concepts. Our study is concluded by a first look at the resulting security issues considering the high impact of potential misuse of the communication links.


title: Impact of DFIG wind turbines on transient stability of power systems — A review

abstract: With the increasing wind power penetration, the wind farms are directly influencing the power systems. The majority of wind farms are using variable speed wind turbines equipped with doubly-fed induction generators (DFIG) due to their advantages over other wind turbine generators. Therefore, the analysis of wind power dynamics with the DFIG wind turbines has become a very important research issue, especially during transient faults. This paper deals with investigating the current research issues in this area, research gaps and limitations in previous works to gestate future research options.


title: A vector model of trust for developing trustworthy systems

abstract: All security services rely to a great extent on some notion of trust. However, even today, there is no accepted formalism or technique for the specification of trust and for reasoning about trust. Secure systems have been developed under the premise that concepts like ”trusted” or ”trustworthy” are well understood, unfortunately without even agreeing to what ”trust” means, how to measure it, how to compare two trust values and how to combine two trust values. In this work we propose a new vector model of trust. Our model proposes the notion of different degrees of trust, differentiates between trust and distrust and formalizes the dependence of trust on time. We believe that our model will help answer some of the questions posed earlier.


Autonomous vehicles


Neural Stance Detectors for Fake News Challenge


title: Switchable Adhesion Actuator for Amphibious Climbing Soft Robot

abstract: Climbing soft robots are of tremendous interest in both science and engineering due to their potential applications in intelligent surveillance, inspection, maintenance, and detection under environments away from the ground. The challenge lies in the design of a fast, robust, switchable adhesion actuator to easily attach and detach the vertical surfaces. Here, we propose a new design of pneumatic-actuated bioinspired soft adhesion actuator working both on ground and under water. It is composed of extremely soft bilayer structures with an embedded spiral pneumatic channel resting on top of a base layer with a cavity. Rather than the traditional way of directly pumping air out of the cavity for suction in hard polymer-based adhesion actuator, we inflate air into the top spiral channel to deform into a stable 3D domed shape for achieving negative pressure in the cavity. The characterization of the maximum shear adhesion force of the proposed soft adhesion actuator shows strong and rapid reversible adhesion on multiple types of smooth and semi-smooth surfaces. Based on the switchable adhesion actuator, we design and fabricate a novel load-carrying amphibious climbing soft robot (ACSR) by combining with a soft bending actuator. We demonstrate that it can operate on a wide range of foreign horizontal and vertical surfaces including dry, wet, slippery, smooth, and semi-smooth ones on ground and also under water with certain load-carrying capability. We show that the vertical climbing speed can reach about 286 mm/min (1.6 body length/min) while carrying over 200g object (over 5 times the weight of ACSR itself) during climbing on ground and under water. This research could largely push the boundaries of soft robot capabilities and multifunctionality in window cleaning and underwater inspection under harsh environment.


title: A framework for monitoring classifiers’ performance: when and why failure occurs?

abstract: Classifier error is the product of model bias and data variance. While understanding the bias involved when selecting a given learning algorithm, it is similarly important to understand the variability in data over time, since even the One True Model might perform poorly when training and evaluation samples diverge. Thus, it becomes the ability to identify distributional divergence is critical towards pinpointing when fracture points in classifier performance will occur, particularly since contemporary methods such as tenfolds and hold-out are poor predictors in divergent circumstances. This article implement a comprehensive evaluation framework to proactively detect breakpoints in classifiers’ predictions and shifts in data distributions through a series of statistical tests. We outline and utilize three scenarios under which data changes: sample selection bias, covariate shift, and shifting class priors. We evaluate the framework with a variety of classifiers and datasets.


title: Body image, eating disorders, and the drive for muscularity in gay and heterosexual men: the influence of media images.

abstract: SUMMARY This Internet research project examined the relationship between consumption of muscle and fitness magazines and/or various indices of pornography and body satisfaction in gay and heterosexual men. Participants (N= 101) were asked to complete body satisfaction questionnaires that addressed maladaptive eating attitudes, the drive for muscularity, and social physique anxiety. Participants also completed scales measuring self-esteem, depression, and socially desirable responding. Finally, respondents were asked about their consumption of muscle and fitness magazines and pornography. Results indicated that viewing and purchasing of muscle and fitness magazines correlated positively with levels of body dissatisfaction for both gay and heterosexual men. Pornography exposure was positively correlated with social physique anxiety for gay men. The limitations of this study and directions for future research are outlined.


Socratic Learning: Correcting Misspecified Generative Models using Discriminative Models


title: An advanced Diffie-Hellman approach to image steganography

abstract: Diffie-Hellman key exchange is one of the most popular key exchange algorithms used in cryptography. Steganography is the approach of hiding information in a medium. When that medium is an image it is called as image steganography. The most popular image steganography algorithm in use presently is the Least Significant bit Algorithm (LSB). However this algorithm has proved to be easy to crack and hence rather insecure. The proposed algorithm uses a variation of the Diffie-Hellman algorithm to determine the pixel positions of the image to embed the information in. Both the sender and receiver maintain private keys. Along with this a public key is shared between them. The sender sends a public key to the receiver and vice versa. This public key is nothing but the product of the private key with the public key. The shared secret key is then the public key received multiplied with the private key of each. Now this value obtained is considered to be the pixel to embed data in. So all pixels with multiples of this value are embedded first using LSB. If still some data persists, then the shared key is substracted by 1 and all multiples of this new value are then filled. The process is repeated till the entire information is hidden.


