title: Convolutional Neural Pyramid for Image Processing

abstract: We propose a principled convolutional neural pyramid (CNP) framework for general low-level vision and image processing tasks. It is based on the essential finding that many applications require large receptive fields for structure understanding. But corresponding neural networks for regression either stack many layers or apply large kernels to achieve it, which is computationally very costly. Our pyramid structure can greatly enlarge the field while not sacrificing computation efficiency. Extra benefit includes adaptive network depth and progressive upsampling for quasi-realtime testing on VGA-size input. Our method profits a broad set of applications, such as depth/RGB image restoration, completion, noise/artifact removal, edge refinement, image filtering, image enhancement and colorization.


title: Big Data as an enabler to prevent failures in the production area

abstract: Failure management in the production area has been intensely analyzed in the research community. Although several efficient methods have been developed and partially successfully implemented, producing companies still face a lot of challenges. The resulting main question is how manufacturers can be assisted by a sustainable approach enabling them to proactively detect and prevent failures before they occur. A high-resolution production system based on analyzed real-time data enables manufacturers to find an answer to the main question. In this context, Big Data technologies have gained importance since the critical success factor is not only to collect real-time data in the production but also to structure the data. Therefore, we present in this paper the implementation of Big Data technologies in the production area using the example of an actual research project. After the literature review, we describe a Big Data based approach to prevent failures in the production area. This approach mainly includes a real-time capable platform including complex event processing algorithms to define appropriate improvement measures.


title: Robust real-time pupil tracking in highly off-axis images

abstract: Robust, accurate, real-time pupil tracking is a key component for online gaze estimation. On head-mounted eye trackers, existing algorithms that rely on circular pupils or contiguous pupil regions fail to detect or accurately track the pupil. This is because the pupil ellipse is often highly eccentric and partially occluded by eyelashes. We present a novel, real-time dark-pupil tracking algorithm that is robust under such conditions. Our approach uses a Haar-like feature detector to roughly estimate the pupil location, performs a k-means segmentation on the surrounding region to refine the pupil centre, and fits an ellipse to the pupil using a novel image-aware Random Sample Concensus (RANSAC) ellipse fitting. We compare our approach against existing real-time pupil tracking implementations, using a set of manually labelled infra-red dark-pupil eye images. We show that our technique has a higher pupil detection rate and greater pupil tracking accuracy.


title: Sequential Data Fusion of GNSS Pseudoranges and Dopplers With Map-Based Vision Systems

abstract: Tightly coupling GNSS pseudorange and Doppler measurements with other sensors is known to increase the accuracy and consistency of positioning information. Nowadays, high-accuracy geo-referenced lane marking maps are seen as key information sources in autonomous vehicle navigation. When an exteroceptive sensor such as a video camera or a lidar is used to detect them, lane markings provide positioning information which can be merged with GNSS data. In this paper, measurements from a forward-looking video camera are merged with raw GNSS pseudoranges and Dopplers on visible satellites. To create a localization system that provides pose estimates with high availability, dead reckoning sensors are also integrated. The data fusion problem is then formulated as sequential filtering. A reduced-order state space modeling of the observation problem is proposed to give a real-time system that is easy to implement. A Kalman filter with measured input and correlated noises is developed using a suitable error model of the GNSS pseudoranges. Our experimental results show that this tightly coupled approach performs better, in terms of accuracy and consistency, than a loosely coupled method using GNSS fixes as inputs.


title: Retailer Web Site Influence On Customer Shopping: Exploratory Study on Key Factors of Customer Satisfaction

abstract: The Internet/World Wide Web (Web) is a critical medium for the sharing of business information between retail firms and their customers (business-to-customer or B2C). Although electronic commerce (e-commerce) has received considerable research attention, little research has examined the effect of e-commerce’s Web presentations on retail customer shopping experiences. This twophase study explored customer reactions to Web retailers’ presentations of their products/services, attempting to identify Web-site characteristics that contribute to customer satisfaction arising from the Web-based shopping experience. Findings from use of the qualitative method of existential phenomenology were explored further with quantitative analyses including logistic regression. Findings suggest that two key elements in “positive” retail Web sites are convenience of site use and simplicity of site design for the customer’s access to information about product/service characteristics and customer-service policies.


title: Vehicle-to-Vehicle Communication: Fair Transmit Power Control for Safety-Critical Information

abstract: Direct radio-based vehicle-to-vehicle communication can help prevent accidents by providing accurate and up-to-date local status and hazard information to the driver. In this paper, we assume that two types of messages are used for traffic safety-related communication: 1) Periodic messages (ldquobeaconsrdquo) that are sent by all vehicles to inform their neighbors about their current status (i.e., position) and 2) event-driven messages that are sent whenever a hazard has been detected. In IEEE 802.11 distributed-coordination-function-based vehicular networks, interferences and packet collisions can lead to the failure of the reception of safety-critical information, in particular when the beaconing load leads to an almost-saturated channel, as it could easily happen in many critical vehicular traffic conditions. In this paper, we demonstrate the importance of transmit power control to avoid saturated channel conditions and ensure the best use of the channel for safety-related purposes. We propose a distributed transmit power control method based on a strict fairness criterion, i.e., distributed fair power adjustment for vehicular environments (D-FPAV), to control the load of periodic messages on the channel. The benefits are twofold: 1) The bandwidth is made available for higher priority data like dissemination of warnings, and 2) beacons from different vehicles are treated with ldquoequal rights,rdquo and therefore, the best possible reception under the available bandwidth constraints is ensured. We formally prove the fairness of the proposed approach. Then, we make use of the ns-2 simulator that was significantly enhanced by realistic highway mobility patterns, improved radio propagation, receiver models, and the IEEE 802.11p specifications to show the beneficial impact of D-FPAV for safety-related communications. We finally put forward a method, i.e., emergency message dissemination for vehicular environments (EMDV), for fast and effective multihop information dissemination of event-driven messages and show that EMDV benefits of the beaconing load control provided by D-FPAV with respect to both probability of reception and latency.


title: Interruptions in manufacturing from a distributed cognition perspective

abstract: This thesis aims at portraying interruptions in the socio-technical domain of manufacturing industry, from a distributed cognition perspective. The research problem addressed is the lack of naturalistic inquiry in prior interruption research. Further, manufacturing is a complex socio-technical domain where interruptions have not previously been studied. In this thesis, a workplace study is applied with distributed cognition as its theoretical framework. The results of the study identify two new types of interruptions, as well as one new dimension of interruptions. This result shows that interruptions are a multifaceted phenomenon that frequently occurs within manufacturing. An integration of the theoretical background and the empirical work resulted in five recommendations concerning how to reduce the amount of interruptions and how to minimize their disruptive effects. This study complements prior interruption research, emphasises the importance of studying interruptions in natural settings, and provides several insights regarding future interruption research.


title: Building a Hybrid Warehouse: Efficient Joins between Data Stored in HDFS and Enterprise Warehouse

abstract: The Hadoop Distributed File System (HDFS) has become an important data repository in the enterprise as the center for all business analytics, from SQL queries and machine learning to reporting. At the same time, enterprise data warehouses (EDWs) continue to support critical business analytics. This has created the need for a new generation of a special federation between Hadoop-like big data platforms and EDWs, which we call the hybrid warehouse. There are many applications that require correlating data stored in HDFS with EDW data, such as the analysis that associates click logs stored in HDFS with the sales data stored in the database. All existing solutions reach out to HDFS and read the data into the EDW to perform the joins, assuming that the Hadoop side does not have efficient SQL support. In this article, we show that it is actually better to do most data processing on the HDFS side, provided that we can leverage a sophisticated execution engine for joins on the Hadoop side. We identify the best hybrid warehouse architecture by studying various algorithms to join database and HDFS tables. We utilize Bloom filters to minimize the data movement and exploit the massive parallelism in both systems to the fullest extent possible. We describe a new zigzag join algorithm and show that it is a robust join algorithm for hybrid warehouses that performs well in almost all cases. We further develop a sophisticated cost model for the various join algorithms and show that it can facilitate query optimization in the hybrid warehouse to correctly choose the right algorithm under different predicate and join selectivities.


title: Holographic Graph Neuron: a Bio-Inspired Architecture for Pattern Processing

abstract: In this paper, we propose a new approach to implementing hierarchical graph neuron (HGN), an architecture for memorizing patterns of generic sensor stimuli, through the use of vector symbolic architectures. The adoption of a vector symbolic representation ensures a single-layer design while retaining the existing performance characteristics of HGN. This approach significantly improves the noise resistance of the HGN architecture, and enables a linear (with respect to the number of stored entries) time search for an arbitrary subpattern.


title: Nonnegative Matrix Factorization: A Comprehensive Review

abstract: Nonnegative Matrix Factorization (NMF), a relatively novel paradigm for dimensionality reduction, has been in the ascendant since its inception. It incorporates the nonnegativity constraint and thus obtains the parts-based representation as well as enhancing the interpretability of the issue correspondingly. This survey paper mainly focuses on the theoretical research into NMF over the last 5 years, where the principles, basic models, properties, and algorithms of NMF along with its various modifications, extensions, and generalizations are summarized systematically. The existing NMF algorithms are divided into four categories: Basic NMF (BNMF), Constrained NMF (CNMF), Structured NMF (SNMF), and Generalized NMF (GNMF), upon which the design principles, characteristics, problems, relationships, and evolution of these algorithms are presented and analyzed comprehensively. Some related work not on NMF that NMF should learn from or has connections with is involved too. Moreover, some open issues remained to be solved are discussed. Several relevant application areas of NMF are also briefly described. This survey aims to construct an integrated, state-of-the-art framework for NMF concept, from which the follow-up research may benefit.


