title: Should Robots Pay Taxes? Tax Policy in the Age of Automation

abstract: Existing technologies can already automate most work functions, and the cost of these technologies is decreasing at a time when human labor costs are increasing. This, combined with ongoing advances in computing, artificial intelligence, and robotics, has led experts to predict that automation will lead to significant job losses and worsening income inequality. Policy makers are actively debating how to deal with these problems, with most proposals focusing on investing in education to train workers in new job types, or investing in social benefits to distribute the gains of automation. ::: The importance of tax policy has been neglected in this debate, which is unfortunate because such policies are critically important. The tax system incentivizes automation even in cases where it is not otherwise efficient. That is because the vast majority of tax revenue is now derived from labor income, so firms avoid taxes by eliminating employees. More importantly, when a machine replaces a person, the government loses a substantial amount of tax revenue—potentially trillions of dollars a year in the aggregate. All of this is the unintended result of a system designed to tax labor rather than capital. Such a system no longer works once the labor is capital. Robots are not good taxpayers. ::: We argue that existing tax policies must be changed. The system should be at least “neutral” as between robot and human workers, and automation should not be allowed to reduce tax revenue. This could be achieved by disallowing corporate tax deductions for automated workers, creating an “automation tax” which mirrors existing unemployment schemes, granting offsetting tax preferences for human workers, levying a corporate self-employment tax, or increasing the corporate tax rate. We argue the ideal solution may be a combination of these proposals.


title: DEXTER: Large-Scale Discovery and Extraction of Product Specifications on the Web

abstract: The web is a rich resource of structured data. There has been an increasing interest in using web structured data for many applications such as data integration, web search and question answering. In this paper, we present Dexter, a system to find product sites on the web, and detect and extract product specifications from them. Since product specifications exist in multiple product sites, our focused crawler relies on search queries and backlinks to discover product sites. To perform the detection, and handle the high diversity of specifications in terms of content, size and format, our system uses supervised learning to classify HTML fragments (e.g., tables and lists) present in web pages as specifications or not. To perform large-scale extraction of the attribute-value pairs from the HTML fragments identified by the specification detector, Dexter adopts two lightweight strategies: a domain-independent and unsupervised wrapper method, which relies on the observation that these HTML fragments have very similar structure; and a combination of this strategy with a previous approach, which infers extraction patterns by annotations generated by automatic but noisy annotators. The results show that our crawler strategy to locate product specification pages is effective: (1) it discovered 1:46AM product specification pages from 3; 005 sites and 9 different categories; (2) the specification detector obtains high values of F-measure (close to 0:9) over a heterogeneous set of product specifications; and (3) our efficient wrapper methods for attribute-value extraction get very high values of precision (0.92) and recall (0.95) and obtain better results than a state-of-the-art, supervised rule-based wrapper.


title: Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions

abstract: Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta, and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve, and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening.


title: Regularization for Deep Learning: A Taxonomy

abstract: Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a novel, systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimization procedures. We identify the atomic building blocks of existing methods, and decouple the assumptions they enforce from the mathematical tools they rely on. We do not provide all details about the listed methods; instead, we present an overview of how the methods can be sorted into meaningful categories and sub-categories. This helps revealing links and fundamental similarities between them. Finally, we include practical recommendations both for users and for developers of new regularization methods.


title: Rank Correlation for Low-Rate DDoS Attack Detection: An Empirical Evaluation

abstract: A low-rate distributed denial of service (DDoS) attack has the ability to obscure its trac because it is very similar to legitimate trac. It can easily evade current detection mechanisms. Rank correlation measures can quantify signicant dierences between attack trac and legitimate trac based on their rank values. In this paper, we use two rank correlation measures, namely, Spearmen Rank Correlation (SRC) and Partial Rank Correlation (PRC) to detect low-rate DDoS attacks. These measures are empirically evaluated using three real-life datasets. Experimental results show that both measures can eectively discriminate legitimate trac from attack trac. We nd


title: Bacterial census of poultry intestinal microbiome

abstract: The objective of this study was to generate a phylogenetic diversity census of bacteria identified in the intestinal tract of chickens and turkeys using a nave analysis of all the curated 16S rRNA gene sequences archived in public databases. High-quality sequences of chicken and turkey gastrointestinal origin (3,184 and 1,345, respectively) were collected from the GenBank, Ribosomal Database Project, and Silva comprehensive ribosomal RNA database. Through phylogenetic and statistical analysis, 915 and 464 species-equivalent operational taxonomic units (defined at 0.03 phylogenetic distance) were found in the chicken and the turkey sequence collections, respectively. Of the 13 bacterial phyla identified in both bird species, Firmicutes, Bacteroidetes, and Proteobacteria were the largest phyla, accounting for >90% of all the sequences. The chicken sequences represent 117 established bacterial genera, and the turkey sequences represent 69 genera. The most predominant genera found in both the chicken and the turkey sequence data sets were Clostridium, Ruminococcus, Lactobacillus, and Bacteroides, but with different distribution between the 2 bird species. The estimated coverage of bacterial diversity of chicken and turkey reached 89 and 68% at species-equivalent and 93 and 73% at genus-equivalent levels, respectively. Less than 7,000 bacterial sequences from each bird species from various locations would be needed to reach 99% coverage for either bird species. Based on annotation of the sequence records, cecum was the most sampled gut segment. Chickens and turkeys were shown to have distinct intestinal microbiomes, sharing only 16% similarity at the species-equivalent level. Besides identifying gaps in knowledge on bacterial diversity in poultry gastrointestinal tract, the bacterial census generated in this study may serve as a framework for future studies and development of analytic tools.


abstract: An improved bandgap reference with high power supply rejection (PSR) is presented. The proposed circuit consists of a simple voltage subtractor circuit incorporated into the conventional Brokaw bandgap reference. Essentially, the subtractor feeds the supply noise directly into the feedback loop of the bandgap circuit which could help to suppress supply noise. The simulation results have been shown to conform well with the theoretical evaluation. The proposed circuit has also shown robust performance across temperature and process variations.