title: The impact of agile practices on communication in software development

abstract: Agile software development practices such as eXtreme Programming (XP) and SCRUM have increasingly been adopted to respond to the challenges of volatile business environments, where the markets and technologies evolve rapidly and present the unexpected. In spite of the encouraging results so far, little is known about how agile practices affect communication. This article presents the results from a study which examined the impact of XP and SCRUM practices on communication within software development teams and within the focal organization. The research was carried out as a case study in F-Secure where two agile software development projects were compared from the communication perspective. The goal of the study is to increase the understanding of communication in the context of agile software development: internally among the developers and project leaders and in the interface between the development team and stakeholders (i.e. customers, testers, other development teams). The study shows that agile practices improve both informal and formal communication. However, it further indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication. The study highlights the fact that hurdles and improvements in the communication process can both affect the feature requirements and task subtask dependencies as described in coordination theory. While the use of SCRUM and some XP practices facilitate team and organizational communication of the dependencies between product features and working tasks, the use of agile practices requires that the team and organization use also additional plan-driven practices to ensure the efficiency of external communication between all the actors of software development.


title: Least Cost Rumor Blocking in Social Networks

abstract: In many real-world scenarios, social network serves as a platform for information diffusion, alongside with positive information (truth) dissemination, negative information (rumor) also spread among the public. To make the social network as a reliable medium, it is necessary to have strategies to control rumor diffusion. In this article, we address the Least Cost Rumor Blocking (LCRB) problem where rumors originate from a community Cr in the network and a notion of protectors are used to limit the bad influence of rumors. The problem can be summarized as identifying a minimal subset of individuals as initial protectors to minimize the number of people infected in neighbor communities of Cr at the end of both diffusion processes. Observing the community structure property, we pay attention to a kind of vertex set, called bridge end set, in which each node has at least one direct in-neighbor in Cr and is reachable from rumors. Under the OOAO model, we study LCRB-P problem, in which α (0 <; α <; 1) fraction of bridge ends are required to be protected. We prove that the objective function of this problem is submodular and a greedy algorithm is adopted to derive a (1-1/e)-approximation. Furthermore, we study LCRB-D problem over the DOAA model, in which all the bridge ends are required to be protected, we prove that there is no polynomial time o(ln n)-approximation for the LCRB-D problem unless P = NP, and propose a Set Cover Based Greedy (SCBG) algorithm which achieves a O(ln n)-approximation ratio. Finally, to evaluate the efficiency and effectiveness of our algorithm, we conduct extensive comparison simulations in three real-world datasets, and the results show that our algorithm outperforms other heuristics.


title: On Automating Basic Data Curation Tasks

abstract: Big data analytics is firmly recognized as a strategic priority for modern enterprises. At the heart of big data analytics lies the data curation process, consists of tasks that transform raw data (unstructured, semi-structured and structured data sources) into curated data, i.e. contextualized data and knowledge that is maintained and made available for use by end-users and applications. To achieve this, the data curation process may involve techniques and algorithms for extracting, classifying, linking, merging, enriching, sampling, and the summarization of data and knowledge. To facilitate the data curation process and enhance the productivity of researchers and developers, we identify and implement a set of basic data curation APIs and make them available as services to researchers and developers to assist them in transforming their raw data into curated data. The curation APIs enable developers to easily add features - such as extracting keyword, part of speech, and named entities such as Persons, Locations, Organizations, Companies, Products, Diseases, Drugs, etc.; providing synonyms and stems for extracted information items leveraging lexical knowledge bases for the English language such as WordNet; linking extracted entities to external knowledge bases such as Google Knowledge Graph and Wikidata; discovering similarity among the extracted information items, such as calculating similarity between string and numbers; classifying, sorting and categorizing data into various types, forms or any other distinct class; and indexing structured and unstructured data - into their data applications. These services can be accessed via a REST API, and the data is returned as a JSON file that can be integrated into data applications. The curation APIs are available as an open source project on GitHub.


title: Interpreting DNA Mixtures with Related Contributors in Subdivided Populations

abstract: In this paper, we study the statistical interpretation of forensic DNA mixtures with related contributors in subdivided populations. Compact general formulae for match probabilities are obtained for two situations: a relative of one tested person is an unknown contributor of a DNA mixture; and two related unknowns are contributors. The effect of kinship and population structure is illustrated using a real case example. Copyright Board of the Foundation of the Scandinavian Journal of Statistics 2004.


title: Generalized Louvain Method for Community Detection in Large Networks

abstract: In this paper we present a novel strategy to discover the community structure of (possibly, large) networks. This approach is based on the well-know concept of network modularity optimization. To do so, our algorithm exploits a novel measure of edge centrality, based on the κ-paths. This technique allows to efficiently compute a edge ranking in large networks in near linear time. Once the centrality ranking is calculated, the algorithm computes the pairwise proximity between nodes of the network. Finally, it discovers the community structure adopting a strategy inspired by the well-known state-of-the-art Louvain method (henceforth, LM), efficiently maximizing the network modularity. The experiments we carried out show that our algorithm outperforms other techniques and slightly improves results of the original LM, providing reliable results. Another advantage is that its adoption is naturally extended even to unweighted networks, differently with respect to the LM.


title: Neural Attentive Session-based Recommendation

abstract: Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. Previous work only considers the user's sequential behavior in the current session, whereas the user's main purpose in the current session is not emphasized. In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem. Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later. We then compute the recommendation scores for each candidate item with a bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session representations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental results show that NARM outperforms state-of-the-art baselines on both datasets. Furthermore, we also find that NARM achieves a significant improvement on long sessions, which demonstrates its advantages in modeling the user's sequential behavior and main purpose simultaneously.