title: Who, when, and why: a machine learning approach to prioritizing students at risk of not graduating high school on time

abstract: Several hundred thousand students drop out of high school every year in the United States. Interventions can help those who are falling behind in their educational goals, but given limited resources, such programs must focus on the right students, at the right time, and with the right message. In this paper, we describe an incremental approach that can be used to select and prioritize students who may be at risk of not graduating high school on time, and to suggest what may be the predictors of particular students going off-track. These predictions can then be used to inform targeted interventions for these students, hopefully leading to better outcomes.


title: The distance correlation t -test of independence in high dimension

abstract: Distance correlation is extended to the problem of testing the independence of random vectors in high dimension. Distance correlation characterizes independence and determines a test of multivariate independence for random vectors in arbitrary dimension. In this work, a modified distance correlation statistic is proposed, such that under independence the distribution of a transformation of the statistic converges to Student t, as dimension tends to infinity. Thus we obtain a distance correlation t-test for independence of random vectors in arbitrarily high dimension, applicable under standard conditions on the coordinates that ensure the validity of certain limit theorems. This new test is based on an unbiased estimator of distance covariance, and the resulting t-test is unbiased for every sample size greater than three and all significance levels. The transformed statistic is approximately normal under independence for sample size greater than nine, providing an informative sample coefficient that is easily interpretable for high dimensional data.


title: A macro study of self-citation

abstract: This study investigates the role of self-citation in the scientific production of Norway (1981-1996). More than 45,000 publications have been analysed. Using a three-year citation window we find that 36% of all citations represent author self-citations. However, this percentage is decreasing when citations are traced for longer periods. We find the highest share of self-citation among the least cited papers. There is a strong positive correlation between the number of self-citations and the number of authors of the publications. Still, only a minor part of the overall increase in citation rates that can be found for multi-authored papers is due to self-citations. Also, the share of self-citation shows significant variations among different scientific disciplines. The results are relevant for the discussion concerning use of citation indicators in research assessments.


Deep-Learning based Analysis of fMRI data: A Visual Recognition Study


title: Generalizing biomedical relation classification with neural adversarial domain adaptation

abstract: Motivation:Creating large datasets for biomedical relation classification can be prohibitively expensive. While some datasets have been curated to extract protein-protein and drug-drug interactions (PPIs and DDIs) from text, we are also interested in other interactions including gene-disease and chemical-protein connections. Also, many biomedical researchers have begun to explore ternary relationships. Even when annotated data are available, many datasets used for relation classification are inherently biased. For example, issues such as sample selection bias typically prevent models from generalizing in the wild. To address the problem of cross-corpora generalization, we present a novel adversarial learning algorithm for unsupervised domain adaptation tasks where no labeled data are available in the target domain. Instead, our method takes advantage of unlabeled data to improve biased classifiers through learning domain-invariant features via an adversarial process. Finally, our method is built upon recent advances in neural network (NN) methods. Results:We experiment by extracting PPIs and DDIs from text. In our experiments, we show domain invariant features can be learned in NNs such that classifiers trained for one interaction type (protein-protein) can be re-purposed to others (drug-drug). We also show that our method can adapt to different source and target pairs of PPI datasets. Compared to prior convolutional and recurrent NN-based relation classification methods without domain adaptation, we achieve improvements as high as 30% in F1-score. Likewise, we show improvements over state-of-the-art adversarial methods. Availability and implementation:Experimental code is available at https://github.com/bionlproc/adversarial-relation-classification. Supplementary information:Supplementary data are available at Bioinformatics online.


title: Context-Aware Synthesis for Video Frame Interpolation

abstract: Video frame interpolation algorithms typically estimate optical flow or its variations and then use it to guide the synthesis of an intermediate frame between two consecutive original frames. To handle challenges like occlusion, bidirectional flow between the two input frames is often estimated and used to warp and blend the input frames. However, how to effectively blend the two warped frames still remains a challenging problem. This paper presents a context-aware synthesis approach that warps not only the input frames but also their pixel-wise contextual information and uses them to interpolate a high-quality intermediate frame. Specifically, we first use a pre-trained neural network to extract per-pixel contextual information for input frames. We then employ a state-of-the-art optical flow algorithm to estimate bidirectional flow between them and pre-warp both input frames and their context maps. Finally, unlike common approaches that blend the pre-warped frames, our method feeds them and their context maps to a video frame synthesis neural network to produce the interpolated frame in a context-aware fashion. Our neural network is fully convolutional and is trained end to end. Our experiments show that our method can handle challenging scenarios such as occlusion and large motion and outperforms representative state-of-the-art approaches.


title: Developing early warning systems to predict students' online learning performance

abstract: We develop early warning systems to predict at-risk students while a course is in progress.Learning portfolios from a fully online course are evaluated by data mining techniques.The results show that CART supplemented by AdaBoost has the best classification performance.Time-dependent variables are essential to identify student online learning performance. An early warning system can help to identify at-risk students, or predict student learning performance by analyzing learning portfolios recorded in a learning management system (LMS). Although previous studies have shown the applicability of determining learner behaviors from an LMS, most investigated datasets are not assembled from online learning courses or from whole learning activities undertaken on courses that can be analyzed to evaluate students' academic achievement. Previous studies generally focus on the construction of predictors for learner performance evaluation after a course has ended, and neglect the practical value of an "early warning" system to predict at-risk students while a course is in progress. We collected the complete learning activities of an online undergraduate course and applied data-mining techniques to develop an early warning system. Our results showed that, time-dependent variables extracted from LMS are critical factors for online learning. After students have used an LMS for a period of time, our early warning system effectively characterizes their current learning performance. Data-mining techniques are useful in the construction of early warning systems; based on our experimental results, classification and regression tree (CART), supplemented by AdaBoost is the best classifier for the evaluation of learning performance investigated by this study.


title: Health literacy: concept analysis

abstract: Aim. This paper reports an analysis of the concept of health literacy in order to clarify its meaning, reduce ambiguities associated with references to it, and promote consistency in using the concept in nursing dialogue and research. ::: ::: Background. Health literacy is a relatively new concept in health promotion research. Only within the last decade have researchers identified the problems associated with health literacy, the role it plays in an individual's ability to comprehend health and self-care information, and its relationship to health outcomes. Clarifying the concept is essential so that nurses develop an awareness of the phenomenon and its relationship to the outcomes of their communication and health education efforts. ::: ::: Method. The method used for this concept analysis was that of Walker and Avant (1995). ::: ::: Findings. Health literacy empowers people to act appropriately in new and changing health-related circumstances through the use of advanced cognitive and social skills. The defining attributes of health literacy are reading and numeracy skills, comprehension, the capacity to use information in health care decision-making, and successful functioning as a healthcare consumer. Antecedents of health literacy are literacy and a health-related experience. Consequences of health literacy include improved self-reported health status, lower health care costs, increased health knowledge, shorter hospitalizations, and less frequent use of health care services. Empirical referents of the concept are the Test of Functional Health Literacy in Adults and the health literacy component of the National Assessment of Adult Literacy. ::: ::: Conclusions. An analysis of the concept of health literacy enhances nurses’ ability to assess more accurately their clients’ levels of health literacy, thus identifying those at risk for misunderstanding health care instructions, shame associated with inadequate reading skills, and inability to adhere to health care recommendations.


title: Cloud Forensics: State-of-the-Art and Research Challenges

abstract: Cloud computing is a new operation model providing utility services over a shared virtualized resources. It is visualized that in future, Cloud can offer everything as a service (EAAS). Cloud Service Provider (CSP) makes infrastructure, platform and software services available over the Internet with greater flexibility at a lower cost. Cloud computing paradigm enables users to access computing resources placed at a remote location. The users also need not necessarily own these physical infrastructures. As more and more service providers and users are getting added to the cloud environment, it has become an significant field attracting more attackers to perform malicious activities for financial gains. Several approaches of security are being considered in order to resolve these threats and provide protection for the resources so that the potential of cloud computing can be exploited to the maximum. Cloud forensics is an approach that attempts to investigate and analyze cloud security threats. It will ensure that attackers will be more cautious to avoid prosecution for their illegal actions. It acts as a deterrent, reducing network crime rate and improving security. The paper aims to provide a better awareness of cloud forensics, understand some of the proposed frameworks and identify the research gaps and challenges. The significance of this work is that it presents the state-of-the-art in cloud forensics, which will be very much useful for security practitioners and researchers.