title: Image-based Human Character Modeling and Reconstruction for Virtual Reality Exposure Therapy

abstract: We design a modeling platform to construct virtual environments for Virtual Reality Exposure Therapy. In this platform, the scene is built by image-based reconstruction followed by manual refinement (when needed). The main technical contribution of this paper is on enhanced human character modeling, which is done through reconstructing 3D face geometry, body shape and posture from provided 2D photos.


title: Random k-Labelsets: An Ensemble Method for Multilabel Classification

abstract: This paper proposes an ensemble method for multilabel classification. The RAndom k-labELsets (RAKEL) algorithm constructs each member of the ensemble by considering a small random subset of labels and learning a single-label classifier for the prediction of each element in the powerset of this subset. In this way, the proposed algorithm aims to take into account label correlations using single-label classifiers that are applied on subtasks with manageable number of labels and adequate number of examples per label. Experimental results on common multilabel domains involving protein, document and scene classification show that better performance can be achieved compared to popular multilabel classification approaches.


title: Autonomous Pedestrian Collision Avoidance Using a Fuzzy Steering Controller

abstract: Collision avoidance is one of the most difficult and challenging automatic driving operations in the domain of intelligent vehicles. In emergency situations, human drivers are more likely to brake than to steer, although the optimal maneuver would, more frequently, be steering alone. This statement suggests the use of automatic steering as a promising solution to avoid accidents in the future. The objective of this paper is to provide a collision avoidance system (CAS) for autonomous vehicles, focusing on pedestrian collision avoidance. The detection component involves a stereo-vision-based pedestrian detection system that provides suitable measurements of the time to collision. The collision avoidance maneuver is performed using fuzzy controllers for the actuators that mimic human behavior and reactions, along with a high-precision Global Positioning System (GPS), which provides the information needed for the autonomous navigation. The proposed system is evaluated in two steps. First, drivers' behavior and sensor accuracy are studied in experiments carried out by manual driving. This study will be used to define the parameters of the second step, in which automatic pedestrian collision avoidance is carried out at speeds of up to 30 km/h. The performed field tests provided encouraging results and proved the viability of the proposed approach.


title: A novel impedance matching structure for a dielectric loaded 0.05 wavelength small meander line antenna

abstract: This paper presents high efficiency small meander line antennas for UHF RFID tags. In this paper, we propose a novel feed model to achieve an impedance matching of small meander line antenna as a means of increasing the antenna efficiency.


title: Short-Term Memory, Working Memory, and Executive Functioning in Preschoolers: Longitudinal Predictors of Mathematical Achievement at Age 7 Years

abstract: This study examined whether measures of short-term memory, working memory, and executive functioning in preschool children predict later proficiency in academic achievement at 7 years of age (third year of primary school). Children were tested in preschool (M age = 4 years, 6 months) on a battery of cognitive measures, and mathematics and reading outcomes (from standardized, norm-referenced school-based assessments) were taken on entry to primary school, and at the end of the first and third year of primary school. Growth curve analyses examined predictors of math and reading achievement across the duration of the study and revealed that better digit span and executive function skills provided children with an immediate head start in math and reading that they maintained throughout the first three years of primary school. Visual-spatial short-term memory span was found to be a predictor specifically of math ability. Correlational and regression analyses revealed that visual short-term and working memory were found to specifically predict math achievement at each time point, while executive function skills predicted learning in general rather than learning in one specific domain. The implications of the findings are discussed in relation to further understanding the role of cognitive skills in different mathematical tasks, and in relation to the impact of limited cognitive skills in the classroom environment.


title: Western diet enhances benzo(a)pyrene-induced colon tumorigenesis in a polyposis in rat coli (PIRC) rat model of colon cancer

abstract: // Kelly L. Harris 1 , Stephanie R. Pulliam 1 , Emmanuel Okoro 2 , Zhongmao Guo 2 , Mary K. Washington 3 , Samuel E. Adunyah 1 , James M. Amos-Landgraf 4 , Aramandla Ramesh 1 1 Department of Biochemistry and Cancer Biology, Meharry Medical College, Nashville, TN 37208, USA 2 Department of Physiology, Meharry Medical College, Nashville, TN 37208, USA 3 Department of Pathology, Vanderbilt University, Nashville, TN 37232, USA 4 Department of Veterinary Pathobiology, University of Missouri, Columbia, MO 65211, USA Correspondence to: Aramandla Ramesh, email: aramesh@mmc.edu Keywords: benzo(a)pyrene, colon cancer, PIRC rat, western diet, polyp Received: January 20, 2016 Accepted: February 15, 2016 Published: March 3, 2016 ABSTRACT Consumption of Western diet (WD), contaminated with environmental toxicants, has been implicated as one of the risk factors for sporadic colon cancer. Our earlier studies using a mouse model revealed that compared to unsaturated dietary fat, the saturated dietary fat exacerbated the development of colon tumors caused by B(a)P. The objective of this study was to study how WD potentiates B(a)P-induced colon carcinogenesis in the adult male rats that carry a mutation in the Apc locus - the polyposis in the rat colon (PIRC) rats. Groups of PIRC rats were fed with AIN-76A standard diet (RD) or Western diet (WD) and received 25, 50, or 100 μg B(a)P/kg body weight (wt) via oral gavage for 60 days. Subsequent to exposure, rats were euthanized; colons were retrieved and preserved in 10% formalin for counting the polyp numbers, measuring the polyp size, and histological analyses. Blood samples were collected and concentrations of cholesterol, triglycerides, glucose, insulin and leptin were measured. Rats that received WD + B(a)P showed increased levels of cholesterol, triglycerides, and leptin in comparison to RD + B(a)P groups or controls. The colon tumor numbers showed a B(a)P dose-response relationship. Adenomas with high grade dysplasia were prominent in B(a)P + WD rats compared to B(a)P + RD rats and controls (p < 0.05). The larger rat model system used in this study allows for studying more advanced tumor phenotypes over a longer duration and delineating the role of diet - toxicant interactions in sporadic colon tumor development.