title: Toward whole-session relevance: exploring intrinsic diversity in web search

abstract: Current research on web search has focused on optimizing and evaluating single queries. However, a significant fraction of user queries are part of more complex tasks [20] which span multiple queries across one or more search sessions [26,24]. An ideal search engine would not only retrieve relevant results for a user's particular query but also be able to identify when the user is engaged in a more complex task and aid the user in completing that task [29,1]. Toward optimizing whole-session or task relevance, we characterize and address the problem of intrinsic diversity (ID) in retrieval [30], a type of complex task that requires multiple interactions with current search engines. Unlike existing work on extrinsic diversity [30] that deals with ambiguity in intent across multiple users, ID queries often have little ambiguity in intent but seek content covering a variety of aspects on a shared theme. In such scenarios, the underlying needs are typically exploratory, comparative, or breadth-oriented in nature. We identify and address three key problems for ID retrieval: identifying authentic examples of ID tasks from post-hoc analysis of behavioral signals in search logs; learning to identify initiator queries that mark the start of an ID search task; and given an initiator query, predicting which content to prefetch and rank.


title: A Case for Transforming Parallel Runtimes Into Operating System Kernels

abstract: The needs of parallel runtime systems and the increasingly sophisticated languages and compilers they support do not line up with the services provided by general-purpose OSes. Furthermore, the semantics available to the runtime are lost at the system-call boundary in such OSes. Finally, because a runtime executes at user-level in such an environment, it cannot leverage hardware features that require kernel-mode privileges---a large portion of the functionality of the machine is lost to it. These limitations warp the design, implementation, functionality, and performance of parallel runtimes. We summarize the case for eliminating these compromises by transforming parallel runtimes into OS kernels. We also demonstrate that it is feasible to do so. Our evidence comes from Nautilus, a prototype kernel framework that we built to support such transformations. After describing Nautilus, we report on our experiences using it to transform three very different runtimes into kernels.


title: Geotagging in multimedia and computer vision—a survey

abstract: Geo-tagging is a fast-emerging trend in digital photography and community photo sharing. The presence of geographically relevant metadata with images and videos has opened up interesting research avenues within the multimedia and computer vision domains. In this paper, we survey geo-tagging related research within the context of multimedia and along three dimensions: (1) Modalities in which geographical information can be extracted, (2) Applications that can benefit from the use of geographical information, and (3) The interplay between modalities and applications. Our survey will introduce research problems and discuss significant approaches. We will discuss the nature of different modalities and lay out factors that are expected to govern the choices with respect to multimedia and vision applications. Finally, we discuss future research directions in this field.


title: Integrality and separability of input devices

abstract: Current input device taxonomies and other frameworks typically emphasize the mechanical structure of input devices. We suggest that selecting an appropriate input device for an interactive task requires looking beyond the physical structure of devices to the deeper perceptual structure of the task, the device, and the interrelationship between the perceptual structure of the task and the control properties of the device. We affirm that perception is key to understanding performance of multidimensional input devices on multidimensional tasks. We have therefore extended the theory of processing of percetual structure to graphical interactive tasks and to the control structure of input devices. This allows us to predict task and device combinations that lead to better performance and hypothesize that performance is improved when the perceptual structure of the task matches the control structure of the device. We conducted an experiment in which subjects performed two tasks with different perceptual structures, using two input devices with correspondingly different control structures, a three-dimensional tracker and a mouse. We analyzed both speed and accuracy, as well as the trajectories generated by subjects as they used the unconstrained three-dimensional tracker to perform each task. The result support our hypothesis and confirm the importance of matching the perceptual structure of the task and the control structure of the input device.


title: Cell Encapsulation in Sub-mm Sized Gel Modules Using Replica Molding

abstract: For many types of cells, behavior in two-dimensional (2D) culture differs from that in three-dimensional (3D) culture. Among biologists, 2D culture on treated plastic surfaces is currently the most popular method for cell culture. In 3D, no analogous standard method--one that is similarly convenient, flexible, and reproducible--exists. This paper describes a soft-lithographic method to encapsulate cells in 3D gel objects (modules) in a variety of simple shapes (cylinders, crosses, rectangular prisms) with lateral dimensions between 40 and 1000 microm, cell densities of 10(5)-10(8) cells/cm(3), and total volumes between 1x10(-7) and 8x10(-4) cm(3). By varying (i) the initial density of cells at seeding, and (ii) the dimensions of the modules, the number of cells per module ranged from 1 to 2500 cells. Modules were formed from a range of standard biopolymers, including collagen, Matrigel, and agarose, without the complex equipment often used in encapsulation. The small dimensions of the modules allowed rapid transport of nutrients by diffusion to cells at any location in the module, and therefore allowed generation of modules with cell densities near to those of dense tissues (10(8)-10(9) cells/cm(3)). This modular method is based on soft lithography and requires little special equipment; the method is therefore accessible, flexible, and well suited to (i) understanding the behavior of cells in 3D environments at high densities of cells, as in dense tissues, and (ii) developing applications in tissue engineering.