title: Baer et al. / FACETS OF MINDFULNESS Using Self-Report Assessment Methods to Explore Facets of Mindfulness

abstract: The authors examine the facet structure of mindfulness using five recently developed mindfulness questionnaires. Two large samples of undergraduate students completed mindfulness questionnaires and measures of other constructs. Psychometric properties of the mindfulness questionnaires were examined, including internal consistency and convergent and discriminant relationships with other variables. Factor analyses of the combined pool of items from the mindfulness questionnaires suggested that collectively they contain five clear, interpretable facets of mindfulness. Hierarchical confirmatory factor analyses suggested that at least four of the identified factors are components of an overall mindfulness construct and that the factor structure of mindfulness may vary with meditation experience. Mindfulness facets were shown to be differentially correlated in expected ways with several other constructs and to have incremental validity in the prediction of psychological symptoms. Findings suggest that conceptua...


title: PSRR improvement technique for amplifiers with Miller capacitor

abstract: A simple circuit technique is presented for improving poor midband power supply rejection ratio (PSRR) of single ended amplifiers that use Miller capacitance to set the location of the dominant pole. The principle of the technique is to create an additional parallel signal path from the power supply to the output, which cancels the dominating unity gain signal path through the output stage and Miller capacitor above the dominant pole frequency. Simulation results of a two-stage amplifier show that more than a 20dB improvement in the midband PSRR is obtainable as compared with an amplifier without the suggested circuit.


title: A comparison of text retrieval models

abstract: Many retrieval models have been proposed as the basis of text retrieval systems. The three main classes that have been investigated are the exact-match, vector space and probabilistic models. The retrieval effectiveness of strategies based on these models has been evaluated experimentally, but there has been little in the way of comparison in terms of their formal properties. In this paper we introduce a recent form of the probabilistic model based on inference networks, and show how the vector space and exact-match models can be described in this framework. Differences between these models can be explained as differences in the estimation of probabilities, both in the initial search and during relevance feedback.


title: Accelerating Levenshtein and Damerau edit distance algorithms using GPU with unified memory

abstract: String matching problems such as sequence alignment is one of the fundamental problems in many computer since fields such as natural language processing (NLP) and bioinformatics. Many algorithms have been proposed in the literature to address this problem. Some of these algorithms compute the edit distance between the two strings to perform the matching. However, these algorithms usually require long execution time. Many researches use high performance computing to reduce the execution time of many string matching algorithms. In this paper, we use the CUDA based Graphics Processing Unit (GPU) and the newly introduced Unified Memory(UM) to speed up the most common algorithms to compute the edit distance between two string. These algorithms are the Levenshtein and Damerau distance algorithms. Our results show that using GPU to implement the Levenshtein and Damerau distance algorithms improvements their execution times of about 11X and 12X respectively when compared to the sequential implementation. And an improvement of about 61X and 71X respectively can be achieved when GPU is used with unified memory.


title: The Health Benefits of Yoga and Exercise: A Review of Comparison Studies

abstract: Abstract Objectives: Exercise is considered an acceptable method for improving and maintaining physical and emotional health. A growing body of evidence supports the belief that yoga benefits physical and mental health via down-regulation of the hypothalamic–pituitary–adrenal (HPA) axis and the sympathetic nervous system (SNS). The purpose of this article is to provide a scholarly review of the literature regarding research studies comparing the effects of yoga and exercise on a variety of health outcomes and health conditions. Methods: Using PubMed® and the key word “yoga,” a comprehensive search of the research literature from core scientific and nursing journals yielded 81 studies that met inclusion criteria. These studies subsequently were classified as uncontrolled (n = 30), wait list controlled (n = 16), or comparison (n = 35). The most common comparison intervention (n = 10) involved exercise. These studies were included in this review. Results: In the studies reviewed, yoga interventions appeared ...


title: Monolayer optical memory cells based on artificial trap-mediated charge storage and release

abstract: Monolayer transition metal dichalcogenides are considered to be promising candidates for flexible and transparent optoelectronics applications due to their direct bandgap and strong light-matter interactions. Although several monolayer-based photodetectors have been demonstrated, single-layered optical memory devices suitable for high-quality image sensing have received little attention. Here we report a concept for monolayer MoS2 optoelectronic memory devices using artificially-structured charge trap layers through the functionalization of the monolayer/dielectric interfaces, leading to localized electronic states that serve as a basis for electrically-induced charge trapping and optically-mediated charge release. Our devices exhibit excellent photo-responsive memory characteristics with a large linear dynamic range of ∼4,700 (73.4 dB) coupled with a low OFF-state current (<4 pA), and a long storage lifetime of over 104 s. In addition, the multi-level detection of up to 8 optical states is successfully demonstrated. These results represent a significant step toward the development of future monolayer optoelectronic memory devices.


title: Understanding GPU errors on large-scale HPC systems and the implications for system design and operation

abstract: Increase in graphics hardware performance and improvements in programmability has enabled GPUs to evolve from a graphics-specific accelerator to a general-purpose computing device. Titan, the world's second fastest supercomputer for open science in 2014, consists of more dum 18,000 GPUs that scientists from various domains such as astrophysics, fusion, climate, and combustion use routinely to run large-scale simulations. Unfortunately, while the performance efficiency of GPUs is well understood, their resilience characteristics in a large-scale computing system have not been fully evaluated. We present a detailed study to provide a thorough understanding of GPU errors on a large-scale GPU-enabled system. Our data was collected from the Titan supercomputer at the Oak Ridge Leadership Computing Facility and a GPU cluster at the Los Alamos National Laboratory. We also present results from our extensive neutron-beam tests, conducted at Los Alamos Neutron Science Center (LANSCE) and at ISIS (Rutherford Appleron Laboratories, UK), to measure the resilience of different generations of GPUs. We present several findings from our field data and neutron-beam experiments, and discuss the implications of our results for future GPU architects, current and future HPC computing facilities, and researchers focusing on GPU resilience.


Reinforcement Learning in Large Discrete Action Spaces


title: WIDER FACE: A Face Detection Benchmark

abstract: Face detection is one of the most studied topics in the computer vision community. Much of the progresses have been made by the availability of face detection benchmark datasets. We show that there is a gap between current face detection performance and the real world requirements. To facilitate future face detection research, we introduce the WIDER FACE dataset, which is 10 times larger than existing datasets. The dataset contains rich annotations, including occlusions, poses, event categories, and face bounding boxes. Faces in the proposed dataset are extremely challenging due to large variations in scale, pose and occlusion, as shown in Fig. 1. Furthermore, we show that WIDER FACE dataset is an effective training source for face detection. We benchmark several representative detection systems, providing an overview of state-of-the-art performance and propose a solution to deal with large scale variation. Finally, we discuss common failure cases that worth to be further investigated. Dataset can be downloaded at: mmlab.ie.cuhk.edu.hk/projects/WIDERFace


title: Content-based organization and visualization of music archives

abstract: With Islands of Music we present a system which facilitates exploration of music libraries without requiring manual genre classification. Given pieces of music in raw audio format we estimate their perceived sound similarities based on psychoacoustic models. Subsequently, the pieces are organized on a 2-dimensional map so that similar pieces are located close to each other. A visualization using a metaphor of geographic maps provides an intuitive interface where islands resemble genres or styles of music. We demonstrate the approach using a collection of 359 pieces of music.


title: Theory of interface-trap-induced NBTI degradation for reduced cross section MOSFETs

abstract: Negative Bias Temperature Instability (NBTI)-induced degradation for ultra-scaled and future-generation MOSFETs is investigated. Numerical simulations based on Reaction-Diffusion framework are implemented. Geometric dependence of degradation arising from the transistor structure and scaling is incorporated into the model. The simulations are applied to narrow-width planar triple-gate and surround-gate MOSFET geometries to estimate the NBTI reliability under several scaling scenarios. Unless the operating voltages are optimized for specific geometry of transistor cross section, the results imply worsened NBTI reliability for the future-generation devices based on the geometric interpretation of the NBTI degradation. A time-efficient and straightforward analysis is developed to predict the degradation. This compact model confirms the numerical simulations.


title: The Impact of Cooperative Adaptive Cruise Control on Traffic-Flow Characteristics

abstract: Cooperative adaptive cruise control (CACC) is an extension of ACC. In addition to measuring the distance to a predecessor, a vehicle can also exchange information with a predecessor by wireless communication. This enables a vehicle to follow its predecessor at a closer distance under tighter control. This paper focuses on the impact of CACC on traffic-flow characteristics. It uses the traffic-flow simulation model MIXIC that was specially designed to study the impact of intelligent vehicles on traffic flow. The authors study the impacts of CACC for a highway-merging scenario from four to three lanes. The results show an improvement of traffic-flow stability and a slight increase in traffic-flow efficiency compared with the merging scenario without equipped vehicles


title: A proposed theoretical framework addressing the effects of informal caregivers on health-related outcomes of elderly recipients in home health care.