Loosely Coupled Traceability for ATL


title: A dynamic split-and-merge approach for evolving cluster models

abstract: This paper describes new dynamic split-and-merge operations for evolving cluster models, which are learned incrementally and expanded on-the-fly from data streams. These operations are necessary to resolve the effects of cluster fusion and cluster delamination, which may appear over time in data stream learning. We propose two new criteria for cluster merging: a touching and a homogeneity criterion for two ellipsoidal clusters. The splitting criterion for an updated cluster applies a 2-means algorithm to its sub-samples and compares the quality of the split cluster with that of the original cluster by using a penalized Bayesian information criterion; the cluster partition of higher quality is retained for the next incremental update cycle. This new approach is evaluated using two-dimensional and high-dimensional streaming clustering data sets, where feature ranges are extended and clusters evolve over time—and on two large streams of classification data, each containing around 500K samples. The results show that the new split-and-merge approach (a) produces more reliable cluster partitions than conventional evolving clustering techniques and (b) reduces impurity and entropy of cluster partitions evolved on the classification data sets.


title: Automatic tagging using deep convolutional neural networks

abstract: We present a content-based automatic music tagging algorithm using fully convolutional neural networks (FCNs). We evaluate different architectures consisting of 2D convolutional layers and subsampling layers only. In the experiments, we measure the AUC-ROC scores of the architectures with different complexities and input types using the MagnaTagATune dataset, where a 4-layer architecture shows state-of-the-art performance with mel-spectrogram input. Furthermore, we evaluated the performances of the architectures with varying the number of layers on a larger dataset (Million Song Dataset), and found that deeper models outperformed the 4-layer architecture. The experiments show that mel-spectrogram is an effective time-frequency representation for automatic tagging and that more complex models benefit from more training data.


title: A Survey on Motion Detection Using WiFi Signals

abstract: WiFi signals based applications, such as indoor localization, human activity recognition and trace tracking, have been increasing attention in the past decade. WiFi signals are sensitive to the change of indoor environment, so the above mentioned applications encounter challenges which contain dynamic indoor environment, device diversity and motion influence. We mainly survey techniques of motion detecting and estimate changes of WiFi signals reflected by motion behaviors. Moreover, we analyze the impact of motion behaviors on applications based on WiFi signals, and deeply research developmental trend of WiFi signals. Finally, we pay closed attention to opportunities and future research directions in this new and large open area.


title: Power line communication in IoT-systems

abstract: Internet of Things (IoT) — a unified network of physical objects that can change the parameters of the environment or their own, gather information and transmit it to other devices. It is emerging as the third wave in the development of the internet. This technology will give immediate access to information about the physical world and the objects in it leading to innovative services and increase in efficiency and productivity. The IoT is enabled by the latest developments, smart sensors, communication technologies, and Internet protocols. This article contains a description of lnternet of things (IoT) networks. Much attention is given to prospects for future of using IoT and it's development. Some problems of development IoT are were noted. The article also gives valuable information on building(construction) IoT systems based on PLC technology.


title: Enhanced Differential Evolution using center-based sampling

abstract: The classical Differential Evolution (DE) has showed to perform efficiently in solving both benchmark functions and real-world problems. However, DE, similar to other evolutionary algorithms deteriorate in performance during solving high-dimensional problems. Opposition-based Differential Evolution (ODE) was introduced and, in general, has shown better performance comparing to classical DE for solving large-scale problems. In this paper, we propose an enhancement to ODE in order to improve its ability to solve large-scale problems more effectively. The proposed modified version of ODE is called Center-Based Differential Evolution (CDE) which utilizes the exact algorithm of ODE except replacing of opposite points with center-based individuals. This paper compares DE and ODE with the proposed algorithm, CDE. Furthermore, CDE with dynamic range (CDE d ) will be compared to CDE with fixed range (CDE f ). Experimental verifications are conducted on seven well-known shifted large-scale benchmark functions for dimensions of 100 and 500, including detailed parameter analysis for CDE. The shifted version of the functions ensures there is no bias towards the center of search space, in favor of CDE algorithm. The results clearly show that the CDE outperforms DE and ODE during solving large-scale problems, and also clarifies the superiority of CDE d to CDE f .


title: A Guide to the Treatment of Adults With ADHD

abstract: While child and adolescent physicians are familiar with the treatment of attention-deficit/hyperactivity disorder (ADHD), many adult physicians have had little experience with the disorder. It is difficult to develop clinical skills in the management of residual adult manifestations of developmental disorders without clinical experience with their presentation in childhood. Adult patients are increasingly seeking treatment for the symptoms of ADHD, and physicians need practice guidelines. Adult ADHD often presents differently from childhood ADHD. Because adult ADHD can be comorbid with other disorders and has symptoms similar to those of other disorders, it is important to understand differential diagnoses. Physicians should work with patients to provide feedback about their symptoms, to educate them about ADHD, and to set treatment goals. Treatment for ADHD in adults should include a medication trial, restructuring of the patient’s environment to make it more compatible with the symptoms of ADHD, and ongoing supportive management to address any residual impairment and to facilitate functional and developmental improvements. (J Clin Psychiatry 2004;65[suppl 3]:27‐37) A 1 and continues into adulthood for approximately 50% of those diagnosed in childhood. 2 Prevalence is estimated at 4.7% of the adult population, 3‐10 indicating that this is both a common and impairing condition for which treatment is now available. Research into the neurobiology of ADHD clearly established the validity of the disorder in adults, 11 which in turn opened a new window of oppor


