title: SIC-GAN: A Self-Improving Collaborative GAN for Decoding Sketch RNNs

abstract: Variational RNNs are proposed to output “creative” sequences. Ideally, a collection of sequences produced by a variational RNN should be of both high quality and high variety. However, existing decoders for variational RNNs suffer from a trade-off between quality and variety. In this paper, we seek to learn a variational RNN that decodes high-quality and high-variety sequences. We propose the Self-Improving Collaborative GAN (SIC-GAN), where there are two generators (variational RNNs) collaborating with each other to output a sequence and aiming to trick the discriminator into believing the sequence is of good quality. By deliberately weakening one generator, we can make another stronger in balancing quality and variety. We conduct experiments using the QuickDraw dataset and the results demonstrate the effectiveness of SIC-GAN empirically.


title: Using a gesture interactive game-based learning approach to improve preschool children's learning performance and motor skills

abstract: Children love to play games, and early childhood is a critical time for developing motor skills. This study combined gesture-based computing technology and a game-based learning model to develop a gesture interactive game-based learning (GIGL) approach that was suitable for preschool children. In this research, the ASUS Xtion PRO was used as a game-based device to build a virtual interactive learning environment for preschoolers. The aim of this study was to implement the GIGL approach to improve the learning performance and motor skills (namely, coordination and agility) of the participants. Based on a quasi-experiment involving 105 preschoolers (average age 5.5 years), the results showed that the participants who used the GIGL approach demonstrated better learning performance and motor skills than those who used the traditional activity game-based learning approach, and the statistics showed a significant deviation between the two approaches. Thus, this study provides additional evidence that using a GIGL approach is an effective learning method that improves both learning performance and motor skills to a greater extent compared with the traditional activity game-based learning approach. This study developed a gesture interactive game-based learning approach.It combined gesture-based computing technology and game-based learning model.A quasi-experiment with 105 preschoolers (average age 5.5 years) was conducted.Learning performance and motor skills (coordination and agility) were evaluated.This approach improved both learning performance and motor skills.


title: Towards a Full-Reference Quality Assessment for Color Images Using Directional Statistics

abstract: This paper presents a novel computational model for quantifying the perceptual quality of color images consistently with subjective evaluations. The proposed full-reference color metric, namely, a directional statistics-based color similarity index, is designed to consistently perform well over commonly encountered chromatic and achromatic distortions. In order to accurately predict the visual quality of color images, we make use of local color descriptors extracted from three perceptual color channels: 1) hue; 2) chroma; and 3) lightness. In particular, directional statistical tools are employed to properly process hue data by considering their periodicities. Moreover, two weighting mechanisms are exploited to accurately combine locally measured comparison scores into a final score. Extensive experimentation performed on large-scale databases indicates that the proposed metric is effective across a wide range of chromatic and achromatic distortions, making it better suited for the evaluation and optimization of color image processing algorithms.


title: Subspace embeddings for the L1-norm with applications

abstract: We show there is a distribution over linear mappings R:l1n -> l1O(d log d), such that with arbitrarily large constant probability, for any fixed d-dimensional subspace L, for all x ∈ L we have |x|1 ≤ |Rx|1 = O(d log d)|x|1. This provides the first analogue of the ubiquitous subspace Johnson-Lindenstrauss embedding for the l1-norm. Importantly, the target dimension and distortion are independent of the ambient dimension n. We give several applications of this result. First, we give a faster algorithm for computing well-conditioned bases. Our algorithm is simple, avoiding the linear programming machinery required of previous algorithms. We also give faster algorithms for least absolute deviation regression and l1-norm best fit hyperplane problems, as well as the first single pass streaming algorithms with low space for these problems. These results are motivated by practical problems in image analysis, spam detection, and tatistics, where the l1-norm is used in studies where outliers may be safely and effectively ignored. This is because the l1-norm is more robust to outliers than the l2-norm.


title: Cognitive Network Neuroscience

abstract: Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time dynamics, and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.


title: Pseudo-Relevance Feedback Based on Matrix Factorization

abstract: In information retrieval, pseudo-relevance feedback (PRF) refers to a strategy for updating the query model using the top retrieved documents. PRF has been proven to be highly effective in improving the retrieval performance. In this paper, we look at the PRF task as a recommendation problem: the goal is to recommend a number of terms for a given query along with weights, such that the final weights of terms in the updated query model better reflect the terms' contributions in the query. To do so, we propose RFMF, a PRF framework based on matrix factorization which is a state-of-the-art technique in collaborative recommender systems. Our purpose is to predict the weight of terms that have not appeared in the query and matrix factorization techniques are used to predict these weights. In RFMF, we first create a matrix whose elements are computed using a weight function that shows how much a term discriminates the query or the top retrieved documents from the collection. Then, we re-estimate the created matrix using a matrix factorization technique. Finally, the query model is updated using the re-estimated matrix. RFMF is a general framework that can be employed with any retrieval model. In this paper, we implement this framework for two widely used document retrieval frameworks: language modeling and the vector space model. Extensive experiments over several TREC collections demonstrate that the RFMF framework significantly outperforms competitive baselines. These results indicate the potential of using other recommendation techniques in this task.


title: An Adaptable, Probabilistic, Next-Best View Algorithm for Reconstruction of Unknown 3-D Objects

abstract: Autonomous mobile robots perform many tasks, such as grasping and inspection, that may require complete models of three-dimensional (3-D) objects in the environment. If little or no knowledge about an object is known a priori, the robot must take sensor measurements from strategically determined viewpoints in order to reconstruct a 3-D model of the object. We propose an autonomous object reconstruction approach for mobile robots that is very general, with no assumptions about object shape or size, such as a bounding box or predetermined set of candidate viewpoints. A probabilistic, volumetric method for determining the optimal next-best view is developed based on a partial model of a 3-D object of unknown shape and size. The proposed method integrates an object probability characteristic to determine sensor views that incrementally reconstruct a 3-D model of the object. Experiments in simulation and on a real-world robot validate the work and compare it to the state of the art.


title: CiteSeerx: an architecture and web service design for an academic document search engine

abstract: CiteSeer is a scientific literature digital library and search engine which automatically crawls and indexes scientific documents in the field of computer and information science. After serving as a public search engine for nearly ten years, CiteSeer is starting to have scaling problems for handling of more documents, adding new feature and more users. Its monolithic architecture design prevents it from effectively making use of new web technologies and providing new services. After analyzing the current system problems, we propose a new architecture and data model, CiteSeerx. CiteSeerx that will overcome the existing problems as well as provide scalability and better performance plus new services and system features.


title: Competitive Multi-scale Convolution

abstract: In this paper, we introduce a new deep convolutional neural network (ConvNet) module that promotes competition among a set of multi-scale convolutional filters. This new module is inspired by the inception module, where we replace the original collaborative pooling stage (consisting of a concatenation of the multi-scale filter outputs) by a competitive pooling represented by a maxout activation unit. This extension has the following two objectives: 1) the selection of the maximum response among the multi-scale filters prevents filter co-adaptation and allows the formation of multiple sub-networks within the same model, which has been shown to facilitate the training of complex learning problems; and 2) the maxout unit reduces the dimensionality of the outputs from the multi-scale filters. We show that the use of our proposed module in typical deep ConvNets produces classification results that are either better than or comparable to the state of the art on the following benchmark datasets: MNIST, CIFAR-10, CIFAR-100 and SVHN.


title: The geodesic dome phased array antenna for satellite operations support - antenna resource management

abstract: The smart antenna resource manager, the brain of the GDPAA, has been developed. The manager, with optimal spherical array geometry and unique concept of operation makes the GD an optimum antenna for multiple, simultaneous TT&C SATOPS. Some of the GD features, that the manager facilitates are: gain-on-demand, beam assignment and management, beam steering, beam shaping, adaptive pattern control, self-healing/compensation for failed elements, antenna health status, O&M under hot conditions, and/operation.


title: Discriminant Analysis for Recognition of Human Face Images

abstract: In this paper the discriminatory power of various human facial features is studied and a new scheme for Automatic Face Recognition (AFR) is proposed. Using Linear Discriminant Analysis (LDA) of different aspects of human faces in spatial domain, we first evaluate the significance of visual information in different parts/features of the face for identifying the human subject. The LDA of faces also provides us with a small set of features that carry the most relevant information for classification purposes. The features are obtained through eigenvector analysis of scatter matrices with the objective of maximizing between-class and minimizing within-class variations. The result is an efficient projection-based feature extraction and classification scheme for AFR. Soft decisions made based on each of the projections are combined, using probabilistic or evidential approaches to multisource data analysis. For medium-sized databases of human faces, good classification accuracy is achieved using very low-dimensional feature vectors.