abstract: PURPOSE ::: The purpose of this review is to develop a theoretical framework addressing the effects of informal caregivers on health-related outcomes of elderly recipients in home health care. ::: ::: ::: METHODS ::: The investigator a) reviewed theories and literature related to informal caregiving and health-related outcomes of elderly recipients, b) critiqued the strengths and limitations of these theories and literature for research in this area, and c) developed a new conceptual framework by deductively applying these theories and literature to the effects of informal caregivers on health-related outcomes of elderly recipients in home health care. ::: ::: ::: RESULTS ::: Social network theories, social support theories, and the literature on informal caregiving are useful in understanding how informal caregivers affect the outcomes of elderly recipients in home health care. This review synthesizes these theories and literature into a new theoretical framework in order to fully address the effects of informal caregivers on health-related outcomes of elderly recipients in home health care. ::: ::: ::: CONCLUSION ::: The proposed theoretical framework suggests that health-related outcomes of elderly recipients are the result of interactions among types of informal caregivers, the nature of the caregiving relationship, caregiving as a function of such relationships, and the internal processes of the care recipient.


title: An Interpretable Model for Scene Graph Generation

abstract: We propose an efficient and interpretable scene graph generator. We consider three types of features: visual, spatial and semantic, and we use a late fusion strategy such that each feature's contribution can be explicitly investigated. We study the key factors about these features that have the most impact on the performance, and also visualize the learned visual features for relationships and investigate the efficacy of our model. We won the champion of the OpenImages Visual Relationship Detection Challenge on Kaggle, where we outperform the 2nd place by 5\% (20\% relatively). We believe an accurate scene graph generator is a fundamental stepping stone for higher-level vision-language tasks such as image captioning and visual QA, since it provides a semantic, structured comprehension of an image that is beyond pixels and objects.


title: Face recognition and retrieval in video

abstract: Automatic face recognition has long been established as one of the most active research areas in computer vision. Face recognition in unconstrained environments remains challenging for most practical applications. In contrast to traditional still-image based approaches, recently the research focus has shifted towards videobased approaches. Video data provides rich and redundant information, which can be exploited to resolve the inherent ambiguities of image-based recognition like sensitivity to low resolution, pose variations and occlusion, leading to more accurate and robust recognition. Face recognition has also been considered in the content-based video retrieval setup, for example, character-based video search. In this chapter, we review existing research on face recognition and retrieval in video. The relevant techniques are comprehensively surveyed and discussed.


title: Classification of watermelon leaf diseases using neural network analysis

abstract: This paper mainly discussed the process to classify Anthracnose and Downey Mildew, watermelon leaf diseases using neural network analysis. A few of infected leaf samples were collected and they were captured using a digital camera with specific calibration procedure under controlled environment. The classification on the watermelon's leaf diseases is based on color feature extraction from RGB color model where the RGB pixel color indices have been extracted from the identified Regions of Interest (ROI). The proposed automated classification model involved the process of diseases classification using Statistical Package for the Social Sciences (SPSS) and Neural Network Pattern Recognition Toolbox in MATLAB. Determinations in this work have shown that the type of leaf diseases achieved 75.9% of accuracy based on its RGB mean color component.


title: Social Media Marketing: Measuring Its Effectiveness and Identifying the Target Market

abstract: The use of social media sites as part of a company’s marketing strategy has increased significantly in the past couple years. Regardless its popularity, there is still very limited information to answer some of the key issues concerning the effectiveness of social media marketing, ways to measure its return on investment, and its target market. Twitter activities and sales performances of five companies from the Fortune 500 companies and its competitors were analyzed to identify the correlation between the company involvement on the social media sites and its financial outcomes (ROI). In addition, a survey of 1,000 University of Wisconsin-La Crosse’s students was conducted to determine whether generation Y is the main audience of the social media marketing, and to find the relation between the respondents’ involvement in social media marketing and changes in their buying decisions. The study finds that social media marketing effectiveness is highly influenced by its messages/contents quality, the company’s involvement, and its association with the other marketing platforms. In addition, a complex and detailed analysis of the strategy is needed in order to accurately measure the return on investment of the social media marketing. The study also finds that generation Y might be the main users of social media sites, but they are not the main target audience of the social media marketing.


title: Accent Conversion Using Phonetic Posteriorgrams

abstract: Accent conversion (AC) aims to transform non-native speech to sound as if the speaker had a native accent. This can be achieved by mapping source spectra from a native speaker into the acoustic space of the non-native speaker. In prior work, we proposed an AC approach that matches frames between the two speakers based on their acoustic similarity after compensating for differences in vocal tract length. In this paper, we propose an approach that matches frames between the two speakers based on their phonetic (rather than acoustic) similarity. Namely, we map frames from the two speakers into a phonetic posteriorgram using speaker-independent acoustic models trained on native speech. We evaluate the proposed algorithm on a corpus containing multiple native and non-native speakers. Compared to the previous AC algorithm, the proposed algorithm improves the ratings of acoustic quality (20% increase in mean opinion score) and native accent (69% preference) while retaining the voice identity of the non-native speaker.


title: Agent-based modeling: Methods and techniques for simulating human systems

abstract: Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed.


title: IoT gateway - implementation proposal based on Arduino board

abstract: The paper presents proposal of practical implementation simple IoT gateway based on Arduino microcontroller, dedicated to use in home IoT environment. Authors are concentrated on research of performance and security aspects of created system. By performed load tests and denial of service attack were investigated performance and capacity limits of implemented gateway.


title: www.elsevier.com/locate/ijforecast The Delphi technique as a forecasting tool: issues and analysis

abstract: Abstract This paper systematically reviews empirical studies looking at the effectiveness of the Delphi technique, and provides a critique of this research. Findings suggest that Delphi groups outperform statistical groups (by 12 studies to two with two ‘ties’) and standard interacting groups (by five studies to one with two ‘ties’), although there is no consistent evidence that the technique outperforms other structured group procedures. However, important differences exist between the typical laboratory version of the technique and the original concept of Delphi, which make generalisations about ‘Delphi’ per se difficult. These differences derive from a lack of control of important group, task, and technique characteristics (such as the relative level of panellist expertise and the nature of feedback used). Indeed, there are theoretical and empirical reasons to believe that a Delphi conducted according to ‘ideal’ specifications might perform better than the standard laboratory interpretations. It is concluded that a different focus of research is required to answer questions on Delphi effectiveness, focusing on an analysis of the process of judgment change within nominal groups.


title: Geometrical workspace analysis of a cable-driven redundant parallel manipulator: KNTU CDRPM

abstract: KNTU CDRPM is a cable driven redundant parallel manipulator, which is under investigation for possible high speed and large workspace applications. This newly developed mechanisms have several advantages compared to the conventional parallel mechanisms. Its rotational motion range is relatively large, its redundancy improves safety for failure in cables, and its design is suitable for long-time high acceleration motions. In this paper, collision-free workspace of the manipulator is derived by applying fast geometrical intersection detection method, which can be used for any fully parallel manipulator. Implementation of the algorithm on the Neuron design of the KNTU CDRPM leads to significant results, which introduce a new style of design of a spatial cable-driven parallel manipulators. The results are elaborated in three presentations; constant-orientation workspace, total orientation workspace and orientation workspace.


title: Aerobic interval training and continuous training equally improve aerobic exercise capacity in patients with coronary artery disease: The SAINTEX-CAD study

abstract: Abstract Background Exercise-based cardiac rehabilitation increases peak oxygen uptake (peak VO 2 ), which is an important predictor of mortality in cardiac patients. However, it remains unclear which exercise characteristics are most effective for improving peak VO 2 in coronary artery disease (CAD) patients. Proof of concept papers comparing Aerobic Interval Training (AIT) and Moderate Continuous Training (MCT) were conducted in small sample sizes and findings were inconsistent and heterogeneous. Therefore, we aimed to compare the effects of AIT and Aerobic Continuous Training (ACT) on peak VO 2 , peripheral endothelial function, cardiovascular risk factors, quality of life and safety, in a large multicentre study. Methods Two-hundred CAD patients (LVEF >40%, 90% men, mean age 58.4±9.1years) were randomized to a supervised 12-week cardiac rehabilitation programme of three weekly sessions of either AIT (90–95% of peak heart rate (HR)) or ACT (70–75% of peak HR) on a bicycle. Primary outcome was peak VO 2 ; secondary outcomes were peripheral endothelial function, cardiovascular risk factors, quality of life and safety. Results Peak VO 2 (ml/kg/min) increased significantly in both groups (AIT 22.7±17.6% versus ACT 20.3±15.3%; p-time Conclusions Contrary to earlier smaller trials, we observed similar improvements in exercise capacity and peripheral endothelial function following AIT and ACT in a large population of CAD patients.


title: Vehicle-Based Relay Assistance for Opportunistic Crowdsensing Over Narrowband IoT (NB-IoT)

abstract: The Internet of Things (IoT) undergoes a fundamental transformation by augmenting its conventional sensor network deployments with more advanced and mobile devices, such as connected and self-driving cars. This fusion of embedded and automotive domains promises to deliver unprecedented mutual benefits, where vehicles will receive timely updates from their proximate sensors while assisting them in delivering their sensory data to the remote network infrastructure. In this paper, we put forward the vision of opportunistic crowdsensing applications, in which the ubiquitous deployments of low-cost and battery-constrained IoT sensors take advantage of more capable and energy-abundant vehicle-mounted mobile relays. In particular, we consider the use of the emerging narrowband IoT radio technology recently ratified by 3GPP and offering efficient means for underlying wireless connectivity. Our rigorous mathematical analysis supported with comprehensive system-level evaluations reveals the effects of vehicle-based relays on the important metrics of interest, such as connection reliability, transmission latency, and communication energy efficiency. These systematic findings advocate for an extensive utilization of vehicular relays as part of the next-generation IoT ecosystem.


