title: InfoVAE: Information Maximizing Variational Autoencoders

abstract: It has been previously observed that variational autoencoders tend to ignore the latent code when combined with a decoding distribution that is too flexible. This undermines the purpose of unsupervised representation learning. We identify the reason for this short-coming in the regularization term used in the ELBO criterion to match the variational posterior to the latent prior distribution. We show that removing this regularization term leads to a model that can still discover meaningful latent features. Even though ancestral sampling is no longer tractable, sampling is possible using a Markov chain. Furthermore, we propose a class of training criteria that use alternative divergences for the regularization term, generalizing the standard ELBO which employs KL divergence. These models can discover meaningful latent features and allow for tractable ancestral sampling. In particular, we propose an alternative based on Maximum Mean Discrepancy (MMD) that is simple to implement, robust, and has similar or better performance in every quantitative and qualitative metric we experimented on.


title: W-Band High-Gain TE$_{220}$-Mode Slot Antenna Array With Gap Waveguide Feeding Network

abstract: In this letter, a high-order-mode substrate integrated cavity (SIC) excited $2 \times 2$ -slot antenna subarray for wideband and high-gain W-band array application is presented. This antenna subarray is designed by using two low-cost double-side printed circuit broads (PCBs). The transmission loss caused by divider can be reduced by using the high-order-mode substrate integrated cavity to excite the slot antenna subarray. Furthermore, such use reduces processing difficulty and fabrication cost. The bandwidth and gain of the proposed antenna are also improved. To validate our design, an $8 \times 8$ antenna array with $4 \times 4$ SIC subarrays has been fabricated and measured. To reduce the loss further, the microstrip-based ridge gap waveguide (GWG) technology is applied to feed the SIC subarrays. Furthermore, for measurement purposes, a wideband transition structure between rectangular waveguide (RWG) and GWG is designed. Measured results show that an $8 \times 8$ slot array with the transition achieves a maximum boresight gain of 25.3 dBi and an impedance matching bandwidth of 82-102 GHz with a boresight gain above 24.5 dBi. In addition, the measured aperture efficiency is 64%.


title: Introducing Docear's research paper recommender system

abstract: In this demo paper we present Docear's research paper recommender system. Docear is an academic literature suite to search, organize, and create research articles. The users' data (papers, references, annotations, etc.) is managed in mind maps and these mind maps are utilized for the recommendations. Using content-based filtering methods, Docear's recommender achieves click-through rates around 6%, in some scenarios even over 10%.


title: Chronic Pain Patients’ Kinesiophobia and Catastrophizing are Associated with Activity Intensity at Different Times of the Day

abstract: Purpose ::: To examine the relationship between baseline kinesiophobia and baseline pain catastrophizing with the 4-day average activity intensity at different times of the day while accounting for different wake and sleep-onset times in chronic pain patients. ::: ::: ::: Methods ::: Twenty-one participants suffering from idiopathic chronic pain completed baseline questionnaires about kinesiophobia, catastrophizing, disability, depression, and pain. We measured the participants' activity using accelerometers and calculated activity intensity in the morning, afternoon, and evening. We performed a 2-way repeated measures ANOVA to compare activity levels at different times of the day, and multiple linear regressions. ::: ::: ::: Results ::: Baseline kinesiophobia was significantly associated with 4-day average evening light activity and sedentary activity at all time periods while baseline catastrophizing was significantly associated with increased 4-day average light activity in the evening and more moderate to vigorous activity in the morning. Our participants engaged in more light activity on average than sedentary activity, and very little moderate-vigorous activity. Participants were most active in the afternoon. ::: ::: ::: Conclusion ::: Baseline kinesiophobia and baseline catastrophizing were not associated with the 4-day average total daily activity; however, they were associated with 4-day average activity intensities at different times throughout the day. Segmenting daily activity into morning, afternoon, evening may influence the relationship between daily activity, and kinesiophobia and pain catastrophizing. Individuals with chronic pain are less sedentary than previously thought which may affect future interventions.


title: The local boundedness and Harnack inequality of p(x)-Laplace equation

abstract: Using Moser's iteration method, we investigate the local boundedness of solutions for p(x)-Laplacian equations and derive a Harnack type inequality important for applications.


title: Generic and Robust Method for Head Pose Estimation

abstract: Head pose estimation has fascinated the research community due to its application in facial motion capture, human-computer interaction and video conferencing. It is a pre-requisite to gaze tracking, face recognition, and facial expression analysis. In this paper, we present a generic and robust method for model-based global 2D head pose estimation from single RGB Image. In our approach we use of the one part the Gabor filters to conceive a robust pose descriptor to illumination and facial expression variations, and that target the pose information. Moreover, we ensure the classification of these descriptors using a SVM classifier. The approach has proved effective view the rate for the correct pose estimations that we got.


title: Holographic Testing Of Stretchable Concave Imaging Mirrors

abstract: A circular, concave, imaging mirror of variable focus is described. The mirror is a very thin plastic membrane with a highly reflective metallised front surface. Mirror curvature is determined by the strength of a vacuum applied to the rear face of the film, increasing the vacuum will increase the curvature. High resolution reflected and focussed images have been observed from targets, using both white light and infra-red. Mirror curvatures from f/10 to f/0.4 have been examined, high resolution images were obtained at all curvatures. Such a wide range covers large f number astronomical telescopes, to the 'fast' small f number concave mirrors used in infra-red imaging. The contents of this paper will describe the application of holographic testing to such devices. The tests were undertaken in order to examine the symmetry of the mirror surface and to study the vibratory response of that surface.


title: Crowdsourcing for multiple-choice question answering

abstract: We leverage crowd wisdom for multiple-choice question answering, and employ lightweight machine learning techniques to improve the aggregation accuracy of crowdsourced answers to these questions. In order to develop more effective aggregation methods and evaluate them empirically, we developed and deployed a crowdsourced system for playing the "Who wants to be a millionaire?" quiz show. Analyzing our data (which consist of more than 200,000 answers), we find that by just going with the most selected answer in the aggregation, we can answer over 90% of the questions correctly, but the success rate of this technique plunges to 60% for the later/harder questions in the quiz show. To improve the success rates of these later/harder questions, we investigate novel weighted aggregation schemes for aggregating the answers obtained from the crowd. By using weights optimized for reliability of participants (derived from the participants' confidence), we show that we can pull up the accuracy rate for the harder questions by 15%, and to overall 95% average accuracy. Our results provide a good case for the benefits of applying machine learning techniques for building more accurate crowdsourced question answering systems.


title: What to do about bad language on the internet

abstract: The rise of social media has brought computational linguistics in ever-closer contact with bad language: text that defies our expectations about vocabulary, spelling, and syntax. This paper surveys the landscape of bad language, and offers a critical review of the NLP community’s response, which has largely followed two paths: normalization and domain adaptation. Each approach is evaluated in the context of theoretical and empirical work on computer-mediated communication. In addition, the paper presents a quantitative analysis of the lexical diversity of social media text, and its relationship to other corpora.


title: “Security begins at home”: Determinants of home computer and mobile device security behavior

abstract: Abstract Personal computing users are vulnerable to information security threats, as they must independently make decisions about how to protect themselves, often with little understanding of technology or its implications. However, personal computing users are under-represented in security research studies, especially for mobile device use. The study described in this paper addresses this research gap by evaluating data from 629 home computer and mobile device users to improve understanding of security behavior in both contexts. The research model extends protection motivation theory by including the roles of social influences and psychological ownership, and by including actual behavior. The model was separately tested with home computer users and mobile device users and data reveals that some of the determinants of security behavior differ between home computer and mobile device use. The results show that perceived vulnerability, self-efficacy, response cost, descriptive norm and psychological ownership all influenced personal computing security intentions and behavior for both home computer users and mobile device users. However, perceived severity was only found to play a role in mobile device security behavior and neither response efficacy nor subjective norm influenced security intentions for either type of user. These findings are discussed in terms of their practical and research implications as well as generating new research opportunities into personal computing security.


title: A study of seismic inverse methods for radar signal processing

abstract: The subject of seismic migration is one of the most varied in seismic data processing. Many algorithms have been developed to perform this task, including Kirchhoff migration, finite-difference reverse time migration, and several types of phase shift migration. The purpose of this study is to investigate the possibility of using seismic inversion algorithms for radar signal processing to improve signal quality and reduce the effects of clutter based on the study of known geophysical inversion algorithms. The finite-difference reverse time migration method was studied in detail since it is one of the most accurate and general depth migration algorithms. It uses the finite difference wave equation modeling as a means of migrating seismic data. Preliminary experiments on the synthetic data generated from different models (a geophysical model and models similar to radar cases) were performed using the reverse-time migration algorithm


GraspNet: An Efficient Convolutional Neural Network for Real-time Grasp Detection for Low-powered Devices


title: Optimization of mutual information for multiresolution image registration

abstract: We propose a new method for the intermodal registration of images using a criterion known as mutual information. Our main contribution is an optimizer that we specifically designed for this criterion. We show that this new optimizer is well adapted to a multiresolution approach because it typically converges in fewer criterion evaluations than other optimizers. We have built a multiresolution image pyramid, along with an interpolation process, an optimizer, and the criterion itself, around the unifying concept of spline-processing. This ensures coherence in the way we model data and yields good performance. We have tested our approach in a variety of experimental conditions and report excellent results. We claim an accuracy of about a hundredth of a pixel under ideal conditions. We are also robust since the accuracy is still about a tenth of a pixel under very noisy conditions. In addition, a blind evaluation of our results compares very favorably to the work of several other researchers.