Zara: A Virtual Interactive Dialogue System Incorporating Emotion, Sentiment and Personality Recognition


title: Write-optimized B-trees

abstract: Large writes are beneficial both on individual disks and on disk arrays, e.g., RAID-5. The presented design enables large writes of internal B-tree nodes and leaves. It supports both in-place updates and large append-only ("log-structured") write operations within the same storage volume, within the same B-tree, and even at the same time. The essence of the proposal is to make page migration inexpensive, to migrate pages while writing them, and to make such migration optional rather than mandatory as in log-structured file systems. The inexpensive page migration also aids traditional defragmentation as well as consolidation of free space needed for future large writes. These advantages are achieved with a very limited modification to conventional B-trees that also simplifies other B-tree operations, e.g., key range locking and compression. ::: ::: Prior proposals and prototypes implemented transacted B-tree on top of log-structured file systems and added transaction support to log-structured file systems. Instead, the presented design adds techniques and performance characteristics of log-structured file systems to traditional B-trees and their standard transaction support, notably without adding a layer of indirection for locating B-tree nodes on disk. The result retains fine-granularity locking, full transactional ACID guarantees, fast search performance, etc. expected of a modern B-tree implementation, yet adds efficient transacted page relocation and large, high-bandwidth writes.


title: Smart city for VANETs using warning messages, traffic statistics and intelligent traffic lights

abstract: Road safety has become a main issue for governments and car manufacturers in the last twenty years. The development of new vehicular technologies has favoured companies, researchers and institutions to focus their efforts on improving road safety. During the last decades, the evolution of wireless technologies has allowed researchers to design communication systems where vehicles participate in the communication networks. Thus, new types of networks, such as Vehicular Ad Hoc Networks (VANETs), have been created to facilitate communication between vehicles themselves and between vehicles and infrastructure. New concepts where vehicular networks play an important role have appeared the last years, such as smart cities and living labs [1]. Smart cities include intelligent traffic management in which data from the TIC (Traffic Information Centre) infrastructures could be reachable at any point. To test the possibilities of these future cities, living labs (cities in which new designed systems can be tested in real conditions) have been created all over Europe. The goal of our framework is to transmit information about the traffic conditions to help the driver (or the vehicle itself) take adequate decisions. In this work, the development of a warning system composed of Intelligent Traffic Lights (ITLs) that provides information to drivers about traffic density and weather conditions in the streets of a city is proposed and evaluated through simulations.


Analysis of Representations for Domain Adaptation


title: Semantic Message Passing for Generating Linked Data from Tables

abstract: We describe work on automatically inferring the intended meaning of tables and representing it as RDF linked data, making it available for improving search, interoperability and integration. We present implementation details of a joint inference module that uses knowledge from the linked open data (LOD) cloud to jointly infer the semantics of column headers, table cell values (e.g., strings and numbers) and relations between columns. The framework generates linked data by mapping column headers to classes, cell values to LOD entities (existing or new) and by identifying relations between columns. We also implement a novel Semantic Message Passing algorithm which uses LOD knowledge to improve existing message passing schemes. We evaluate our implemented techniques on tables from the Web and Wikipedia. This is an extended version of a paper under review at XYZZY.


Novel Multi-user Conjunctive Keyword Search Against Keyword Guessing Attacks Under Simple Assumptions


title: Face Analysis in the Wild

abstract: With the global demand for extra security systems, and the growing of human-machine interaction, facial analysis in unconstrained environments (in the wild) became a hot-topic in recent computer vision research.Unconstrained environments include surveillance footage, social media photos and live broadcasts.This type of images and videos include no control over illumination, position, size, occlusion, and facial expressions. Successful facial processing methods for controlled scenarios are unable to pledge with challenging circumstances. Consequently, methods tailored for handling those situations are indispensable for the face analysis research progress. This work presents a comprehensive review of state-of-the-art methods, drawing attention to the complications derived from in the wild scenarios and the behavior differences when applied to the controlled images.The main topics to be covered are: (1) face detection; (2) facial image quality; (3) head pose estimation; (4) face alignment; (5) 3D face reconstruction; (6) gender and age estimation; (7) facial expressions and emotions; and (8) face recognition. Finally, available code and applications for in the wild face analysis are presented,followed by a discussion on future directions.


title: Attention-based Vocabulary Selection for NMT Decoding

abstract: Neural Machine Translation (NMT) models usually use large target vocabulary sizes to capture most of the words in the target language. The vocabulary size is a big factor when decoding new sentences as the final softmax layer normalizes over all possible target words. To address this problem, it is widely common to restrict the target vocabulary with candidate lists based on the source sentence. Usually, the candidate lists are a combination of external word-to-word aligner, phrase table entries or most frequent words. In this work, we propose a simple and yet novel approach to learn candidate lists directly from the attention layer during NMT training. The candidate lists are highly optimized for the current NMT model and do not need any external computation of the candidate pool. We show significant decoding speedup compared with using the entire vocabulary, without losing any translation quality for two language pairs.


title: Deep Neural Networks for Optimal Team Composition

abstract: Cooperation is a fundamental social mechanism, whose effects on human performance have been investigated in several environments. Online games are modern-days natural settings in which cooperation strongly affects human behavior. Every day, millions of players connect and play together in team-based games: the patterns of cooperation can either foster or hinder individual skill learning and performance. This work has three goals: (i) identifying teammates' influence on players' performance in the short and long term, (ii) designing a computational framework to recommend teammates to improve players' performance, and (iii) setting to demonstrate that such improvements can be predicted via deep learning. We leverage a large dataset from Dota 2, a popular Multiplayer Online Battle Arena game. We generate a directed co-play network, whose links' weights depict the effect of teammates on players' performance. Specifically, we propose a measure of network influence that captures skill transfer from player to player over time. We then use such framing to design a recommendation system to suggest new teammates based on a modified deep neural autoencoder and we demonstrate its state-of-the-art recommendation performance. We finally provide insights into skill transfer effects: our experimental results demonstrate that such dynamics can be predicted using deep neural networks.