title: S-Caffe: Co-designing MPI Runtimes and Caffe for Scalable Deep Learning on Modern GPU Clusters

abstract: Availability of large data sets like ImageNet and massively parallel computation support in modern HPC devices like NVIDIA GPUs have fueled a renewed interest in Deep Learning (DL) algorithms. This has triggered the development of DL frameworks like Caffe, Torch, TensorFlow, and CNTK. However, most DL frameworks have been limited to a single node. In order to scale out DL frameworks and bring HPC capabilities to the DL arena, we propose, S-Caffe; a scalable and distributed Caffe adaptation for modern multi-GPU clusters. With an in-depth analysis of new requirements brought forward by the DL frameworks and limitations of current communication runtimes, we present a co-design of the Caffe framework and the MVAPICH2-GDR MPI runtime. Using the co-design methodology, we modify Caffe's workflow to maximize the overlap of computation and communication with multi-stage data propagation and gradient aggregation schemes. We bring DL-Awareness to the MPI runtime by proposing a hierarchical reduction design that benefits from CUDA-Aware features and provides up to a massive 133x speedup over OpenMPI and 2.6x speedup over MVAPICH2 for 160 GPUs. S-Caffe successfully scales up to 160 K-80 GPUs for GoogLeNet (ImageNet) with a speedup of 2.5x over 32 GPUs. To the best of our knowledge, this is the first framework that scales up to 160 GPUs. Furthermore, even for single node training, S-Caffe shows an improvement of 14\% and 9\% over Nvidia's optimized Caffe for 8 and 16 GPUs, respectively. In addition, S-Caffe achieves up to 1395 samples per second for the AlexNet model, which is comparable to the performance of Microsoft CNTK.


title: An effective key management scheme for heterogeneous sensor networks

abstract: Abstract Security is critical for sensor networks used in military, homeland security and other hostile environments. Previous research on sensor network security mainly considers homogeneous sensor networks. Research has shown that homogeneous ad hoc networks have poor performance and scalability. Furthermore, many security schemes designed for homogeneous sensor networks suffer from high communication overhead, computation overhead, and/or high storage requirement. Recently deployed sensor network systems are increasingly following heterogeneous designs. Key management is an essential cryptographic primitive to provide other security operations. In this paper, we present an effective key management scheme that takes advantage of the powerful high-end sensors in heterogeneous sensor networks. The performance evaluation and security analysis show that the key management scheme provides better security with low complexity and significant reduction on storage requirement, compared with existing key management schemes.


title: A Novel Robot Fish With Wire-Driven Active Body and Compliant Tail

abstract: In the past two decades, a large number of robot fishes have been built, though their performances are still far inferior compared with that of the real fish. One of the key reasons is the design. This paper presents a novel robot fish. The key innovation is the combination of an active wire-driven body with a soft compliant tail to accomplish undulatory swimming (i.e., flapping in “S” motion). First, the design is presented. Second, the design and analysis model is given. In particular, it is shown that the compliant tail can be modeled through the proposed multipseudolink model. Then, a prototype and experimental results are presented. The experiment results demonstrate that the kinematics of the robot fish can be predicted by the presented model with reasonable errors. Moreover, it has several advantages; simple in structure, easy to control, capable of high-speed swimming (the maximum speed is 2.15 body lengths/s), and good maneuverability (the average turning speed is 63°/s). Most importantly, it is highly efficient; the swimming performance of the robot fish is close to that of a real fish.


title: Image Segmentation Based on Poisson Equation

abstract: Image segmentation is an important part of the image processing. Currently, image segmentation methods are mainly the threshold-based segmentation method, the region-based segmentation method, the edge-based segmentation method and the Snake model based on energy function etc. This paper presents a novel image segmentation method based on the Poisson equation. The goal of the segmentation method is to divide the image into two homogeneous parts, the boundary portion and the non-boundary portion, which have similar gray values in homogeneous part. The key of the method is to build a Poisson equation with Dirichlet boundary condition. It sets a gradient threshold as the Dirichlet boundary condition of the Poisson equation, and gets a binary image by retaining the image boundary and smoothing the non-image boundary. Then simple binary segmentation will be able to get the image boundary. The experimental results show that this segmentation method can get accurate image boundaries for non-noise images and the weak noise images.


title: Fingerprint Minutiae: A Constructive Definition

abstract: The flow pattern of ridges in a fingerprint is unique to the person in that no two people with the same fingerprints have yet been found. Fingerprints have been in use in forensic applications for many years and, more recently, in computer-automated identification and authentication. For automated fingerprint image matching, a machine representation of a fingerprint image is often a set of minutiae in the print; a minimal, but fundamental, representation is just a set of ridge endings and bifurcations. Oddly, however, after all the years of using minutiae, a precise definition of minutiae has never been formulated. We provide a formal definition of a minutia based on the gray scale image. This definition is constructive, in that, given a minutia image, the minutia location and orientation can be uniquely determined.


title: Assessing the Extent of Randomization Bias in the Canadian Self-Sufficiency Experiment

abstract: In Canada, a policy aiming at helping single parents on social assistance become selfreliant was implemented on an experimental basis. The Self-Sufficiency Entry Effects Demonstration randomly selected a sample of 4,142 single parents who had applied for welfare between January 1994 and March 1995. It turned out only 3,315 agreed to be part of the experiment despite a 50% chance of receiving a generous, time-limited, earnings supplement conditional on finding a full-time jobs and leaving income assistance. The purpose of this paper is to determine whether a refusal rate as high as 20% is likely to bias the measurement of the treatment effect. We compare the estimated impact of the program using experimental data only to those obtained using additional data on individuals not taking part in the experiment. We write the likelihood of various sets of information and obtain relevant estimates of program impact on welfare spell durations. We find strong evidence of randomization bias in the data. When we correct for the bias, we find that estimates that rely on experimental data only significantly underestimate the true impact of the program.


title: Turkish Language Resources: Morphological Parser, Morphological Disambiguator and Web Corpus

abstract: In this paper, we propose a set of language resources for building Turkish language processing applications. Specifically, we present a finite-state implementation of a morphological parser, an averaged perceptron-based morphological disambiguator, and compilation of a web corpus. Turkish is an agglutinative language with a highly productive inflectional and derivational morphology. We present an implementation of a morphological parser based on two-level morphology. This parser is one of the most complete parsers for Turkish and it runs independent of any other external system such as PC-KIMMO in contrast to existing parsers. Due to complex phonology and morphology of Turkish, parsing introduces some ambiguous parses. We developed a morphological disambiguator with accuracy of about 98% using averaged perceptron algorithm. We also present our efforts to build a Turkish web corpus of about 423 million words.