title: Childhood sexual abuse and psychiatric disorder in young adulthood : II. Psychiatric outcomes of childhood sexual abuse

abstract: ABSTRACT Objective This is the second in a series of articles that describe the prevalence, correlates, and consequences of childhood sexual abuse (CSA) in a birth cohort of more than 1,000 New Zealand children studied to the age of 18 years. This article examines the associations between reports of CSA at age 18 and DSM-IV diagnostic classifications at age 18. Method A birth cohort of New Zealand children was studied at annual intervals from birth to age 16 years. At age 18 years retrospective reports of CSA prior to age 16 and concurrently measured psychiatric symptoms were obtained. Results Those reporting CSA had higher rates of major depression, anxiety disorder, conduct disorder, substance use disorder, and suicidal behaviors than those not reporting CSA ( p Conclusions The findings suggest that CSA, and particularly severe CSA, was associated with increased risk of psychiatric disorder in young adults even when due allowance was made for prospectively measured confounding factors.


title: An Application of Business Process Management to Health Care Facilities

abstract: The purpose of this article is to help health care facility managers and personnel identify significant elements of their facilities to address, and steps and actions to follow, when applying business process management to them. The ABPMP (Association of Business Process Management Professionals) life-cycle model of business process management is adopted, and steps from Lean, business process reengineering, and Six Sigma, and actions from operations management are presented to implement it. Managers of health care facilities can find in business process management a more comprehensive approach to improving their facilities than Lean, Six Sigma, business process reengineering, and ad hoc approaches that does not conflict with them because many of their elements can be included under its umbrella. Furthermore, the suggested application of business process management can guide and relieve them from selecting among these approaches, as well as provide them with specific steps and actions that they can follow. This article fills a gap in the literature by presenting a much needed comprehensive application of business process management to health care facilities that has specific steps and actions for implementation.


title: Vehicle Tracking, Monitoring and Alerting System: A Review

abstract: The goal of this paper is to review the past work of vehicle tracking, monitoring and alerting system, to categorize various methodologies and identify new trends. Vehicle tracking, monitoring and alerting system is challenging problem. There are various challenges encounter in vehicle tracking, monitoring and alerting due to deficiency in proper real time vehicle location and problem of alerting system. GPS (Global Positioning System) is most widely used technology for vehicle tracking and keep regular monitoring of vehicle. The objective of tracking system is to manage and control the transport using GPS transreceiver to know the current location of vehicle. In number of system, RFID (Radio Frequency Identification) is chosen as one of technology implemented for bus monitoring system. GSM (Global System for Mobile Communication) is most widely used for alerting system. Alerting system is essential for providing the location and information about vehicle to passenger, owner or user. General Terms Vehicle Tracking, Vehicle Monitoring, Alerting system


title: Substance Use Among College Students

abstract: Emerging adulthood has heightened risk for substance use. College students experience unique challenges, making them prone to use of alcohol, marijuana, and nonmedical use of prescription drugs. This article reviews rates of college students' substance use, risk factors, and populations at elevated risk. Consequences include legal, academic, and mental health problems; engagement in other risky behaviors; increased rates of injury; and death. Researchers, clinicians, and university administrators must identify those at greatest risk and provide prevention and intervention programs. Despite broad evidence supporting such programs, many students fail to access appropriate treatment. Future research should elucidate treatment barriers.Copyright © 2016 Elsevier Inc. All rights reserved. Language: en


History of Cloud Application Architectures From Deployment Monoliths via Microservices to Serverless Architectures and Possible Roads Ahead-A Review from the Frontline ( invited paper )


title: Manage Risks through the Enterprise Architecture

abstract: The goal of Risk Management activities is to define prevention and control mechanisms to address the risks attached to specify activities and valuable assets. Many Risk Management efforts operate in silos with narrowly focused, functionally driven, and disjointed activities. That fact leads to a fragmented view of risks, where each activity uses its own language, customs and metrics. The lack of interconnection and holistic view of risks limits an organization-wide perception of risks, where interdependent risks are not anticipated, controlled or managed. In order to address the Risk Management interoperability and standardization issues, this paper proposes an alignment between Risk Management, Governance and Enterprise Architecture activities, providing a systematic support to map and trace identified risks to enterprise artifacts modeled within the Enterprise Architecture, supporting the overall strategy of any organization. We discuss the main relationships between Risk Management and Enterprise Architecture and propose an architecture to integrate risks concerns into the overall organization environment.


title: INVESTIGATING PREDICTIVE POWER OF STOCK MICRO BLOG SENTIMENT IN FORECASTING FUTURE STOCK PRICE DIRECTIONAL MOVEMENT

abstract: This study attempts to discover and evaluate the predictive power of stock micro blog sentiment on future stock price directional movements. We construct a set of robust models based on sentiment analysis and data mining algorithms. Using 72,221 micro blog postings for 1909 stock tickers and 3874 distinct authors, our study reveals not only that stock micro blog sentiments do have predictive power for simple and marketadjusted returns respectively, but also that this predictive accuracy is consistent with the underreaction hypothesis observed in behavioral finance. We establish that stock micro blog with its succinctness, high volume and real-time features do have predictive power over future stock price movements. Furthermore, this study provides support for the model of irrational investor sentiment, recommends a complimentary investing approach using user-generated content and validates an instrument that may contribute to the monetization schemes for Virtual Investing Communities.


title: Intestinal Rehabilitation and the Short Bowel Syndrome: Part 1

abstract: The management of patients with intestinal failure due to short bowel syndrome (SBS) is complex, requiring a comprehensive approach that frequently necessitates long-term, if not life-long, use of parenteral nutrition (PN). Despite tremendous advances in the provision of PN over the past three decades, which have allowed significant improvements in the survival and quality of life of these patients, this mode of nutritional support carries with it significant risks to the patient, is very costly and, ultimately, does not attempt to improve the function of the remaining bowel. Intestinal rehabilitation refers to the process of restoring enteral autonomy and, thus, allowing freedom from parenteral nutrition, usually by means of dietary, medical, and, occasionally, surgical strategies. While recent investigations have focused on the use of trophic substances to increase the absorptive function of the remaining gut, whether intestinal rehabilitation occurs as a consequence of enhanced bowel adaptation or is simply a result of an optimized, comprehensive approach to the care of these patients remains unclear. In Part 1 of this review, an overview of SBS and pathophysiological considerations related to the remaining bowel anatomy in these patients will be provided. Additionally, a review of intestinal adaptation and factors that may enhance the adaptive process, focusing on evidence derived from animal studies, will also be discussed. In Part 2, relevant data on the development of intestinal adaptation in studies involving humans will be reviewed as will the general management of SBS. Lastly, the potential benefits of a multidisciplinary intestinal rehabilitation program in the care of these patients will also be discussed.


title: CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting

abstract: Estimating crowd count in densely crowded scenes is an extremely challenging task due to non-uniform scale variations. In this paper, we propose a novel end-to-end cascaded network of CNNs to jointly learn crowd count classification and density map estimation. Classifying crowd count into various groups is tantamount to coarsely estimating the total count in the image thereby incorporating a high-level prior into the density estimation network. This enables the layers in the network to learn globally relevant discriminative features which aid in estimating highly refined density maps with lower count error. The joint training is performed in an end-to-end fashion. Extensive experiments on highly challenging publicly available datasets show that the proposed method achieves lower count error and better quality density maps as compared to the recent state-of-the-art methods.


title: Multi-modal Factorized Bilinear Pooling with Co-attention Learning for Visual Question Answering

abstract: Visual question answering (VQA) is challenging because it requires a simultaneous understanding of both the visual content of images and the textual content of questions. The approaches used to represent the images and questions in a fine-grained manner and questions and to fuse these multi-modal features play key roles in performance. Bilinear pooling based models have been shown to outperform traditional linear models for VQA, but their high-dimensional representations and high computational complexity may seriously limit their applicability in practice. For multi-modal feature fusion, here we develop a Multi-modal Factorized Bilinear (MFB) pooling approach to efficiently and effectively combine multi-modal features, which results in superior performance for VQA compared with other bilinear pooling approaches. For fine-grained image and question representation, we develop a co-attention mechanism using an end-to-end deep network architecture to jointly learn both the image and question attentions. Combining the proposed MFB approach with co-attention learning in a new network architecture provides a unified model for VQA. Our experimental results demonstrate that the single MFB with co-attention model achieves new state-of-the-art performance on the real-world VQA dataset. Code available at this https URL.


title: Machine learning based mobile malware detection using highly imbalanced network traffic

abstract: Abstract In recent years, the number and variety of malicious mobile apps have increased drastically, especially on Android platform, which brings insurmountable challenges for malicious app detection. Researchers endeavor to discover the traces of malicious apps using network traffic analysis. In this study, we combine network traffic analysis with machine learning methods to identify malicious network behavior, and eventually to detect malicious apps. However, most network traffic generated by malicious apps is benign, while only a small portion of traffic is malicious, leading to an imbalanced data problem when the traffic model skews towards modeling the benign traffic. To address this problem, we introduce imbalanced classification methods, including the synthetic minority oversampling technique (SMOTE) + support vector machine (SVM), SVM cost-sensitive (SVMCS), and C4.5 cost-sensitive (C4.5CS) methods. However, when the imbalance rate reaches a certain threshold, the performance of common imbalanced classification algorithms degrades significantly. To avoid performance degradation, we propose to use the imbalanced data gravitation-based classification (IDGC) algorithm to classify imbalanced data. Moreover, we develop a simplex imbalanced data gravitation classification (S-IDGC) model to further reduce the time costs of IDGC without sacrificing the classification performance. In addition, we propose a machine learning based comparative benchmark prototype system, which provides users with substantial autonomy, such as multiple choices of the desired classifiers or traffic features. Using this prototype system, users can compare the detection performance of different classification algorithms on the same data set, as well as the performance of a specific classification algorithm on multiple data sets.


