title: Training support vector machines: an application to face detection

abstract: We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs., 1985) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision surfaces are found by solving a linearly constrained quadratic programming problem. This optimization problem is challenging because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. We present a decomposition algorithm that guarantees global optimality, and can be used to train SVM's over very large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of optimality conditions which are used both to generate improved iterative values, and also establish the stopping criteria for the algorithm. We present experimental results of our implementation of SVM, and demonstrate the feasibility of our approach on a face detection problem that involves a data set of 50,000 data points.


title: BM3D-PRGAMP: Compressive phase retrieval based on BM3D denoising

abstract: The explosion of computational imaging has seen the frontier of image processing move past linear problems, like denoising and deblurring, and towards non-linear problems such as phase retrieval. There has a been a corresponding research thrust into non-linear image recovery algorithms, but in many ways this research is stuck where linear problem research was twenty years ago: Models, if used at all, are simple designs like sparsity or smoothness. In this paper we use denoisers to impose elaborate and accurate models in order to perform inference on generalized linear systems. More specifically, we use the state-of-the-art BM3D denoiser within the Generalized Approximate Message Passing (GAMP) framework to solve compressive phase retrieval in a variety of different contexts. Our method demonstrates recovery performance equivalent to existing techniques using fewer than half as many measurements. This dramatic improvement in compressive phase retrieval performance opens the door for a whole new class of imaging systems.


title: Single target detection and tracking using direction-of-arrival spectrum

abstract: This paper presents a track-before-detect algorithm based on the particle filter for simultaneously detecting and tracking a single target with nearly constant velocity. The measurement data available to the algorithm is the raw direction-of-arrival spectrum of the received wideband signals at a single observation platform. The proposed method does not require prior information on the target existence probability and it is capable of detecting a target of very low signal-to-noise ratio (-30dB). Simulation results show that the proposed algorithm demonstrates good performance in detecting and tracking a target with reasonably low errors in the estimated target states over the observation period.


title: Transmission line metamaterials based on pairs of coupled split ring resonators (SRRs) and complementary split ring resonators (CSRR): A comparison to the light of the lumped element equivalent circuits

abstract: This paper analyzes and compares two types of transmission line metamaterials: (i) coplanar waveguide (CPW) transmission lines loaded with pairs of magnetically coupled split ring resonators (SRRs), and (ii) microstrip lines loaded with pairs of electrically coupled complementary split ring resonators (CSRRs). Both structures are described by lumped element equivalent circuit models rather different. However, both structures exhibit very similar phenomenology, and there is a mapping between the elements of both models that gives identical results for the analytical expressions providing the resonance frequencies (transmission zeros) that these lines exhibit. It is shown that these artificial lines are useful as dual-notched filters and as differential sensors and comparators.


title: 3D segmentation and labeling of fractured bone from CT images

abstract: The segmentation of fractured bone from computed tomographies (CT images) is an important process in medical visualization and simulation, because it enables such applications to use data of a specific patient. On the other hand, the labeling of fractured bone usually requires the participation of an expert. Moreover, close fragment can be joined after the segmentation because of their proximity and the resolution of the CT image. Classical methods perform well in the segmentation of healthy bone, but they are not able to identify bone fragments separately. In this paper, we propose a method to segment and label bone fragments from CT images. Labeling involves the identification of bone fragments separately. The method is based on 2D region growing and requires minimal user interaction. In addition, the presented method is able to separate wrongly joined fragments during the segmentation process.


title: Uncovering Topic Dynamics of Social Media and News: The Case of Ferguson

abstract: Looking at the dynamics of news content and social media content can help us understand the increasingly complex dynamics of the relationship between the media and the public surrounding noteworthy news events. Although topic models such as latent Dirichlet allocation (lda) are valuable tools, they are a poor fit for analyses in which some documents, like news articles, tend to incorporate multiple topics, while others, like tweets, tend to be focused on just one. In this paper, we propose Single Topic lda (st-lda) which jointly models news-type documents as distributions of topics and tweets as having a single topic; the model improves topic discovery in news and tweets within a unified topic space by removing noisy topics that conventional lda tends to assign to tweets. Using st-lda, we focus on the unrest in Ferguson, Missouri after the fatal shooting of Michael Brown on August 9, 2014, looking in particular at the topic dynamics of tweets in and out of St. Louis area, and at differences and relationships between topic coverage in news and tweets.


title: Machine Learning Framework for the Detection of Mental Stress at Multiple Levels

abstract: Mental stress has become a social issue and could become a cause of functional disability during routine work. In addition, chronic stress could implicate several psychophysiological disorders. For example, stress increases the likelihood of depression, stroke, heart attack, and cardiac arrest. The latest neuroscience reveals that the human brain is the primary target of mental stress, because the perception of the human brain determines a situation that is threatening and stressful. In this context, an objective measure for identifying the levels of stress while considering the human brain could considerably improve the associated harmful effects. Therefore, in this paper, a machine learning (ML) framework involving electroencephalogram (EEG) signal analysis of stressed participants is proposed. In the experimental setting, stress was induced by adopting a well-known experimental paradigm based on the montreal imaging stress task. The induction of stress was validated by the task performance and subjective feedback. The proposed ML framework involved EEG feature extraction, feature selection (receiver operating characteristic curve, t-test and the Bhattacharya distance), classification (logistic regression, support vector machine and naive Bayes classifiers) and tenfold cross validation. The results showed that the proposed framework produced 94.6% accuracy for two-level identification of stress and 83.4% accuracy for multiple level identification. In conclusion, the proposed EEG-based ML framework has the potential to quantify stress objectively into multiple levels. The proposed method could help in developing a computer-aided diagnostic tool for stress detection.


title: Ocular and orbital metastatic carcinoma.

abstract: One hundred cases of metastatic carcinoma to the eye and orbit were reviewed. Fifty-nine patients were female and 35 were male. The average age of the females was 60 years and the males 52 years. Breast carcinoma was the most common primary in the females and lung carcinoma in the males. The left eye and orbit were involved more frequently than the right (30 to 27). The choroid and the orbit were the most common site of metastasis.


title: The effect of big data analytics capability on firm performance

abstract: Big data analytics (BDA) has emerged as an important area of study for both academics and practitioners. Despite of rising potential value of BDA, a few studies have been conducted to investigate the effect of BDA on firm performance. In this research in progress, according to the challenges of BDA dimensions (volume, variety, velocity, veracity and value) we propose the BDA capability dimensions in line with IT capability concept. BDA infrastructure capability, BDA management capability, BDA personnel capability and relational BDA capability provide the overall BDA Capability concept. The study, by employing dynamic capability, proposes that BDA capability impacts on firm financial and market performance by mediated effect of operational performance. The finding of this research by providing essential BDA capability and its effect on firm performance can apply as a roadmap and fill the gap between managers' expectation of BDA and what is emerged of BDA implementation.


title: Gunshot detection and localization based on Non-negative Matrix Factorization and SRP-hat

abstract: This paper describes a sound source detection and localization system where the sound source is a gunshot. The proposed system is composed of two consecutive stages: detection and localization. In this manner, the localization system is activated when a gunshot is detected. The detection stage is based on a Non-negative Matrix Factorization (NMF) approach. The localization stage proposes a modification of the method called Steered Response Power Phase transform (SRP-Phat). Firstly, we estimate the delay between multichannel signals that are captured by a six microphone array. Generalized Cross-Correlation (GCC), Generalized Cross-Correlation Phase transform (GCC-Phat), Steered Response Power Phase transform (SRP-Phat) and the proposed localization method are compared. Evaluation indicates that the proposed detection and localization method provides promising results in many acoustic scenarios including noisy environments with low signal-to-noise ratios. The proposed application is designed following the paradigm of smart sound processing and is focused on security needs in a real life scenario.


title: SAR Image Texture Classification Based on Kernel Fisher Discriminant Analysis

abstract: Texture analysis plays an important role in remote sensing image processing. Most methods of texture classification, for the identification of different texture surfaces, are based on wavelet features, MRF models, STFT features, and geometric shape of texels and PCA analysis. Kernel Fisher discriminant (KFD) is a state-of-the-art nonlinear machine learning method, and it has great potential to identify image texture. Meanwhile, Texture analysis is also an important technique of SAR image classification. In this paper, a nonlinear discriminative texture feature extraction method based on kernel Fisher discriminant (KFD) is proposed for SAR image texture classification.


title: Prediction of the content popularity in the 5G network: Auto-regressive, moving-average and exponential smoothing approaches

abstract: Today's mobile users want faster data and more reliable services. The next generation of wireless networks 5G promises to deal with this, and more. In this context, to enable ultra-short response times, fast relocation of service instances between edge nodes and reduce migration time its required to cope with user mobility to guarantee the (QoE). In this new paradigm called 5G cellular systems, the technique of content caching in small base stations (SBS) is considered to be a suitable approach to improve the efficiency and to alleviate the backhaul burden and reduce user perceived latency in wireless content delivery. Proactively serving predictable user demands, via caching at base stations (BS) and users' devices is crucial due to storage limitations, but it requires knowledge about the content popularity distribution, which is often not available in advance. Moreover, local content popularity is subject to fluctuations since mobile users with different interests connect to the caching entity over time. In this paper we focus on the prediction of popularity evolution of video contents. Based on the observation of past solicitations of individual video contents. The popularity prediction in this proactive approach relies on AR (Auto-Regressive), MA (Moving-Average) and Exponential smoothing techniques to complete a proposal caching Algorithm to manage cache decisions.