title: Employing Event Inference to Improve Semi-Supervised Chinese Event Extraction

abstract: Although semi-supervised model can extract the event mentions matching frequent event patterns, it suffers much from those event mentions, which match infrequent patterns or have no matching pattern. To solve this issue, this paper introduces various kinds of linguistic knowledge-driven event inference mechanisms to semi-supervised Chinese event extraction. These event inference mechanisms can capture linguistic knowledge from four aspects, i.e. semantics of argument role, compositional semantics of trigger, consistency on coreference events and relevant events, to further recover missing event mentions from unlabeled texts. Evaluation on the ACE 2005 Chinese corpus shows that our event inference mechanisms significantly outperform the refined state-of-the-art semi-supervised Chinese event extraction system in F1-score by 8.5%.


title: AMW: A Generic Model Weaver

abstract: Abstract In the MDA approach proposed by the OMG (Object Management Group) as well as in other MDE (Model Driven Engineering) approaches such as Microsoft “Software Factories”, model transformation is a central operation. However it is often necessary to create links between models, where distinct operations will be executed based on the link semantics. These links are captured by weaving models. One of the possible application domains is data mapping. We have been engaged in building a model weaver prototype as part of the construction of AMMA, a MDE platform. The basic assumption in such platforms is to consider models as first class entities. In the present work we go one step beyond to consider also model weaving as models, and thus as manageable entities. This paper suggests that several situations in software engineering could be captured by model weaving techniques. Present achievements (mainly output generation of a weaving model, extensible weaving metamodels, partial GUI generation, etc.) and future potential extensions are discussed.


title: Personality Processes and Individual Differences - Words of Wisdom: Language Use Over the Life Span

abstract: Two projects explored the links between language use and aging. In the first project, written or spoken text samples from disclosure studies from over 3,000 research participants from 45 different studies representing 21 laboratories in 3 countries were analyzed to determine how people change in their use of 14 text dimensions as a function of age. A separate project analyzed the collected works of 10 well-known novelists, playwrights, and poets who lived over the last 500 years. Both projects found that with increasing age, individuals use more positive and fewer negative affect words, use fewer self-references, use more future-tense and fewer past-tense verbs, arid demonstrate a general pattern of increasing cognitive complexity. Implications for using language as a marker of personality among current and historical texts are discussed.


PREVENTION OF HEART PROBLEM USING ARTIFICIAL INTELLIGENCE


title: Person re-identification across different datasets with multi-task learning

abstract: This paper presents an approach to tackle the re-identification problem. This is a challenging problem due to the large variation of pose, illumination or camera view. More and more datasets are available to train machine learning models for person re-identification. These datasets vary in conditions: cameras numbers, camera positions, location, season, in size, i.e. number of images, number of different identities. Finally in labeling: there are datasets annotated with attributes while others are not. To deal with this variety of datasets we present in this paper an approach to take information from different datasets to build a system which performs well on all of them. Our model is based on a Convolutional Neural Network (CNN) and trained using multitask learning. Several losses are used to extract the different information available in the different datasets. Our main task is learned with a classification loss. To reduce the intra-class variation we experiment with the center loss. Our paper ends with a performance evaluation in which we discuss the influence of the different losses on the global re-identification performance. We show that with our method, we are able to build a system that performs well on different datasets and simultaneously extracts attributes. We also show that our system outperforms recent re-identification works on two datasets.


Meta-analysis of psychological intervention`s effectiveness on reduction of pain intensity among patients with chronic pain


title: Making Graphene Luminescent by Oxygen Plasma Treatment

abstract: We show that strong photoluminescence (PL) can be induced in single-layer graphene using an oxygen plasma treatment. The PL is spatially uniform across the flakes and connected to elastic scattering spectra distinctly different from those of gapless pristine graphene. Oxygen plasma can be used to selectively convert the topmost layer when multilayer samples are treated.


title: Controlled Aggregate Tree Shaped Questions over

abstract: Controlled languages (CLs) are ambiguity-free subsets of natural languages such as English offering a good trade-off between the formal rigor of ontology and query languages and the intuitive appeal of natural language. They compositionally map (modulo a compositional translation *** (·)) into (or express ) formal query languages and ontology languages. Modulo compositionality, they inherit the computational properties of such ontology/query languages. In the setting of OBDAS, we are interested in capturing query answering and measuring computational complexity w.r.t. the data queried (a.k.a. data complexity ). In this paper we focus in defining a CL capable of expressing a subset SQL aggregate queries , and study its data complexity w.r.t. several ontology languages and extensions of the query language.


title: Wireless Relay Communications with Unmanned Aerial Vehicles: Performance and Optimization

abstract: In this paper, we investigate a communication system in which unmanned aerial vehicles (UAVs) are used as relays between ground-based terminals and a network base station. We develop an algorithm for optimizing the performance of the ground-to-relay links through control of the UAV heading angle. To quantify link performance, we define the ergodic normalized transmission rate (ENTR) for the links between the ground nodes and the relay, and derive a closed-form expression for it in terms of the eigenvalues of the channel correlation matrix. We show that the ENTR can be approximated as a sinusoid with an offset that depends on the heading of the UAV. Using this observation, we develop a closed-form expression for the UAV heading that maximizes the uplink network data rate while keeping the rate of each individual link above a certain threshold. When the current UAV relay assignments cannot meet the minimum link requirements, we investigate the deployment and heading control problem for new UAV relays as they are added to the network, and propose a smart handoff algorithm that updates node and relay assignments as the topology of the network evolves.


title: Asymptotics in statistics and probability : papers in honor of George Gregory Roussas

abstract: Part 1 Trend estimation in correlated noise, Rudolf Beran: the lumber-thickness data asymptotically minimax estimators proofs. Part 2 Higher order analysis at Lebesgue points, A. Berlinet and S. Levallois: definitions and main results two examples with infinite derivatives example with discontinuity of the second kind conclusion. Part 3 Regression analysis for multivariate failure time observations, T. Cai and L.J. Wei: estimation of regression parameters simultaneous predictions of survival probabilities and related quantities example appendix -asymptotic joint distribution of {b*k} with discrete covariates. Part 4 Local estimation of a biometric function with covariate effects, Zongwu Cai and Lianfen Qian: local likelihood method an exponential regression approach simulation study appendix. Part 5 The estimation of conditional densities, X. Chen et al: kernel conditional density estimates asymptotic theory of conditional density estimates and bandwidth choice. Part 6 Functional limit theorems for induced order statistics of a sample from a domain of attraction of a-stable law, a I (0,2), Yu. Davydov and V. Egorov: notation auxillary facts main results lemmas proofs concluding remarks. Part 7 Limit laws for kernel density estimators for kernels with unbounded supports, Paul Deheuvel: introduction and results proofs. Part 8 Inequalities for a new data-based method for selecting nonparametric density estimates, Luc Devroye et al: the basic estimate standard kernel estimate -Riemann kernels standard kernel estimate - general kernels multiparameter kernel estimates - product kernels multiparameter kernel estimates - ellipsoidal kernels the transformed kernel estimate Monte Carlo simulations proof of theorem 1. Part 9 B-fuzzy stochastics, C.A. Drossos et al: preliminaries basic results B-fuzzy structures B-fuzzy statistics. Part 10 Detecting jumps in nonparametric regression, Ch. Dubowik and U. Stadtmuller: the proposed model new results simulations appendix. (Part contents).


title: Student perceptions of collaborative learning, social presence and satisfaction in a blended learning environment: Relationships and critical factors

abstract: The purpose of this study was to examine the relationships of the students' perceived levels of collaborative learning, social presence and overall satisfaction in a blended learning environment. This research studied the relationship of these three variables and identified critical factors related to them. The participants were 48 graduate students who took a blended-format course in health education and worked on a collaborative group project related to the development of a comprehensive HIV-AIDS prevention plan. Data was collected from the Student Perception Questionnaire and face-to-face interviews. The analysis of quantitative data indicated that student perceptions of collaborative learning have statistically positive relationships with perceptions of social presence and satisfaction. This means that students who perceived high levels of collaborative learning tended to be more satisfied with their distance course than those who perceived low levels of collaborative learning. Similarly, students with high perceptions of collaborative learning perceived high levels of social presence as well. Surprisingly, the relationship between social presence and overall satisfaction was positive but not statistically significant. Interview data revealed that (a) course structure, (b) emotional support, and (c) communication medium were critical factors associated with student perceptions of collaborative learning, social presence, and satisfaction. Explanations about findings and implications for instructional design are discussed in the conclusion.


title: SoundNet: Learning Sound Representations from Unlabeled Video

abstract: We learn rich natural sound representations by capitalizing on large amounts of unlabeled sound data collected in the wild. We leverage the natural synchronization between vision and sound to learn an acoustic representation using two-million unlabeled videos. Unlabeled video has the advantage that it can be economically acquired at massive scales, yet contains useful signals about natural sound. We propose a student-teacher training procedure which transfers discriminative visual knowledge from well established visual recognition models into the sound modality using unlabeled video as a bridge. Our sound representation yields significant performance improvements over the state-of-the-art results on standard benchmarks for acoustic scene/object classification. Visualizations suggest some high-level semantics automatically emerge in the sound network, even though it is trained without ground truth labels.