title: Personalized Response Generation via Domain adaptation

abstract: In this paper, we propose a novel personalized response generation model via domain adaptation (PRG-DM). First, we learn the human responding style from large general data (without user-specific information). Second, we fine tune the model on a small size of personalized data to generate personalized responses with a dual learning mechanism. Moreover, we propose three new rewards to characterize good conversations that are personalized, informative and grammatical. We employ the policy gradient method to generate highly rewarded responses. Experimental results show that our model can generate better personalized responses for different users.


title: Mutations in HIVEP2 are associated with developmental delay, intellectual disability, and dysmorphic features

abstract: Human immunodeficiency virus type I enhancer binding protein 2 (HIVEP2) has been previously associated with intellectual disability and developmental delay in three patients. Here, we describe six patients with developmental delay, intellectual disability, and dysmorphic features with de novo likely gene-damaging variants in HIVEP2 identified by whole-exome sequencing (WES). HIVEP2 encodes a large transcription factor that regulates various neurodevelopmental pathways. Our findings provide further evidence that pathogenic variants in HIVEP2 lead to intellectual disabilities and developmental delay.


title: A Visual Text Mining approach for Systematic Reviews

abstract: The software engineering research community has been adopting systematic reviews as an unbiased and fair way to assess a research topic. Despite encouraging early results, a systematic review process can be time consuming and hard to conduct. Thus, tools that help on its planning or execution are needed. This article suggests the use of visual text mining (VTM) to aid systematic reviews. A feasibility study was conducted comparing the proposed approach with a manual process. We observed that VTM can contribute to systematic review and we propose a new strategy called VTM-Based systematic review.


title: Secure Content-Based Routing Using Intel Software Guard Extensions

abstract: Content-based routing (CBR) is a powerful model that supports scalable asynchronous communication among large sets of geographically distributed nodes. Yet, preserving privacy represents a major limitation for the wide adoption of CBR, notably when the routers are located in public clouds. Indeed, a CBR router must see the content of the messages sent by data producers, as well as the filters (or subscriptions) registered by data consumers. This represents a major deterrent for companies for which data is a key asset, as for instance in the case of financial markets or to conduct sensitive business-to-business transactions. While there exists some techniques for privacy-preserving computation, they are either prohibitively slow or too limited to be usable in real systems. In this paper, we follow a different strategy by taking advantage of trusted hardware extensions that have just been introduced in off-the-shelf processors and provide a trusted execution environment. We exploit Intel's new software guard extensions (SGX) to implement a CBR engine in a secure enclave. Thanks to the hardware-based trusted execution environment (TEE), the compute-intensive CBR operations can operate on decrypted data shielded by the enclave and leverage efficient matching algorithms. Extensive experimental evaluation shows that SGX adds only limited overhead to insecure plaintext matching outside secure enclaves while providing much better performance and more powerful filtering capabilities than alternative software-only solutions. To the best of our knowledge, this work is the first to demonstrate the practical benefits of SGX for privacy-preserving CBR.


title: The Emerging Field of Adolescent Romantic Relationships

abstract: Romantic relationships are central in adolescents’ lives. They have the potential to affect development positively, but also place adolescents at risk for problems. Romantic experiences change substantially over the course of adolescence; the peer context plays a critical role as heterosexual adolescents initially interact with the other sex in a group context, then begin group dating, and finally have dyadic romantic relationships. Adolescents’ expectations and experiences in romantic relationships are related to their relationships with their peers as well as their parents. Although research on adolescents’ romantic relationships has blossomed in the past decade, further work is needed to identify the causes and consequences of romantic experiences, examine the diversity of romantic experiences, and integrate the field with work on sexuality and adult romantic relationships.


title: Q-Aware: A new approach towards the task scheduling process using nature-inspired meta-heuristic algorithms

abstract: Cloud Computing is the modern invocation being recited by the IT industry, and has set an example for accessing as well as configuring large-scale distributed computing applications across the clusters in datacenters. Today, it is in control of the greater percentage of computing quite recently, subsequently physical assets are being over-stacked. In-order to manage and assign tasks for the physical or virtual resources, techniques of clustering, scheduling and load balancing are being used. The current techniques are profoundly complex for non-pre-emptive tasks, which has remained as an irrecoverable restraint on the part of scheduling mechanism. In recent times, researches have implemented nature-inspired algorithms into the field of computing to solve the problems associated to complexity and to provide optimal solutions. In this paper, our focus lies in computing the makeSpan and costs incurred for resource management in the task scheduling process with a new methodology. We are proposing a heuristic approach namely Q-aware algorithm which takes the decisions based on analytical scheming. It works by forming a cluster of VMs using K means algorithm based on the memory, CPU, and band-width. This proposed technique uses a pool of nature-inspired algorithms namely SA,GA,IWO,PSO,ABC for the scheduling of tasks. This experiment is performed using cloudsim as simulator in-order to create a datacenter environment and schedule the tasks onto the virtual resources. The results show that the proposed system successfully performs the hyper-analytical scheming and measures the makespan, and calculates the resource utilization during the process.


title: Advances in the Inspection of Unpiggable Pipelines

abstract: The field of in-pipe robotics covers a vast and varied number of approaches to the inspection of pipelines with robots specialising in pipes ranging anywhere from 10 mm to 1200 mm in diameter. Many of these developed systems focus on overcoming in-pipe obstacles such as T-sections and elbows, as a result important aspects of exploration are treated as sub-systems, namely shape adaptability. One of the most prevalent methods of hybridised locomotion today is wall-pressing; generating traction using the encompassing pipe walls. A review of wall-pressing systems has been performed, covering the different approaches taken since their introduction. The advantages and disadvantages of these systems is discussed as well as their effectiveness in the inspection of networks with highly varying pipe diameters. When compared to unconventional in-pipe robotic techniques, traditional full-bore wall-pressing robots were found to be at a disadvantage.


title: Talking heads synthesis from audio with deep neural networks

abstract: Talking heads synthesis with expressions from speech is proposed in this paper. Talking heads synthesis can be considered as a learning problem of sequence-to-sequence mapping, which consists of audio as input and video as output. To synthesize talking heads, we use SAVEE database which consists of videos of multiple sentences speeches recorded from front of face. Audiovisual data can be considered as two parallel sequential data of audio and visual features and it is composed of continuous value. Thus, audio and visual features of our dataset are represented by a regression model. In this research, the regression model is trained with long short-term memory (LSTM) by minimizing mean squared error (MSE). Then, audio features are used as input and visual features are used as target of LSTM. Thereby, talking heads are synthesized from speech. Our method is proposed to use lower level audio features than phonemes and it enables to synthesize talking heads with expressions while existing researches which use phonemes as audio features only can synthesize neutral expression talking heads. With SAVEE database, we achieved the minimum MSE 17.03 on our testing dataset. In experiment, we use mel-frequency cepstral coefficient (MFCC), AMFCC and A2 MFCC with energy as audio feature and active appearance model (AAM) on entire face region as visual feature.


title: Learning Important Features Through Propagating Activation Differences

abstract: The purported "black box" nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input. DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. By optionally giving separate consideration to positive and negative contributions, DeepLIFT can also reveal dependencies which are missed by other approaches. Scores can be computed efficiently in a single backward pass. We apply DeepLIFT to models trained on MNIST and simulated genomic data, and show significant advantages over gradient-based methods. Video tutorial: http://goo.gl/qKb7pL, code: http://goo.gl/RM8jvH.


Algorithms and Methods in Recommender Systems


title: LTE-M Evolution Towards 5G Massive MTC

abstract: Massive machine type communication (mMTC) has been identified as an important use case for 5G New Radio wireless technology. In 4G Long-Term Evolution (LTE), 3GPP has previously introduced LTE-M for low-power, wide-area networks supporting the Internet of Things. Rel-13 specifications for LTE-M were completed in 2016. Rel-14 enhancements were completed in 2017 while Rel-15 enhancements are ongoing and expected to be completed in 2018. In this paper, we provide an overview of LTE-M and describe its evolution in subsequent releases. The features of the technology that have specifically been designed for mMTC are discussed. In addition, we present evaluations of LTE-M against 5G performance targets and show that mMTC requirements can be satisfied by LTE-M. To meet the 5G mMTC requirements, 3-dB power spectral density boosting is used in the downlink. In addition, for some requirements, 4 receive antennas at the eNB are required instead of the more typical 2 receive antennas. Thus, LTE-M will comprise an important component of 5G New Radio technology.