title: Gamification in theory and action

abstract: Gamification has drawn the attention of academics, practitioners and business professionals in domains as diverse as education, information studies, human-computer interaction, and health. As yet, the term remains mired in diverse meanings and contradictory uses, while the concept faces division on its academic worth, underdeveloped theoretical foundations, and a dearth of standardized guidelines for application. Despite widespread commentary on its merits and shortcomings, little empirical work has sought to validate gamification as a meaningful concept and provide evidence of its effectiveness as a tool for motivating and engaging users in non-entertainment contexts. Moreover, no work to date has surveyed gamification as a field of study from a human-computer studies perspective. In this paper, we present a systematic survey on the use of gamification in published theoretical reviews and research papers involving interactive systems and human participants. We outline current theoretical understandings of gamification and draw comparisons to related approaches, including alternate reality games (ARGs), games with a purpose (GWAPs), and gameful design. We present a multidisciplinary review of gamification in action, focusing on empirical findings related to purpose and context, design of systems, approaches and techniques, and user impact. Findings from the survey show that a standard conceptualization of gamification is emerging against a growing backdrop of empirical participants-based research. However, definitional subjectivity, diverse or unstated theoretical foundations, incongruities among empirical findings, and inadequate experimental design remain matters of concern. We discuss how gamification may to be more usefully presented as a subset of a larger effort to improve the user experience of interactive systems through gameful design. We end by suggesting points of departure for continued empirical investigations of gamified practice and its effects. We present findings from a survey of the gamification literature.Theoretical findings suggest that gamification is a distinct concept.Conceptual foundations tend to converge on psychological theories of motivation.Early applied work suggests positive-leaning but mixed results.Empirical work on specific elements with direct ties to theory and stronger experimental designs is needed.


title: An Overview of Multi-Task Learning in Deep Neural Networks

abstract: Multi-task learning (MTL) has led to successes in many applications of machine learning, from natural language processing and speech recognition to computer vision and drug discovery. This article aims to give a general overview of MTL, particularly in deep neural networks. It introduces the two most common methods for MTL in Deep Learning, gives an overview of the literature, and discusses recent advances. In particular, it seeks to help ML practitioners apply MTL by shedding light on how MTL works and providing guidelines for choosing appropriate auxiliary tasks.


title: Crushed cartilage grafts for concealing irregularities in rhinoplasty.

abstract: OBJECTIVE ::: To assess the clinical outcome of crushed cartilage grafts used to conceal contour irregularities in rhinoplasty. ::: ::: ::: METHODS ::: We reviewed the medical records of 462 patients in whom crushed autogenous cartilage grafts were used, selected from a total of 669 patients in whom rhinoplasty procedures were performed at our institution between June 1, 1999, and June 1, 2006. The grafts were used as slightly, moderately, significantly, or severely crushed. ::: ::: ::: RESULTS ::: Eight hundred nine cartilage grafts (41 slightly crushed grafts [5%], 650 moderately crushed grafts [80%], and 118 significantly crushed grafts [15%]) were used in 462 patients. Resorption occurred in 11 of the 462 patients (2.4%). All of the resorbed grafts (6 moderately crushed grafts and 5 significantly crushed grafts) had been placed in the dorsal area. The resorption rate of those grafts was lower in the moderately crushed cartilage grafts (6 of 284 grafts [2.1%]) than in the significantly crushed grafts (5 of 38 grafts [13.1%]). There was no resorption of slightly crushed grafts. ::: ::: ::: CONCLUSIONS ::: The degree of crushing applied is important for long-term clinical outcome of autogenous crushed cartilage grafts. Slight or moderate crushing of cartilage creates an outstanding graft material for concealing irregularities and provides both excellent long-term clinical outcome and predictable esthetic results.


Targeting and Privacy in Mobile Advertising


title: Imitation Learning with Recurrent Neural Networks

abstract: We present a novel view that unifies two frameworks that aim to solve sequential prediction problems: learning to search (L2S) and recurrent neural networks (RNN). We point out equivalences between elements of the two frameworks. By complementing what is missing from one framework comparing to the other, we introduce a more advanced imitation learning framework that, on one hand, augments L2S s notion of search space and, on the other hand, enhances RNNs training procedure to be more robust to compounding errors arising from training on highly correlated examples.


Object Detection


title: ZFN, TALEN, and CRISPR/Cas-based methods for genome engineering.

abstract: Zinc-finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs) comprise a powerful class of tools that are redefining the boundaries of biological research. These chimeric nucleases are composed of programmable, sequence-specific DNA-binding modules linked to a nonspecific DNA cleavage domain. ZFNs and TALENs enable a broad range of genetic modifications by inducing DNA double-strand breaks that stimulate error-prone nonhomologous end joining or homology-directed repair at specific genomic locations. Here, we review achievements made possible by site-specific nuclease technologies and discuss applications of these reagents for genetic analysis and manipulation. In addition, we highlight the therapeutic potential of ZFNs and TALENs and discuss future prospects for the field, including the emergence of clustered regulatory interspaced short palindromic repeat (CRISPR)/Cas-based RNA-guided DNA endonucleases.


title: On cortical coding of vocal communication sounds in primates

abstract: Understanding how the brain processes vocal communication sounds is one of the most challenging problems in neuroscience. Our understanding of how the cortex accomplishes this unique task should greatly facilitate our understanding of cortical mechanisms in general. Perception of species-specific communication sounds is an important aspect of the auditory behavior of many animal species and is crucial for their social interactions, reproductive success, and survival. The principles of neural representations of these behaviorally important sounds in the cerebral cortex have direct implications for the neural mechanisms underlying human speech perception. Our progress in this area has been relatively slow, compared with our understanding of other auditory functions such as echolocation and sound localization. This article discusses previous and current studies in this field, with emphasis on nonhuman primates, and proposes a conceptual platform to further our exploration of this frontier. It is argued that the prerequisite condition for understanding cortical mechanisms underlying communication sound perception and production is an appropriate animal model. Three issues are central to this work: (i) neural encoding of statistical structure of communication sounds, (ii) the role of behavioral relevance in shaping cortical representations, and (iii) sensory-motor interactions between vocal production and perception systems.


title: Emerging Robotic Platforms for Minimally Invasive Surgery

abstract: Recent technological advances in surgery have resulted in the development of a range of new techniques that have reduced patient trauma, shortened hospitalization, and improved diagnostic accuracy and therapeutic outcome. Despite the many appreciated benefits of minimally invasive surgery (MIS) compared to traditional approaches, there are still significant drawbacks associated with conventional MIS including poor instrument control and ergonomics caused by rigid instrumentation and its associated fulcrum effect. The use of robot assistance has helped to realize the full potential of MIS with improved consistency, safety and accuracy. The development of articulated, precision tools to enhance the surgeon's dexterity has evolved in parallel with advances in imaging and human-robot interaction. This has improved hand-eye coordination and manual precision down to micron scales, with the capability of navigating through complex anatomical pathways. In this review paper, clinical requirements and technical challenges related to the design of robotic platforms for flexible access surgery are discussed. Allied technical approaches and engineering challenges related to instrument design, intraoperative guidance, and intelligent human-robot interaction are reviewed. We also highlight emerging designs and research opportunities in the field by assessing the current limitations and open technical challenges for the wider clinical uptake of robotic platforms in MIS.


title: Alternating Multi-bit Quantization for Recurrent Neural Networks

abstract: Recurrent neural networks have achieved excellent performance in many applications. However, on portable devices with limited resources, the models are often too large to deploy. For applications on the server with large scale concurrent requests, the latency during inference can also be very critical for costly computing resources. In this work, we address these problems by quantizing the network, both weights and activations, into multiple binary codes {-1,+1}. We formulate the quantization as an optimization problem. Under the key observation that once the quantization coefficients are fixed the binary codes can be derived efficiently by binary search tree, alternating minimization is then applied. We test the quantization for two well-known RNNs, i.e., long short term memory (LSTM) and gated recurrent unit (GRU), on the language models. Compared with the full-precision counter part, by 2-bit quantization we can achieve ~16x memory saving and ~6x real inference acceleration on CPUs, with only a reasonable loss in the accuracy. By 3-bit quantization, we can achieve almost no loss in the accuracy or even surpass the original model, with ~10.5x memory saving and ~3x real inference acceleration. Both results beat the exiting quantization works with large margins. We extend our alternating quantization to image classification tasks. In both RNNs and feedforward neural networks, the method also achieves excellent performance.


title: Cultural Humility Versus Cultural Competence: A Critical Distinction in Defining Physician Training Outcomes in Multicultural Education

abstract: Researchers and program developers in medical education presently face the challenge of implementing and evaluating curricula that teach medical students and house staff how to effectively and respectfully deliver health care to the increasingly diverse populations of the United States. Inherent in this challenge is clearly defining educational and training outcomes consistent with this imperative. The traditional notion of competence in clinical training as a detached mastery of a theoretically finite body of knowledge may not be appropriate for this area of physician education. Cultural humility is proposed as a more suitable goal in multicultural medical education. Cultural humility incorporates a lifelong commitment to self-evaluation and self-critique, to redressing the power imbalances in the patient-physician dynamic, and to developing mutually beneficial and nonpaternalistic clinical and advocacy partnerships with communities on behalf of individuals and defined populations.