title: Design and implementation of an intelligent end-to-end network QoS system

abstract: End-to-End guaranteed network QoS is a requirement for predictable data transfers between geographically distant end-hosts. Existing QoS systems, however, do not have the capability/intelligence to decide what resources to reserve and which paths to choose when there are multiple and flexible resource reservation requests. In this paper, we design and implement an intelligent system that can guarantee end-to-end network QoS for multiple flexible reservation requests. At the heart of this system is a polynomial time algorithm called resource reservation and path construction (RRPC). The RRPC algorithm schedules multiple flexible end-to-end data transfer requests by jointly optimizing the path construction and bandwidth reservation along these paths. We show that constructing such schedules is NP-hard. We implement our intelligent QoS system, and present the results of deployment on real world production networks (ESnet and Internet2). Our implementation does not require modifications or new software to be deployed on the routers within network.


title: Cost-sensitive boosting for classification of imbalanced data

abstract: Classification of data with imbalanced class distribution has posed a significant drawback of the performance attainable by most standard classifier learning algorithms, which assume a relatively balanced class distribution and equal misclassification costs. The significant difficulty and frequent occurrence of the class imbalance problem indicate the need for extra research efforts. The objective of this paper is to investigate meta-techniques applicable to most classifier learning algorithms, with the aim to advance the classification of imbalanced data. The AdaBoost algorithm is reported as a successful meta-technique for improving classification accuracy. The insight gained from a comprehensive analysis of the AdaBoost algorithm in terms of its advantages and shortcomings in tacking the class imbalance problem leads to the exploration of three cost-sensitive boosting algorithms, which are developed by introducing cost items into the learning framework of AdaBoost. Further analysis shows that one of the proposed algorithms tallies with the stagewise additive modelling in statistics to minimize the cost exponential loss. These boosting algorithms are also studied with respect to their weighting strategies towards different types of samples, and their effectiveness in identifying rare cases through experiments on several real world medical data sets, where the class imbalance problem prevails.


title: Spatialization Design: Comparing Points and Landscapes

abstract: Spatializations represent non-spatial data using a spatial layout similar to a map. We present an experiment comparing different visual representations of spatialized data, to determine which representations are best for a non-trivial search and point estimation task. Primarily, we compare point-based displays to 2D and 3D information landscapes. We also compare a colour (hue) scale to a grey (lightness) scale. For the task we studied, point-based spatializations were far superior to landscapes, and 2D landscapes were superior to 3D landscapes. Little or no benefit was found for redundantly encoding data using colour or greyscale combined with landscape height. 3D landscapes with no colour scale (height-only) were particularly slow and inaccurate. A colour scale was found to be better than a greyscale for all display types, but a greyscale was helpful compared to height-only. These results suggest that point-based spatializations should be chosen over landscape representations, at least for tasks involving only point data itself rather than derived information about the data space.


title: Test battery for measuring the perception and recognition of facial expressions of emotion

abstract: Despite the importance of the perception and recognition of facial expressions in everyday life there is no comprehensive test battery for the multivariate assessment of these abilities. As a first step towards such a compilation, we present 16 tasks for measuring the perception and the recognition of emotional expressions, along with their difficulty and reliability. The scoring of these tasks focuses on either the speed or accuracy of performance. A sample of 269 healthy young adults completed all tasks. In general, accuracy and reaction time measures for overall emotion scores showed acceptable and high estimates of internal consistency and factor reliability. Deriving emotion-specific scores yielded reliabilities that were somewhat lower, yet mostly high enough to encourage studies of such measures. Analyses of task difficulty revealed that all tasks are suitable for measuring emotion perception- and recognition-related abilities in normal populations.


title: Cloud forensics: Evidence collection and preliminary analysis

abstract: Cloud computing systems host most of today's commercial business applications yielding it high revenue which makes it a target of cyber attacks. This emphasizes the need for a digital forensic mechanism for the cloud environment. Conventional digital forensics cannot be directly presented as a cloud forensic solution due to the multi tenancy and virtualization of resources prevalent in cloud. While we do cloud forensics, the data to be inspected are cloud component logs, virtual machine disk images, volatile memory dumps, console logs and network captures. In this paper, we have come up with a remote evidence collection and pre-processing framework using Struts and Hadoop distributed file system. Collection of VM disk images, logs etc., are initiated through a pull model when triggered by the investigator, whereas cloud node periodically pushes network captures to HDFS. Pre-processing steps such as clustering and correlation of logs and VM disk images are carried out through Mahout and Weka to implement cross drive analysis.


title: Neutral Point Clamped MOSFET Inverter With Full-Bridge Configuration for Nonisolated Grid-Tied Photovoltaic System

abstract: Existing nonisolated full-bridge neutral point clamped (NPC) inverters for a single-phase grid-tied photovoltaic (PV) system have limitations such as shoot-through and low European Union (EU) efficiency. In order to address these limitations, an NPC superjunction MOSFET nonisolated inverter with full-bridge configuration (NIIFBC) is proposed in this paper. This inverter reduces the possibility of shoot-through, thereby improving the reliability of the grid-tied PV system. It controls the grid current by energizing two coupled inductors individually during positive and negative half-grid cycles. This obviates the possibility of reverse recovery loss in switches due to their body diodes. Furthermore, two external silicon carbide diodes of a clamping branch cause negligible reverse recovery loss in switches besides a constant common-mode voltage. Therefore, the main claims of NIIFBC are low leakage current, high EU efficiency, and reliability. A generalized leakage current model for the proposed inverter is developed. In order to validate the veracity of the model and the claims of NIIFBC, a 1-kW prototype is designed and developed. The experimental results of NIIFBC validate the claims made by the authors. Its performance comparison with the existing nonisolated full-bridge inverters is given. Furthermore, a variant circuit of NIIFBC operating at nonunity power factor is proposed.


title: An efficient deep convolutional neural networks model for compressed image deblocking

abstract: Convolutional neural networks (CNNs) have been widely used in image processing community. Image deblocking is a post-processing strategy, which aims to reduce the visually annoying blocking artifacts that are caused by block-based transform coding at low bit rates. In recent years, CNNs based methods have been proposed to solve this classic image processing problem. In this paper, we present an efficient deep C-NNs model for image deblocking. Our model can well alleviate the conflict between bit reduction and quality preservation by taking local small patches into consideration. Our trained model can be used to deblock lossy compressed images with different quality factors. The proposed model can be easily integrated into the existing codecs as a post-processing procedure without changing the codec. Experimental results verify that our proposed model outperforms the state-of-the-art methods in both the objective quality and the perceptual quality.


title: Asymmetric Paternalism to Improve Health Behaviors

abstract: INDIVIDUAL BEHAVIOR PLAYS A CENTRAL ROLE IN THE DISease burden faced by society. Many major health problems in the United States and other developed nations, such as lung cancer, hypertension, and diabetes, are exacerbated by unhealthy behaviors. Modifiable behaviors such as tobacco use, overeating, and alcohol abuse account for nearly one-third of all deaths in the United States. Moreover, realizing the potential benefit of some of the most promising advances in medicine, such as medications to control blood pressure, lower cholesterol levels, and prevent stroke, has been stymied by poor adherence rates among patients. For example, by 1 year after having a myocardial infarction, nearly half of patients prescribed cholesterol-lowering medications have stopped taking them. Reducing morbidity and mortality may depend as much on motivating changes in behavior as on developing new treatments. Economics, as the social science discipline traditionally most closely tied to public policy, could be a key discipline in addressing behaviors that are potentially harmful to health. Yet conventional economics does not provide satisfactory policy solutions to problems caused by self-harmful behavior. Economics is premised on a rational choice perspective which, by assuming that individuals make optimal decisions given their information, resources, and preferences, in effect assumes away these problems. The main policy tools suggested by conventional economics, providing information or changing prices, only partially address these problems because they fail to exploit what is known about human motivation and behavior change. Responding in part to these limitations of conventional economics, the new field of behavioral economics has, over the last few decades, begun to import concepts from psychology. Behavioral economists have identified a number of decision biases and pitfalls in decision making that can help explain when and why individuals engage in selfharming behaviors that contribute to poor health outcomes. Insights from behavioral economics can contribute to solutions for public health problems such as medication nonadherence and sedentary lifestyles that have challenged clinicians and public health professionals for years. In this Commentary, we identify some key decision biases that ordinarily lead to self-harming behavior and show how they can be exploited in interventions to instead promote healthy behaviors.