title: Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion

abstract: Americans spend about a third of their time online, with many participating in online conversations on social and political issues. We hypothesize that social media arguments on such issues may be more engaging and persuasive than traditional media summaries, and that particular types of people may be more or less convinced by particular styles of argument, e.g. emotional arguments may resonate with some personalities while factual arguments resonate with others. We report a set of experiments testing at large scale how audience variables interact with argument style to affect the persuasiveness of an argument, an under-researched topic within natural language processing. We show that belief change is affected by personality factors, with conscientious, open and agreeable people being more convinced by emotional arguments.


title: BRNGLR: a cubic Tomita-style GLR parsing algorithm

abstract: Tomita-style generalised LR (GLR) algorithms extend the standard LR algorithm to non-deterministic grammars by performing all possible choices of action. Cubic complexity is achieved if all rules are of length at most two. In this paper we shall show how to achieve cubic time bounds for all grammars by binarising the search performed whilst executing reduce actions in a GLR-style parser. We call the resulting algorithm Binary Right Nulled GLR (BRNGLR) parsing. The binarisation process generates run-time behaviour that is related to that shown by a parser which pre-processes its grammar or parse table into a binary form, but without the increase in table size and with a reduced run-time space overhead. BRNGLR parsers have worst-case cubic run time on all grammars, linear behaviour on LR(1) grammars and produce, in worst-case cubic time, a cubic size binary SPPF representation of all the derivations of a given sentence.


title: Partial and complete trisomy 9: Delineation of a trisomy 9 syndrome

abstract: Two infants with trisomy involving chromosome 9 are described. One had complete trisomy 9 and the other karyotype 47,XX,+der(9),t(7;9)(p22;q32)mat. A trisomy 9 syndrome is delineated, consisting of features of the trisomy 9p syndrome and various other malformations. These include abnormalities of the cardiovascular and urogenital systems, cranial suture anomalies, dislocation of the hips and knees and early death. A possible relationship of some of these findings to regions of 9q involved in cases of partial trisomy 9 is suggested.


Neural Network based Closed loop Speed Control of DC Motor using Arduino Uno


title: Controlled Functional Encryption

abstract: Motivated by privacy and usability requirements in various scenarios where existing cryptographic tools (like secure multi-party computation and functional encryption) are not adequate, we introduce a new cryptographic tool called Controlled Functional Encryption (C-FE). As in functional encryption, C-FE allows a user (client) to learn only certain functions of encrypted data, using keys obtained from an authority. However, we allow (and require) the client to send a fresh key request to the authority every time it wants to evaluate a function on a ciphertext. We obtain efficient solutions by carefully combining CCA2 secure public-key encryption (or rerandomizable RCCA secure public-key encryption, depending on the nature of security desired) with Yao's garbled circuit. Our main contributions in this work include developing and for- mally defining the notion of C-FE; designing theoretical and practical constructions of C-FE schemes achieving these definitions for specific and general classes of functions; and evaluating the performance of our constructions on various application scenarios.


title: A Three-Frame Approach to Constraint-Consistent Motion Estimation

abstract: We study the motion estimation across three images by simultaneously enforcing the geometric constraint of trilinearity and the constant-brightness constraint. An iterative and hierarchical three-frame scheme is proposed to solve the geometry-consistent and appearance-consistent motion fields. It provides an uncalibrated approach to dense motion estimation, applicable to both 2-D and 3-D rigid scenes under perspective projection with unconstrained camera motion. Valid and consistent constraints are explored to alleviate the inherent aperture and over smoothing problems.


title: Software architecture for farm management information systems in precision agriculture

abstract: Farm management information systems (FMIS) have steadily increased in their level of sophistication as they have included new technologies with Internet connectivity being the latest addition. However, few FMIS have used the full capabilities of the Internet, and the emerging concept of precision agriculture has little or no support in the current commercially available FMIS. FMIS for precision agriculture have certain additional requirements to traditional FMIS, which makes the implementation of these systems technically more complicated in several aspects. Our research aimed to identify the requirements posed by precision agriculture on FMIS and then evaluate a modern Web-based approach to the implementation of an FMIS that fulfilled these additional requirements.


title: Ultrasonic Assistive Headset for visually impaired people

abstract: This paper presents a developed device to solve the problem of moving and navigating of visually impaired and blind people. This device, called Ultrasonic Assistive Headset, is light, simple and low-cost option compared with other assistive devices. Ultrasonic Assistive Headset will guide for them among obstacles by employing ultrasonic distance sensors, microcontroller, voice storage circuit and solar panels to be battery-free. In proposed method, ultrasonic waves arrive to the ultrasonic sensors on the spherical membrane of the headset after sending this waves from ultrasonic distance sensors reflection from obstacles. A microcontroller determines the location of the obstacles according to the sensor ID and the information of distance. The system produces an voice data defining the location, and then it speaks to blind person where the obstacles are. Ultrasonic Assistive Headset can be used easily by them both indoor and outdoor, so they can avoid obstacles quickly and accurately.


title: A General Framework for Multi-focal Image Classification and Authentication: Application to Microscope Pollen Images

abstract: In this article, we propose a general framework for multi-focal image classification and authentication, the methodology being demonstrated on microscope pollen images. The framework is meant to be generic and based on a brute force-like approach aimed to be efficient not only on any kind, and any number, of pollen images (regardless of the pollen type), but also on any kind of multi-focal images. All stages of the framework's pipeline are designed to be used in an automatic fashion. First, the optimal focus is selected using the absolute gradient method. Then, pollen grains are extracted using a coarse-to-fine approach involving both clustering and morphological techniques (coarse stage), and a snake-based segmentation (fine stage). Finally, features are extracted and selected using a generalized approach, and their classification is tested with four classifiers: Weighted Neighbor Distance, Neural Network, Decision Tree and Random Forest. The latter method, which has shown the best and more robust classification accuracy results (above 97\% for any number of pollen types), is finally used for the authentication stage.