title: An analysis of rogue AV campaigns

abstract: Rogue antivirus software has recently received extensive attention, justified by the diffusion and efficacy of its propagation. We present a longitudinal analysis of the rogue antivirus threat ecosystem, focusing on the structure and dynamics of this threat and its economics. To that end, we compiled and mined a large dataset of characteristics of rogue antivirus domains and of the servers that host them. ::: ::: The contributions of this paper are threefold. Firstly, we offer the first, to our knowledge, broad analysis of the infrastructure underpinning the distribution of rogue security software by tracking 6,500 malicious domains. Secondly, we show how to apply attack attribution methodologies to correlate campaigns likely to be associated to the same individuals or groups. By using these techniques, we identify 127 rogue security software campaigns comprising 4,549 domains. Finally, we contextualize our findings by comparing them to a different threat ecosystem, that of browser exploits. We underline the profound difference in the structure of the two threats, and we investigate the root causes of this difference by analyzing the economic balance of the rogue antivirus ecosystem. We track 372,096 victims over a period of 2 months and we take advantage of this information to retrieve monetization insights. While applied to a specific threat type, the methodology and the lessons learned from this work are of general applicability to develop a better understanding of the threat economies.


title: A Review of Online Advertising Effects on the User Experience

abstract: This article reviews empirical research conducted in the last decade on the subject of how online display advertising affects the usability and quality of user experience of websites. In particular, from an in-depth analysis of research questions, methods, and findings of the reviewed studies, the following is discussed: (a) which conceptual and theoretical background knowledge, based on psychological explanations of user cognition, affection and behavior, can best support the design and investigation of online advertising, and (b) which specific adverts features and properties are key to understand and favor certain types of effects on users. By capitalizing on this benchmark knowledge on benefits of adverts and their hidden costs, web researchers and practitioners are encouraged to approach online advertising from a deeper and more comprehensive perspective, which is centered on qualities of web interaction that go beyond traditional usability factors. It is speculated that many of the theories and mode...


title: Learning Task Grouping and Overlap in Multi-task Learning

abstract: In the paradigm of multi-task learning, multiple related prediction tasks are learned jointly, sharing information across the tasks. We propose a framework for multi-task learning that enables one to selectively share the information across the tasks. We assume that each task parameter vector is a linear combination of a finite number of underlying basis tasks. The coefficients of the linear combination are sparse in nature and the overlap in the sparsity patterns of two tasks controls the amount of sharing across these. Our model is based on the assumption that task parameters within a group lie in a low dimensional subspace but allows the tasks in different groups to overlap with each other in one or more bases. Experimental results on four datasets show that our approach outperforms competing methods.


title: Video eCommerce: Towards Online Video Advertising

abstract: The prevalence of online videos provides an opportunity for e-commerce companies to exhibit their product ads in videos by recommendation. In this paper, we propose an advertising system named Video eCommerce to exhibit appropriate product ads to particular users at proper time stamps of videos, which takes into account video semantics, user shopping preference and viewing behavior feedback by a two-level strategy. At the first level, Co-Relation Regression (CRR) model is novelly proposed to construct the semantic association between keyframes and products. Heterogeneous information network (HIN) is adopted to build the user shopping preference from two different e-commerce platforms, Tmall and MagicBox, which alleviates the problems of data sparsity and cold start. In addition, Video Scene Importance Model (VSIM) utilizes the viewing behavior of users to embed ads at the most attractive position within the video stream. At the second level, taking the results of CRR, HIN and VSIM as the input, Heterogeneous Relation Matrix Factorization (HRMF) is applied for product advertising. Extensive evaluation on a variety of online videos from Tmall MagicBox demonstrates that Video eCommerce achieves promising performance, which significantly outperforms the state-of-the-art advertising methods.


title: Visual analytics of movement: An overview of methods, tools and procedures

abstract: Analysis of movement is currently a hot research topic in visual analytics. A wide variety of methods and tools for analysis of movement data has been developed in recent years. They allow analysts to look at the data from different perspectives and fulfil diverse analytical tasks. Visual displays and interactive techniques are often combined with computational processing, which, in particular, enables analysis of a larger number of data than would be possible with purely visual methods. Visual analytics leverages methods and tools developed in other areas related to data analytics. particularly statistics, machine learning and geographic information science. We present an illustrated structured survey of the state of the art in visual analytics concerning the analysis of movement data. Besides reviewing the existing works, we demonstrate, using examples. how different visual analytics techniques can support our understanding of various aspects of movement.


title: Integrated anchor and social link predictions across social networks

abstract: To enjoy more social network services, users nowadays are usually involved in multiple online social media sites at the same time. Across these social networks, users can be connected by both intranetwork links (i.e., social links) and inter-network links (i.e., anchor links) simultaneously. In this paper, we want to predict the formation of social links among users in the target network as well as anchor links aligning the target network with other external social networks. The problem is formally defined as the "collective link identification" problem. To solve the collective link identification problem, a unified link prediction framework, CLF (Collective Link Fusion) is proposed in this paper, which consists of two phases: step (1) collective link prediction of anchor and social links, and step (2) propagation of predicted links across the partially aligned "probabilistic networks" with collective random walk. Extensive experiments conducted on two real-world partially aligned networks demonstrate that CLF can perform very well in predicting social and anchor links concurrently.


title: Ordering-Based Search: A Simple and Effective Algorithm for Learning Bayesian Networks

abstract: One of the basic tasks for Bayesian networks (BNs) is that of learning a network structure from data. The BN-learning problem is NP-hard, so the standard solution is heuristic search. Many approaches have been proposed for this task, but only a very small number outperform the baseline of greedy hill-climbing with tabu lists; moreover, many of the proposed algorithms are quite complex and hard to implement. In this paper, we propose a very simple and easy-to-implement method for addressing this task. Our approach is based on the well-known fact that the best network (of bounded in-degree) consistent with a given node ordering can be found very efficiently. We therefore propose a search not over the space of structures, but over the space of orderings, selecting for each ordering the best network consistent with it. This search space is much smaller, makes more global search steps, has a lower branching factor, and avoids costly acyclicity checks. We present results for this algorithm on both synthetic and real data sets, evaluating both the score of the network found and in the running time. We show that ordering-based search outperforms the standard baseline, and is competitive with recent algorithms that are much harder to implement.


title: Exact clothing retrieval approach based on deep neural network

abstract: In recent years, the trend of shopping for clothing on an onscreen character has been raising. Unfortunately, clothes that appear on screen could be difficult to track down, much less to purchase directly. It is a challenging task to find out exactly what the clothing in a TV show is from massive amounts of clothing items in online clothing retails due to the clearly noticeable visual discrepancies of the same clothing in video and in online shop photos. In order to tackle the problem, we adopt the deep neural network to learn recognizable clothing-specific feature for finding the exact clothing item in the video scenario among a large collection of online shop images. The experiments shows the clothing-specific feature is more effective in retrieval task and color filtering improve the retrieval performance.


title: An Example-Based Japanese Proofreading System for Offshore Development

abstract: Yuchang CHENG Tomoki NAGASE Speech & Language Technologies Laboratory of Media Processing Systems Laboratories FUJITSU LABORATORIES LTD. 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan cheng.yuchang@jp.fujitsu.com, nagase.tomoki@jp.fujitsu.com ABSTRACT More than 70% of Japanese IT companies are engaged in the offshore development of their products in China. However, a decrease in the quality of the accompanying Japanese engineering documentation has become a serious problem due to errors in Japanese grammar. A proofreading system is therefore required for offshore development cases. The goal of this research is to construct an automatic proofreading system for the Japanese language that can be used in offshore development. We considered an example-based proofreading approach that can effectively use our proofreading corpus and simultaneously process multiple types of error. There are three main steps in the proofreading system. They are the search step, the check step and the replace step. We will make a demostration for the proofreading system and simulated the use of our example-based approach. The results show that using the entire corpus can reduce the errors by over 66%. Title and abstract in another language (Japanese)


title: Personality prediction based on Twitter information in Bahasa Indonesia

abstract: The sheer usage of social media presents an opportunity for an automated analysis of a social media user based on his/her information, activities, or status updates. This opportunity is due to the abundant amount of information shared by the user. This fact is especially true for countries with high number of active social media users such as Indonesia. Extraction of information from social media can yield insightful results if done correctly. Recent studies have managed to leverage associations between language and personality and build a personality prediction system based on those associations. The current study attempts to build a personality prediction system based on a Twitter user's information for Bahasa Indonesia, the native language of Indonesia. The personality prediction system is built on Support Vector Machine and XGBoost trained with 329 instances (users). Evaluation results using 10-fold cross validation shows that the system managed to reach highest average accuracy of 76.2310% with Support Vector Machine and 97.9962% with XGBoost.