title: Enabling End-To-End Machine Learning Replicability: A Case Study in Educational Data Mining

abstract: The use of machine learning techniques has expanded in education research, driven by the rich data from digital learning environments and institutional data warehouses. However, replication of machine learned models in the domain of the learning sciences is particularly challenging due to a confluence of experimental, methodological, and data barriers. We discuss the challenges of end-to-end machine learning replication in this context, and present an open-source software toolkit, the MOOC Replication Framework (MORF), to address them. We demonstrate the use of MORF by conducting a replication at scale, and provide a complete executable container, with unique DOIs documenting the configurations of each individual trial, for replication or future extension at this https URL. This work demonstrates an approach to end-to-end machine learning replication which is relevant to any domain with large, complex or multi-format, privacy-protected data with a consistent schema.


title: Physiologically Based Pharmacokinetic (PBPK) Modeling of Everolimus (RAD001) in Rats Involving Non-Linear Tissue Uptake

abstract: Everolimus is a novel macrolide immunosuppressant developed for the prophylaxis of allogeneic renal or cardiac transplant rejection. Treatments with immunosuppressants are often associated with organ toxicity that is linked to high organ exposure. Therefore, gaining insight into the pharmacokinetics of everolimus in various organs is highly desirable especially those organs of therapeutic interest or those that pose safety concerns. The aim of this work was to characterize the disposition kinetics of everolimus in rats by physiologically based pharmacokinetic (PBPK) modeling. Blood and tissue samples were collected from male Wistar rats over 24 hr following intravenous (iv) bolus and iv infusion of 1 mg/kg and 10 mg/kg/2 hr of everolimus. Further blood samples were collected between 1 and 170 hr from a third group of rats, which received iv infusion of 1 mg/kg/2 hr of everolimus. Drug concentrations in blood and tissues were determined by a liquid chromatography reverse dilution method. Distribution of everolimus between blood fractions was determined in vitro at 37 degrees C. The results of the study demonstrated that everolimus exhibited moderate non-linear binding to red blood cells. Also, the tissue-to-blood concentration ratio decreased in all tissues as blood concentration increased. A PBPK model involving non-linear tissue binding was able to successfully describe the observed data in blood and all the organs investigated. The highest binding potential was observed in thymus, lungs, and spleen with the greatest tissue affinity observed in thymus, skin, and muscle as compared to other tissues. Everolimus exhibited a high clearance rate that was limited to the hepatic blood flow (47.2 ml/min/kg). The PBPK model was also able to predict the venous blood concentration reasonably well following oral administration. The oral bioavailability value, as estimated with the PBPK, was 12% and was similar to the value obtained by non-compartmental analysis. In conclusion, A PBPK model has been developed that successfully predicts the time course of everolimus in blood and a variety of organs. This model takes into account the non- linear binding of everolimus to red blood cells and tissues. This model may be used to predict everolimus concentration-time course in organs from other species including humans.


title: Application-Driven End-to-End Slicing: When Wireless Network Virtualization Orchestrates With NFV-Based Mobile Edge Computing

abstract: Recently, to adapt to the various quality-of-service (QoS) requirements of emerging applications, application-driven network slicing has attracted intensive interests. In this paper, we apply the idea of software-defined wireless network virtualization (WiNV) to WiFi networks and design and demonstrate a novel network system, namely, ADE2WiNFV. The proposed system can orchestrate software-defined WiNV with network-function-virtualization-based mobile edge computing (MEC) to realize application-driven end-to-end (E2E) slicing over heterogeneous wireline/wireless networks. Our experimental demonstrations verify that ADE2WiNFV can realize application-aware E2E slices on demand, each of which contains not only guaranteed E2E bandwidth resources (i.e., in the forms of virtual links, virtual switches, and virtual access points) but also isolated IT resources (i.e., in the form of virtual network functions) to carry specific applications with QoS guarantees.


title: Density-Based Multiscale Analysis for Clustering in Strong Noise Settings With Varying Densities

abstract: Finding meaningful clustering patterns in data can be very challenging when the clusters are of arbitrary shapes, different sizes, or densities, and especially when the data set contains high percentage (e.g., 80%) of noise. Unfortunately, most existing clustering techniques cannot properly handle this tough situation and often result in dramatically deteriorating performance. In this paper, a purposefully designed clustering algorithm called Density-Based Multiscale Analysis for Clustering (DBMAC)-II is proposed, which is an improved version of the latest strong-noise clustering algorithm DBMAC. DBMAC is proposed under the assumption that all clusters are homogeneous and cannot work well when the data set contains clusters of varying densities. DBMAC-II overcomes the limitation of DBMAC by executing the multiscale analysis iteratively and can conduct strong noise-robust clustering without any strict assumption on the shapes and densities of clusters. In DBMAC-II, each data point or object is mapped into a feature space using its ${r}$ -neighborhood statistics with different ${r}$ (radius) values, which is similar to DBMAC. In general, the higher the value of ${r}$ -neighborhood statistics, the more likely the object is considered as a “clustered” object. Instead of trying to find a single optimal ${r}$ value, a set of radius values appropriate for separating “clustered” objects and “noisy” objects is identified, using a formal statistical method for multimodality test, referred to as multiscale analysis. For clusters with varying densities, multiscale analysis is applied to extract the clusters with the highest density from the current data set iteratively. Moreover, a statistical uniformity test for measuring clustering tendency is used as the self-adaptive stopping criterion of the iteration. Comprehensive experimental studies on a series of challenging benchmark data sets demonstrate that DBMAC-II is not only superior to classical density-based clustering approaches, including DBSCAN, OPTICS, and HDBSCAN, but also can consistently outperform the latest strong-noise robust clustering techniques, such as Skinny-dip.


title: End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks

abstract: One impressive advantage of convolutional neural networks (CNNs) is their ability to automatically learn feature representation from raw pixels, eliminating the need for hand-designed procedures. However, recent methods for single image super-resolution (SR) fail to maintain this advantage. They utilize CNNs in two decoupled steps, i.e., first upsampling the low resolution (LR) image to the high resolution (HR) size with hand-designed techniques (e.g., bicubic interpolation), and then applying CNNs on the upsampled LR image to reconstruct HR results. In this paper, we seek an alternative and propose a new image SR method, which jointly learns the feature extraction, upsampling and HR reconstruction modules, yielding a completely end-to-end trainable deep CNN. As opposed to existing approaches, the proposed method conducts upsampling in the latent feature space with filters that are optimized for the task of image SR. In addition, the HR reconstruction is performed in a multi-scale manner to simultaneously incorporate both short- and long-range contextual information, ensuring more accurate restoration of HR images. To facilitate network training, a new training approach is designed, which jointly trains the proposed deep network with a relatively shallow network, leading to faster convergence and more superior performance. The proposed method is extensively evaluated on widely adopted data sets and improves the performance of state-of-the-art methods with a considerable margin. Moreover, in-depth ablation studies are conducted to verify the contribution of different network designs to image SR, providing additional insights for future research.


title: Vision-based vehicle detection for nighttime with discriminately trained mixture of weighted deformable part models

abstract: Vehicle detection at night time is a challenging problem due to low visibility and light distortion caused by motion and illumination in urban environments. This paper presents a method based on the deformable object model for detecting and classifying vehicles by using monocular infra-red cameras. As some features of vehicles, such as headlight and taillights are more visible at night time, we propose a weighted version of the deformable part model. We define weights for different features in the deformable part model of the vehicle and try to learn the weights through an enormous number of positive and negative samples. Experimental results prove the effectiveness of the algorithm for detecting close and medium range vehicles in urban scenes at night time.


title: Multi Robot Object-Based SLAM

abstract: We propose a multi robot SLAM approach that uses 3D objects as landmarks for localization and mapping. The approach is fully distributed in that the robots only communicate during rendezvous and there is no centralized server gathering the data. Moreover, it leverages local computation at each robot (e.g., object detection and object pose estimation) to reduce the communication burden. We show that object-based representations reduce the memory requirements and information exchange among robots, compared to point-cloud-based representations; this enables operation in severely bandwidth-constrained scenarios. We test the approach in simulations and field tests, demonstrating its advantages over related techniques: our approach is as accurate as a centralized method, scales well to large teams, and is resistant to noise.


title: A Review on Challenging Issues in Arabic Sentiment Analysis

abstract: Understanding what people think about an idea or how they evaluate a product, a service or a policy is important for individuals, companies and governments. Sentiment analysis is the process of automatically identifying opinions expressed in text on certain subjects. The accuracy of sentiment analysis has a direct effect on decision making in both business and government. Working with the Arabic language is very important because of the growing number of online contents in Arabic and the existing resources are limited and the accuracy of existing methods is low. In this study, we do a survey to highlight Arabic sentiment analysis challenging issues based on two main perspectives: Arabic-specific and general linguistic issues. The Arabic-specific challenges are mainly caused by Arabic morphological complexity, limited resources and dialects, while the general linguistic issues include polarity fuzziness, polarity strength, implicit sentiment, sarcasm, spam, review quality and domain dependence.


title: On the Feasibility of Predicting News Popularity at Cold Start

abstract: We perform a study on cold-start news popularity prediction using a collection of 13,319 news articles obtained from Yahoo News. We characterise the online popularity of news articles by two different metrics and try to predict them using machine learning techniques. Contrary to a prior work on the same topic, our findings indicate that predicting the news popularity at cold start is a difficult task and the previously published results may be superficial.


title: Decentralized Traceable Attribute-Based Signatures

abstract: We provide a formal security model for traceable attribute-based signatures. Our focus is on the more practical case where attribute management is distributed among different authorities rather than relying on a single central authority. By specializing our model to the single attribute authority setting, we overcome some of the shortcomings of the existing model for the same setting.