title: Robust Natural Language Processing - Combining Reasoning, Cognitive Semantics and Construction Grammar for Spatial Language

abstract: We present a system for generating and understanding of dynamic and static spatial relations in robotic interaction setups. Robots describe an environment of moving blocks using English phrases that include spatial relations such as "across" and "in front of". We evaluate the system in robot-robot interactions and show that the system can robustly deal with visual perception errors, language omissions and ungrammatical utterances.


title: Recommendations with Optimal Combination of Feature-Based and Item-Based Preferences

abstract: Many recommender systems rely on item ratings to predict users' preferences and generate recommendations. However, users often express preferences by referring to features of the items, e.g., "I like Tarantino's movies". But, it has been shown that user models based on feature preferences may lead to wrong recommendations. In this paper we cope with this issue and we introduce a novel prediction model that generate better item recommendations, especially in cold-start situations, by exploiting both item-based and feature-based preferences. We also show that it is possible to optimize the combination of the two types of preferences when actively requesting them to users.


title: Smart energy monitoring system with ADE7758 IC

abstract: Brazil is experiencing its greatest power supply crisis since the 2001 rationing. With the growing risks of blackouts, the largest country in Latin America must face the challenges arising from the power failure looking for ways to improve the efficiency of energy supply. In recent years, with the improvement of the population's purchasing power, there was a substantial increase in energy demand and the need to seek improvements in energy parameters in the generation and supply. Therefore, the investigation for the energy parameters evaluation methods is imperative as well as the advance of equipment performance. This paper details the development of a smart energy system incorporating features to monitor energy parameters of equipments and perform functions like real time energy monitoring, power factor and alerts against overvoltage and overcurrent. The proposed meter uses the capabilities of ADE7758 IC as well an ATmega16 microcontroller.


title: Mapping Seismic Site Classes in Monterrey Metropolitan Area, northeast Mexico

abstract: A first microzonation ::: map of seismic site class distribution in Monterrey Metropolitan ::: area is presented. This was prepared using borehole information, ::: seismic refraction profiling and surficial geology. According to ::: geotechnical site categories proposed by RodriguezMarek et al. ::: (2001) and NEHRP soil classification (VS30), C, C1-C2 sites classes ::: are located to the south and central of the study area. In the ::: northwest zone, B site classes are dominant. Lowest S-wave velocity ::: (average of 268 m/s) correspond to silts, whereas highest velocities ::: (average 2311 m/s) are related to Cretaceous limestones. Maximum ::: thicknesses (16 to 20 m) of the soft sediments C-C2 classes are ::: located at the center area being correlated to two paleo-channels. ::: This study provides an initial attempt to understand and ::: characterize the seismic site classes in the study area


title: Fast and scalable reachability queries on graphs by pruned labeling with landmarks and paths

abstract: Answering reachability queries on directed graphs is ubiquitous in many applications involved with graph-shaped data as one of the most fundamental and important operations. However, it is still highly challenging to efficiently process them on large-scale graphs. Transitive-closure-based methods consume prohibitively large index space, and online-search-based methods answer queries too slowly. Labeling-based methods attain both small index size and query time, but previous indexing algorithms are not scalable at all for processing large graphs of the day. In this paper, we propose new labeling-based methods for reachability queries, referred to as pruned landmark labeling and pruned path labeling. They follow the frameworks of 2-hop cover and 3-hop cover, but their indexing algorithms are based on the recent notion of pruned labeling and improve the indexing time by several orders of magnitude, resulting in applicability to large graphs with tens of millions of vertices and edges. Our experimental results show that they attain remarkable trade-offs between fast query time, small index size and scalability, which previous methods have never been able to achieve. Furthermore, we also discuss the ingredients of the efficiency of our methods by a novel theoretical analysis based on the graph minor theory.


Challenges and Opportunities with Fault Injection in Virtualized Systems


title: Contextual Equivalence for Probabilistic Programs with Continuous Random Variables and Scoring

abstract: We present a logical relation for proving contextual equivalence in a probabilistic programming language (PPL) with continuous random variables and with a scoring operation for expressing observations and soft constraints.


title: Physical and Numerical Simulation Based Optimization in Continuous Casting of Cu-SMA Alloys

abstract: Kontinuirno litje je pogosto uporabljen in privlacen tehnoloski proces v sodobni metalurski industriji. Kljub razsirjeni uporabi zaradi zapletenosti fizikalnih procesov v ozadju se vedno ni dobro opisano. Cilj tega prispevka je osnovati model termomehanskega vedenja pri kontinuirnem litju bakrenih zlitin z oblikovnim spominom. Sklopljeno termomehansko numericno simulacijo palice v procesu strjevanja smo izvedli v zbirki programske opreme ProCAST, zmožni modeliranja procesov v polno neuravnoteženih procesnih pogojih. Model smo uporabili za izracun temperature, odvisne od polnega casa, in porazdelitve obremenitve med kontinuirnim litjem. Zlasti smo se osredotocili na prepoznavanje obcutljivosti fronte strjevanja na razlicne procesne parametre: hitrost litja, toplotno prevodnost ob stiku in toplotno ucinkovitost vodnega hladilnega sistema. Proucili smo stabilnost profila fronte strjevanja in predlagali tipicne procesne parametre. Rezultati simulacij so pozitivno primerljivi z eksperimentalnimi izkusnjami, in sicer, da je eden kljucnih parametrov hitrost litja, odvisna od polnega casa.