title: Dolus: Cyber Defense using Pretense against DDoS Attacks in Cloud Platforms

abstract: Cloud-hosted services are being increasingly used in online businesses in e.g., retail, healthcare, manufacturing, entertainment due to benefits such as scalability and reliability. These benefits are fueled by innovations in orchestration of cloud platforms that make them totally programmable as Software Defined everything Infrastructures (SDxI). At the same time, sophisticated targeted attacks such as Distributed Denial-of-Service (DDoS) are growing on an unprecedented scale threatening the availability of online businesses. In this paper, we present a novel defense system called Dolus to mitigate the impact of DDoS attacks launched against high-value services hosted in SDxI-based cloud platforms. Our Dolus system is able to initiate a 'pretense' in a scalable and collaborative manner to deter the attacker based on threat intelligence obtained from attack feature analysis in a two-stage ensemble learning scheme. Using foundations from pretense theory in child play, Dolus takes advantage of elastic capacity provisioning via 'quarantine virtual machines' and SDxI policy co-ordination across multiple network domains to deceive the attacker by creating a false sense of success. From the time gained through pretense initiation, Dolus enables cloud service providers to decide on a variety of policies to mitigate the attack impact, without disrupting the cloud services experience for legitimate users. We evaluate the efficacy of Dolus using a GENI Cloud testbed and demonstrate its real-time capabilities to: (a) detect DDoS attacks and redirect attack traffic to quarantine resources to engage the attacker under pretense, and (b) coordinate SDxI policies to possibly block DDoS attacks closer to the attack source(s).


title: Transformation of formants for voice conversion using artificial neural networks

abstract: In this paper we propose a scheme for developing a voice conversion system that converts the speech signal uttered by a source speaker to a speech signal having the voice characteristics of the target speaker. In particular, we address the issue of transformation of the vocal tract system features from one speaker to another. Formants are used to represent the vocal tract system features and a formant vocoder is used for synthesis. The scheme consists of a formant analysis phase, followed by a learning phase in which the implicit formant transformation is captured by a neural network. The transformed formants together with the pitch contour modified to suit the average pitch of the target speaker are used to synthesize speech with the desired vocal tract system characteristics.


A 5-Level Three-Phase Cascaded Hybrid Multilevel Inverter


title: Distractions, Distractions: Does Instant Messaging Affect College Students' Performance on a Concurrent Reading Comprehension Task?

abstract: Abstract Instant messaging (IM) has become one of the most popular forms of computer-mediated communication (CMC) and is especially prevalent on college campuses. Previous research suggests that IM users often multitask while conversing online. To date, no one has yet examined the cognitive effect of concurrent IM use. Participants in the present study (N = 69) completed a reading comprehension task uninterrupted or while concurrently holding an IM conversation. Participants who IMed while performing the reading task took significantly longer to complete the task, indicating that concurrent IM use negatively affects efficiency. Concurrent IM use did not affect reading comprehension scores. Additional analyses revealed that the more time participants reported spending on IM, the lower their reading comprehension scores. Finally, we found that the more time participants reported spending on IM, the lower their self-reported GPA. Implications and future directions are discussed.


title: A Meta-Analytic Review of Predictors of Job Performance for Salespeople

abstract: This meta-analysis evaluated predictors of both objective and subjective sales performance. Biodata measures and sales ability inventories were good predictors of the ratings criterion, with corrected rs of .52 and .45, respectively. Potency (a subdimension of the Big 5 personality dimension Extraversion) predicted supervisor ratings of performance (r = .28) and objective measures of sales (r = .26). Achievement (a component of the Conscientiousness dimension) predicted ratings (r = .25) and objective sales (r = .41). General cognitive ability showed a correlation of.40 with ratings but only .04 with objective sales. Similarly, age predicted ratings (r = .26) but not objective sales (r = -.06). On the basis of a small number of studies, interest appears to be a promising predictor of sales success.


title: Improving Fall Detection Using an On-Wrist Wearable Accelerometer

abstract: Fall detection is a very important challenge that affects both elderly people and the carers. Improvements in fall detection would reduce the aid response time. This research focuses on a method for fall detection with a sensor placed on the wrist. Falls are detected using a published threshold-based solution, although a study on threshold tuning has been carried out. The feature extraction is extended in order to balance the dataset for the minority class. Alternative models have been analyzed to reduce the computational constraints so the solution can be embedded in smart-phones or smart wristbands. Several published datasets have been used in the Materials and Methods section. Although these datasets do not include data from real falls of elderly people, a complete comparison study of fall-related datasets shows statistical differences between the simulated falls and real falls from participants suffering from impairment diseases. Given the obtained results, the rule-based systems represent a promising research line as they perform similarly to neural networks, but with a reduced computational cost. Furthermore, support vector machines performed with a high specificity. However, further research to validate the proposal in real on-line scenarios is needed. Furthermore, a slight improvement should be made to reduce the number of false alarms.


title: Spacer-First Damascene-Gate FinFET Architecture Featuring Stringer-Free Integration

abstract: This letter presents a new Damascene-gate FinFET process that inherently suppresses stringers, resulting from gate and spacers patterning. The so-called spacer-first integration scheme relies on the engineering of a hydrogen silsesquioxane layer by electron beam lithography followed by two selective compartmentalized development steps to successively release the Damascene-gate cavity and the source/drain (S/D) contact regions. In contrast to the existing gate-first and gate-last integration approaches, the resulting FinFET process does not impose any restriction or interdependency on the sizing of the fins, gate, spacers, and S/D regions. A complete morphological and electrical validation is proposed in the particular case of wrap-around self-aligned metallic Schottky S/D contacts.


Adaptive PID Controller Based on Reinforcement Learning for Wind Turbine Control


title: Trust Based Recommender System for Semantic Web

abstract: This paper proposes the design of a recommender system that uses knowledge stored in the form of ontologies. The interactions amongst the peer agents for generating recommendations are based on the trust network that exists between them. Recommendations about a product given by peer agents are in the form of Intuitionistic Fuzzy Sets specified using degree of membership, non membership and uncertainty. In literature, the recommender systems use databases to generate recommendations. The presented design uses ontologies, a knowledge representation technique for creating annotated content for Semantic Web. Seeing the potential and popularity of ontologies among researchers, we believe that ontologies will be build and maintained in numerous knowledge domains for the Semantic Web and future applications. The presented recommender system uses temporal ontologies that absorb the effect of changes in the ontologies due to the dynamic nature of domains, in addition to the benefits of ontologies. A case study of tourism recommender system is chosen to generate the recommendations for the selection of destination, travel agents and the flight schedule. A comparison of the generated recommendations with the manual recommendations by peers establishes the validity of the presented recommender system.


title: Fully Supervised Speaker Diarization

abstract: In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN). Given extracted speaker-discriminative embeddings (a.k.a. d-vectors) from input utterances, each individual speaker is modeled by a parameter-sharing RNN, while the RNN states for different speakers interleave in the time domain. This RNN is naturally integrated with a distance-dependent Chinese restaurant process (ddCRP) to accommodate an unknown number of speakers. Our system is fully supervised and is able to learn from examples where time-stamped speaker labels are annotated. We achieved a 7.6% diarization error rate on NIST SRE 2000 CALLHOME, which is better than the state-of-the-art method using spectral clustering. Moreover, our method decodes in an online fashion while most state-of-the-art systems rely on offline clustering.


title: High-directivity single- and dual-band directional couplers based on substrate integrated coaxial line technology

abstract: The high-directivity single- and dual-band directional couplers based on substrate integrated coaxial line (SICL) are first proposed in this paper. As the SICL features the propagation of TEM mode, the SICL directional coupler can keep its high-directivity compared with the microstrip one. The SICL coupler also characterizes a vias-shielded structure keeping it away from lateral leakage, cross-talk or unwanted parallel-plate mode. The design approaches of the single- and dual-band SICL couplers with proper width-spacing are given. Experiments are carried on a SICL 2.35 GHz coupler and a SICL 0.8/2.35 GHz coupler. Compared with microstrip couplers, the SICL ones show a great enhancement in directivity.


title: Natural language annotations for question answering

abstract: This paper presents strategies and lessons learned from the use of natural language annotations to facilitate question answering in the START information access system.


title: Chronic Pain and Suicide: Understanding the Association

abstract: Chronic pain conditions are associated with an elevated risk for suicide. Of particular importance is the question of why pain conditions might be linked to increased suicide risk. We discuss the association between chronic pain and psychological pain, particularly in the context of depression, and the use of suicide as an attempt to escape from what is perceived as unbearable suffering. We also consider the role that chronic pain may play in increasing the capacity for suicide. Bridging across research areas and drawing on the interpersonal-psychological theory of suicide, we suggest that chronic pain may facilitate the development of a key risk factor for suicide: fearlessness about death. Given that chronic pain can lead to (and be exacerbated by) depression, engender hopelessness, facilitate a desire for escape through death, and erode the natural fear of dying, clinicians must be aware of psychological processes that can combine to create elevated suicide risk in patients with chronic pain, and they should also assess and treat suicide risk factors in these patients.