title: Stock price prediction based on stock-specific and sub-industry-specific news articles

abstract: Accurate forecasting of upcoming trends in the capital markets is extremely important for algorithmic trading and investment management. Before making a trading decision, investors estimate the probability that a certain news item will influence the market based on the available information. Speculation among traders is often caused by the release of a breaking news article and results in price movements. Publications of news articles influence the market state that makes them a powerful source of data in financial forecasting. Recently, researchers have developed trend and price prediction models based on information extracted from news articles. However, to date no previous research that investigates the advantages of using news articles with different levels of relevance to the target stock has been conducted. This research study uses the multiple kernel learning technique to effectively combine information extracted from stock-specific and sub-industry-specific news articles for prediction of an upcoming price movement. News articles are divided into these two categories based on their relevance to a targeted stock and analyzed by separate kernels. The experimental results show that utilizing two categories of news improves the prediction accuracy in comparison with methods based on a single news category.


title: Fuzzy job shop scheduling problem with availability constraints

abstract: This paper presents the fuzzy job shop scheduling problem with availability constraints. The objective is to find a schedule that maximizes the minimum agreement index subject to periodic maintenance, non-resumable jobs and fuzzy due-date. A random key genetic algorithm (RKGA) is proposed for the problem, in which a novel random key representation, a new decoding strategy incorporating maintenance operation and discrete crossover (DX) are used. RKGA is applied to some fuzzy scheduling problem with availability constraints and compared with other algorithms. Computational results show that RKGA performs better than other algorithms.


title: Social Factors Influencing Child Health in Ghana

abstract: Objectives: ::: ::: Social factors have profound effects on health. Children are especially vulnerable to social influences, particularly in their early years. Adverse social exposures in childhood can lead to chronic disorders later in life. Here, we sought to identify and evaluate the impact of social factors on child health in Ghana. As Ghana is unlikely to achieve the Millennium Development Goals’ target of reducing child mortality by two-thirds between 1990 and 2015, we deemed it necessary to identify social determinants that might have contributed to the non-realisation of this goal. ::: ::: Methods: ::: ::: ScienceDirect, PubMed, MEDLINE via EBSCO and Google Scholar were searched for published articles reporting on the influence of social factors on child health in Ghana. After screening the 98 articles identified, 34 of them that met our inclusion criteria were selected for qualitative review. ::: ::: Results: ::: ::: Major social factors influencing child health in the country include maternal education, rural-urban disparities (place of residence), family income (wealth/poverty) and high dependency (multiparousity). These factors are associated with child mortality, nutritional status of children, completion of immunisation programmes, health-seeking behaviour and hygiene practices. ::: ::: Conclusions: ::: ::: Several social factors influence child health outcomes in Ghana. Developing more effective responses to these social determinants would require sustainable efforts from all stakeholders including the Government, healthcare providers and families. We recommend the development of interventions that would support families through direct social support initiatives aimed at alleviating poverty and inequality, and indirect approaches targeted at eliminating the dependence of poor health outcomes on social factors. Importantly, the expansion of quality free education interventions to improve would-be-mother’s health knowledge is emphasised.


title: Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples

abstract: We propose a retrieval-augmented convolutional network and propose to train it with local mixup, a novel variant of the recently proposed mixup algorithm. The proposed hybrid architecture combining a convolutional network and an off-the-shelf retrieval engine was designed to mitigate the adverse effect of off-manifold adversarial examples, while the proposed local mixup addresses on-manifold ones by explicitly encouraging the classifier to locally behave linearly on the data manifold. Our evaluation of the proposed approach against five readily-available adversarial attacks on three datasets--CIFAR-10, SVHN and ImageNet--demonstrate the improved robustness compared to the vanilla convolutional network.


1 IMAGE PROCESSING TECHNIQUES FOR MACHINE VISION


title: Using super resolution to enhance license plates recognition accuracy

abstract: License Plate Recognition (LPR) has become one of the most widely used applications. One of the main factors affecting the accuracy of LPR is the quality of the image containing the plate. In case of video-based solutions, many frames are captured for the same car. Super Resolution (SR) techniques can be used for enhancing LPR accuracy by constructing one high resolution image from a number of low resolution images that belong to the same car. However because of the nature of the detected object (car plate), motion analysis as well as object perspective correction need to be taken into consideration when applying the SR algorithm over the provided series of video frames containing the moving car, in order to enhance the resolution of the plate area. This paper aims at providing an implementation for a SR algorithm where input frames are put together into one high-resolution image after detecting the license plate and removing the noisy frames to provide one clear and focused image as an output. OpenCV library was modified to support this implementation. This implementation enhanced the accuracy of an LPR solution by approximately 7% enhancement while trying to not increase the computational complexity of the original solution.


title: A generative model with hypergraph regularizers for protein function prediction

abstract: Heterogeneous data sources and multi-label are two important characteristics of protein function prediction. They describe protein data from two different aspects. However, it is of considerable challenge to integrate multiple data sources and multi-label simultaneously for predicting protein functions, especially when there are only a limited number of labeled proteins. In this paper, we propose a generative model with hypergraph regularizers algorithm, called GMHR, for predicting proteins with multiple functions. The GMHR algorithm integrates all data sources that are available, including protein attribute features, interaction networks, label correlations, and unlabeled data. Experimental results on the real-world datasets predicting the functions of proteins demonstrate the superiority of our proposed method compared with the state-of-the-art baselines.