title: A polyhedral branch-and-cut approach to global optimization

abstract: A variety of nonlinear, including semidefinite, relaxations have been developed in recent years for nonconvex optimization problems. Their potential can be realized only if they can be solved with sufficient speed and reliability. Unfortunately, state-of-the-art nonlinear programming codes are significantly slower and numerically unstable compared to linear programming software.In this paper, we facilitate the reliable use of nonlinear convex relaxations in global optimization via a polyhedral branch-and-cut approach. Our algorithm exploits convexity, either identified automatically or supplied through a suitable modeling language construct, in order to generate polyhedral cutting planes and relaxations for multivariate nonconvex problems. We prove that, if the convexity of a univariate or multivariate function is apparent by decomposing it into convex subexpressions, our relaxation constructor automatically exploits this convexity in a manner that is much superior to developing polyhedral outer approximators for the original function. The convexity of functional expressions that are composed to form nonconvex expressions is also automatically exploited.Root-node relaxations are computed for 87 problems from globallib and minlplib, and detailed computational results are presented for globally solving 26 of these problems with BARON 7.2, which implements the proposed techniques. The use of cutting planes for these problems reduces root-node relaxation gaps by up to 100% and expedites the solution process, often by several orders of magnitude.


title: Real-time sign language fingerspelling recognition using convolutional neural networks from depth map

abstract: Sign language recognition is important for natural and convenient communication between deaf community and hearing majority. We take the highly efficient initial step of automatic fingerspelling recognition system using convolutional neural networks (CNNs) from depth maps. In this work, we consider relatively larger number of classes compared with the previous literature. We train CNNs for the classification of 31 alphabets and numbers using a subset of collected depth data from multiple subjects. While using different learning configurations, such as hyper-parameter selection with and without validation, we achieve 99.99% accuracy for observed signers and 83.58% to 85.49% accuracy for new signers. The result shows that accuracy improves as we include more data from different subjects during training. The processing time is 3 ms for the prediction of a single image. To the best of our knowledge, the system achieves the highest accuracy and speed. The trained model and dataset is available on our repository.


title: A Framework for Tag-Based Research Paper Recommender System: An IR Approach

abstract: The Internet and the World Wide Web provide a way to store and share information, especially in academic fields. Community-based research paper sharing systems, such as CiteULike, have become popular among researchers. This paper proposes a framework for a tag-based research paper recommender system. The proposed approach exploits the use of sets of tags for recommending research papers to each user. The preliminary evaluation shows that user self-defined tags could be used as a profile for each individual user. This recommender system demonstrated an encouraging preliminary result with the overall accuracy percentage up to 91.66%.


title: Fuzzy based approach for weather advisory system

abstract: In this paper, we propose study of weather advisory system development by using Fuzzy Logic based approach. Agricultural productivity largely depends upon weather. Weather forecasts in all temporal ranges are desirable for effective planning and management of agricultural practices. The development of response strategy helped farmers realize the potential benefits of using weather-based agro meteorological information in minimizing the losses due to adverse weather conditions, thereby improving yield, quantity and quality of agricultural productions. The nature of weather is uncertain and viable we are using Fuzzy approach for the development of system.


title: An Unsupervised Approach to Sentence Classification

abstract: Many real-life databases include columns containing textual data, which need to be processed to produce novel and actionable insights. We consider the novel problem of identifying specific sentences in the given text, since they tend to be actionable and novel. We demonstrate that specific sentences in the textual responses collected in an employee satisfaction survey and a product review are useful in understanding concrete issues, grievances and actionable suggestions for improvements. We propose an unsupervised approach for identifying specific sentences: We define and compute several features for each sentence and compute a specificity score for each sentence. Top k sentences in terms of this score are identified as specific. Some features are semantic in the sense that they use a supporting ontology (that of WordNet). We use the theory of speech acts to build an unsupervised (knowledgebased) classifier to further classify the identified specific sentences into suggestion and complaint classes. Our contributions here include treating suggestion and complaint as speech acts as well as the use of sentiment analysis in identifying speech acts. We demonstrate the utility of the proposed work in two real-life domains: employee surveys and product reviews.


title: A broadband slotted ridge waveguide antenna array

abstract: A longitudinally-slotted ridge waveguide antenna array with a compact transverse dimension is presented. To broaden the bandwidth of the array, it is separated into two subarrays fed by a novel compact convex waveguide divider. A 16-element uniform linear array at X-band was fabricated and measured to verify the validity of the design. The measured bandwidth of S11les-15 dB is 14.9% and the measured cross- polarization level is less than -36 dB over the entire bandwidth. This array can be combined with the edge-slotted waveguide array to build a two-dimensional dual-polarization antenna array for the synthetic aperture radar (SAR) application


title: A Fast Deep Convolutional Neural Network for Face Detection in Big Visual Data

abstract: Deep learning methods are powerful approaches but often require expensive computations and lead to models of high complexity which need to be trained with large amounts of data. In this paper, we consider the problem of face detection and we propose a light-weight deep convolutional neural network that achieves a state-of-the-art recall rate of 90 % at the challenging FDDB dataset. Our model is designed with a view to minimize both training and run time and outperforms the convolutional network used in [2] for the same task. Our model consists of only 76.554 free parameters whereas the previously proposed CNN for face detection had 60 million parameters. Our model also requires 250 times fewer floating point operations than AlexNet. We propose a new training method that gradually increases the difficulty of both negative and positive examples and has proved to drastically improve training speed and accuracy. The proposed method is able to detect faces under severe occlusion and unconstrained pose variation and meets the difficulties and the large variations of real-world face detection..


title: Towards a dynamic process for business model innovation: A review of the state-of-the-art

abstract: Business models are not only necessary for entrepreneurs starting a company but also for established companies wishing to sustain a competitive advantage. Knowing when or how to change a model is difficult and implementation is risky. Business model innovation takes art and skill and implementation requires an iterative process. Despite the often experimental nature of creating a successful business model, there are few dynamic methods for business model generation. The most frequently referenced method - The Business Model Canvas (BMC) - maps business models into a framework but is static and leaves entrepreneurs and managers to struggle with ad-hoc trial and error experimentation. This paper lays the groundwork for improved approaches to business model innovation by outlining directions for future process and tool developments.


title: Applying Clickstream Data Mining to Real-Time Web Crawler Detection and Containment Using ClickTips Platform

abstract: Web crawler uncontrolled widespread has led to undesired situations of server overload and contents misuse. Most programs still have legitimate and useful goals, but standard detection heuristics have not evolved along with Web crawling technology and are now unable to identify most of today’s programs. In this paper, we propose an integrated approach to the problem that ensures the generation of up-to-date decision models, targeting both monitoring and clickstream differentiation. The ClickTips platform sustains Web crawler detection and containment mechanisms and its data webhousing system is responsible for clickstream processing and further data mining. Web crawler detection and monitoring helps preserving Web server performance and Web site privacy and clickstream differentiated analysis provides focused report and interpretation of navigational patterns. The generation of up-to-date detection models is based on clickstream data mining and targets not only well-known Web crawlers, but also camouflaging and previously unknown programs. Experiments with different real-world Web sites are optimistic, proving that the approach is not only feasible but also adequate.


Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques


title: Robot Path Integration in Manufacturing Processes: Genetic Algorithm Versus Ant Colony Optimization

abstract: Tool path planning for automated manufacturing processes is a computationally complex task. This paper addresses the problem of tool path integration in the context of spray-forming processes. Tool paths for geometry-complicated parts are generated by partitioning them into individual freeform surfaces, generating the paths for each partition, and then, finally, interconnecting the paths from the different patches so as to minimize the overall path length. We model the problem as a variant of the rural postman problem (RPP), which we call open-RPP. In this paper, we present two different solutions to the open-RPP. The first solution is based on genetic algorithms and the second one is based on ant colony optimization. This paper presents and compares the results from both methods on sample data and on real-world automotive body parts. We conclude this paper with remarks about the effectiveness of our implementations and the pros and cons of each method.


title: Surround video: a multihead camera approach

abstract: We describe algorithms for creating, storing and viewing high-resolution immersive surround videos. Given a set of unit cameras designed to be almost aligned at a common nodal point, we first present a versatile process for stitching seamlessly synchronized streams of videos into a single surround video corresponding to the video of the multihead camera. We devise a general registration process onto raymaps based on minimizing a tailored objective function. We review and introduce new raymaps with good sampling properties. We then give implementation details on the surround video viewer and present experimental results on both real-world acquired and computer-graphics rendered full surround videos. We conclude by mentioning potential applications and discuss ongoing related activities. ::: ::: Video supplements: http://www.csl.sony.co.jp/person/nielsen


title: Towards Machine Learning on the Automata Processor

abstract: A variety of applications employ ensemble learning models, using a collection of decision trees, to quickly and accurately classify an input based on its vector of features. In this paper, we discuss the implementation of such a method, namely Random Forests, as the first machine learning algorithm to be executed on the Automata Processor (AP). The AP is an upcoming reconfigurable co-processor accelerator which supports the execution of numerous automata in parallel against a single input data-flow. Owing to this execution model, our approach is fundamentally different, translating Random Forest models from existing memory-bound tree-traversal algorithms to pipelined designs that use multiple automata to check all of the required thresholds independently and in parallel. We also describe techniques to handle floating-point feature values which are not supported in the native hardware, pipelining of the execution stages, and compression of automata for the fastest execution times. The net result is a solution which when evaluated using two applications, namely handwritten digit recognition and sentiment analysis, produce up to 63 and 93 times speed-up respectively over single-core state-of-the-art CPU-based solutions. We foresee these algorithmic techniques to be useful not only in the acceleration of other applications employing Random Forests, but also in the implementation of other machine learning methods on this novel architecture.