title: 3D Constrained Local Model for rigid and non-rigid facial tracking

abstract: We present 3D Constrained Local Model (CLM-Z) for robust facial feature tracking under varying pose. Our approach integrates both depth and intensity information in a common framework. We show the benefit of our CLM-Z method in both accuracy and convergence rates over regular CLM formulation through experiments on publicly available datasets. Additionally, we demonstrate a way to combine a rigid head pose tracker with CLM-Z that benefits rigid head tracking. We show better performance than the current state-of-the-art approaches in head pose tracking with our extension of the generalised adaptive view-based appearance model (GAVAM).


title: Towards Online Iris and Periocular Recognition Under Relaxed Imaging Constraints

abstract: Online iris recognition using distantly acquired images in a less imaging constrained environment requires the development of a efficient iris segmentation approach and recognition strategy that can exploit multiple features available for the potential identification. This paper presents an effective solution toward addressing such a problem. The developed iris segmentation approach exploits a random walker algorithm to efficiently estimate coarsely segmented iris images. These coarsely segmented iris images are postprocessed using a sequence of operations that can effectively improve the segmentation accuracy. The robustness of the proposed iris segmentation approach is ascertained by providing comparison with other state-of-the-art algorithms using publicly available UBIRIS.v2, FRGC, and CASIA.v4-distance databases. Our experimental results achieve improvement of 9.5%, 4.3%, and 25.7% in the average segmentation accuracy, respectively, for the UBIRIS.v2, FRGC, and CASIA.v4-distance databases, as compared with most competing approaches. We also exploit the simultaneously extracted periocular features to achieve significant performance improvement. The joint segmentation and combination strategy suggest promising results and achieve average improvement of 132.3%, 7.45%, and 17.5% in the recognition performance, respectively, from the UBIRIS.v2, FRGC, and CASIA.v4-distance databases, as compared with the related competing approaches.


title: Curved Gabor Filters for Fingerprint Image Enhancement

abstract: Gabor filters (GFs) play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved GFs that locally adapt their shape to the direction of flow. These curved GFs enable the choice of filter parameters that increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved GFs are applied to the curved ridge and valley structures of low-quality fingerprint images. First, we combine two orientation-field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation. Subsequently, these curved regions are used for estimating the local ridge frequency. Finally, curved GFs are defined based on curved regions, and they apply the previously estimated orientations and ridge frequencies for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison with state-of-the-art enhancement methods.


title: BioSpaun: A large-scale behaving brain model with complex neurons

abstract: We describe a large-scale functional brain model that includes detailed, conductance-based, compartmental models of individual neurons. We call the model BioSpaun, to indicate the increased biological plausibility of these neurons, and because it is a direct extension of the Spaun model \cite{Eliasmith2012b}. We demonstrate that including these detailed compartmental models does not adversely affect performance across a variety of tasks, including digit recognition, serial working memory, and counting. We then explore the effects of applying TTX, a sodium channel blocking drug, to the model. We characterize the behavioral changes that result from this molecular level intervention. We believe this is the first demonstration of a large-scale brain model that clearly links low-level molecular interventions and high-level behavior.


title: An objective and subjective study of the role of semantics and prosodic features in building corpora for emotional TTS

abstract: Building a text corpus suitable to be used in corpus-based speech synthesis is a time-consuming process that usually requires some human intervention to select the desired phonetic content and the necessary variety of prosodic contexts. If an emotional text-to-speech (TTS) system is desired, the complexity of the corpus generation process increases. This paper presents a study aiming to validate or reject the use of a semantically neutral text corpus for the recording of both neutral and emotional (acted) speech. The use of this kind of texts would eliminate the need to include semantically emotional texts into the corpus. The study has been performed for Basque language. It has been made by performing subjective and objective comparisons between the prosodic characteristics of recorded emotional speech using both semantically neutral and emotional texts. At the same time, the performed experiments allow for an evaluation of the capability of prosody to carry emotional information in Basque language. Prosody manipulation is the most common processing tool used in concatenative TTS. Experiments of automatic recognition of the emotions considered in this paper (the "Big Six emotions") show that prosody is an important emotional indicator, but cannot be the only manipulated parameter in an emotional TTS system-at least not for all the emotions. Resynthesis experiments transferring prosody from emotional to neutral speech have also been performed. They corroborate the results and support the use of a neutral-semantic-content text in databases for emotional speech synthesis


title: Torque ripple minimisation of permanent magnet synchronous motor using a new proportional resonant controller

abstract: When applying the permanent magnetic synchronous motors (PMSM) to refrigerant system, the suppression of speed ripples of the PMSM in low-feed range becomes the focus of consideration. The authors start with the generation mechanism and the periodical characteristics of the speed ripples to propose an adaptive control method. In the proposed method, the authors place a frequency variable resonance controller in parallel with the conditional proportion and integral (PI) controller to form a PI-resonance (PI-RES) controller. The compensation torque current generated by the resonance controller and the main reference current generated by the PI controller constitute the reference torque current. Because of the existence of the compensation torque current, the electromagnetic torque can follow the variation of the load torque much better, thus the speed ripples are suppressed. Performance indices of the conventional PI controller and the PI-RES controller are compared and evaluated through experimental investigations. The results validate the effectiveness of the proposed method.