Proposal : CRF Autoencoder Models for Structured Prediction with Partial Supervision


title: Log-based Anomaly Detection of CPS Using a Statistical Method

abstract: Detecting anomalies of a cyber physical system (CPS), which is a complex system consisting of both physical and software parts, is important because a CPS often operates autonomously in an unpredictable environment. However, because of the ever-changing nature and lack of a precise model for a CPS, detecting anomalies is still a challenging task. To address this problem, we propose applying an outlier detection method to a CPS log. By using a log obtained from an actual aquarium management system, we evaluated the effectiveness of our proposed method by analyzing outliers that it detected. By investigating the outliers with the developer of the system, we confirmed that some outliers indicate actual faults in the system. For example, our method detected failures of mutual exclusion in the control system that were unknown to the developer. Our method also detected transient losses of functionalities and unexpected reboots. On the other hand, our method did not detect anomalies that were too many and similar. In addition, our method reported rare but unproblematic concurrent combinations of operations as anomalies. Thus, our approach is effective at finding anomalies, but there is still room for improvement.


title: Internet of Things(IoT) digital forensic investigation model: Top-down forensic approach methodology

abstract: The Internet of Things (IoT) is the interconnection of uniquely identifiable embedded computing devices within the existing Internet infrastructure. Typically, internet of things (IoT) is expected to offer advanced connectivity of devices, systems, and services that goes beyond machine-to-machine communications (M2M) and covers a variety of protocols, domains, and applications. The interconnection of these embedded devices including smart objects, is expected to usher in automation in nearly all fields, while also enabling advanced applications like a Smart Grid. The main research challenge in Internet of things (IoT) for the forensic investigators is based size of the objects of forensic interest, relevancy, blurry network boundaries and edgeless networks, especially on method for conducting the investigation. The aim of this paper is to identify the best approach by designing a novel model to conduct the investigation situations for digital forensic professionals and experts. There was existing research works which introduce models for identifying the objects of forensics interest in investigations, but there were no rigorous testing for accepting the approach. Currently in this work, an integrated model is designed based on triage model and 1-2-3 zone model for volatile based data preservation.


title: Text Classification: A Sequential Reading Approach

abstract: We propose to model the text classification process as a sequential decision process. In this process, an agent learns to classify documents into topics while reading the document sentences sequentially and learns to stop as soon as enough information was read for deciding. The proposed algorithm is based on a modelisation of Text Classification as a Markov Decision Process and learns by using Reinforcement Learning. Experiments on four different classical mono-label corpora show that the proposed approach performs comparably to classical SVM approaches for large training sets, and better for small training sets. In addition, the model automatically adapts its reading process to the quantity of training information provided.


title: Learning How to Listen: A Temporal-Frequential Attention Model for Sound Event Detection

abstract: In this paper, we propose a temporal-frequential attention model for sound event detection (SED). Our network learns how to listen with two attention models: a temporal attention model and a frequential attention model. Proposed system learns when to listen using the temporal attention model while it learns where to listen on the frequency axis using the frequential attention model. With these two models, we attempt to make our system pay more attention to important frames or segments and important frequency components for sound event detection. Our proposed method is demonstrated on the task 2 of Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 Challenge and achieves competitive performance.


title: CULTURAL DETERMINANTS OF ORGANIZATIONAL SOCIAL MEDIA ADOPTION

abstract: In this research-in-progress paper, we present a two-step approach to measure the impact of cultural values on organizational social media adoption. We build on the GLOBE framework to measure societal culture and the Competing Values Framework to measure organizational culture. We define organizational social media adoption as the use of social networking, blogs, and media sharing tools in order to communicate and collaborate with customers, partners, and organization members. Cultural values appear to be salient factors for the ongoing adoption of social media in organizations.


title: Discrete Optimization A simulated annealing and hillclimbing algorithm for the traveling tournament problem

abstract: The Traveling Tournament Problem (TTP) [E. Easton, G. Nemhauser, M. Trick, The traveling tournament problem description and benchmarks, in: Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming, CP 2001, 2001, pp. 580–584; M. Trick, Challenge Traveling Tournament Problems, 2004] schedules a double round-robin tournament to minimize the total distance traveled by competing teams. It involves issues of feasibility and optimality and is a challenge to constraint and integer programming. In this work, we divide the search space and use simulated annealing (SA) to search a timetable space and hill-climbing to explore a team assignment space. The SA component mutates timetables using conditional local jumps to find timetables which lead to better schedules while hill-climbing is enhanced by pre-computation and dynamic cost updating to provide fast and efficient search. Computational experiments using this hybrid approach on benchmark sets give results comparable to or better than current best known solutions. � 2005 Elsevier B.V. All rights reserved.


title: Visualizing Patterns of Drug Prescriptions with EventFlow: A Pilot Study of Asthma Medications in the Military Health System

abstract: The Food and Drug Administration and Department of Defense were interested in detecting sub-optimal use of long-acting beta-agonists (LABAs) in asthmatics within the Military Health System (MHS). Visualizing the patterns of asthma medication use surrounding a LABA prescription is a quick way to detect possible sub-optimal use for further evaluation. Analysis of 100 asthma patients was conducted in EventFlow to display and summarize timepoint and interval data. Epidemiologists reported that EventFlow was a powerful tool for rapidly visualizing possible patterns of sub-optimal LABA use that can be targeted for intervention.


title: Technical Debt in Test Automation

abstract: Automated test execution is one of the more popular and available strategies to minimize the cost for software testing, and is also becoming one of the central concepts in modern software development as methods such as test-driven development gain popularity. Published studies on test automation indicate that the maintenance and development of test automation tools commonly encounter problems due to unforeseen issues. To further investigate this, we performed a case study on a telecommunication subsystem to seek factors that contribute to inefficiencies in use, maintenance, and development of the automated testing performed within the scope of responsibility of a software design team. A qualitative evaluation of the findings indicates that the main areas of improvement in this case are in the fields of interaction design and general software design principles, as applied to test execution system development.


title: Multilevel converters-a new breed of power converters

abstract: Multilevel voltage source converters are emerging as a new breed of power converter options for high-power applications. The multilevel voltage source converters typically synthesize the staircase voltage wave from several levels of DC capacitor voltages. One of the major limitations of the multilevel converters is the voltage unbalance between different levels. The techniques to balance the voltage between different levels normally involve voltage clamping or capacitor charge control. There are several ways of implementing voltage balance in multilevel converters. Without considering the traditional magnetic coupled converters, this paper presents three recently developed multilevel voltage source converters: (1) diode-clamp, (2) flying-capacitors, and (3) cascaded-inverters with separate DC sources. The operating principle, features, constraints, and potential applications of these converters are discussed.


title: Analysis of Arabic Bot Behaviors

abstract: Nowadays, online social media became an essential communication platform that affects all our lives. However, it goes unnoticed that there are entities found in social media that affect and manipulate society and pose as a human to do so; these entities are called bots. Over the years, researchers have conducted tremendous work in developing bot detection applications, but unfortunately, most of them focus on analyzing and detecting English bots, whereas there is a noticeable lack in addressing Arabic bots. Therefore, in this paper, we have studied and analyzed Arabic Twitter bots’ behavior, and highlighted the most significant features of these bots. In addition, we addressed the language and cultural effect on these features and whether the bots’ behavior changes based on language and culture. The study started by collecting datasets using crowdturfing campaign and manual labeling. Then we extracted the features that were identified and proven to be useful by other researchers. After that, we built a classifier that tested the probability of an account being human or bot. Finally, the results were analyzed and discussed.


Quantitative ultrasound bone measurement.


title: Revisiting the Effectiveness of Off-the-shelf Temporal Modeling Approaches for Large-scale Video Classification

abstract: This paper describes our solution for the video recognition task of ActivityNet Kinetics challenge that ranked the 1st place. Most of existing state-of-the-art video recognition approaches are in favor of an end-to-end pipeline. One exception is the framework of DevNet. The merit of DevNet is that they first use the video data to learn a network (i.e. fine-tuning or training from scratch). Instead of directly using the end-to-end classification scores (e.g. softmax scores), they extract the features from the learned network and then fed them into the off-the-shelf machine learning models to conduct video classification. However, the effectiveness of this line work has long-term been ignored and underestimated. In this submission, we extensively use this strategy. Particularly, we investigate four temporal modeling approaches using the learned features: Multi-group Shifting Attention Network, Temporal Xception Network, Multi-stream sequence Model and Fast-Forward Sequence Model. Experiment results on the challenging Kinetics dataset demonstrate that our proposed temporal modeling approaches can significantly improve existing approaches in the large-scale video recognition tasks. Most remarkably, our best single Multi-group Shifting Attention Network can achieve 77.7% in term of top-1 accuracy and 93.2% in term of top-5 accuracy on the validation set.