title: A novel approach for quantification of time-intensity curves in a DCE-MRI image series with an application to prostate cancer

abstract: This paper considers the problem of an automatic quantification of DCE-MRI curve shape patterns. In particular, the semi-quantitative approach which classifies DCE time-intensity curves into clusters representing the tree main shape patterns is proposed. The approach combines heuristic rules with the naive Bayes classifier. In particular, the descriptive parameters are firstly derived from pixel-by-pixel analysis of the DCE time intensity curves and then used to recognise the curves which without a doubt represent the three main shape patterns. These curves are next used to train the naive Bayes classifier intended to classify the remaining curves within the dataset. Results of applying the proposed approach to the DCE-MRI scans of patients with prostate cancer are presented and discussed. Additionally, the overall performance of the approach is estimated through the comparison with the ground truth results provided by the expert. HighlightsThe method for quantification of DCE time-intensity curve shape patterns is proposed.To the best of author's knowledge this is the first method to the considered problem which uses the supervised classification.The proposed method can be used to improve specificity of MRI in characterizing cancerous lesions.


title: Design Identification of Curve Patterns on Cultural Heritage Objects: Combining Template Matching and CNN-based Re-Ranking

abstract: The surfaces of many cultural heritage objects were embellished with various patterns, especially curve patterns. In practice, most of the unearthed cultural heritage objects are highly fragmented, e.g., sherds of potteries or vessels, and each of them only shows a very small portion of the underlying full design, with noise and deformations. The goal of this paper is to address the challenging problem of automatically identifying the underlying full design of curve patterns from such a sherd. Specifically, we formulate this problem as template matching: curve structure segmented from the sherd is matched to each location with each possible orientation of each known full design. In this paper, we propose a new two-stage matching algorithm, with a different matching cost in each stage. In Stage 1, we use a traditional template matching, which is highly computationally efficient, over the whole search space and identify a small set of candidate matchings. In Stage 2, we derive a new matching cost by training a dual-source Convolutional Neural Network (CNN) and apply it to re-rank the candidate matchings identified in Stage 1. We collect 600 pottery sherds with 98 full designs from the Woodland Period in Southeastern North America for experiments and the performance of the proposed algorithm is very competitive.


title: On-line learning of parametric mixture models for light transport simulation

abstract: Monte Carlo techniques for light transport simulation rely on importance sampling when constructing light transport paths. Previous work has shown that suitable sampling distributions can be recovered from particles distributed in the scene prior to rendering. We propose to represent the distributions by a parametric mixture model trained in an on-line (i.e. progressive) manner from a potentially infinite stream of particles. This enables recovering good sampling distributions in scenes with complex lighting, where the necessary number of particles may exceed available memory. Using these distributions for sampling scattering directions and light emission significantly improves the performance of state-of-the-art light transport simulation algorithms when dealing with complex lighting.


title: Applications of Geometry Processing: CudaHull: Fast parallel 3D convex hull on the GPU

abstract: In this paper, we present a novel parallel algorithm for computing the convex hull of a set of points in 3D using the CUDA programming model. It is based on the QuickHull approach and starts by constructing an initial tetrahedron using four extreme points, discards the internal points, and distributes the external points to the four faces. It then proceeds iteratively. In each iteration, it refines the faces of the polyhedron, discards the internal points, and redistributes the remaining points for each face among its children faces. The refinement of a face is performed by selecting the furthest point from its associated points and generating three children triangles. In each iteration, concave edges are swapped, and concave vertices are removed to maintain convexity. The face refinement procedure is performed on the CPU, because it requires a very small fraction of the execution time (approximately 1%), and the intensive point redistribution is performed in parallel on the GPU. Our implementation outpaced the CPU-based Qhull implementation by 30 times for 10 million points and 40 times for 20 million points.


title: Automated Identification of Innocent Still's Murmur in Children

abstract: Objective: Still's murmur is the most common innocent heart murmur in children. It is also the most commonly misdiagnosed murmur, resulting in a high number of unnecessary referrals to pediatric cardiologist. The purpose of this study was to develop a computer algorithm for automated identification of Still's murmur that may help reduce unnecessary referrals. Methods: We first developed an accurate segmentation algorithm to locate the first and the second heart sounds. Once these sounds were identified, we extracted signal features specific to Still's murmur. Subsequently, machine learning-based classifiers, artificial neural network and support vector machine, were used to identify Still's murmur. Results: We evaluated our classifiers using the jackknife method using 87 Still's murmurs and 170 non-Still's murmurs. Our algorithm identified Still's murmur accurately with 84–93% sensitivity and 91–99% specificity. Conclusion: We have achieved accurate automated identification of Still's murmur while minimizing false positives. The performance of our algorithm is comparable to the rate of murmur identification by auscultation by pediatric cardiologists. Significance: To our knowledge, our solution is the first murmur classifier that focuses singularly on Still's murmur. Following further refinement and testing, the presented algorithm could reduce the number of children with Still's murmur referred unnecessarily to pediatric cardiologists.


SPECTRE: A Fast and Scalable Cryptocurrency Protocol


title: A 10-Gb/s 5-Tap DFE/4-Tap FFE Transceiver in 90-nm CMOS Technology

abstract: This paper presents a 90-nm CMOS 10-Gb/s transceiver for chip-to-chip communications. To mitigate the effects of channel loss and other impairments, a 5-tap decision feedback equalizer (DFE) is included in the receiver and a 4-tap baud-spaced feed-forward equalizer (FFE) in the transmitter. This combination of DFE and FFE permits error-free NRZ signaling over channels with losses exceeding 30 dB. Low jitter clocks for the transmitter and receiver are supplied by a PLL with LC VCO. Operation at 10-Gb/s with good power efficiency is achieved by using half-rate architectures in both transmitter and receiver. With the transmitter producing an output signal of 1200mVppd, one transmitter/receiver pair and one PLL consume 300mW. Design enhancements of a half-rate DFE employing one tap of speculative feedback and four taps of dynamic feedback allow its loop timing requirements to be met. Serial link experiments with a variety of test channels demonstrate the effectiveness of the FFE/DFE equalization


title: © 2011 INFORMS An Empirical Analysis of User Content Generation and Usage Behavior on the Mobile Internet

abstract: We quantify how user mobile Internet usage relates to unique characteristics of the mobile Internet. In particular, we focus on examining how the mobile-phone-based content generation behavior of users relates to content usage behavior. The key objective is to analyze whether there is a positive or negative interdependence between the two activities. We use a unique panel data set that consists of individual-level mobile Internet usage data that encompass individual multimedia content generation and usage behavior. We combine this knowledge with data on user calling patterns, such as duration, frequency, and locations from where calls are placed, to construct their social network and to compute their geographical mobility. We build an individual-level simultaneous equation panel data model that controls for the different sources of endogeneity of the social network. We find that there is a negative and statistically significant temporal interdependence between content generation and usage. This finding implies that an increase in content usage in the previous period has a negative impact on content generation in the current period and vice versa. The marginal effect of this interdependence is stronger on content usage (up to 8.7%) than on content generation (up to 4.3%). The extent of geographical mobility of users has a positive effect on their mobile Internet activities. Users more frequently engage in content usage compared to content generation when they are traveling. In addition, the variance of user mobility has a stronger impact on their mobile Internet activities than does the mean. We also find that the social network has a strong positive effect on user behavior in the mobile Internet. These analyses unpack the mechanisms that stimulate user behavior on the mobile Internet. Implications for shaping user mobile Internet usage behavior are discussed. ::: ::: This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors. ::: ::: This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors.


title: Host load prediction with long short-term memory in cloud computing

abstract: Host load prediction is significant for improving resource allocation and utilization in cloud computing. Due to the higher variance than that in a grid, accurate prediction remains a challenge in the cloud system. In this paper, we apply a concise yet adaptive and powerful model called long short-term memory to predict the mean load over consecutive future time intervals and actual load multi-step-ahead. Two real-world load traces were used to evaluate the performance. One is the load trace in the Google data center, and the other is that in a traditional distributed system. The experiment results show that our proposed method achieves state-of-the-art performance with higher accuracy in both datasets.


title: Learning relational event models from video

abstract: Event models obtained automatically from video can be used in applications ranging from abnormal event detection to content based video retrieval. When multiple agents are involved in the events, characterizing events naturally suggests encoding interactions as relations. Learning event models from this kind of relational spatio-temporal data using relational learning techniques such as Inductive Logic Programming (ILP) hold promise, but have not been successfully applied to very large datasets which result from video data. In this paper, we present a novel framework remind (Relational Event Model INDuction) for supervised relational learning of event models from large video datasets using ILP. Efficiency is achieved through the learning from interpretations setting and using a typing system that exploits the type hierarchy of objects in a domain. The use of types also helps prevent over generalization. Furthermore, we also present a type-refining operator and prove that it is optimal. The learned models can be used for recognizing events from previously unseen videos. We also present an extension to the framework by integrating an abduction step that improves the learning performance when there is noise in the input data. The experimental results on several hours of video data from two challenging real world domains (an airport domain and a physical action verbs domain) suggest that the techniques are suitable to real world scenarios.