title: Knowledge, attitude, and practices on usage, disposal, and effect of plastic bags on sheep and goats

abstract: The objective of this study was to evaluate knowledge, attitudes, and practices of people in the Nairobi and Kajiado Counties, Kenya, on the usage, disposal, and effect of plastic waste on sheep and goats (shoats). A semi-structured questionnaire was used to collect data from 384 respondents in four communities in the two counties. Most of the people irrespective of their age, occupation, and educational status used plastic bags of some type on a daily basis. A high proportion of the respondents (37.0%, 142) used plastic bags because of the low cost. Approximately, 79.1% (304) disposed used plastic bags in open dumps. A total of 147 (38.3%) households kept shoats. Out of these, 38.1% (56) purchased feed and also allowed their animals to roam. Most of them (45.3%, 174) thought that lack of feed for the animals was the main reason why shoats roam and scavenge at refuse dump sites and road sides. A large proportion of the respondents (44.5%, 143) mentioned death of animals as the ultimate consequence of ingestion of waste plastic bags. Though, the respondents were aware that indiscriminate disposal of used plastic bags could result in death of the animals from which they derive their livelihoods, they nevertheless continued with the practice. There is a need for a paradigm shift in the way and manner plastic bags are used and disposed.


title: A 77-GHz cavity antenna array in PCB technology

abstract: In this paper the design and the implementation of a cavity antenna array in PCB technology for automotive radar applications is presented. The array consists of four cavity antennas fed by a microstrip-line feeding network and H-shaped coupling apertures in the ground plane. Simulation and measurement results show that the proposed structure is a suitable candidate for the integration in automotive radar sensors.


title: Importance masks for revealing occluded objects in augmented reality

abstract: When simulating "X-ray vision" in Augmented Reality, a critical aspect is ensuring correct perception of the occluded objects position. Naive overlay rendering of occluded objects on top of real-world occluders can lead to a misunderstanding of the visual scene and a poor perception of the depth. We present a simple technique to enhance the perception of the spatial arrangements in the scene. An importance mask associated with occluders informs the rendering what information can be overlaid and what should be preserved. This technique is independent of scene properties such as illumination and surface properties, which may be unknown. The proposed solution is computed efficiently in a single-pass fragment shaders on the GPU.


title: A Theory of ICOs: Diversification, Agency, and Information Asymmetry

abstract: This paper develops a theory of financing of entrepreneurial ventures via crypto tokens, which is not limited to platform-based ventures. We compare token financing with traditional equity financing, focusing on agency problems and information asymmetry frictions associated with the two financing methods, as well as on risk sharing between entrepreneurs and investors. Token financing introduces an agency problem not present under equity financing -- underproduction, while mitigating an agency problem often associated with equity financing -- entrepreneurial effort underprovision. Our theory abstracts from all institutional and potentially transient differences between tokens and equity and is based on a single intrinsic characteristic of tokens -- they represent claims to a venture's output. We show that tokens are likely to dominate equity for ventures developing information goods or services, those for which entrepreneurial effort is crucial, and/or those with relatively low payoff volatility. In addition, tokens have can have an advantage over equity in signaling venture quality to outside investors.


title: Energy Efficiency and Asymptotic Performance Evaluation of Beamforming Structures in Doubly Massive MIMO mmWave Systems

abstract: Future cellular systems based on the use of millimeter waves will heavily rely on the use of antenna arrays both at the transmitter and at the receiver. For complexity reasons and energy consumption issues, fully digital precoding and postcoding structures may turn out to be unfeasible, and thus suboptimal structures, making use of simplified hardware and a limited number of RF chains, have been investigated. This paper considers and makes a comparative assessment, both from a spectral efficiency and energy efficiency point of view, of several suboptimal precoding and postcoding beamforming structures for a cellular multiuser MIMO (MU-MIMO) system with large number of antennas. Analytical formulas for the asymptotic achievable spectral efficiency and for the global energy efficiency of several beamforming structures are derived in the large number of antennas regime. Using the most recently available data for the energy consumption of phase shifters and switches, we show that fully-digital beamformers may actually achieve a larger energy efficiency than lower-complexity solutions, as well as that low-complexity beam-steering purely analog beamforming may in some cases represent a good performance-complexity trade-off solution.


title: Learning ReLUs via Gradient Descent

abstract: In this paper we study the problem of learning Rectified Linear Units (ReLUs) which are functions of the form $max(0, )$ with $w$ denoting the weight vector. We study this problem in the high-dimensional regime where the number of observations are fewer than the dimension of the weight vector. We assume that the weight vector belongs to some closed set (convex or nonconvex) which captures known side-information about its structure. We focus on the realizable model where the inputs are chosen i.i.d.~from a Gaussian distribution and the labels are generated according to a planted weight vector. We show that projected gradient descent, when initialization at 0, converges at a linear rate to the planted model with a number of samples that is optimal up to numerical constants. Our results on the dynamics of convergence of these very shallow neural nets may provide some insights towards understanding the dynamics of deeper architectures.


title: Gazing and frowning as a new human--computer interaction technique

abstract: The present aim was to study a new technique for human--computer interaction. It combined the use of two modalities, voluntary gaze direction and voluntary facial muscle activation for object pointing and selection. Fourteen subjects performed a series of pointing tasks with the new technique and with a mouse. At short distances the mouse was significantly faster than the new technique. However, there were no statistically significant differences at medium and long distances between the techniques. Fitts' law analyses were performed both by using only error-free trials and using also data including error trials (i.e., effective target width). In all cases both techniques seemed to follow Fitts' law, although for the new technique the effective target width correlation coefficient was smaller R = 0.776 than for the mouse R = 0.991. The regression slopes suggested that at very long distances (i.e., beyond 800 pixels) the new technique might be faster than the mouse. The new technique showed promising results already after a short practice and in the future it could be useful especially for physically challenged persons.


title: Aligning users across social networks using network embedding

abstract: In this paper, we adopt the representation learning approach to align users across multiple social networks where the social structures of the users are exploited. In particular, we propose to learn a network embedding with the followership/ followee-ship of each user explicitly modeled as input/output context vector representations so as to preserve the proximity of users with "similar" followers/followees in the embedded space. For the alignment, we add both known and potential anchor users across the networks to facilitate the transfer of context information across networks. We solve both the network embedding problem and the user alignment problem simultaneously under a unified optimization framework. The stochastic gradient descent and negative sampling algorithms are used to address scalability issues. Extensive experiments on real social network datasets demonstrate the effectiveness and efficiency of the proposed approach compared with several state-of-the-art methods.