title: Defining and identifying Sleeping Beauties in science

abstract: A Sleeping Beauty (SB) in science refers to a paper whose importance is not recognized for several years after publication. Its citation history exhibits a long hibernation period followed by a sudden spike of popularity. Previous studies suggest a relative scarcity of SBs. The reliability of this conclusion is, however, heavily dependent on identification methods based on arbitrary threshold parameters for sleeping time and number of citations, applied to small or monodisciplinary bibliographic datasets. Here we present a systematic, large-scale, and multidisciplinary analysis of the SB phenomenon in science. We introduce a parameter-free measure that quantifies the extent to which a specific paper can be considered an SB. We apply our method to 22 million scientific papers published in all disciplines of natural and social sciences over a time span longer than a century. Our results reveal that the SB phenomenon is not exceptional. There is a continuous spectrum of delayed recognition where both the hibernation period and the awakening intensity are taken into account. Although many cases of SBs can be identified by looking at monodisciplinary bibliographic data, the SB phenomenon becomes much more apparent with the analysis of multidisciplinary datasets, where we can observe many examples of papers achieving delayed yet exceptional importance in disciplines different from those where they were originally published. Our analysis emphasizes a complex feature of citation dynamics that so far has received little attention, and also provides empirical evidence against the use of short-term citation metrics in the quantification of scientific impact.


title: RUFUS: Remote Supporter Feedback for Long-Distance Runners

abstract: This paper explores remote cheering during amateur races through a formative design inquiry. Friends and family of advanced amateur runners are part of their running experience. Runners rely on support during the race day and it is usually provided in the form of co-located cheers. RUFUS -- a prototype remote ambient runner support system -- was developed. The system enables supporters to send three types of signals to runners during a race and runners can send signals back to supporters. Input from supporters is sent through a webpage and received by runners through a device designed to lower distraction. An in situ study was conducted to evaluate the prototype during an organized race. Results show that two-way communication between runners and supporters was achieved. We also found that our system reflected varying user needs correctly. Runners and supporters reported increased motivation and enhanced race experience through feeling connected.


title: Path tracking Model Predictive Control of a Tilt-rotor UAV carrying a suspended load

abstract: This paper proposes a constrained Model Predictive Controller (MPC) to solve the path tracking problem of a Tilt-rotor UAV carrying a suspended load. Initially, a nonlinear model for the multi-body system is obtained via Euler-Lagrange formulation. Then, to design the constrained linear MPC, the equations of motion of the Tilt-rotor UAV with suspended load are linearized around the reference trajectory. Furthermore, to achieve null steady state error throughout the trajectory in presence of constant external disturbances and parametric uncertainties, the state vector is augmented with the integral action of some selected states. In addition, since the system is underactuated, the prediction model is also formulated in the incremental form to cope with disturbances affecting the system. Simulation results are carried out to corroborate the good performance of the proposed MPC when performing path tracking of the UAV with reduced load's swing.


title: Fake News Detection on Social Media: A Data Mining Perspective

abstract: Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of "fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ineffective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.


title: A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI

abstract: Intensity inhomogeneity often occurs in real-world images, which presents a considerable challenge in image segmentation. The most widely used image segmentation algorithms are region-based and typically rely on the homogeneity of the image intensities in the regions of interest, which often fail to provide accurate segmentation results due to the intensity inhomogeneity. This paper proposes a novel region-based method for image segmentation, which is able to deal with intensity inhomogeneities in the segmentation. First, based on the model of images with intensity inhomogeneities, we derive a local intensity clustering property of the image intensities, and define a local clustering criterion function for the image intensities in a neighborhood of each point. This local clustering criterion function is then integrated with respect to the neighborhood center to give a global criterion of image segmentation. In a level set formulation, this criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, by minimizing this energy, our method is able to simultaneously segment the image and estimate the bias field, and the estimated bias field can be used for intensity inhomogeneity correction (or bias correction). Our method has been validated on synthetic images and real images of various modalities, with desirable performance in the presence of intensity inhomogeneities. Experiments show that our method is more robust to initialization, faster and more accurate than the well-known piecewise smooth model. As an application, our method has been used for segmentation and bias correction of magnetic resonance (MR) images with promising results.


title: Spatial augmented reality — A tool for 3D data visualization

abstract: This paper presents a proposal for the use of Spatial Augmented Reality as a tool for 3D data visualizations. The use of traditional display technologies such as LCD monitors provides a fish tank metaphor for the user, i.e. the information is behind a plane of glass. The use of VR technologies allows the user to be immersed in the 3D volume and remove this fish tank problem, but can limit the set of techniques that allow users to interact with spatial data. Spatial Augmented Reality employed in conjunction with a highresolution monitor provides an elegant blend of spatial reasoning, tangible interaction, and detailed information viewing. This paper purposes a range of usages for SAR in 3D visualizations.


title: Materialized Views in Oracle

abstract: Oracle Materialized Views (MVs) are designed for data warehousing and replication. For data warehousing, MVs based on inner/outer equijoins with optional aggregation, can be refreshed on transaction boundaries, on demand, or periodically. Refreshes are optimized for bulk loads and can use a multi-MV scheduler. MVs based on subqueries on remote tables support bidirectional replication. Optimization with MVs includes transparent query rewrite based on costbased selection method. The ability to rewrite a large class of queries based on a small set of MVs is supported by using Dimensions (new Oracle object), losslessness of joins, functional dependency, column equivalence, join derivability, joinback and aggregate rollup.


title: A Reconfigurable Differential CMOS RF Energy Scavenger With 60% Peak Efficiency and -21 dBm Sensitivity

abstract: A differential RF-DC CMOS converter for RF energy scavenging based on a reconfigurable voltage rectifier topology is presented. The converter efficiency and sensitivity are optimized thanks to the proposed reconfigurable architecture. Prototypes, realized in 130 nm, provide a regulated output voltage of ~2 V when working at 868 MHz, with a -21 dBm sensitivity. The circuit efficiency peaks at 60%, remaining above the 40% for a 18 dB input power range.


title: Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

abstract: Synaptic plasticity, the putative basis of learning and memory formation, manifests in various forms and across different timescales. Here we show that the interaction of Hebbian homosynaptic plasticity with rapid non-Hebbian heterosynaptic plasticity is, when complemented with slower homeostatic changes and consolidation, sufficient for assembly formation and memory recall in a spiking recurrent network model of excitatory and inhibitory neurons. In the model, assemblies were formed during repeated sensory stimulation and characterized by strong recurrent excitatory connections. Even days after formation, and despite ongoing network activity and synaptic plasticity, memories could be recalled through selective delay activity following the brief stimulation of a subset of assembly neurons. Blocking any component of plasticity prevented stable functioning as a memory network. Our modelling results suggest that the diversity of plasticity phenomena in the brain is orchestrated towards achieving common functional goals.


title: Assisting developers of Big Data Analytics Applications when deploying on Hadoop clouds

abstract: Big data analytics is the process of examining large amounts of data (big data) in an effort to uncover hidden patterns or unknown correlations. Big Data Analytics Applications (BDA Apps) are a new type of software applications, which analyze big data using massive parallel processing frameworks (e.g., Hadoop). Developers of such applications typically develop them using a small sample of data in a pseudo-cloud environment. Afterwards, they deploy the applications in a large-scale cloud environment with considerably more processing power and larger input data (reminiscent of the mainframe days). Working with BDA App developers in industry over the past three years, we noticed that the runtime analysis and debugging of such applications in the deployment phase cannot be easily addressed by traditional monitoring and debugging approaches. In this paper, as a first step in assisting developers of BDA Apps for cloud deployments, we propose a lightweight approach for uncovering differences between pseudo and large-scale cloud deployments. Our approach makes use of the readily-available yet rarely used execution logs from these platforms. Our approach abstracts the execution logs, recovers the execution sequences, and compares the sequences between the pseudo and cloud deployments. Through a case study on three representative Hadoop-based BDA Apps, we show that our approach can rapidly direct the attention of BDA App developers to the major differences between the two deployments. Knowledge of such differences is essential in verifying BDA Apps when analyzing big data in the cloud. Using injected deployment faults, we show that our approach not only significantly reduces the deployment verification effort, but also provides very few false positives when identifying deployment failures.


title: Person Recognition in Social Media Photos

abstract: People nowadays share large parts of their personal lives through social media. Being able to automatically recognise people in personal photos may greatly enhance user convenience by easing photo album organisation. For human identification task, however, traditional focus of computer vision has been face recognition and pedestrian re-identification. Person recognition in social media photos sets new challenges for computer vision, including non-cooperative subjects (e.g. backward viewpoints, unusual poses) and great changes in appearance. To tackle this problem, we build a simple person recognition framework that leverages convnet features from multiple image regions (head, body, etc.). We propose new recognition scenarios that focus on the time and appearance gap between training and testing samples. We present an in-depth analysis of the importance of different features according to time and viewpoint generalisability. In the process, we verify that our simple approach achieves the state of the art result on the PIPA benchmark, arguably the largest social media based benchmark for person recognition to date with diverse poses, viewpoints, social groups, and events. ::: Compared the conference version of the paper, this paper additionally presents (1) analysis of a face recogniser (DeepID2+), (2) new method naeil2 that combines the conference version method naeil and DeepID2+ to achieve state of the art results even compared to post-conference works, (3) discussion of related work since the conference version, (4) additional analysis including the head viewpoint-wise breakdown of performance, and (5) results on the open-world setup.