title: Friendlink: Link prediction in social networks via bounded local path traversal

abstract: Online social networks (OSNs) like Facebook, Myspace, and Hi5 have become popular, because they allow users to easily share content or expand their social circle. OSNs recommend new friends to registered users based on local graph features (i.e. based on the number of common friends that two users share). However, OSNs do not exploit all different length paths of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-size social networks. In this paper, we provide friend recommendations, also known as the link prediction problem, by traversing all paths of a bounded length, based on the “algorithmic small world hypothesis”. As a result, we are able to provide more accurate and faster friend recommendations. We perform an extensive experimental comparison of the proposed method against existing link prediction algorithms, using two real data sets (Hi5 and Epinions). Our experimental results show that our FriendLink algorithm outperforms other approaches in terms of effectiveness and efficiency in both real data sets.


title: Competing Algorithm Detection from Research Papers

abstract: We propose an unsupervised approach to extract all competing algorithms present in a given scholarly article. The algorithm names are treated as named entities and natural language processing techniques are used to extract them. All extracted entity names are linked with their respective original papers in the reference section by our novel entity-citation linking algorithm. Then these entity-citation pairs are ranked based on the number of comparison related cue-words present in the entity-citation context. We manually annotated a small subset of DBLP Computer Science conference papers and report both qualitative and quantitative results of our algorithm on it.


title: Training cellular automata for image processing

abstract: Experiments were carried out to investigate the possibility of training cellular automata to to perform processing. Currently, only binary images are considered, but the space of rule sets is still very large. Various objective functions were considered, and sequential floating forward search used to select good rule sets for a range of tasks, namely: noise filtering, thinning, and convex hulls. Several modifications to the standard CA formulation were made (the B-rule and 2-cycle CAs) which were found to improve performance.


title: Load Frequency Control For Distributed Grid Power System Single Area&Multi-area System

abstract: This project presents decentralized control scheme for Load-Frequency Control in power System. In this era renewable energy is most promising solution to man's ever increasing energy needs. But the power production by these resources cannot be controlled unlike in thermal plants. A number of optimal control techniques are adopted to implement a reliable stabilizing controller. It is necessary to interconnect more distributed generation in power systems because of environmental concerns. Primary concern includes global environmental and energy depletion problem. A serious attempt has been undertaken aiming a investigating the load frequency control problem in a power system consisting of two power generation unit and multiple variable load units. The robustness and reliability of the various control schemes is examined through simulations. A system involving thermal plants and a hydro plant is modeled using MAT LAB 2011b.


title: Intersubjectification and clause periphery

abstract: Ways of identifying subjectification and especially intersubjectification are discussed using data from the history of English no doubt and surely. These adverbs arose out of non-modal expressions and were recruited for use as epistemic adverbs and metadiscursive markers. The data are shown not to support the hypothesis that expressions at left periphery are likely to be subjective (oriented toward turn-taking and discourse coherence), those at right periphery intersubjective (oriented toward turn-giving or elicitation of response, and toward the Addressee’s stance and participation in the communicative situation.). While no doubt is subjective at both left and right periphery, surely is intersubjective at both peripheries


title: Recent advances in image dehazing

abstract: Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles, which reduces the contrast, changes the color, and makes the object features difficult to identify by human vision and by some outdoor computer vision systems. Therefore image dehazing is an important issue and has been widely researched in the field of computer vision. The role of image dehazing is to remove the influence of weather factors in order to improve the visual effects of the image and provide benefit to post-processing. This paper reviews the main techniques of image dehazing that have been developed over the past decade. Firstly, we innovatively divide a number of approaches into three categories: image enhancement based methods, image fusion based methods and image restoration based methods. All methods are analyzed and corresponding sub-categories are introduced according to principles and characteristics. Various quality evaluation methods are then described, sorted and discussed in detail. Finally, research progress is summarized and future research directions are suggested.


title: Demo2Vec: Reasoning Object Affordances from Online Videos

abstract: Watching expert demonstrations is an important way for humans and robots to reason about affordances of unseen objects. In this paper, we consider the problem of reasoning object affordances through the feature embedding of demonstration videos. We design the Demo2Vec model which learns to extract embedded vectors of demonstration videos and predicts the interaction region and the action label on a target image of the same object. We introduce the Online Product Review dataset for Affordance (OPRA) by collecting and labeling diverse YouTube product review videos. Our Demo2Vec model outperforms various recurrent neural network baselines on the collected dataset.


title: Resource Allocation in Spectrum-Sharing OFDMA Femtocells With Heterogeneous Services

abstract: Femtocells are being considered a promising technique to improve the capacity and coverage for indoor wireless users. However, the cross-tier interference in the spectrum-sharing deployment of femtocells can degrade the system performance seriously. The resource allocation problem in both the uplink and the downlink for two-tier networks comprising spectrum-sharing femtocells and macrocells is investigated. A resource allocation scheme for cochannel femtocells is proposed, aiming to maximize the capacity for both delay-sensitive users and delay-tolerant users subject to the delay-sensitive users' quality-of-service constraint and an interference constraint imposed by the macrocell. The subchannel and power allocation problem is modeled as a mixed-integer programming problem, and then, it is transformed into a convex optimization problem by relaxing subchannel sharing; finally, it is solved by the dual decomposition method. Subsequently, an iterative subchannel and power allocation algorithm considering heterogeneous services and cross-tier interference is proposed for the problem using the subgradient update. A practical low-complexity distributed subchannel and power allocation algorithm is developed to reduce the computational cost. The complexity of the proposed algorithms is analyzed, and the effectiveness of the proposed algorithms is verified by simulations.


title: Maximum likelihood thresholding based on population mixture models

abstract: Maximum likelihood thresholding methods are presented on the basis of population mixture models. It turns out that the standard thresholding proposed by Otsu, which is based on a discriminant criterion and also minimizes the mean square errors between the original image and the resultant binary image, is equivalent to the maximization of the likelihood of the conditional distribution in the population mixture model under the assumption of normal distributions with a common variance. It is also shown that Kittler and Illingworth's thresholding, which minimizes a criterion related to the average classification error rate assuming normal distribution with different variances, is equivalent to the maximization of the likelihood of the joint distribution in the population mixture model. A multi-thresholding algorithm based on Dynamic Programming is also presented.


title: A compact 12-watt high-efficiency 2.1-2.7 GHz class-E GaN HEMT power amplifier for base stations

abstract: A compact broadband class-E power amplifier design is presented. High broadband power efficiency is observed from 2.0–2.5 GHz, where drain efficiency ≫74% and PAE ≫71%, when using 2nd-harmonic input tuning. The highest in-band efficiency performance is observed at 2.14 GHz from a 40V supply with peak drain-efficiency of 77.3% and peak PAE of 74.0% at 12W output power and 14dB gain. The best broadband output power performance is observed from 2.1–2.7 GHz without 2nd-harmonic input tuning, where the output power variation is within 1.5dB and power efficiency is between 53% and 66%.


title: Robust video fingerprinting for content-based video identification

abstract: Video fingerprints are feature vectors that uniquely characterize one video clip from another. The goal of video fingerprinting is to identify a given video query in a database (DB) by measuring the distance between the query fingerprint and the fingerprints in the DB. The performance of a video fingerprinting system, which is usually measured in terms of pairwise independence and robustness, is directly related to the fingerprint that the system uses. In this paper, a novel video fingerprinting method based on the centroid of gradient orientations is proposed. The centroid of gradient orientations is chosen due to its pairwise independence and robustness against common video processing steps that include lossy compression, resizing, frame rate change, etc. A threshold used to reliably determine a fingerprint match is theoretically derived by modeling the proposed fingerprint as a stationary ergodic process, and the validity of the model is experimentally verified. The performance of the proposed fingerprint is experimentally evaluated and compared with that of other widely-used features. The experimental results show that the proposed fingerprint outperforms the considered features in the context of video fingerprinting.


title: The inhibitors of apoptosis (IAPs) as cancer targets

abstract: Apoptosis has been accepted as a fundamental component in the pathogenesis of cancer, in addition to other human diseases including neurodegeneration, coronary disease and diabetes. The origin of cancer involves deregulated cellular proliferation and the suppression of apoptotic processes, ultimately leading to tumor establishment and growth. Several lines of evidence point toward the IAP family of proteins playing a role in oncogenesis, via their effective suppression of apoptosis. The central mechanisms of IAP apoptotic suppression appear to be through direct caspase and pro-caspase inhibition (primarily caspase 3 and 7) and modulation of, and by, the transcription factor NF-kappaB. Thus, when the IAPs are over-expressed or over-active, as is the case in many cancers, cells are no longer able to die in a physiologically programmed fashion and become increasingly resistant to standard chemo- and radiation therapies. To date several approaches have been taken to target and eliminate IAP function in an attempt to re-establish sensitivity, reduce toxicity, and improve efficacy of cancer treatment. In this review, we address IAP proteins as therapeutic targets for the treatment of cancer and emphasize the importance of novel therapeutic approaches for cancer therapy. Novel targets of IAP function are being identified and include gene therapy strategies and small molecule inhibitors that are based on endogenous IAP antagonists. As well, molecular mechanistic approaches, such as RNAi to deplete IAP expression, are in development.