title: Providing all global energy with wind , water , and solar power , Part II : Reliability , system and transmission costs , and policies

abstract: This is Part II of two papers evaluating the feasibility of providing all energy for all purposes (electric power, transportation, and heating/cooling), everywhere in the world, from wind, water, and the sun (WWS). In Part I, we described the prominent renewable energy plans that have been proposed and discussed the characteristics of WWS energy systems, the global demand for and availability of WWS energy, quantities and areas required for WWS infrastructure, and supplies of critical materials. Here, we discuss methods of addressing the variability of WWS energy to ensure that power supply reliably matches demand (including interconnecting geographically dispersed resources, using hydroelectricity, using demand-response management, storing electric power on site, over-sizing peak generation capacity and producing hydrogen with the excess, storing electric power in vehicle batteries, and forecasting weather to project energy supplies), the economics of WWS generation and transmission, the economics of WWS use in transportation, and policy measures needed to enhance the viability of a WWS system. We find that the cost of energy in a 100% WWS will be similar to the cost today. We conclude that barriers to a 100% conversion to WWS power worldwide are primarily social and political, not technological or even economic.


title: Determining the Model Order of Nonlinear Input-Output Systems by Fuzzy Clustering

abstract: Selecting the order of an input-output model of a dynamical system is a key step toward the goal of system identification. By determining the smallest regression vector dimension that allows accurate prediction of the output, the false nearest neighbors algorithm (FNN) is a useful tool for linear and also for nonlinear systems. The one parameter that needs to be determined before performing FNN is the threshold constant that is used to compute the percentage of false neighbors. For this purpose heuristic rules can be followed. However, for nonlinear systems choosing a suitable threshold is extremely important, the optimal choice of this parameter will depend on the system. While this advanced FNN uses nonlinear input-output data based models, the computational effort of the method increases along with the number of data and the dimension of the model. To increase the efficiency of the method this paper proposes the application of a fuzzy clustering algorithm. The advantage of the generated solutions is that it remains in the horizon of the data, hence there is no need to apply nonlinear model identification tools. The efficiency of the algorithm is supported by a data driven identification of a polymerization reactor.


title: Coco-Q: Learning in Stochastic Games with Side Payments

abstract: COCO ("cooperative/competitive") values are a solution concept for two-player normal-form games with transferable utility, when binding agreements and side payments between players are possible. In this paper, we show that COCO values can also be defined for stochastic games and can be learned using a simple variant of Q-learning that is provably convergent. We provide a set of examples showing how the strategies learned by the COCO-Q algorithm relate to those learned by existing multiagent Q-learning algorithms.


title: Iterative Nearest Neighbors for classification and dimensionality reduction

abstract: Representative data in terms of a set of selected samples is of interest for various machine learning applications, e.g. dimensionality reduction and classification. The best-known techniques probably still are k-Nearest Neighbors (kNN) and its variants. Recently, richer representations have become popular. Examples are methods based on l 1 -regularized least squares (Sparse Representation (SR)) or l 2 -regularized least squares (Collaborative Representation (CR)), or on l 1 -constrained least squares (Local Linear Embedding (LLE)). We propose Iterative Nearest Neighbors (INN). This is a novel sparse representation that combines the power of SR and LLE with the computational simplicity of kNN. We test our method in terms of dimensionality reduction and classification, using standard benchmarks such as faces (AR), traffic signs (GTSRB), and PASCAL VOC 2007. INN performs better than NN and comparable with CR and SR, while being orders of magnitude faster than the latter.


title: Very Deep Convolutional Networks for Large-Scale Image Recognition

abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.


title: Message authentication in driverless cars

abstract: Driverless cars and driver-assisted vehicles are becoming prevalent in today's transportation systems. Companies like TESLA, Audi, and Google are among the leaders in this new era of the automotive industry. Modern vehicles, equipped with sophisticated navigation devices, complex driver-assisted systems, and a whole lot of safety features, bring broader impacts to our quality of life, economic development, and environmental sustainability. Instant safety messages such as pre-collision warnings, blind-spot detection, pedestrian and object awareness significantly improve the safety for drivers, passengers, and pedestrians. As a result, vehicles would be able to travel closely yet safely together, forming a platoon, thus resulting in a reduction of traffic congestion and fuel consumption. Driverless cars also have non-safety-related applications: which are used to facilitate traffic management and infotainment dissemination for drivers and passengers. Vehicular Ad hoc Network (VANET), the wireless communication technology enabling driverless cars, features not only dynamic topology but also high mobility. The vehicle nodes move rapidly on streets and highways. Their movements also depend on but not limited to road traffic, speed limits, and behavior of nearby vehicles. With massive amount of messages exchanged among driverless cars that command the cars' movements at high speeds and in close distances, any malicious alternation could lead to disastrous accidents. Message authentication to ensure data integrity is paramount to attack preparation. This paper presents a novel message authentication scheme that protects cars from bogus messages and makes VANET resilient to Denial-of-Service (DoS) attacks. The work includes a simulation framework that integrates vehicle and data traffic models to validate the effectiveness of the proposed message authentication scheme.


title: Bayesian factorization and learning for monaural source separation

abstract: This paper presents a new Bayesian nonnegative matrix factorization (NMF) for monaural source separation. Using this approach, the reconstruction error based on NMF is represented by a Poisson distribution, and the NMF parameters, consisting of the basis and weight matrices, are characterized by the exponential priors. A variational Bayesian inference procedure is developed to learn variational parameters and model parameters. The randomness in separation process is faithfully represented so that the system robustness to model variations in heterogeneous environments could be achieved. Importantly, the exponential prior parameters are used to impose sparseness in basis representation. The variational lower bound of log marginal likelihood is adopted as the objective to control model complexity. The dependencies of variational objective on model parameters are fully characterized in the derived closed-form solution. A clustering algorithm is performed to find the groups of bases for unsupervised source separation. The experiments on speech/music separation and singing voice separation show that the proposed Bayesian NMF (BNMF) with adaptive basis representation outperforms the NMF with fixed number of bases and the other BNMFs in terms of signal-to-distortion ratio and the global normalized source to distortion ratio.


title: Selective Facial Nerve Stimulation with an Array Electrode

abstract: Over the years, Neuromuscular Electrical Stimulation has successfully been used for generating muscle contractions for limb movements and rehabilitation. Although single pair of electrodes has been the standard practice for stimulation, recently there have been some researches on the use multi field array electrodes for electrical stimulation. Array electrodes have many advantages in selective innervation of closely distributed bunch of nerves. In this study we have designed a 16 field partially flexible Platinum electrode array. The 28×28 [mm] electrode array, which is suitable for intracranial stimulations, provides a higher selectivity while being unobtrusive. We have been investigating the supporting of expressiveness for the facially paralyzed and during this study we have investigated the use of above array electrode to contract facial muscles, selectively. We have also investigated the possibility of controlling the amount of muscle contraction and simultaneous stimulation of multiple pair of electrodes. In this paper, we present our findings that could pave the way to a successful implementation of externally stimulated facial expressions.


title: Representational and executive selection resources in ‘theory of mind’: Evidence from compromised belief-desire reasoning in old age

abstract: Abstract Effective belief-desire reasoning requires both specialized representational capacities—the capacity to represent the mental states as such—as well as executive selection processes for accurate performance on tasks requiring the prediction and explanation of the actions of social agents. Compromised belief-desire reasoning in a given population may reflect failures in either or both of these systems. We report evidence supporting this two-process model from belief-desire reasoning tasks conducted with younger and older adult populations. When task inferential complexity is held constant, neither group showed specific difficulty with reasoning about mental state content as compared with non-mental state content. However, manipulations that systematically increase executive performance demands within belief-desire reasoning caused systematic decreases in task performance in both older and younger adult groups. Moreover, the effect of increasing executive demands was disproportionately greater in the older group. Regression analysis indicated that measures of processing speed and inhibition contributed most to explaining variance in accuracy and response times in the belief-desire reasoning tasks. These results are consistent with the idea that compromised belief-desire reasoning in old age is likely the result of age-related decline in executive selection skills that supplement core mental state representational abilities, rather than as a result of failures in the representational system itself.


title: 3GPP LTE traffic offloading onto WiFi Direct

abstract: Mobile network operators are struggling to handle the rapidly increasing amounts of data traffic on their networks. As a result, 3GPP is currently investigating possible scenarios for device-to-device (D2D) offloading in LTE networks. However, it remains unclear what kinds of gains can be expected from in-band D2D solutions. By comparison, the already available WiFi Direct D2D technology enables offloading onto unlicensed bands at data rates generally higher than infrastructure cellular links. In this paper1, we study the performance of WiFi Direct as a prominent technology for D2D communications in urban environments. We also discuss some of the potential performance gains for WiFi Direct communications in the presence of 3GPP LTE network-level management. Our extensive simulation study of LTE traffic offloading onto WiFi Direct covers a range of D2D loads and interference levels. We focus on the case where the D2D overlay network is assisted by the cellular infrastructure network; or more specifically, where the cellular network supplies its clients with service discovery information to facilitate the establishment of D2D sessions.