title: The Effect of Culture on User Acceptance of Information Technology

abstract: The UTAUT (Unified Theory of Acceptance and Use of Technology), a unified model of the acceptance of technology by workers in the United States, explains individual acceptance and usage decisions of a technology in organizations (R 2


title: A graph based approach to scientific paper recommendation

abstract: When looking for recently published scientific papers, a researcher usually focuses on the topics related to her/his scientific interests. The task of a recommender system is to provide a list of unseen papers that match these topics. The core idea of this paper is to leverage the latent topics of interest in the publications of the researchers, and to take advantage of the social structure of the researchers (relations among researchers in the same field) as reliable sources of knowledge to improve the recommendation effectiveness. In particular, we introduce a hybrid approach to the task of scientific papers recommendation, which combines content analysis based on probabilistic topic modeling and ideas from collaborative filtering based on a relevance-based language model. We conducted an experimental study on DBLP, which demonstrates that our approach is promising.


title: The role of necroptosis in pulmonary diseases

abstract: Abstract By regulating the cell number and eliminating harmful cells, programmed cell death plays a critical role in development, homeostasis, and disease. While apoptosis is a recognized form of programmed cell death, necrosis was considered a type of uncontrolled cell death induced by extreme physical or chemical stress. However, recent studies have revealed the existence of a genetically programmed and regulated form of necrosis, termed necroptosis. Necroptosis is defined as necrotic cell death that is dependent on receptor-interacting protein kinase 3 (RIPK3). RIPK3, receptor-interacting protein kinase 1 (RIPK1), and a mixed-lineage kinase domain-like protein (MLKL) form a multiprotein complex called a necrosome. Although necroptosis generally provides a cell-autonomous host defense, on the other hand, cell rupture caused by necroptosis induces inflammation through the release of damage-associated molecular patterns, such as mitochondrial DNA, HMGB1, and IL-1. Previously, necroptosis was considered an alternative to apoptosis, but it is becoming increasingly clear that necroptosis itself is relevant to clinical disease, independent of apoptosis. According to some recent studies, autophagy, a cellular process for organelle and protein turnover, regulates necroptosis. This review outlines the principal components of necroptosis and provides an overview of the emerging importance of necroptosis in the pathogenesis of pulmonary disease, including chronic obstructive pulmonary disease, lung cancer, infection, and sepsis. We also discuss the molecular relationship between necroptosis and autophagy. Strategies targeting necroptosis may yield novel therapies for pulmonary diseases.


title: ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections

abstract: Deep neural networks have become ubiquitous for applications related to visual recognition and language understanding tasks. However, it is often prohibitive to use typical neural networks on devices like mobile phones or smart watches since the model sizes are huge and cannot fit in the limited memory available on such devices. While these devices could make use of machine learning models running on high-performance data centers with CPUs or GPUs, this is not feasible for many applications because data can be privacy sensitive and inference needs to be performed directly "on" device. ::: We introduce a new architecture for training compact neural networks using a joint optimization framework. At its core lies a novel objective that jointly trains using two different types of networks--a full trainer neural network (using existing architectures like Feed-forward NNs or LSTM RNNs) combined with a simpler "projection" network that leverages random projections to transform inputs or intermediate representations into bits. The simpler network encodes lightweight and efficient-to-compute operations in bit space with a low memory footprint. The two networks are trained jointly using backpropagation, where the projection network learns from the full network similar to apprenticeship learning. Once trained, the smaller network can be used directly for inference at low memory and computation cost. We demonstrate the effectiveness of the new approach at significantly shrinking the memory requirements of different types of neural networks while preserving good accuracy on visual recognition and text classification tasks. We also study the question "how many neural bits are required to solve a given task?" using the new framework and show empirical results contrasting model predictive capacity (in bits) versus accuracy on several datasets.


title: Double-Switch Equalizer Using Parallel- or Series-Parallel-Resonant Inverter and Voltage Multiplier for Series-Connected Supercapacitors

abstract: A double-switch cell voltage equalizer using a parallel-resonant-inverter (PRI) or series-parallel-resonant inverter (SPRI) and voltage multiplier is proposed for series-connected supercapacitors (SCs), such as electric double-layer capacitors (EDLCs) and lithium-ion capacitors. The double-switch operation without the need for a multiwinding transformer offers simpler circuitry as well as better modularity than conventional equalizers requiring multiple switches and/or a multiwinding transformer. Furthermore, the inherent constant current characteristic of the PRI/SPRI at a fixed frequency, not only removes the need for feedback control to limit currents under desired levels but the proposed equalizer can also operate safely, even when some cell voltage is 0 V. Detailed operation analyses were separately performed for the voltage multiplier and PRI/SPRI, and a dc equivalent circuit for the proposed equalizer was mathematically derived. A 10-W prototype for 12 cells connected in series was built, and an experimental equalization test was performed for EDLCs from an initially voltage imbalanced condition. Voltage imbalance of the series-connected EDLCs was successfully eliminated by the equalizer, demonstrating the equalization performance of the proposed equalizer.


title: Reduction in the Incidence of Type 2 Diabetes with Lifestyle Intervention or Metformin

abstract: Background Type 2 diabetes affects approximately 8 percent of adults in the United States. Some risk factors — elevated plasma glucose concentrations in the fasting state and after an oral glucose load, overweight, and a sedentary lifestyle — are potentially reversible. We hypothesized that modifying these factors with a lifestyle-intervention program or the administration of metformin would prevent or delay the development of diabetes. Methods We randomly assigned 3234 nondiabetic persons with elevated fasting and post-load plasma glucose concentrations to placebo, metformin (850 mg twice daily), or a lifestyle modification program with the goals of at least a 7 percent weight loss and at least 150 minutes of physical activity per week. The mean age of the participants was 51 years, and the mean body-mass index (the weight in kilograms divided by the square of the height in meters) was 34.0; 68 percent were women, and 45 percent were members of minority groups. Results The average follow-up was 2.8 years. The incidence of diabetes was 11.0, 7.8, and 4.8 cases per 100 person-years in the placebo, metformin, and lifestyle groups, respectively. The lifestyle intervention reduced the incidence by 58 percent (95 percent confidence interval, 48 to 66 percent) and metformin by 31 percent (95 percent confidence interval, 17 to 43 percent), as compared with placebo; the lifestyle intervention was significantly more effective than metformin. To prevent one case of diabetes during a period of three years, 6.9 persons would have to participate in the lifestyle-intervention program, and 13.9 would have to receive metformin. Conclusions Lifestyle changes and treatment with metformin both reduced the incidence of diabetes in persons at high risk. The lifestyle intervention was more effective than metformin.


title: Visual Classification With Multitask Joint Sparse Representation

abstract: We address the problem of visual classification with multiple features and/or multiple instances. Motivated by the recent success of multitask joint covariate selection, we formulate this problem as a multitask joint sparse representation model to combine the strength of multiple features and/or instances for recognition. A joint sparsity-inducing norm is utilized to enforce class-level joint sparsity patterns among the multiple representation vectors. The proposed model can be efficiently optimized by a proximal gradient method. Furthermore, we extend our method to the setup where features are described in kernel matrices. We then investigate into two applications of our method to visual classification: 1) fusing multiple kernel features for object categorization and 2) robust face recognition in video with an ensemble of query images. Extensive experiments on challenging real-world data sets demonstrate that the proposed method is competitive to the state-of-the-art methods in respective applications.


title: A Regression Approach to Speech Enhancement Based on Deep Neural Networks

abstract: In contrast to the conventional minimum mean square error (MMSE)-based noise reduction techniques, we propose a supervised method to enhance speech by means of finding a mapping function between noisy and clean speech signals based on deep neural networks (DNNs). In order to be able to handle a wide range of additive noises in real-world situations, a large training set that encompasses many possible combinations of speech and noise types, is first designed. A DNN architecture is then employed as a nonlinear regression function to ensure a powerful modeling capability. Several techniques have also been proposed to improve the DNN-based speech enhancement system, including global variance equalization to alleviate the over-smoothing problem of the regression model, and the dropout and noise-aware training strategies to further improve the generalization capability of DNNs to unseen noise conditions. Experimental results demonstrate that the proposed framework can achieve significant improvements in both objective and subjective measures over the conventional MMSE based technique. It is also interesting to observe that the proposed DNN approach can well suppress highly nonstationary noise, which is tough to handle in general. Furthermore, the resulting DNN model, trained with artificial synthesized data, is also effective in dealing with noisy speech data recorded in real-world scenarios without the generation of the annoying musical artifact commonly observed in conventional enhancement methods.


title: Induction of transformed phenotypes in sheep fibroblasts by culture fluids from cells persistently infected with bovine leukemia virus.

abstract: FLK cells are fetal lamb kidney cells persistently infected with bovine leukemia virus (BLV). 3178 cells, originating from calf-form bovine lymphosarcoma, also showed persistent production of BLV and alteration of cell morphology, after treatment with 5'-iodo-2'-deoxyuridine. In the present paper, the first in vitro transformation of sheep fibroblasts by inoculation with BLV materials from these two cell lines is described. In a few passages after inoculation with these viral materials, morphological alteration occurred. The morphologically altered cells were grown as stable cultures and showed such transformed phenotypes as growth in soft agar medium, increased uptake of 2-deoxy-D-glucose and tumorigenicity in athymic nude mice. This result, together with our previous observation of simultaneous induction of BLV expression and morphological alteration of 3178 cells, suggests the presence of some transforming capacity in these BLV materials similar to that in, for example, murine or avian acute leukemia viruses. The possible acquisition of such capacity during the prolonged passage is discussed.