Mobile Banknote Recognition and Conversion


title: Nonharmonic multitone of singing bowls and digital signal generators

abstract: This paper presents the issue of a nonharmonic multitone generation with the use of singing bowls and the digital signal processors. The authors show the possibility of therapeutic applications of such multitone signals. Some known methods of the digital generation of the tone signal with the additional modulation are evaluated. Two projects of the very precise multitone generators are presented. In described generators, the digital signal processors synthesize the signal, while the additional microcontrollers realize the operator's interface. As a final result, the sound of the original singing bowls is confronted with the sound synthesized by one of the generators.


title: Jumper firefly algorithm

abstract: Firefly algorithm is one of the metaheuristic algorithms which are used to solve optimization problems. Firefly algorithm is such a population-based algorithm so that their group behavior and interaction result in a swarm Intelligence that is used to find the best and fittest firefly. Since this algorithm is based on the search agents, it utilizes different agents with different qualities in searching the problem space. In order to increase the quality of firefly society to strengthen it to search the problem space efficiently and find the optimal solution, we have proposed a new algorithm based on the firefly algorithm. This algorithm with the aid of a Status Table, records behavior of the fireflies in details and identifies weak agents. Then the proposed algorithm enables weak ones to jump to new positions in order to attain a high probability of finding the qualified solutions. Modification of the search agents individually, leads to modification of the whole population and subsequently causes to increase the performance of algorithm in finding the optimal solutions.


title: Biomedical ontology alignment: an approach based on representation learning

abstract: BackgroundWhile representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance.ResultsAn ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results.ConclusionsOur proposed representation learning approach leverages terminological embeddings to capture semantic similarity. Our results provide evidence that the approach produces embeddings that are especially well tailored to the ontology matching task, demonstrating a novel pathway for the problem.


Integrating Question Classification and Deep Learning for improved Answer Selection


title: Segmentation of unknown objects in indoor environments

abstract: We present a framework for segmenting unknown objects in RGB-D images suitable for robotics tasks such as object search, grasping and manipulation. While handling single objects on a table is solved, handling complex scenes poses considerable problems due to clutter and occlusion. After pre-segmentation of the input image based on surface normals, surface patches are estimated using a mixture of planes and NURBS (non-uniform rational B-splines) and model selection is employed to find the best representation for the given data. We then construct a graph from surface patches and relations between pairs of patches and perform graph cut to arrive at object hypotheses segmented from the scene. The energy terms for patch relations are learned from user annotated training data, where support vector machines (SVM) are trained to classify a relation as being indicative of two patches belonging to the same object. We show evaluation of the relations and results on a database of different test sets, demonstrating that the approach can segment objects of various shapes in cluttered table top scenes.


title: Jointly Extracting Relations with Class Ties via Effective Deep Ranking

abstract: Connections between relations in relation extraction, which we call class ties, are common. In distantly supervised scenario, one entity tuple may have multiple relation facts. Exploiting class ties between relations of one entity tuple will be promising for distantly supervised relation extraction. However, previous models are not effective or ignore to model this property. In this work, to effectively leverage class ties, we propose to make joint relation extraction with a unified model that integrates convolutional neural network (CNN) with a general pairwise ranking framework, in which three novel ranking loss functions are introduced. Additionally, an effective method is presented to relieve the severe class imbalance problem from NR (not relation) for model training. Experiments on a widely used dataset show that leveraging class ties will enhance extraction and demonstrate the effectiveness of our model to learn class ties. Our model outperforms the baselines significantly, achieving state-of-the-art performance.


title: New Insights into the Mechanism of Action of Amphetamines

abstract: AbstractAmphetamine is a psychostimulant commonly used to treat several disorders, including attention deficit, narcolepsy, and obesity. Plasmalemmal and vesicular monoamine transporters, such as the neuronal dopamine transporter and the vesicular monoamine transporter-2, are two of its principal targets. This review focuses on new insights, obtained from both in vivo and in vitro studies, into the molecular mechanisms whereby amphetamine, and the closely related compounds methamphetamine and methylenedioxymethamphetamine, cause monoamine, and particularly dopamine, release. These mechanisms include amphetamine-induced exchange diffusion, reverse transport, and channel-like transport phenomena as well as the weak base properties of amphetamine. Additionally, amphetamine analogs may affect monoamine transporters through phosphorylation, transporter trafficking, and the production of reactive oxygen and nitrogen species. All of these mechanisms have potential implications for both amphetamine- and methamphe...


title: Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings

abstract: Collecting a large dataset with high quality annotations is expensive and time-consuming. Recently, Shrivastava et al. (2017) propose Simulated+Unsupervised (S+U) learning: It first learns a mapping from synthetic data to real data, translates a large amount of labeled synthetic data to the ones that resemble real data, and then trains a learning model on the translated data. Bousmalis et al. (2017) propose a similar framework that jointly trains a translation mapping and a learning model. While these algorithms are shown to achieve the state-of-the-art performances on various tasks, it may have a room for improvement, as they do not fully leverage flexibility of data simulation process and consider only the forward (synthetic to real) mapping. While these algorithms are shown to achieve the state-of-the-art performances on various tasks, it may have a room for improvement, as it does not fully leverage flexibility of data simulation process and consider only the forward (synthetic to real) mapping. Inspired by this limitation, we propose a new S+U learning algorithm, which fully leverage the flexibility of data simulators and bidirectional mappings between synthetic data and real data. We show that our approach achieves the improved performance on the gaze estimation task, outperforming (Shrivastava et al., 2017).


title: Graphene Photonics, Plasmonics, and Broadband Optoelectronic Devices

abstract: Graphene has been hailed as a wonderful material in electronics, and recently, it is the rising star in photonics, as well. The wonderful optical properties of graphene afford multiple functions of signal emitting, transmitting, modulating, and detection to be realized in one material. In this paper, the latest progress in graphene photonics, plasmonics, and broadband optoelectronic devices is reviewed. Particular emphasis is placed on the ability to integrate graphene photonics onto the silicon platform to afford broadband operation in light routing and amplification, which involves components like polarizer, modulator, and photodetector. Other functions like saturable absorber and optical limiter are also reviewed.


title: Learning to predict indoor illumination from a single image

abstract: We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous work that relies on specialized image capture, user input, and/or simple scene models, we train an end-to-end deep neural network that directly regresses a limited field-of-view photo to HDR illumination, without strong assumptions on scene geometry, material properties, or lighting. We show that this can be accomplished in a three step process: 1) we train a robust lighting classifier to automatically annotate the location of light sources in a large dataset of LDR environment maps, 2) we use these annotations to train a deep neural network that predicts the location of lights in a scene from a single limited field-of-view photo, and 3) we fine-tune this network using a small dataset of HDR environment maps to predict light intensities. This allows us to automatically recover high-quality HDR illumination estimates that significantly outperform previous state-of-the-art methods. Consequently, using our illumination estimates for applications like 3D object insertion, we can achieve results that are photo-realistic, which is validated via a perceptual user study.


title: Astrocyte-Neuron Lactate Transport Is Required for Long-Term Memory Formation

abstract: We report that, in the rat hippocampus, learning leads to a significant increase in extracellular lactate levels that derive from glycogen, an energy reserve selectively localized in astrocytes. Astrocytic glycogen breakdown and lactate release are essential for long-term but not short-term memory formation, and for the maintenance of long-term potentiation (LTP) of synaptic strength elicited in vivo. Disrupting the expression of the astrocytic lactate transporters monocarboxylate transporter 4 (MCT4) or MCT1 causes amnesia, which, like LTP impairment, is rescued by L-lactate but not equicaloric glucose. Disrupting the expression of the neuronal lactate transporter MCT2 also leads to amnesia that is unaffected by either L-lactate or glucose, suggesting that lactate import into neurons is necessary for long-term memory. Glycogenolysis and astrocytic lactate transporters are also critical for the induction of molecular changes required for memory formation, including the induction of phospho-CREB, Arc, and phospho-cofilin. We conclude that astrocyte-neuron lactate transport is required for long-term memory formation.


Theories of Computer-Mediated Communication and Interpersonal Relations


title: Logistic regression Gaussian mixture model and softmax discriminant classifier for epilepsy classification from EEG signals

abstract: The patient suffers from spontaneous seizures under a neurological condition termed epilepsy. Due to the severe disturbances happening in the electrical activity of the brain, the seizures occur. An epileptic seizure is simply a transient occurrence of symptoms because of excessive and abnormal neuronal firing of neurons in the brain. Due to the hypersynchronous and abnormal activity of neurons in the brain, epilepsy manifests itself in the form of seizures. The most disabling aspect and the characteristic feature of this disease are witnessed by the occurrence of seizures. It is quite difficult to identify the presence of epileptic activities in order to characterize and understand the spatio-temporal patterns. The automatic classification and detection procedure of seizures from Electroencephalography (EEG) signals is quite crucial for the localization and epileptic seizure activity classification. The seizures are typically non stationary and highly dynamic in nature so that the rhythmic activities cannot be easily interpreted. In this paper, a two level classification procedure is employed, firstly the risk of epilepsy is classified with Softmax Discriminant Classifier (SDC) and secondly it is further optimized and classified with Logistic Regression Gaussian Mixture Model (LRGMM) Classifier. Results show that a classification accuracy of about 93.38% is obtained when classified with SDC and a classification accuracy of about 97.91% is obtained with classified with LRGMM.