title: MATLAB for Signal Processing on Multiprocessors and Multicores

abstract: MATLAB is a popular choice for algorithm development in signal and image processing. While traditionally done using sequential MATLAB running on desktop systems, in recent years there has been a surge of interest in running MATLAB in parallel to take advantage of multiprocessor and multicore systems. In this article, we discuss three variations of multiprocessor parallel MATLAB, two of which are available as commercial, supported products. We also consider running MATLAB with key computations speeded up using multithreaded computations on multicore general-purpose graphical processing units (GPGPUs). Two signal processing kernels (fast Fourier transform (FFT) and convolution) and two full applications [synthetic aperture radar (SAR) imaging and superconducting quantum interference devices (SQIF)] are used to illustrate the use of parallel MATLAB.


title: Approximation and Convergence Properties of Generative Adversarial Learning

abstract: Generative adversarial networks (GAN) approximate a target data distribution by jointly optimizing an objective function through a "two-player game" between a generator and a discriminator. Despite their empirical success, however, two very basic questions on how well they can approximate the target distribution remain unanswered. First, it is not known how restricting the discriminator family affects the approximation quality. Second, while a number of different objective functions have been proposed, we do not understand when convergence to the global minima of the objective function leads to convergence to the target distribution under various notions of distributional convergence. ::: In this paper, we address these questions in a broad and unified setting by defining a notion of adversarial divergences that includes a number of recently proposed objective functions. We show that if the objective function is an adversarial divergence with some additional conditions, then using a restricted discriminator family has a moment-matching effect. Additionally, we show that for objective functions that are strict adversarial divergences, convergence in the objective function implies weak convergence, thus generalizing previous results.


A Data Sorting and Searching Scheme Based on Distributed Asymmetric Searchable Encryption


title: The Dark Triad and Interpersonal Perception: Similarities and Differences in the Social Consequences of Narcissism, Machiavellianism, and Psychopathy

abstract: The subclinical Dark Triad traits narcissism, Machiavellianism, and psychopathy (Paulhus & Williams, 2002) are related to antagonistic behaviors in interpersonal situations. The current study addresses whether these three traits entail different social consequences by investigating self-ratings, ratings of others, and ratings by others for the Dark Triad. In a naturalistic setting, 93 informal, minimally acquainted student dyads worked briefly on a cooperative task and subsequently provided self- and other- ratings on the Big Five and intelligence, self-ratings on the Dark Triad, and ratings on properties of the interaction. Overall, narcissism, Machiavellianism, and psychopathy manifested differently: profile analyses indicated that Machiavellians diverged from narcissists and psychopaths in self-ratings, ratings of others, and ratings by others, while narcissists and psychopaths converged to a moderate degree. Findings are discussed regarding the distinction of the Dark Triad traits.


title: Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

abstract: We propose a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization. Our method iteratively transports a set of particles to match the target distribution, by applying a form of functional gradient descent that minimizes the KL divergence. Empirical studies are performed on various real world models and datasets, on which our method is competitive with existing state-of-the-art methods. The derivation of our method is based on a new theoretical result that connects the derivative of KL divergence under smooth transforms with Stein's identity and a recently proposed kernelized Stein discrepancy, which is of independent interest.


title: Support Vector Regression

abstract: Instead of minimizing the observed training error, Support Vector Regression (SVR) attempts to minimize the generalization error bound so as to achieve generalized performance. The idea of SVR is based on the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. SVR has been applied in various fields - time series and financial (noisy and risky) prediction, approximation of complex engineering analyses, convex quadratic programming and choices of loss functions, etc. In this paper, an attempt has been made to review the existing theory, methods, recent developments and scopes of SVR.


title: Textureless object detection using cumulative orientation feature

abstract: We propose a novel image feature for textureless object detection. The feature is based on quantized gradient orientations those have been shown to be robust to cluttered backgrounds and illumination changes. We make this feature robust to the appearance changes of a targeted object itself induced by its transformations and small deformations. In our proposed method, we add small random values to the similarity transformation parameters and synthesize many model images. Then quantized orientations are extracted on these images and the orientations are cumulated at each pixel. The frequencies of selected features are utilized as weights when calculating scores. Our proposed feature is evaluated on publicly available dataset and achieve top-class performance both in speed and detection accuracy compared to state-of-the-art techniques.


title: Scapular dyskinesis: the surgeon’s perspective

abstract: The scapula fulfils many roles to facilitate optimal function of the shoulder. Normal function of the shoulder joint requires a scapula that can be properly aligned in multiple planes of motion of the upper extremity. Scapular dyskinesis, meaning abnormal motion of the scapula during shoulder movement, is a clinical finding commonly encountered by shoulder surgeons. It is best considered an impairment of optimal shoulder function. As such, it may be the underlying cause or the accompanying result of many forms of shoulder pain and dysfunction. The present review looks at the causes and treatment options for this indicator of shoulder pathology and aims to provide an overview of the management of disorders of the scapula.


title: A corpus of Australian contract language

abstract: Written contracts are a fundamental framework for economic and cooperative transactions in society. Little work has been reported on the application of natural language processing or corpus linguistics to contracts. In this paper we report the design, profiling and initial analysis of a corpus of Australian contract language. This corpus enables a quantitative and qualitative characterisation of Australian contract language as an input to the development of contract drafting tools. Profiling of the corpus is consistent with its suitability for use in language engineering applications. We provide descriptive statistics for the corpus and show that document length and document vocabulary size approximate to log normal distributions. The corpus conforms to Zipf's law and comparative type to token ratios are consistent with lower term sparsity (an expectation for legal language). We highlight distinctive term usage in Australian contract language. Results derived from the corpus indicate a longer prepositional phrase depth in sentences in contract rules extracted from the corpus, as compared to other corpora.


title: Falls and consequent injuries in hospitalized patients: effects of an interdisciplinary falls prevention program

abstract: BackgroundPatient falls in hospitals are common and may lead to negative outcomes such as injuries, prolonged hospitalization and legal liability. Consequently, various hospital falls prevention programs have been implemented in the last decades. However, most of the programs had no sustained effects on falls reduction over extended periods of time.MethodsThis study used a serial survey design to examine in-patient fall rates and consequent injuries before and after the implementation of an interdisciplinary falls prevention program (IFP) in a 300-bed urban public hospital. The population under study included adult patients, hospitalized in the departments of internal medicine, geriatrics, and surgery. Administrative patient data and fall incident report data from 1999 to 2003 were examined and summarized using frequencies, proportions, means and standard deviations and were analyzed accordingly.ResultsA total of 34,972 hospitalized patients (mean age: 67.3, SD ± 19.3 years; female 53.6%, mean length of stay: 11.9 ± 13.2 days, mean nursing care time per day: 3.5 ± 1.4 hours) were observed during the study period. Overall, a total of 3,842 falls affected 2,512 (7.2%) of the hospitalized patients. From these falls, 2,552 (66.4%) were without injuries, while 1,142 (29.7%) falls resulted in minor injuries, and 148 (3.9%) falls resulted in major injuries. The overall fall rate in the hospitals' patient population was 8.9 falls per 1,000 patient days. The fall rates fluctuated slightly from 9.1 falls in 1999 to 8.6 falls in 2003. After the implementation of the IFP, in 2001 a slight decrease to 7.8 falls per 1,000 patient days was observed (p = 0.086). The annual proportion of minor and major injuries did not decrease after the implementation of the IFP. From 1999 to 2003, patient characteristics changed in terms of slight increases (female gender, age, consumed nursing care time) or decreases (length of hospital stay), as well as the prevalence of fall risk factors increased up to 46.8% in those patients who fell.ConclusionFollowing the implementation of an interdisciplinary falls prevention program, neither the frequencies of falls nor consequent injuries decreased substantially. Future studies need to incorporate strategies to maximize and evaluate ongoing adherence to interventions in hospital falls prevention programs.


title: Big Data Computing and Clouds: Challenges, Solutions, and Future Directions

abstract: This paper discusses approaches and environments for carrying out analytics on Clouds for Big Data applications. It revolves around four important areas of analytics and Big Data, namely (i) data management and supporting architectures; (ii) model development and scoring; (iii) visualisation and user interaction; and (iv) business models. Through a detailed survey, we identify possible gaps in technology and provide recommendations for the research community on future directions on Cloud-supported Big Data computing and analytics solutions.


title: Blockchained Post-Quantum Signatures

abstract: Inspired by the blockchain architecture and existing Merkle tree based signature schemes, we propose BPQS, an extensible post-quantum (PQ) resistant digital signature scheme best suited to blockchain and distributed ledger technologies (DLTs). One of the unique characteristics of the protocol is that it can take advantage of application-specific chain/graph structures in order to decrease key generation, signing and verification costs as well as signature size. Compared to recent improvements in the field, BPQS outperforms existing hash-based algorithms when a key is reused for reasonable numbers of signatures, while it supports a fallback mechanism to allow for a practically unlimited number of signatures if required. We provide an open source implementation of the scheme and benchmark it.