Current Source Inverter fed Induction Motor Drives : A Survey


title: Lipid Composition of the Human Eye: Are Red Blood Cells a Good Mirror of Retinal and Optic Nerve Fatty Acids?

abstract: Background ::: The assessment of blood lipids is very frequent in clinical research as it is assumed to reflect the lipid composition of peripheral tissues. Even well accepted such relationships have never been clearly established. This is particularly true in ophthalmology where the use of blood lipids has become very common following recent data linking lipid intake to ocular health and disease. In the present study, we wanted to determine in humans whether a lipidomic approach based on red blood cells could reveal associations between circulating and tissue lipid profiles. To check if the analytical sensitivity may be of importance in such analyses, we have used a double approach for lipidomics. ::: ::: ::: Methodology and Principal Findings ::: Red blood cells, retinas and optic nerves were collected from 9 human donors. The lipidomic analyses on tissues consisted in gas chromatography and liquid chromatography coupled to an electrospray ionization source-mass spectrometer (LC-ESI-MS). Gas chromatography did not reveal any relevant association between circulating and ocular fatty acids except for arachidonic acid whose circulating amounts were positively associated with its levels in the retina and in the optic nerve. In contrast, several significant associations emerged from LC-ESI-MS analyses. Particularly, lipid entities in red blood cells were positively or negatively associated with representative pools of retinal docosahexaenoic acid (DHA), retinal very-long chain polyunsaturated fatty acids (VLC-PUFA) or optic nerve plasmalogens. ::: ::: ::: Conclusions and Significance ::: LC-ESI-MS is more appropriate than gas chromatography for lipidomics on red blood cells, and further extrapolation to ocular lipids. The several individual lipid species we have identified are good candidates to represent circulating biomarkers of ocular lipids. However, further investigation is needed before considering them as indexes of disease risk and before using them in clinical studies on optic nerve neuropathies or retinal diseases displaying photoreceptors degeneration.


title: Improved Current Controller Based on SVPWM for Three-phase Grid-connected Voltage Source Inverters

abstract: Space vector pulse-width modulation (SVPWM) has been widely employed for the current control of three-phase voltage source inverters (VSI). However, in grid-connected distributed generation (DG) systems, SVPWM also brings drawbacks to current controllers, such as the compromised output current due to the grid harmonic disturbance and nonlinearity of the system, the lack of inherent over-current protection, etc. In this paper, an improved current controller has been developed for three-phase grid-connected VSIs. A grid harmonic feed-forward compensation method is employed to depress the grid disturbance. Based on a dual-timer sampling scheme, a software predictor and filter is proposed for both the current feedback loop and grid voltage feed-forward loop to eliminate the system nonlinearity due to the control delay. Both simulation and experimental results have verified the superior performance of the proposed current controller


title: Human Action Laws in Electronic Virtual Worlds: An Empirical Study of Path Steering Performance in VR

abstract: This paper is concerned with simple human performance "laws of action" for three classes of tasks--pointing, crossing, and steering, as well as their applications in Virtual Reality research. In comparison to Fitts' law of pointing, the "law of steering"-- the quantitative relationship between human temporal performance and the movement path's spatial characteristics--has been notably under investigated. After a review of research on the law of steering in different domains and time periods, we examine the applicability of the law of steering in a VR locomotion task. Participants drove a virtual vehicle in a virtual environment on paths whose shape and width were systematically manipulated. Results showed that the law of steering indeed applies to locomotion in Virtual Environments. Participants' mean trial completion times linearly correlated (r2 between 0.985 and 0.999) with an index of difficulty quantified as path length to width ratio for the straight and circular paths used in this experiment. On average both the mean and the maximum speeds of the participants were linearly proportional to path width. Such human performance regularity provides a quantitative too for 3D human-machine interface design and evaluation. We also propose to use the law-of-steering model in Virtual Reality manipulation tasks such as the "ring and wire" task in the future.


title: Study on 5.8 GHz band rectenna rectifying circuit for internal wireless system of satellite

abstract: For reducing the development period of satellite, we propose an internal wireless system of satellite. In this paper, we designed a highly efficient 5.8 GHz rectifier based charge pump circuit, and introduced a class-F load that can make it on a process the third harmonic wave. For adjusting the most suitable applied voltage of diodes, we designed a new filter. Owing to inserting the class-F load and the new filter into charge pump circuit, the conversion efficiency increase to 82% at 30 mW in circuit simulations. In addition, the output voltage is higher than 5 V at 1700 Ω.


title: Evaluation Datasets for Twitter Sentiment Analysis: A survey and a new dataset, the STS-Gold

abstract: Sentiment analysis over Twitter offers organisations and individuals a fast and effective way to monitor the publics' feelings towards them and their competitors. To assess the performance of sentiment analysis methods over Twitter a small set of evaluation datasets have been released in the last few years. In this paper we present an overview of eight publicly available and manually annotated evaluation datasets for Twitter sentiment analysis. Based on this review, we show that a common limitation of most of these datasets, when assessing sentiment analysis at target (entity) level, is the lack of distinctive sentiment annotations among the tweets and the entities contained in them. For example, the tweet "I love iPhone, but I hate iPad" can be annotated with a mixed sentiment label, but the entity iPhone within this tweet should be annotated with a positive sentiment label. Aiming to overcome this limitation, and to complement current evaluation datasets, we present STS-Gold, a new evaluation dataset where tweets and targets (entities) are annotated individually and therefore may present different sentiment labels. This paper also provides a comparative study of the various datasets along several dimensions including: total number of tweets, vocabulary size and sparsity. We also investigate the pair-wise correlation among these dimensions as well as their correlations to the sentiment classification performance on different datasets.


title: Pulsed laser deposition of thin films: applications-led growth of functional materials

abstract: SECTION I. 1. Pulsed Laser Deposition of Complex Materials: Progress Towards Applications (D. Norton). SECTION II. 2. Resonant Infrared Pulsed Laser Ablation and Deposition of Thin Polymer Films (D. Bubb & R. Haglund). 3. Deposition of Polymers and Biomaterials Using the Matrix Assisted Pulsed Laser Eveporation (MAPLE) Process (A. Pique). 4. In situ Diagnostics by High Pressure RHEED during PLD (G. Rijnders & D. Blank). 5. Ultra-fast laser Ablation and Film Deposition (E. Gamaly, et al.). 6. Cross-beam PLD: Metastable Film Structures from Intersecting Plumes (A. Gorbunoff). 7. Combinatorial Pulsed Laser Deposition (I. Takeuchi). 8. Growth Kinetics During Pulsed Laser Deposition (G. Rijnders & D. Blank). 9. Large Area Commercial Pulsed Laser Deposition (J. Greer). SECTION III. 10. Coating Powders for Drug Delivery Systems Using Pulsed Laser Deposition (J. Talton, et al.). 11. Transparent Conducting Oxide Films (H. Kim). 12. ZnO and ZnO-related Compounds (J. Perriere, et al.). 13. Group III Nitride Growth (D. O'Mahony & J. Lunney). 14. Pulsed Laser Deposition of High-Temperature Superconducting Thin Films and Their Applications (B. Schey). 15. DLC: Medical and Mechanical Applications (R. Narayan). 16. Pulsed Laser Deposition of Metals (H. Krebs). SECTION IV. 17. Optical Waveguide Growth and Applications (R. Eason, et al.). 18. Biomaterials: New issues and Breakthroughs for Biomedical Applications (V. Nelea, et al.). 19. Thermoelectric Materials (A. Dauscher & B. Lenoir). 20. Piezoelectrics (F. Cracium & M. Dinescu). 21. Ferroelectric Thin Films for Microwave Device Applications (C. Chen & J. Horwitz). 22. Films for Electrochemical Applications (M. Montenegro & T. Lippert). 23. Pulsed Laser Deposition of Tribological Coatings (A. Voevodin, et al.). SECTION V. 24. Laser Ablation Synthesis of Single-wall Carbon Nanotubes: The SLS Model (A. Gorbunoff & O. Jost). 25. Quasicrystalline Thin Films (P. Willmott).


title: Early History of SQL

abstract: One of the most important research areas in computer science in the early 1970s was the development of systems and languages for handling what computer scientists call persistent data. This term denotes data that remains in a computer system indefinitely, until it is explicitly deleted. Systems for managing persistent data were spreading quickly in the business world. A database management language proposed by the Codasyl Data Base Task Group (DBTG) was receiving a lot of attention. In this paper, the early history of SQL is discussed.