title: The Management of Visual Attention in Graphic Displays

abstract: This chapter presents an overview of several recent developments in vision science, and outlines some of their implications for the management of visual attention in graphic displays. These include ways of sending attention to the right item at the right time, techniques to improve attentional efficiency, and possibilities for offloading some of the processing typically done by attention onto nonattentional mechanisms. In addition it is argued that such techniques not only allow more effective use to be made of visual attention, but also open up new possibilities for human-machine interaction.


title: Clustering-based KPI data association analysis method in cellular networks

abstract: With the rapid development of cellular network systems, the operators need more experience to deal with complicated network management system and wide range of Key Performance Indicators (KPIs). There are many indicators related to each other due to the definition or communication process. But several implicit associations still exist among these KPIs. This paper proposes an approach to figure out the implicit linear relationship among indicators clearly in which a new clustering technique is used for distinguishing different relationships. Data analysis using real network data shows that the approach can well divide data into clusters, and each cluster can effectively reflect the relationship between indicators.


title: Recent Trends in Text Classification Techniques

abstract: Text Mining is the discovery of valuable, yet hidden, information from the text document. Text classification (Also called Text Categorization) is one of the important research issues in the field of text mining. With the dramatic increase in the amount of content available in digital forms gives rise to a problem to manage this online textual data. As a result, it has become a necessary to classify/categorize large texts (documents) into specific classes. Text Classification assigns a text document to one of a set of predefined classes. This paper covers different text classification techniques and also includes Classifier Architecture and Text Classification Applications. Terms Text Mining, Text Classification, Applications, Classifier Architecture, Classification Techniques.


title: jMWE: A Java Toolkit for Detecting Multi-Word Expressions

abstract: jMWE is a Java library for implementing and testing algorithms that detect Multi-Word Expression (MWE) tokens in text. It provides (1) a detector API, including implementations of several detectors, (2) facilities for constructing indices of MWE types that may be used by the detectors, and (3) a testing framework for measuring the performance of a MWE detector. The software is available for free download.


title: Design and Motion Planning of a Two-Module Collaborative Indoor Pipeline Inspection Robot

abstract: This paper deals with a design and motion planning algorithm of a caterpillar-based pipeline robot that can be used for inspection of 80-100-mm pipelines in an indoor pipeline environment. The robot system uses a differential drive to steer the robot and spring loaded four-bar mechanisms to assure that the robot expands to grip the pipe walls. Unique features of this robot are the caterpillar wheels, the analysis of the four-bar mechanism supporting the treads, a closed-form kinematic approach, and an intuitive user interface. In addition, a new motion planning approach is proposed, which uses springs to interconnect two robot modules and allows the modules to cooperatively navigate through difficult segments of the pipes. Furthermore, an analysis method of selecting optimal compliance to assure functionality and cooperation is suggested. Simulation and experimental results are used throughout the paper to highlight algorithms and approaches.


title: Structured Parallel Programming for Monte Carlo Tree Search

abstract: In this paper, we present a new algorithm for parallel Monte Carlo tree search (MCTS). It is based on the pipeline pattern and allows flexible management of the control flow of the operations in parallel MCTS. The pipeline pattern provides for the first structured parallel programming approach to MCTS. Moreover, we propose a new lock-free tree data structure for parallel MCTS which removes synchronization overhead. The Pipeline Pattern for Parallel MCTS algorithm (called 3PMCTS), scales very well to higher numbers of cores when compared to the existing methods.


title: Dual-Band Circularly Polarized Annular-Ring Microstrip Antenna for GNSS Applications

abstract: A dual-band circularly polarized antenna fed by four apertures that covers the bands of GPS (L1, L2, L5), Galileo (E5a, E5b, E1, E2, L1), and GLONASS (L1, L3) is introduced. A lotus-shaped aperture is added to optimize the coupling between the microstrip lines and the rings. Three wideband planar baluns are used to achieve good axial ratio (lower than 2.1 dB in both bands) and VSWR (41.2%). The measured results of the annular-ring microstrip antenna show good performance of a dual-band operation, and they confirm the validity of this design, which meets the requirement of Global Navigation Satellite System (GNSS) applications.


title: A Randomized Controlled Trial Investigating the Effects of Neurofeedback, Methylphenidate, and Physical Activity on Event-Related Potentials in Children with Attention-Deficit/Hyperactivity Disorder

abstract: OBJECTIVE ::: Electroencephalographic (EEG) neurofeedback (NF) is considered a nonpharmacological alternative for medication in attention-deficit/hyperactivity disorder (ADHD). Comparisons of the behavioral efficacy of NF and medication have produced inconsistent results. EEG measures can provide insight into treatment mechanisms, but have received little consideration. In this randomized controlled trial (RCT), effects of NF were compared with methylphenidate (MPH), and physical activity (PA) in children with ADHD on event-related potential (ERP) indices of response inhibition, which are involved in ADHD psychopathology. ::: ::: ::: METHODS ::: Using a multicenter three way parallel group RCT design, 112 children with a Diagnostic and Statistical Manual of Mental Disorders, 4th ed. (DSM-IV) (American Psychiatric Association 1994 ) diagnosis of ADHD, between 7 and 13 years of age, were initially included. NF training consisted of 30 sessions of theta/beta training at Cz over a 10 week period. PA training was a semiactive control group, matched in frequency and duration. MPH was titrated using a double-blind placebo controlled procedure in 6 weeks, followed by a stable dose for 4 weeks. ERP measures of response inhibition, N2 and P3, were available for 81 children at pre- and postintervention (n = 32 NF, n = 25 MPH, n = 24 PA). ::: ::: ::: RESULTS ::: Only the medication group showed a specific increase in P3 amplitude compared with NF (partial eta-squared [ηp(2) ] = 0.121) and PA (ηp(2) = 0.283), which was related to improved response inhibition. Source localization of medication effects on P3 amplitude indicated increased activation primarily in thalamic and striatal nuclei. ::: ::: ::: CONCLUSIONS ::: This is the first study that simultaneously compared NF with stimulant treatment and a semiactive control group. Only stimulant treatment demonstrated specific improvements in brain function related to response inhibition. These results are in line with recent doubts on the efficacy and specificity of NF as treatment for ADHD. ::: ::: ::: CLINICAL TRIALS REGISTRATION ::: Train Your Brain? Exercise and Neurofeedback Intervention for ADHD, https://clinicaltrials.gov/show/NCT01363544 , Ref. No. NCT01363544.