title: Dynamic Spectrum Tracking Using Energy and Cyclostationarity-Based Multi-Variate Non-Parametric Quickest Detection for Cognitive Radios

abstract: A novel non-parametric, multi-variate quickest detection method is proposed for cognitive radios (CRs) using both energy and cyclostationary features. The proposed approach can be used to track state dynamics of communication channels. This capability can be useful for both dynamic spectrum sharing (DSS) and future CRs, as in practice, centralized channel synchronization is unrealistic and the prior information of the statistics of channel usage is, in general, hard to obtain. The proposed multi-variate non-parametric average sample power and cyclostationarity-based quickest detection scheme is shown to achieve better performance compared to traditional energy-based schemes. We also develop a parallel on-line quickest detection/off-line change-point detection algorithm to achieve self-awareness of detection delays and false alarms for future automation. Compared to traditional energy-based quickest detection schemes, the proposed multi-variate non-parametric quickest detection scheme has comparable computational complexity. The simulated performance shows improvements in terms of small detection delays and significantly higher percentage of spectrum utilization.


title: Genetics, personality and wellbeing. A twin study of traits, facets and life satisfaction

abstract: Human wellbeing is influenced by personality traits, in particular neuroticism and extraversion. Little is known about which facets that drive these associations, and the role of genes and environments. Our aim was to identify personality facets that are important for life satisfaction, and to estimate the contribution of genetic and environmental factors in the association between personality and life satisfaction. Norwegian twins (N = 1,516, age 50–65, response rate 71%) responded to a personality instrument (NEO-PI-R) and the Satisfaction With Life Scale (SWLS). Regression analyses and biometric modeling were used to examine influences from personality traits and facets, and to estimate genetic and environmental contributions. Neuroticism and extraversion explained 24%, and personality facets accounted for 32% of the variance in life satisfaction. Four facets were particularly important; anxiety and depression in the neuroticism domain, and activity and positive emotions within extraversion. Heritability of life satisfaction was 0.31 (0.22–0.40), of which 65% was explained by personality-related genetic influences. The remaining genetic variance was unique to life satisfaction. The association between personality and life satisfaction is driven mainly by four, predominantly emotional, personality facets. Genetic factors play an important role in these associations, but influence life satisfaction also beyond the effects of personality.


title: Audio event classification using deep neural networks.

abstract: We present in this paper our work on audio event classification for outdoor events. As the main classification method we employ a deep neural network (DNN) and compare this to other classification methods. We propose a novel improvement to the pre-training process of the network which is useful when training with Gaussian data. Our experimental results are based on an audio corpus extracted from the FreeSound.org website repository. We show that the DNN has some advantage over other classification methods and that fusion of two methods can produce the best results.


title: StressAware: An app for real-time stress monitoring on the amulet wearable platform

abstract: Stress is the root cause of many diseases and unhealthy behaviors. Being able to monitor when and why a person is stressed could inform personal stress management as well as interventions when necessary. In this work, we present StressAware, an application on the Amulet wearable platform that classifies the stress level (low, medium, high) of individuals continuously and in real time using heart rate (HR) and heart-rate variability (HRV) data from a commercial heart-rate monitor. We developed our stress-detection model using a Support Vector Machine (SVM). We trained and tested our model using data from three sources and had the following preliminary results: PhysioNet, a public physiological database (94.5% accurate with 10-fold cross validation), a field study (100% accurate with 10-fold cross validation) and a lab study (64.3% accurate with leave-one-out cross-validation). Testing the StressAware app revealed a projected battery life of up to 12 days. Also, the usability feedback from subjects showed that the Amulet has a potential to be used by people for monitoring their stress levels. The results are promising, indicating that the app may be used for stress detection, and eventually for the development of stress-related intervention that could improve the health of individuals.


title: Composite right/left-handed substrate integrated waveguide leaky-wave antennas

abstract: Leaky-wave antennas based on composite right/left-handed (CRLH) substrate integrated waveguide (SIW) are investigated and presented in this paper. By introducing series interdigital capacitors, which are realized by etching slots on the waveguide surface, a balanced CRLH waveguide leaky-wave antenna with a continuous beam-steering capability has been implemented. The backfire radiation is obtained when the waveguide is operated below its characteristic cutoff frequency while the broadside radiation is achieved at the balancing frequency point. By embedding the series interdigital capacitors in both the top surface and the ground of the SIW, a frequency-scanned leaky-wave antenna characterized by double-side radiation or a quasi-omnidirectional feature is achieved. The measured results show that the proposed antennas offer a scanning angle covering almost backfire-to-endfire directions with relatively high gains.


title: Relapse-remission and remission-relapse switches in rheumatoid arthritis patients were modeled by random effects.

abstract: OBJECTIVE ::: To compare statistical models for the analysis of two-state disease processes. ::: ::: ::: STUDY DESIGN AND SETTING ::: A two-armed randomized trial of patients with early rheumatoid arthritis (RA) treated by either combination therapy (sulfasazaline, methotrextate, prednisolone) or monotherapy (sulfasazaline). Disease activity (remission or relapse) was analyzed with the logistic regression model, the proportional hazards regression model, and the continuous-time Markov process model for panel data. The dependence among the switching times was studied by (1) including correlated normal random patient effects for the relapse-remission and remission-relapse switching probabilities; (2) assuming the population to be a mixture of patients responsive and nonresponsive to therapy; (3) including separate parameters for the first and subsequent relapse-remission switch; and (4) combining (1) and (3). The four approaches were compared using parametric bootstrap checks. ::: ::: ::: RESULTS ::: The logistic regression model, the proportional hazards regression model, and the continuous-time Markov process model for panel data yielded similar combination therapy effects. The inclusion of random patient effects (approaches 1 and 4) gave the best fit to the observed disease activity pattern. ::: ::: ::: CONCLUSION ::: Models with correlated random effects can provide a satisfactory fit to two-state disease patterns.


title: Development of a Survey to Measure Self-Efficacy and Attitudes toward Web-Based Professional Development among Elementary School Teachers.

abstract: The major purpose of this study was to develop a survey to measure elementary school teachers' self-efficacy for web-based professional development. Based on interviews with eight elementary school teachers, three scales of web-based professional development self-efficacy (WPDSE) were formed, namely, general self-efficacy (measuring teachers' general capabilities of using Internet tools or completing basic web-based tasks), interaction self-efficacy (assessing teachers' perceived abilities of engaging in interactions in online professional development activities), and applying self-efficacy (evaluating teachers' confidence level of applying what they have learned in web-based professional development to their future teaching). The results of Exploratory Factor Analysis (EFA) indicated that the three scales of WPDSE arising from the interviews and the Cronbach alpha values, ranging from 0.92 to 0.85, were acceptable. Subsequently, a large-scale survey was conducted to examine the relationships between teachers' self-efficacy and attitudes toward web-based professional development with a sample of 214 elementary school teachers in Taiwan using a questionnaire that included WPDSE and an established scale for measuring attitudes toward web-based professional development (AWPD). The results indicated that teachers' self-efficacy played a positive role in their attitudes towards web-based professional development. The results also indicated that teachers' confidence in their interaction self-efficacy and in their applying self-efficacy was more important than their confidence in their general self-efficacy in predicting their attitudes toward web-based professional development. Implications of the study findings were also discussed.


title: Web-based dynamic assessment : Taking assessment as teaching and learning strategy for improving students ’ e-Learning effectiveness

abstract: This research combines the idea of cake format dynamic assessment defined by Sternberg and Grigorenko (2001) and the 'graduated prompt approach' proposed by Campione and Brown (1985, 1987) to develop a multiple-choice Web-based dynamic assessment system. This research adopts a quasi-experimental design to investigate the effectiveness of this Web-based dynamic assessment system (GPAM-WATA) and normal Web-based test (N-WBT). One hundred and sixteen sixth grade elementary students from four classes participated in this research. These four classes were randomly divided into the GPAM-WATA group and N-WBT group. Before e-Learning instruction, all the students took the prior knowledge assessment and the pre-test of the summative assessment. After 2-week e-Learning instruction, all the students took the post-test of the summative assessment. The research findings show that students in the GPAM-WATA group experience better e-Learning effectiveness than those in the N-WBT group. GPAM-WATA is also found to be effective in improving the e-Learning effectiveness of students with low-level prior knowledge.


title: Fast parallel similarity search in multimedia databases

abstract: Most similarity search techniques map the data objects into some high-dimensional feature space. The similarity search then corresponds to a nearest-neighbor search in the feature space which is computationally very intensive. In this paper, we present a new parallel method for fast nearest-neighbor search in high-dimensional feature spaces. The core problem of designing a parallel nearest-neighbor algorithm is to find an adequate distribution of the data onto the disks. Unfortunately, the known declustering methods to not perform well for high-dimensional nearest-neighbor search. In contrast, our method has been optimized based on the special properties of high-dimensional spaces and therefore provides a near-optimal distribution of the data items among the disks. The basic idea of our data declustering technique is to assign the buckets corresponding to different quadrants of the data space to different disks. We show that our technique - in contrast to other declustering methods - guarantees that all buckets corresponding to neighboring quadrants are assigned to different disks. We evaluate our method using large amounts of real data (up to 40 MBytes) and compare it with the best known data declustering method, the Hilbert curve. Our experiments show that our method provides an almost linear speed-up and a constant scale-up. Additionally, it outperforms the Hilbert approach by a factor of up to 5.