Sampling Generative Networks: Notes on a Few Effective Techniques


title: Sleep and rest facilitate auditory learning

abstract: Sleep is superior to waking for promoting performance improvements between sessions of visual perceptual and motor learning tasks. Few studies have investigated possible effects of sleep on auditory learning. A key issue is whether sleep specifically promotes learning, or whether restful waking yields similar benefits. According to the "interference hypothesis," sleep facilitates learning because it prevents interference from ongoing sensory input, learning and other cognitive activities that normally occur during waking. We tested this hypothesis by comparing effects of sleep, busy waking (watching a film) and restful waking (lying in the dark) on auditory tone sequence learning. Consistent with recent findings for human language learning, we found that compared with busy waking, sleep between sessions of auditory tone sequence learning enhanced performance improvements. Restful waking provided similar benefits, as predicted based on the interference hypothesis. These findings indicate that physiological, behavioral and environmental conditions that accompany restful waking are sufficient to facilitate learning and may contribute to the facilitation of learning that occurs during sleep.


title: Searching in metric spaces

abstract: The problem of searching the elements of a set that are close to a given query element under some similarity criterion has a vast number of applications in many branches of computer science, from pattern recognition to textual and multimedia information retrieval. We are interested in the rather general case where the similarity criterion defines a metric space, instead of the more restricted case of a vector space. Many solutions have been proposed in different areas, in many cases without cross-knowledge. Because of this, the same ideas have been reconceived several times, and very different presentations have been given for the same approaches. We present some basic results that explain the intrinsic difficulty of the search problem. This includes a quantitative definition of the elusive concept of "intrinsic dimensionality." We also present a unified view of all the known proposals to organize metric spaces, so as to be able to understand them under a common framework. Most approaches turn out to be variations on a few different concepts. We organize those works in a taxonomy that allows us to devise new algorithms from combinations of concepts not noticed before because of the lack of communication between different communities. We present experiments validating our results and comparing the existing approaches. We finish with recommendations for practitioners and open questions for future development.


An Annotated Corpus and Method for Analysis of Ad-Hoc Structures Embedded in Text.


title: Accelerating CNN inference on FPGAs: A Survey

abstract: Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of problems, ranging from speech recognition to image classification and segmentation. The large amount of processing required by CNNs calls for dedicated and tailored hardware support methods. Moreover, CNN workloads have a streaming nature, well suited to reconfigurable hardware architectures such as FPGAs. The amount and diversity of research on the subject of CNN FPGA acceleration within the last 3 years demonstrates the tremendous industrial and academic interest. This paper presents a state-of-the-art of CNN inference accelerators over FPGAs. The computational workloads, their parallelism and the involved memory accesses are analyzed. At the level of neurons, optimizations of the convolutional and fully connected layers are explained and the performances of the different methods compared. At the network level, approximate computing and datapath optimization methods are covered and state-of-the-art approaches compared. The methods and tools investigated in this survey represent the recent trends in FPGA CNN inference accelerators and will fuel the future advances on efficient hardware deep learning.


title: Videos as Space-Time Region Graphs

abstract: How do humans recognize the action"opening a book"? We argue that there are two important cues: modeling temporal shape dynamics and modeling functional relationships between humans and objects. In this paper, we propose to represent videos as space-time region graphs which capture these two important cues. Our graph nodes are defined by the object region proposals from different frames in a long range video. These nodes are connected by two types of relations: (i) similarity relations capturing the long range dependencies between correlated objects and (ii) spatial-temporal relations capturing the interactions between nearby objects. We perform reasoning on this graph representation via Graph Convolutional Networks. We achieve state-of-the-art results on both Charades and Something-Something datasets. Especially for Charades, we obtain a huge 4.4% gain when our model is applied in complex environments.


title: Future directions in rolebased access control models

abstract: In the past five years there has been tremendous activity in role-based access control (RBAC) models. Consensus has been achieved on a standard core RBAC model that is in process of publication by the US National Institute of Standards and Technology (NIST). An early insight was that RBAC cannot be encompassed by a single model since RBAC concepts range from very simple to very sophisticated. Hence a family of models is more appropriate than a single model. The NIST model reflects this approach. In fact RBAC is an open-ended concept which can be extended in many different directions as new applications and systems arise. The consensus embodied in the NIST model is a substantial achievement. All the same it just a starting point. There are important aspects of RBAC models, such as administration of RBAC, on which consensus remains to be reached. Recent RBAC models have studied newer concepts such as delegation and personalization, which are not captured in the NIST model. Applications of RBAC in workflow management systems have been investigated by several researchers. Research on RBAC systems that cross organizational boundaries has also been initiated. Thus RBAC models remain a fertile area for future research. In this paper we discuss some of the directions which we feel are likely to result in practically useful enhancements to the current state of art in RBAC models.


title: Building a Sustainable Career: The Role of Work-home Balance in Career Decision Making

abstract: Several economic, organizational and societal trends in the new work environment have emphasized the need for contemporary employees to take charge and build sustainable careers in which they remain employable, healthy and happy over the long term. To do so, employees need to proactively shape their own career trajectory through a series of important career decisions, such as the decision to move upwards, to become self-employed or to start working part-time. According to contemporary career theories, such as the kaleidoscope career, a key driver of career decisions is work–home balance. To date, however, it remains unclear how work–home balance drives career decisions. To get an insight into this process, we have developed a model explaining how employees’ experiences of low or high work–home balance induce feelings of frustration or satisfaction respectively, which stimulates employees to make different career decisions focusing on a reduction of job demands and/or an increment of job resources. These different career decisions, in turn, influence the sustainability of careers by impacting employees’ health and employability.


title: Trust as an Organizing Principle

abstract: Although research on trust in an organizational context has advanced considerably in recent years, the literature has yet to produce a set of generalizable propositions that inform our understanding of the organization and coordination of work. We propose that conceptualizing trust as an organizing principle is a powerful way of integrating the diverse trust literature and distilling generalizable implications for how trust affects organizing. We develop the notion of trust as an organizing principle by specifying structuring and mobilizing as two sets of causal pathways through which trust influences several important properties of organizations. We further describe specific mechanisms within structuring and mobilizing that influence interaction patterns and organizational processes. The principal aim of the framework is to advance the literature by connecting the psychological and sociological micro-foundations of trust with the macro-bases of organizing. The paper concludes by demonstrating how the framework can be applied to yield novel insights into traditional views of organizations and to stimulate original and innovative avenues of organizational research that consider both the benefits and downsides of trust.


title: A Ku-Band Planar Antenna Array for Mobile Satellite TV Reception With Linear Polarization

abstract: We present a high gain linearly polarized Ku-band planar array for mobile satellite TV reception. In contrast with previously presented three dimensional designs, the approach presented here results in a low profile planar array with a similar performance. The elevation scan is performed electronically, whereas the azimuth scan is done mechanically using an electric motor. The incident angle of the arriving satellite signal is generally large, varying between 25° to 65° depending on the location of the receiver, thereby creating a considerable off-axis scan loss. In order to alleviate this problem, and yet maintaining a planar design, the antenna array is designed to be consisting of subarrays with a fixed scanned beam at 45°. Therefore, the array of fixed-beam subarrays needs to be scanned ±20° around their peak beam, which results in a higher combined gain/directivity. The proposed antenna demonstrates the minimum measured gain of 23.1 dBi throughout the scan range (for 65° scan) with the peak gain of 26.5 dBi (for 32° scan) at 12 GHz while occupying a circular aperture of 26 cm in diameter.


title: Video similarity detection using fixed-length Statistical Dominant Colour Profile (SDCP) signatures

abstract: This paper presents a fast and effective technique for videos’ visual similarity detection and measurement using compact fixed-length signatures. The proposed technique facilitates for building real-time and scalable video matching/retrieval systems through generating a representative signature for a given video shot. The generated signature (Statistical Dominant Colour Profile, SDCP) effectively encodes the colours’ spatio-temporal patterns in a given shot, towards a robust real-time matching. Furthermore, the SDCP signature is engineered to better address the visual similarity problem, through its relaxed representation of shot contents. The compact fixed-length aspect of the proposed signature is the key to its high matching speed (>1000 fps) compared to the current techniques that relies on exhaustive processing, such as dense trajectories. The SDCP signature encodes a given video shot with only 294 values, regardless of the shot length, which facilitates for speedy signature extraction and matching. To maximize the benefit of the proposed technique, compressed-domain videos are utilized as a case study following their wide availability. However, the proposed technique avoids full video decompression and operates on tiny frames, rather than full-size decompressed frames. This is achievable through using the tiny DC-images sequence of the MPEG compressed stream. The experiments on various standard and challenging datasets (e.g. UCF101 13k videos) shows the technique’s robust performance, in terms of both, retrieval ability and computational performances.