Seismic vulnerability assessment of wharf structures


title: Short-circuit critical frequency for induction heating parallel resonant inverters

abstract: In induction heating applications, the component being heated is very close to the heating coil. Due to the high temperatures reached during the process, isolation is not always possible and short-circuits may happen. Two main types of short-circuit should be considered: complete inductor's short-circuit and short-circuit between windings. Parallel resonant oscillators are widely used in induction heating applications together with a current fed inverter to generate the alternating current. Parasitic inductance in the connection between the inverter and the oscillator tank may generate high over-voltages if the switching is uncontrolled. The aim of the paper is to describe the protections needed to avoid the failure of the transistors due to over-voltages which may occur during the short-circuit process. Short-circuit critical frequency is calculated and determines whether over-voltages will appear after short-circuit occurs.


title: From On-Premise Software to Cloud Services: The Impact of Cloud Computing on Enterprise Software Vendors' Business Models

abstract: Cloud computing is an emerging paradigm that allows users to conveniently access computing resources as pay-per-use services. Whereas cloud offerings such as Amazon's Elastic Compute Cloud and Google Apps are rapidly gaining a large user base, enterprise software's migration towards the cloud is still in its infancy. For software vendors the move towardscloud solutions implies profound changes in their value-creation logic. Not only are they forced to deliver fully web-enabled solutions and to replace their license model with service fees, they also need to build the competencies to host and manage business-critical applications for their customers. This motivates our research, which investigates cloud computing's implications for enterprise software vendors' business models. From multiple case studies covering traditional and pure cloud providers, we find that moving from on-premise software to cloud services affects all business model components, that is, the customer value proposition, resource base, value configuration, and financial flows. It thus underpins cloud computing's disruptive nature in the enterprise software domain. By deriving two alternative business model configurations, SaaS and SaaS+PaaS, our research synthesizes the strategic choices for enterprise software vendors and provides guidelines for designing viable business models.


title: Database forensic analysis through internal structure carving

abstract: Forensic tools assist analysts with recovery of both the data and system events, even from corrupted storage. These tools typically rely on "file carving" techniques to restore files after metadata loss by analyzing the remaining raw file content. A significant amount of sensitive data is stored and processed in relational databases thus creating the need for database forensic tools that will extend file carving solutions to the database realm. Raw database storage is partitioned into individual "pages" that cannot be read or presented to the analyst without the help of the database itself. Furthermore, by directly accessing raw database storage, we can reveal things that are normally hidden from database users.There exists a number of database-specific tools developed for emergency database recovery, though not usually for forensic analysis of a database. In this paper, we present a universal tool that seamlessly supports many different databases, rebuilding table and other data content from any remaining storage fragments on disk or in memory. We define an approach for automatically (with minimal user intervention) reverse engineering storage in new databases, for detecting volatile data changes and discovering user action artifacts. Finally, we empirically verify our tool's ability to recover both deleted and partially corrupted data directly from the internal storage of different databases.


ECDSA Security in Bitcoin and Ethereum : a Research Survey


title: Factoring Variations in Natural Images with Deep Gaussian Mixture Models

abstract: Generative models can be seen as the swiss army knives of machine learning, as many problems can be written probabilistically in terms of the distribution of the data, including prediction, reconstruction, imputation and simulation. One of the most promising directions for unsupervised learning may lie in Deep Learning methods, given their success in supervised learning. However, one of the current problems with deep unsupervised learning methods, is that they often are harder to scale. As a result there are some easier, more scalable shallow methods, such as the Gaussian Mixture Model and the Student-t Mixture Model, that remain surprisingly competitive. In this paper we propose a new scalable deep generative model for images, called the Deep Gaussian Mixture Model, that is a straightforward but powerful generalization of GMMs to multiple layers. The parametrization of a Deep GMM allows it to efficiently capture products of variations in natural images. We propose a new EM-based algorithm that scales well to large datasets, and we show that both the Expectation and the Maximization steps can easily be distributed over multiple machines. In our density estimation experiments we show that deeper GMM architectures generalize better than more shallow ones, with results in the same ballpark as the state of the art.


Using Personality Information in Collaborative Filtering for New Users


title: Effects of stunting, diarrhoeal disease, and parasitic infection during infancy on cognition in late childhood: a follow-up study

abstract: BACKGROUND ::: Chronic malnutrition during infancy, marked by stunting, has been associated with poor cognitive function. We assessed the effect of stunting, diarrhoeal disease, and parasitic infections during infancy on cognitive function in late childhood. ::: ::: ::: METHODS ::: We followed up from birth to 2 years, a cohort of 239 Peruvian children for anthropometrics, stool samples, and diarrhoeal status. At 9 years of age, we assessed cognitive function in 143 (69%) with the full-scale intelligence quotient of the Wechsler intelligence scale for children-revised (WISC-R). Findings All findings were adjusted for socioeconomic status and schooling; in addition, findings related to diarrhoea prevalence, Giardia lamblia, and Cryptosporidium parvum were adjusted for severe stunting. During the first 2 years of life, 46 (32%) of 143 children were stunted. Children with severe stunting in the second year of life scored 10 points lower on the WISC-R test (95% CI 2.4--17.5) than children without severe stunting. Children with more than one episode of G lamblia per year scored 4.1 points (0.2--8.0) lower than children with one episode or fewer per year. Neither diarrhoea prevalence nor Cparvum infection was associated with WISC-R scores. ::: ::: ::: INTERPRETATION ::: Malnutrition in early childhood, indexed by stunting, and potentially G lamblia, are associated with poor cognitive function at age 9 years. If the observed associations are causal, then intervention programmes designed to prevent malnutrition and G lamblia early in life could lead to significant improvement in cognitive function of children in similar lower-income communities throughout the less-developed world.


Home Automation System ( HAS ) using Android for Mobile Phone


title: Privacy and human behavior in the age of information

abstract: This Review summarizes and draws connections between diverse streams of empirical research on privacy behavior. We use three themes to connect insights from social and behavioral sciences: people’s uncertainty about the consequences of privacy-related behaviors and their own preferences over those consequences; the context-dependence of people’s concern, or lack thereof, about privacy; and the degree to which privacy concerns are malleable—manipulable by commercial and governmental interests. Organizing our discussion by these themes, we offer observations concerning the role of public policy in the protection of privacy in the information age.


title: A polyhedral-based SystemC modeling and generation framework for effective low-power design space exploration

abstract: With the prevalence of System-on-Chips there is a growing need for automation and acceleration of the design process. A classical approach is to take a C/C++ specification of the application, convert it to a SystemC (or equivalent) description of hardware implementing this application, and perform successive refinement of the description to improve various design metrics. In this work, we present an automated SystemC generation and design space exploration flow alleviating several productivity and design time issues encountered in the current design process. We first automatically convert a subset of C/C++, namely affine program regions, into a full SystemC description through polyhedral model-based techniques while performing powerful data locality and parallelism transformations. We then leverage key properties of affine computations to design a fast and accurate latency and power characterization flow. Using this flow, we build analytical models of power and performance that can effectively prune away a large amount of inferior design points very fast and generate Pareto-optimal solution points. Experimental results show that (1) our SystemC models can evaluate system performance and power that is only 0.57% and 5.04% away from gate-level evaluation results, respectively; (2) our latency and power analytical models are 3.24% and 5.31% away from the actual Pareto points generated by SystemC simulation, with 2091x faster design-space exploration time on average. The generated Pareto-optimal points provide effective low-power design solutions given different latency constraints.


title: Identifying Media Frames and Frame Dynamics Within and Across Policy Issues

abstract: Framing is a central concept in political communication and a powerful political tool. Understanding what frames are used to dene specic issues and also what general patterns are evidenced by the evolution of frames over time is hugely important. It is also a serious challenge, thanks to the volume of text data, the dynamic nature of language, and the variance in applicable frames across issues (e.g., the ‘innocence’ frame of the death penalty debate is not used in discussing smoking bans). We describe a project that advances framing research methodology in two ways. First, we are developing a unied coding scheme for content analysis across issues, whereby issue-specic frames (e.g., innocence) are nested within high-level dimensions (or frame types) that cross-cut issues (e.g., fairness); we are validating this coding scheme by applying it to news coverage of immigration, same-sex marriage, and smoking/tobacco in the United States over the course of the past twenty-three years. Second, we are developing methods for semi-automated and automated frame discovery aimed at both replicating manual coding and isolating patterns of frame evolution that might not be readily visible to human inspection. Our goal is to employ strategies heavily informed by existing work in natural language processing, but tailored to the specic needs and professional sensibilities of political communications scholars.