title: Reinforcement Learning for Humanoid Robotics

abstract: Reinforcement learning offers one of the most general framework to take traditional robotics towards true autonomy and versatility. However, applying reinforcement learning to high dimensional movement systems like humanoid robots remains an unsolved problem. In this paper, we discuss different approaches of reinforcement learning in terms of their applicability in humanoid robotics. Methods can be coarsely classified into three different categories, i.e., greedy methods, ‘vanilla’ policy gradient methods, and natural gradient methods. We discuss that greedy methods are not likely to scale into the domain humanoid robotics as they are problematic when used with function approximation. ‘Vanilla’ policy gradient methods on the other hand have been successfully applied on real-world robots including at least one humanoid robot [3]. We demonstrate that these methods can be significantly improved using the natural policy gradient instead of the regular policy gradient. A derivation of the natural policy gradient is provided, proving that the average policy gradient of Kakade [10] is indeed the true natural gradient. A general algorithm for estimating the natural gradient, the Natural Actor-Critic algorithm, is introduced. This algorithm converges to the nearest local minimum of the cost function with respect to the Fisher information metric under suitable conditions. The algorithm outperforms non-natural policy gradients by far in a cart-pole balancing evaluation, and for learning nonlinear dynamic motor primitives for humanoid robot control. It offers a promising route for the development of reinforcement learning for truly high-dimensionally continuous state-action systems.


title: CONDENSATION—Conditional Density Propagation for Visual Tracking

abstract: The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimo dal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together with visual observations, to propagate the random set over time. The result is highly robust tracking of agile motion. Notwithstanding the use of stochastic methods, the algorithm runs in near real-time.


title: ALEF: From Application to Platform for Adaptive Collaborative Learning

abstract: Web 2.0 has had a tremendous impact on education. It facilitates access and availability of learning content in variety of new formats, content creation, learning tailored to students’ individual preferences, and collaboration. The range of Web 2.0 tools and features is constantly evolving, with focus on users and ways that enable users to socialize, share and work together on (user-generated) content. In this chapter we present ALEF—Adaptive Learning Framework that responds to the challenges posed on educational systems in Web 2.0 era. Besides its base functionality—to deliver educational content—ALEF particularly focuses on making the learning process more efficient by delivering tailored learning experience via personalized recommendation, and enabling learners to collaborate and actively participate in learning via interactive educational components. Our existing and successfully utilized solution serves as the medium for presenting key concepts that enable realizing Web 2.0 principles in education, namely lightweight models, and three components of framework infrastructure important for constant evolution and inclusion of students directly into the educational process—annotation framework, feedback infrastructure and widgets. These make possible to devise and implement various mechanisms for recommendation and collaboration—we also present selected methods for personalized recommendation and collaboration together with their evaluation in ALEF.


title: Factors hindering acceptance of using cloud services in university: a case study

abstract: Purpose – The aim of this paper is to propose a novel evaluation framework to explore the “root causes” that hinder the acceptance of using internal cloud services in a university.Design/methodology/approach – The proposed evaluation framework incorporates the duo‐theme DEMATEL (decision making trial and evaluation laboratory) with TAM (technology acceptance model). The operational procedures were proposed and tested on a university during the post‐implementation phase after introducing the internal cloud services.Findings – According to the results, clear understanding and operational ease under the theme perceived ease of use (PEOU) are more imperative; whereas improved usefulness and productivity under the theme perceived usefulness (PU) are more urgent to foster the usage of internal clouds in the case university.Research limitations/implications – Based on the findings, some intervention activities were suggested to enhance the level of users' acceptance of internal cloud solutions in the case univer...


title: Best Match: New relevance search for PubMed

abstract: PubMed is a free search engine for biomedical literature accessed by millions of users from around the world each day. With the rapid growth of biomedical literature—about two articles are added every minute on average—finding and retrieving the most relevant papers for a given query is increasingly challenging. We present Best Match, a new relevance search algorithm for PubMed that leverages the intelligence of our users and cutting-edge machine-learning technology as an alternative to the traditional date sort order. The Best Match algorithm is trained with past user searches with dozens of relevance-ranking signals (factors), the most important being the past usage of an article, publication date, relevance score, and type of article. This new algorithm demonstrates state-of-the-art retrieval performance in benchmarking experiments as well as an improved user experience in real-world testing (over 20% increase in user click-through rate). Since its deployment in June 2017, we have observed a significant increase (60%) in PubMed searches with relevance sort order: it now assists millions of PubMed searches each week. In this work, we hope to increase the awareness and transparency of this new relevance sort option for PubMed users, enabling them to retrieve information more effectively.


title: Question Answering by Reasoning Across Documents with Graph Convolutional Networks

abstract: Most research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. We introduce a neural model which integrates and reasons relying on information spread within documents and across multiple documents. We frame it as an inference problem on a graph. Mentions of entities are nodes of this graph while edges encode relations between different mentions (e.g., within- and cross-document co-reference). Graph convolutional networks (GCNs) are applied to these graphs and trained to perform multi-step reasoning. Our Entity-GCN method is scalable and compact, and it achieves state-of-the-art results on a multi-document question answering dataset, WikiHop (Welbl et al., 2018).


title: Architectural Pattern of Progressive Web and Background Synchronization

abstract: Progressive Web Applications emerged as a better alternative to traditional mobile application development. Possessing the best of both native applications and web applications, PWA also offers advantages like offline loading of the application as well as services like push notifications and background synchronization to enhance user engagement. The paper provides an overview of using Progressive Web Apps as modern application development cycle. We have discussed in detail in the paper how progressiveness in a web application offers advantage over native application. The paper also discusses about the architectural pattern followed to develop progressive web apps to improve loading time of the app. API provided by the service workers have also been discussed in detail including push notifications and background synchronization to provide native app like experience. A case study has been carried out to depict the working of various progressive features.