Chinese word segmentation


Supplier Selection Criteria and Methods in Supply Chains: A Review


title: Analysis, Design, and Implementation of an APWM ZVZCS Full-Bridge DC–DC Converter for Battery Charging in Electric Vehicles

abstract: An efficient power converter system plays a significant role in the design of battery charging systems for electric vehicles (EVs). In this paper, a new zero-voltage and zero-current switching (ZVZCS) full-bridge dc–dc converter is proposed to reduce the power conversion losses. The proposed converter incorporates a new asymmetrical pulse width modulation (APWM) gating technique for the dc–dc conversion stage in the battery charging system. The proposed dc–dc converter topology achieves zero-voltage switching (ZVS) for all the active switches and near zero-current switching (ZCS) for low-side active switches throughout the charging range of the battery. The proposed APWM technique can reduce the switching and conduction losses compared to the conventional phase-shift modulation (PSM) gating technique. The auxiliary inductance required to ensure ZVS with APWM can also be reduced compared to PSM. Analysis, design, and implementation of the proposed APWM ZVZCS full-bridge dc-dc converter are discussed in this paper. A 100-kHz 1.2-kW laboratory prototype is developed and the experimental results are presented. The results validate the analysis and performance of the proposed converter.


title: Olfactory bulb atrophy in migraine patients

abstract: Objective ::: Osmophobia and headache triggered by odors are commonly seen in migraine, and these are symptoms that differentiate migraine from other primary headaches. Since these odor-related symptoms are disease-specific, we aimed to measure the volume of olfactory bulb and depth of olfactory sulcus in migraine patients.


title: Integrated STEM Education: A Systematic Review of Instructional Practices in Secondary Education.

abstract: The shortage of graduates in Science, Technology, Engineering and Mathematics (STEM), has led to numerous attempts to increase students’ interest in STEM. One emerging approach that has the potential to improve students’ motivation for STEM is integrated STEM education. Nonetheless, the implementation of this new instructional strategy is not straightforward due to the lack of consensus about instructional practices in integrated STEM. This paper contributes to this challenge by providing a well-defined framework for instructional practices in integrated STEM in secondary education, based on the results of a systematic review of existing literature. The framework contains five key principles: integration of STEM content, problem-centered learning, inquiry-based learning, design-based learning and cooperative learning. The proposed framework has several benefits, including its applicability in the classroom and the possibility to describe integrated STEM on multiple dimensions. Nonetheless, further research is necessary to investigate the effects of integrated STEM on students’ cognitive and affective learning outcomes.


title: Stay on the Wikipedia task: When task-related disagreements slip into personal and procedural conflicts

abstract: In Wikipedia, volunteers collaboratively author encyclopedic entries, and therefore managing conflict is a key factor in group success. Behavioral research describes 3 conflict types: task‐related, affective, and process. Affective and process conflicts have been consistently found to impede group performance; however, the effect of task conflict is inconsistent. We propose that these inconclusive results are due to underspecification of the task conflict construct, and focus on the transition phase where task‐related disagreements escalate into affective and process conflict. We define these transitional phases as distinct constructs—task‐affective and task‐process conflict—and develop a theoretical model that explains how the various task‐related conflict constructs, together with the composition of the wiki editor group, determine the quality of the collaboratively authored wiki article. Our empirical study of 96 Wikipedia articles involved multiple data‐collection methods, including analysis of Wikipedia system logs, manual content analysis of articles' discussion pages, and a comprehensive assessment of articles' quality using the Delphi method. Our results show that when group members' disagreements—originally task related—escalate into personal attacks or hinge on procedure, these disagreements impede group performance. Implications for research and practice are discussed.


title: Position control on center of mass for a power lines inspection robot

abstract: In order to implement autonomous inspection of a robot with self-balance dual-arm mechanism, the position of the robot's center of mass must be controlled precisely. This paper analyzes the capability of attitude control and self-balancing control when the robot is operating on power lines, and proposes a mathematical relationship between the robot's attitude and its center of mass by combining the forward kinematics model with the equations of multibody system's center of mass. Besides, the solving equation of the robot's center of mass position is derived, whose inverse solution determines the configuration of the self-balance adjusting mechanism under desired position of the robot's center of mass. Meanwhile, the relationship between the angle of lifting-arm and the robot's center of mass are obtained using the lever principle. Besides, the trajectory planning in the Lifting-arm Mode is also discussed in this paper. And experiment is introduced to validate the reasonability of the center of mass position solving equation and the effectiveness of the control algorithm proposed in this paper.


title: Inductive principles for restricted boltzmann machine learning

abstract: Recent research has seen the proposal of several new inductive principles designed specifically to avoid the problems associated with maximum likelihood learning in models with intractable partition functions. In this paper, we study learning methods for binary restricted Boltzmann machines (RBMs) based on ratio matching and generalized score matching. We compare these new RBM learning methods to a range of existing learning methods including stochastic maximum likelihood, contrastive divergence, and pseudo-likelihood. We perform an extensive empirical evaluation across multiple tasks and data sets.


title: Food Balance Estimation by Using Personal Dietary Tendencies in a Multimedia Food Log

abstract: We have investigated the “FoodLog” multimedia food-recording tool, whereby users upload photographs of their meals and a food diary is constructed using image-processing functions such as food-image detection and food-balance estimation. In this paper, following a brief introduction to FoodLog, we propose a Bayesian framework that makes use of personal dietary tendencies to improve both food-image detection and food-balance estimation. The Bayesian framework facilitates incremental learning. It incorporates three personal dietary tendencies that influence food analysis: likelihood, prior distribution, and mealtime category. In the evaluation of the proposed method using images uploaded to FoodLog, both food-image detection and food-balance estimation are improved. In particular, in the food-balance estimation, the mean absolute error is significantly reduced from 0.69 servings to 0.28 servings on average for two persons using more than 200 personal images, and 0.59 servings to 0.48 servings on average for four persons using 100 personal images. Among the works analyzing food images, this is the first to make use of statistical personal bias to improve the performance of the analysis.


title: A fluxgate magnetic sensor with micro-solenoids and electroplated permalloy cores

abstract: Abstract In this paper, a fluxgate magnetic sensor with silicon micro-technology is presented. The sensor is composed of micro-structured solenoids for the excitation and pick-up coils and permalloy cores formed by electropolating. This device is for a sensing element of the integrated silicon magnetic sensor with high sensitivity and high resolution. Two types of core structures have been fabricated. One is a rod-core sensor based on micromachining technology by using anisotropic etching for the deep groove formation, direct electron-beam lithography for patterning the metal wires on the groove, and selective electroplating for the rod-shaped permalloy core formation in the groove. The other is a thin-film core sensor made by using a similar but simpler process without the groove formation. Relatively high sensitivity and low-offset characteristics have been achieved compared with those of the conventional silicon magnetic sensors.


title: On Learning Invariant Representation for Domain Adaptation

abstract: Due to the ability of deep neural nets to learn rich representations, recent advances in unsupervised domain adaptation have focused on learning domain-invariant features that achieve a small error on the source domain. The hope is that the learnt representation, together with the hypothesis learnt from the source domain, can generalize to the target domain. In this paper, we first construct a simple counterexample showing that, contrary to common belief, the above conditions are not sufficient to guarantee successful domain adaptation. In particular, the counterexample exhibits \emph{conditional shift}: the class-conditional distributions of input features change between source and target domains. To give a sufficient condition for domain adaptation, we propose a natural and interpretable generalization upper bound that explicitly takes into account the aforementioned shift. Moreover, we shed new light on the problem by proving an information-theoretic lower bound on the joint error of \emph{any} domain adaptation method that attempts to learn invariant representations. Our result characterizes a fundamental tradeoff between learning invariant representations and achieving small joint error on both domains when the marginal label distributions differ from source to target. Finally, we conduct experiments on real-world datasets that corroborate our theoretical findings. We believe these insights are helpful in guiding the future design of domain adaptation and representation learning algorithms.


title: Towards cooperative guidance and control of highly automated vehicles: H-Mode and Conduct-by-Wire

abstract: This article provides a general ergonomic framework of cooperative guidance and control for vehicles with an emphasis on the cooperation between a human and a highly automated vehicle. In the twenty-first century, mobility and automation technologies are increasingly fused. In the sky, highly automated aircraft are flying with a high safety record. On the ground, a variety of driver assistance systems are being developed, and highly automated vehicles with increasingly autonomous capabilities are becoming possible. Human-centred automation has paved the way for a better cooperation between automation and humans. How can these highly automated systems be structured so that they can be easily understood, how will they cooperate with the human? The presented research was conducted using the methods of iterative build-up and refinement of framework by triangulation, i.e. by instantiating and testing the framework with at least two derived concepts and prototypes. This article sketches a general, conceptual ergonomic framework of cooperative guidance and control of highly automated vehicles, two concepts derived from the framework, prototypes and pilot data. Cooperation is exemplified in a list of aspects and related to levels of the driving task. With the concept 'Conduct-by-Wire', cooperation happens mainly on the guidance level, where the driver can delegate manoeuvres to the automation with a specialised manoeuvre interface. With H-Mode, a haptic-multimodal interaction with highly automated vehicles based on the H(orse)-Metaphor, cooperation is mainly done on guidance and control with a haptically active interface. Cooperativeness should be a key aspect for future human-automation systems. Especially for highly automated vehicles, cooperative guidance and control is a research direction with already promising concepts and prototypes that should be further explored. The application of the presented approach is every human-machine system that moves and includes high levels of assistance/automation.


title: Multi-label Classification Using Ensembles of Pruned Sets

abstract: This paper presents a pruned sets method (PS) for multi-label classification. It is centred on the concept of treating sets of labels as single labels. This allows the classification process to inherently take into account correlations between labels. By pruning these sets, PS focuses only on the most important correlations, which reduces complexity and improves accuracy. By combining pruned sets in an ensemble scheme (EPS), new label sets can be formed to adapt to irregular or complex data. The results from experimental evaluation on a variety of multi-label datasets show that [E]PS can achieve better performance and train much faster than other multi-label methods.