title: Cross-modal adaptation for RGB-D detection

abstract: In this paper we propose a technique to adapt convolutional neural network (CNN) based object detectors trained on RGB images to effectively leverage depth images at test time to boost detection performance. Given labeled depth images for a handful of categories we adapt an RGB object detector for a new category such that it can now use depth images in addition to RGB images at test time to produce more accurate detections. Our approach is built upon the observation that lower layers of a CNN are largely task and category agnostic and domain specific while higher layers are largely task and category specific while being domain agnostic. We operationalize this observation by proposing a mid-level fusion of RGB and depth CNNs. Experimental evaluation on the challenging NYUD2 dataset shows that our proposed adaptation technique results in an average 21% relative improvement in detection performance over an RGB-only baseline even when no depth training data is available for the particular category evaluated. We believe our proposed technique will extend advances made in computer vision to RGB-D data leading to improvements in performance at little additional annotation effort.


title: Holistic Planimetric prediction to Local Volumetric prediction for 3D Human Pose Estimation

abstract: We propose a novel approach to 3D human pose estimation from a single depth map. Recently, convolutional neural network (CNN) has become a powerful paradigm in computer vision. Many of computer vision tasks have benefited from CNNs, however, the conventional approach to directly regress 3D body joint locations from an image does not yield a noticeably improved performance. In contrast, we formulate the problem as estimating per-voxel likelihood of key body joints from a 3D occupancy grid. We argue that learning a mapping from volumetric input to volumetric output with 3D convolution consistently improves the accuracy when compared to learning a regression from depth map to 3D joint coordinates. We propose a two-stage approach to reduce the computational overhead caused by volumetric representation and 3D convolution: Holistic 2D prediction and Local 3D prediction. In the first stage, Planimetric Network (P-Net) estimates per-pixel likelihood for each body joint in the holistic 2D space. In the second stage, Volumetric Network (V-Net) estimates the per-voxel likelihood of each body joints in the local 3D space around the 2D estimations of the first stage, effectively reducing the computational cost. Our model outperforms existing methods by a large margin in publicly available datasets.


title: A flexible solution to AX=XB for robot hand-eye calibration

abstract: A direct linear closed-form solution followed by Jacobian optimization is proposed to solve AX=XB for hand-eye calibration. The approach does not require A,B satisfying rigid transformation rather than the classic ones based on quaternion algebra or screw rule, so the technique is more flexible. Rigid deduction and demonstration are given based on matrix theory. Computer simulation and real data implementation indicate that: (1)In computation of initial value, our technique has good precision and robustness. (2) As more equations are added, initial value will converge to final value gradually, which shows it credible to regard initial value as final solution when many equations are supplied.


title: Wilmington robotic exoskeleton: a novel device to maintain arm improvement in muscular disease.

abstract: BACKGROUND ::: Upper-extremity movement is limited in individuals with muscular weakness. This paper describes a novel, articulated upper-extremity orthosis, the Wilmington Robotic Exoskeleton (WREX), which helps people overcome this movement deficit. ::: ::: ::: METHODS ::: This prospective, case-controlled study involved an ambulatory patient with arthrogryposis multiplex congenita and 2 nonambulatory patients with spinal muscular atrophy type II. The WREX uses elastic bands to negate the effects of gravity; it allows a person with neuromuscular weakness to move their arm in 3 dimensions. The WREX can be fixed on a brace for ambulatory patients and on the wheelchair for nonambulatory patients. Assessment was performed through motion analysis (with and without the WREX), clinical examination, and qualitative questionnaire. ::: ::: ::: RESULTS ::: Motion analysis showed a marked improvement in upper-extremity function with the WREX. The questionnaire illustrated enhanced functionality with the WREX including self-feeding, fine motor control, and use of a television remote control. Enhanced functionality resulted in improved quality of life by increasing participation in school, raising self-esteem, and increasing social interaction. Two unexpected outcomes were increased security with trunk inclination and amelioration of the effects of contractures. ::: ::: ::: CONCLUSIONS ::: The WREX provided an increase in functionality and improved the quality of life of the patients. The device has become an integral part of the lives of the 3 patients. ::: ::: ::: LEVEL OF EVIDENCE ::: Level III in Therapeutic Studies-Investigating the Results of Treatment.


title: Effectiveness of low level laser therapy for treating male infertility

abstract: In half of the cases, the infertility of the couple is due to the disorder of the male fertility. The leading factors that cause male infertility are urogenital infections, disorders of the immune system, testicular and prostate pathology, as well as endocrine disorders. Low level laser therapy (LLLT) is a very effective physical therapy method, used in many areas of medicine, including obstetrics and gynaecology, andrology and urology; and it is recommended as an integral part of the complex treatment of infertility. The literature review showed that LLLT is beneficial in treating male infertility. Laser can significantly improve the survival, motility and speed of movement of spermatozoa. Laser therapy of patients with prostatitis and vesiculitis can eliminate infiltrative-exudative changes, improve reproductive and copulatory functions. Local illumination of red (635 nm) and infrared (904 nm) spectra should be combined with intravenous laser blood illumination (ILBI) of red (635 nm) and ultraviolet (UV) (365 nm) spectra.


title: Honey as Medicinal and food resource. Comparison between polish and multiethnic settlements of the Atlantic Forest, Misiones, Argentina

abstract: We present a comparative study on the use of wild honeys produced by insects of the order Hymenoptera in the Criollos (of mixed origin) and Polish populations in the northern part of the province of Misiones, Argentina. The principal questions of the study are: do different types of honey exist that are used for both nutritional and medicinal purposes or on the contrary: do exclusively nutritional and/or medicinal honeys exist? When used medicinally, are these honeys involved in the cure or prevention of similar ailments? The previous report of the use of complex drugs consisting of honey and medicinal plants or other products, led us to inquire about alternative or complementary roles among the sapid and medicinal properties of honeys in these blends. This question is approached from the local understanding of the role of honey in the preparation of medicines, treatments and prevention of common diseases in the area. This study is based partly on the results of two larger projects in progress in the mentioned areas. We prepared an open-ended questionnaire and worked with 16 Polish settlers, and with 23 Criollos farmers. The honeys of two species: bee (Apis mellifera) and yatei (Tetrogonisca angustula) obtained the greatest number of reports within alimentary and medicinal categories of use. They were also employed as functional and medicinal food. In general terms, we found a similar corpus of traditional medical knowledge for both populations. The relation between phytotherapy and zootherapy is evident for Criollos and Polish groups. Nevertheless, they show differences in forms of preparation and administrations. Criollos reported the use of greater number of plant species and more combinations of complex remedies.


title: Multi-task, multi-label and multi-domain learning with residual convolutional networks for emotion recognition

abstract: Automated emotion recognition in the wild from facial images remains a challenging problem. Although recent advances in Deep Learning have supposed a significant breakthrough in this topic, strong changes in pose, orientation and point of view severely harm current approaches. In addition, the acquisition of labeled datasets is costly, and current state-of-the-art deep learning algorithms cannot model all the aforementioned difficulties. In this paper, we propose to apply a multi-task learning loss function to share a common feature representation with other related tasks. Particularly we show that emotion recognition benefits from jointly learning a model with a detector of facial Action Units (collective muscle movements). The proposed loss function addresses the problem of learning multiple tasks with heterogeneously labeled data, improving previous multi-task approaches. We validate the proposal using two datasets acquired in non controlled environments, and an application to predict compound facial emotion expressions.


title: Introduction to Queueing Theory and Stochastic Teletraffic Models

abstract: The aim of this textbook is to provide students with basic knowledge of stochastic models that may apply to telecommunications research areas, such as traffic modelling, resource provisioning and traffic management. These study areas are often collectively called teletraffic. This book assumes prior knowledge of a programming language, mathematics, probability and stochastic processes normally taught in an electrical engineering course. For students who have some but not sufficiently strong background in probability and stochastic processes, we provide, in the first few chapters, background on the relevant concepts in these areas.


title: Exploration and exploitation in evolutionary algorithms: A survey

abstract: “Exploration and exploitation are the two cornerstones of problem solving by search.” For more than a decade, Eiben and Schippers' advocacy for balancing between these two antagonistic cornerstones still greatly influences the research directions of evolutionary algorithms (EAs) [1998]. This article revisits nearly 100 existing works and surveys how such works have answered the advocacy. The article introduces a fresh treatment that classifies and discusses existing work within three rational aspects: (1) what and how EA components contribute to exploration and exploitation; (2) when and how exploration and exploitation are controlled; and (3) how balance between exploration and exploitation is achieved. With a more comprehensive and systematic understanding of exploration and exploitation, more research in this direction may be motivated and refined.


title: Entrepreneurial Activities in Europe - Evaluation of Inclusive Entrepreneurship Programmes

abstract: Inclusive entrepreneurship policies are intended to give everybody the opportunity to start up in business or self-employment regardless of their social background and to improve labour market outcomes for people who are under-represented or disadvantaged in entrepreneurship and self-employment. This may occur directly, through increasing the number and quality of businesses and self-employment start-up activities, or indirectly, by providing an improved pathway to employment for people who do not eventually start-up or remain in business or self-employment. They work by targeting specific populations such as youth, seniors, women, the disabled, ex-offenders, ethnic minorities, and the unemployed with tailored interventions or improved accessibility to mainstream actions in areas such as access to start-up financing; training, mentoring and consultancy; entrepreneurship education and awareness raising; network building; or improvements to social security and business regulation systems.