title: Local Learning Regularized Nonnegative Matrix Factorization

abstract: Nonnegative Matrix Factorization (NMF) has been widely used in machine learning and data mining. It aims to find two nonnegative matrices whose product can well approximate the nonnegative data matrix, which naturally lead to parts-based representation. In this paper, we present a local learning regularized nonnegative matrix factorization (LLNMF) for clustering. It imposes an additional constraint on NMF that the cluster label of each point can be predicted by the points in its neighborhood. This constraint encodes both the discriminative information and the geometric structure, and is good at clustering data on manifold. An iterative multiplicative updating algorithm is proposed to optimize the objective, and its convergence is guaranteed theoretically. Experiments on many benchmark data sets demonstrate that the proposed method outperforms NMF as well as many state of the art clustering methods.


title: Parametric analysis of multi section slot coupled quadrature coupler

abstract: This paper presents a design procedure of an ultra wideband three section slot coupled hybrid coupler employing a parametric analysis of different design parameters. The coupler configuration is composed of a modified hexagonal shape at the top and bottom conductor plane along with a hexagonal slot at the common ground plane. The coupler performance for different design parameters is studied through full wave simulations. A final design providing a return loss and isolation better than 20dB, an amplitude imbalance between output ports of less than 0.9dB and a phase imbalance of ±1.9° across the 3.1–10.6 GHz band is confirmed.


title: Combining News and Technical Indicators in Daily Stock Price Trends Prediction

abstract: Stock market prediction has always been one of the hottest topics in research, as well as a great challenge due to its complex and volatile nature. However, most of the existing methods neglect the impact from mass media that will greatly affect the behavior of investors. In this paper we present a system that combines the information from both related news releases and technical indicators to enhance the predictability of the daily stock price trends. The performance shows that this system can achieve higher accuracy and return than a single source system.


title: A Maturity Model for Assessing the Digital Readiness of Manufacturing Companies

abstract: “The most profound technologies are those that disappear… They weave themselves into the fabric of everyday life until they are indistinguishable from it” wrote computer scientist and visionary Mark Weiser nearly 25 years ago in his essay “The Computer for the 21st Century.” It turns out he was right: in the age of “Industry 4.0”, digital technologies are the core driver for the manufacturing transformation. In fact, the introduction of such technologies allows companies to find solutions capable to turn increasing complexity into opportunities for ensuring sustainable competitiveness and profitable growth. Nonetheless, the effective implementation in manufacturing still depends on the state of practice: it may slow down, or even worst, may prevent from implementation. Indeed, we assume that a minimum level of capabilities is required before implementing the digital technologies in a company. Based on this concern, our research question is “are manufacturing companies ready to go digital?”. This paper wants to illustrate a “tool” to answer this question by building a maturity assessment method to measure the digital readiness of manufacturing firms. Based on the inspiring principles of the CMMI (Capability Maturity Model Integration) framework, we propose a model to set the ground for the investigation of company digital maturity. Different dimensions are used to assess 5 areas in which manufacturing key processes can be grouped: (1) design and engineering, (2) production management, (3) quality management, (4) maintenance management and (5) logistics management. Thus, the maturity model provides a normative description of practices in each area and dimension, building a ranked order of practices (i.e. from low to high maturity). A scoring method for maturity assessment is subsequently defined, in order to identify the criticalities in implementing the digital transformation and to subsequently drive the improvement of the whole system. The method should be useful both to manufacturing companies and researchers interested in understanding the digital readiness level in the state of practice.


title: Gaussian Prototypical Networks for Few-Shot Learning on Omniglot

abstract: We propose a novel architecture for $k$-shot classification on the Omniglot dataset. Building on prototypical networks, we extend their architecture to what we call Gaussian prototypical networks. Prototypical networks learn a map between images and embedding vectors, and use their clustering for classification. In our model, a part of the encoder output is interpreted as a confidence region estimate about the embedding point, and expressed as a Gaussian covariance matrix. Our network then constructs a direction and class dependent distance metric on the embedding space, using uncertainties of individual data points as weights. We show that Gaussian prototypical networks are a preferred architecture over vanilla prototypical networks with an equivalent number of parameters. We report state-of-the-art performance in 1-shot and 5-shot classification both in 5-way and 20-way regime (for 5-shot 5-way, we are comparable to previous state-of-the-art) on the Omniglot dataset. We explore artificially down-sampling a fraction of images in the training set, which improves our performance even further. We therefore hypothesize that Gaussian prototypical networks might perform better in less homogeneous, noisier datasets, which are commonplace in real world applications.


Distributed component systems: the next new computing model


title: ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition

abstract: In this paper, we introduce a new and challenging large-scale food image dataset called"ChineseFoodNet", which aims to automatically recognizing pictured Chinese dishes. Most of the existing food image datasets collected food images either from recipe pictures or selfie. In our dataset, images of each food category of our dataset consists of not only web recipe and menu pictures but photos taken from real dishes, recipe and menu as well. ChineseFoodNet contains over 180,000 food photos of 208 categories, with each category covering a large variations in presentations of same Chinese food. We present our efforts to build this large-scale image dataset, including food category selection, data collection, and data clean and label, in particular how to use machine learning methods to reduce manual labeling work that is an expensive process. We share a detailed benchmark of several state-of-the-art deep convolutional neural networks (CNNs) on ChineseFoodNet. We further propose a novel two-step data fusion approach referred as"TastyNet", which combines prediction results from different CNNs with voting method. Our proposed approach achieves top-1 accuracies of 81.43% on the validation set and 81.55% on the test set, respectively. The latest dataset is public available for research and can be achieved at https://sites.google.com/view/chinesefoodnet.


title: Twitter Sentiment Analysis Using Deep Convolutional Neural Network

abstract: In the work presented in this paper, we conduct experiments on sentiment analysis in Twitter messages by using a deep convolutional neural network. The network is trained on top of pre-trained word embeddings obtained by unsupervised learning on large text corpora. We use CNN with multiple filters with varying window sizes on top of which we add 2 fully connected layers with dropout and a softmax layer. Our research shows the effectiveness of using pre-trained word vectors and the advantage of leveraging Twitter corpora for the unsupervised learning phase. The experimental evaluation is made on benchmark datasets provided on the SemEval 2015 competition for the Sentiment analysis in Twitter task. Despite the fact that the presented approach does not depend on hand-crafted features, we achieve comparable performance to state-of-the-art methods on the Twitter2015 set, measuring F1 score of 64.85 %.


title: Personality traits and knowledge sharing in online communities

abstract: Adopting diffusion theory and the concept of social value orientation, the effects of personality traits on knowledge sharing in a virtual open content community are investigated. In addition to the main effects of personality, it was hypothesized that intrinsic motivations would moderate the effects on knowledge sharing. A sample of N=256 active users of Wikipedia provided measures of personality, motivation, and knowledge sharing. Latent regression analyses support the notion that authorship of Wikipedia is associated with higher levels of trendsetting and a prosocial value orientation. Moreover, moderation analyses demonstrate that the effect of the latter is moderated by individual differences in motivations to write. Differences with regard to opinion leadership could not be confirmed.


title: A Taxi Driving Fraud Detection System

abstract: Advances in GPS tracking technology have enabled us to install GPS tracking devices in city taxis to collect a large amount of GPS traces under operational time constraints. These GPS traces provide unparallel opportunities for us to uncover taxi driving fraud activities. In this paper, we develop a taxi driving fraud detection system, which is able to systematically investigate taxi driving fraud. In this system, we first provide functions to find two aspects of evidences: travel route evidence and driving distance evidence. Furthermore, a third function is designed to combine the two aspects of evidences based on Dempster-Shafer theory. To implement the system, we first identify interesting sites from a large amount of taxi GPS logs. Then, we propose a parameter-free method to mine the travel route evidences. Also, we introduce route mark to represent a typical driving path from an interesting site to another one. Based on route mark, we exploit a generative statistical model to characterize the distribution of driving distance and identify the driving distance evidences. Finally, we evaluate the taxi driving fraud detection system with large scale real-world taxi GPS logs. In the experiments, we uncover some regularity of driving fraud activities and investigate the motivation of drivers to commit a driving fraud by analyzing the produced taxi fraud data.


title: OBSTACLES IN MOVING TO AGILE SOFTWARE DEVELOPMENT METHODS; AT A GLANCE

abstract: It is only less than a decade that agile SD methods were introduced and got popular steadily. The defined values in these methods and their outcomes have motivated many software producers to use these methods. Since migration from traditional sof tware development methods to agile methods is growing highly, managers of the companies should be aware of problems, hindrances and challenges they may face with during the agile transformation process. This study focused on challenges which companies may face with and it is necessary that ma nagers think about solving them. Classifying them into four main categories; organization and management, people, process and tools are the areas that challenges have been seen in recent studies.


title: Zero-Knowledge Simulation of Boolean Circuits

abstract: A zero-knowledge interactive proof is a protocol by which Alice can convince a polynomially-bounded Bob of the truth of some theorem without giving him any hint as to how the proof might proceed. Under cryptographic assumptions, we give a general technique for achieving this goal for every problem in NP. This extends to a presumably larger class, which combines the powers of non-determinism and randomness. Our protocol is powerful enough to allow Alice to convince Bob of theorems for which she does not even have a proof: it is enough for Alice to convince herself probabilistically of a theorem, perhaps thanks to her knowledge of some trap-door information, in order for her to be able to convince Bob as well, without compromising the trap-door in any way.


A S EMANTIC L OSS F UNCTION FOR D EEP L EARNING WITH S YMBOLIC K NOWLEDGE


title: Black sheep get the blues : A psychobiological model of social rejection and depression

abstract: Major life events involving social rejection are strongly associated with onset of depression. To account for this relation, we propose a psychobiological model in which rejection-related stressors elicit a distinct and integrated set of cognitive, emotional, and biological changes that may evoke depression. In this model, social rejection events activate brain regions involved in processing negative affect and rejection-related distress (e.g., anterior insula, dorsal anterior cingulate cortex). They also elicit negative self-referential cognitions (e.g., “I’m undesirable,” “Other people don’t like me”) and related self-conscious emotions (e.g., shame, humiliation). Downstream biological consequences include upregulation of the hypothalamic-pituitary-adrenal axis, sympathetic-adrenal-medullary axis, and inflammatory response. Pro-inflammatory cytokines play an important role in this process because they induce a constellation of depressotypic behaviors called sickness behaviors. Although these changes can be short-lived, sustained inflammation may occur via glucocorticoid resistance, catecholamines, sympathetic innervation of immune organs, and immune cell aging. This response also may be moderated by several factors, including prior life stress, prior depression, and genes implicated in stress reactivity.


title: Scalable Variational Gaussian Process Classification

abstract: Gaussian process classification is a popular method with a number of appealing properties. We show how to scale the model within a variational inducing point framework, outperforming the state of the art on benchmark datasets. Importantly, the variational formulation can be exploited to allow classification in problems with millions of data points, as we demonstrate in experiments.


title: Scaling Deep Learning Workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing

abstract: Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors — including NVIDIA, Intel, AMD and IBM — have architectural road-maps influenced by DL workloads. Furthermore, several vendors have recently advertised new computing products as accelerating DL workloads. Unfortunately, it is difficult for data scientists to quantify the potential of these different products. This paper provides a performance and power analysis of important DL workloads on two major parallel architectures: NVIDIA DGX-1 (eight Pascal P100 GPUs interconnected with NVLink) and Intel Knights Landing (KNL) CPUs interconnected with Intel Omni-Path. Our evaluation consists of a cross section of convolutional neural net workloads: CifarNet, CaffeNet, AlexNet and GoogleNet topologies using the Cifar10 and ImageNet datasets. The workloads are vendor optimized for each architecture. GPUs provide the highest overall raw performance. Our analysis indicates that although GPUs provide the highest overall performance, the gap can close for some convolutional networks; and KNL can be competitive when considering performance/watt. Furthermore, NVLink is critical to GPU scaling.


title: Towards multiobjective procedural map generation

abstract: A search-based procedural content generation (SBPCG) algorithm for strategy game maps is proposed. Two representations for strategy game maps are devised, along with a number of objectives relating to predicted player experience. A multiobjective evolutionary algorithm is used for searching the space of maps for candidates that satisfy pairs of these objectives. As the objectives are inherently partially conflicting, the algorithm generates Pareto fronts showing how these objectives can be balanced. Such fronts are argued to be a valuable tool for designers looking to balance various design needs. Choosing appropriate points (manually or automatically) on the Pareto fronts, maps can be found that exhibit good map design according to specified criteria, and could either be used directly in e.g. an RTS game or form the basis for further human design.


title: Differentially Private Empirical Risk Minimization

abstract: Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the $\epsilon$-differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance.


title: Chemical-Reaction-Inspired Metaheuristic for Optimization

abstract: We encounter optimization problems in our daily lives and in various research domains. Some of them are so hard that we can, at best, approximate the best solutions with (meta-) heuristic methods. However, the huge number of optimization problems and the small number of generally acknowledged methods mean that more metaheuristics are needed to fill the gap. We propose a new metaheuristic, called chemical reaction optimization (CRO), to solve optimization problems. It mimics the interactions of molecules in a chemical reaction to reach a low energy stable state. We tested the performance of CRO with three nondeterministic polynomial-time hard combinatorial optimization problems. Two of them were traditional benchmark problems and the other was a real-world problem. Simulation results showed that CRO is very competitive with the few existing successful metaheuristics, having outperformed them in some cases, and CRO achieved the best performance in the real-world problem. Moreover, with the No-Free-Lunch theorem, CRO must have equal performance as the others on average, but it can outperform all other metaheuristics when matched to the right problem type. Therefore, it provides a new approach for solving optimization problems. CRO may potentially solve those problems which may not be solvable with the few generally acknowledged approaches.


title: Rotation-invariant and scale-invariant Gabor features for texture image retrieval

abstract: Conventional Gabor representation and its extracted features often yield a fairly poor performance in retrieving the rotated and scaled versions of the texture image under query. To address this issue, existing methods exploit multiple stages of transformations for making rotation and/or scaling being invariant at the expense of high computational complexity and degraded retrieval performance. The latter is mainly due to the lost of image details after multiple transformations. In this paper, a rotation-invariant and a scale-invariant Gabor representations are proposed, where each representation only requires few summations on the conventional Gabor filter impulse responses. The optimum setting of the orientation parameter and scale parameter is experimentally determined over the Brodatz and MPEG-7 texture databases. Features are then extracted from these new representations for conducting rotation-invariant or scale-invariant texture image retrieval. Since the dimension of the new feature space is much reduced, this leads to a much smaller metadata storage space and faster on-line computation on the similarity measurement. Simulation results clearly show that our proposed invariant Gabor representations and their extracted invariant features significantly outperform the conventional Gabor representation approach for rotation-invariant and scale-invariant texture image retrieval.


Learning CNN-LSTM Architectures for Image Caption Generation


title: Development of an Airborne Ground Penetrating Radar System: Antenna Design, Laboratory Experiment, and Numerical Simulation

abstract: A stepped-frequency continuous wave (SFCW) airborne ground penetrating radar (GPR) system was built using a vector network analyzer (VNA), an optical-to-electric ( \mmb O/\mmb E) converter, an electric-to-optical ( \mmb E/\mmb O) converter, and the resistively loaded planer dipole antennas. The resistively loaded antennas are based on the Altshuler discrete resistive loading approach. The distance from the loading point to the feeding point and the resistance has been optimized; in addition, a balun at the feeding point was introduced to convert the unbalanced signal from the coax to balanced signal. Test results show that the manufactured antennas have a wide bandwidth frequency range from 55 to 275 MHz. A laboratory experiment using the developed system was conducted in a building with high ceilings. The results registered all basic features including the ground, sub-ground surface, and the lamp hanging from the ceiling. Numerical modeling by finite-difference time-domain (FDTD) method using two-dimensional (2-D) transverse electric (TE) verifies the presence of these registered features in the measured airborne data. A bedrock outcrop in ice sheet model is reconstructed based on a piece of real airborne GPR data, the modeling profile results show excellent consistent with the observed data. The capability of airborne GPR detection of subsurface features is clearly validated.


title: Pain Research in Complementary and Alternative Medicine in Australia: A Critical Review

abstract: Abstract Background: Sixty percent (60%) to 80% of patients who visit chiropractic, osteopathic, or Chinese medicine practitioners are seeking pain relief. Objectives: This article aimed to identify the amount, quality, and type of complementary and alternative medicine (CAM) pain research in Australia by systematically and critically reviewing the literature. Methods: PubMed, Scopus, Australasian Medical Index, and Cochrane library were searched from their inception to July 2009. Australian and New Zealand Clinical Trial Registration and National Health and Medical Research Council databases were searched for human studies yet to be completed. Predefined search terms and selection criteria were used for data identification. Results: Of 204 studies selected, 54% were on chiropractic, 27% on Chinese medicine, 15% about multitherapy, and 4% on osteopathy. Chronic spinal pain was the most studied condition, with visceral pain being the least studied. Half of the articles in Chinese medicine or multitherapy w...


title: Document-level sentiment classification: An empirical comparison between SVM and ANN

abstract: Document-level sentiment classification aims to automate the task of classifying a textual review, which is given on a single topic, as expressing a positive or negative sentiment. In general, supervised methods consist of two stages: (i) extraction/selection of informative features and (ii) classification of reviews by using learning models like Support Vector Machines (SVM) and Nai@?ve Bayes (NB). SVM have been extensively and successfully used as a sentiment learning approach while Artificial Neural Networks (ANN) have rarely been considered in comparative studies in the sentiment analysis literature. This paper presents an empirical comparison between SVM and ANN regarding document-level sentiment analysis. We discuss requirements, resulting models and contexts in which both approaches achieve better levels of classification accuracy. We adopt a standard evaluation context with popular supervised methods for feature selection and weighting in a traditional bag-of-words model. Except for some unbalanced data contexts, our experiments indicated that ANN produce superior or at least comparable results to SVM's. Specially on the benchmark dataset of Movies reviews, ANN outperformed SVM by a statistically significant difference, even on the context of unbalanced data. Our results have also confirmed some potential limitations of both models, which have been rarely discussed in the sentiment classification literature, like the computational cost of SVM at the running time and ANN at the training time.