Social Exchange Theory . does it matter for employee performance


title: Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

abstract: Recent molecular studies have revealed that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels, and phenotypic output 1–5 , with important functional consequences 4,5 . Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs 1,2 or proteins 5,6 simultaneously because genomic profiling methods 3 could not be applied to single cells until very recently 7–10 . Here, we use single-cell RNA-Seq to investigate heterogeneity in the response of bone marrow derived dendritic cells (BMDCs) to lipopolysaccharide (LPS). We find extensive, and previously unobserved, bimodal variation in mRNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization (RNA-FISH) for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.


title: Deep Depth Completion of a Single RGB-D Image

abstract: The goal of our work is to complete the depth channel of an RGB-D image. Commodity-grade depth cameras often fail to sense depth for shiny, bright, transparent, and distant surfaces. To address this problem, we train a deep network that takes an RGB image as input and predicts dense surface normals and occlusion boundaries. Those predictions are then combined with raw depth observations provided by the RGB-D camera to solve for depths for all pixels, including those missing in the original observation. This method was chosen over others (e.g., inpainting depths directly) as the result of extensive experiments with a new depth completion benchmark dataset, where holes are filled in training data through the rendering of surface reconstructions created from multiview RGB-D scans. Experiments with different network inputs, depth representations, loss functions, optimization methods, inpainting methods, and deep depth estimation networks show that our proposed approach provides better depth completions than these alternatives.


title: Low Power Wide Area Networks: An Overview

abstract: Low power wide area (LPWA) networks are attracting a lot of attention primarily because of their ability to offer affordable connectivity to the low-power devices distributed over very large geographical areas. In realizing the vision of the Internet of Things, LPWA technologies complement and sometimes supersede the conventional cellular and short range wireless technologies in performance for various emerging smart city and machine-to-machine applications. This review paper presents the design goals and the techniques, which different LPWA technologies exploit to offer wide-area coverage to low-power devices at the expense of low data rates. We survey several emerging LPWA technologies and the standardization activities carried out by different standards development organizations (e.g., IEEE, IETF, 3GPP, ETSI) as well as the industrial consortia built around individual LPWA technologies (e.g., LoRa Alliance, Weightless-SIG, and Dash7 alliance). We further note that LPWA technologies adopt similar approaches, thus sharing similar limitations and challenges. This paper expands on these research challenges and identifies potential directions to address them. While the proprietary LPWA technologies are already hitting the market with large nationwide roll-outs, this paper encourages an active engagement of the research community in solving problems that will shape the connectivity of tens of billions of devices in the next decade.


Real-Time Semantic Segmentation Benchmarking Framework


title: A Comparative Review and Evaluation of Approximate Adders

abstract: As an important arithmetic module, the adder plays a key role in determining the speed and power consumption of a digital signal processing (DSP) system. The demands of high speed and power efficiency as well as the fault tolerance nature of some applications have promoted the development of approximate adders. This paper reviews current approximate adder designs and provides a comparative evaluation in terms of both error and circuit characteristics. Simulation results show that the equal segmentation adder (ESA) is the most hardware-efficient design, but it has the lowest accuracy in terms of error rate (ER) and mean relative error distance (MRED). The error-tolerant adder type II (ETAII), the speculative carry select adder (SCSA) and the accuracy-configurable approximate adder (ACAA) are equally accurate (provided that the same parameters are used), however ETATII incurs the lowest power-delay-product (PDP) among them. The almost correct adder (ACA) is the most power consuming scheme with a moderate accuracy. The lower-part-OR adder (LOA) is the slowest, but it is highly efficient in power dissipation.


title: Moringa oleifera Lam: Targeting Chemoprevention

abstract: Moringa oleifera Lam, family Moringaceae, is a perennial plant which is called various names, but is locally ::: known in Malaysia as ‘’murungai’’ or ‘’kelor’’. Glucomoringin, a glucosinolate with from M. oleifera is a major secondary metabolite compound. The seeds and leaves of the plant are reported to have the highest amount of ::: glucosinolates. M. oleifera is well known for its many uses health and benefits. It is claimed to have nutritional, ::: medicinal and chemopreventive potentials. Chemopreventive effects of M. oleifera are expected due to the existence ::: of glucosinolate which it is reported to have the ability to induce apoptosis in anticancer studies. Furthermore, ::: chemopreventive value of M. oleifera has been demonstrated in studies utilizing its leaf extract to inhibit the ::: growth of human cancer cell lines. This review highlights the advantages of M. oleifera targeting chemoprevention ::: where glucosinolates could help to slow the process of carcinogenesis through several molecular targets. It is ::: also includes inhibition of carcinogen activation and induction of carcinogen detoxification, anti-inflammatory, ::: anti-tumor cell proliferation, induction of apoptosis and inhibition of tumor angiogenesis. Finally, for synergistic ::: effects of M. oleifera with other drugs and safety, essential for chemoprevention, it is important that it safe to be consumed by human body and works well. Although there there were promising evident about M. oleifera in ::: chemoprevention extensive research need to be done due to the expected rise of cancer in coming years and to ::: gain more information about the mechanisms involved in M. oleifera influence, which could be a good source to ::: inhibit several major mechanisms involved in cancer development.


title: Word Sense Disambiguation-based Sentence Similarity

abstract: Previous works tend to compute the similarity between two sentences based on the comparison of their nearest meanings. However, the nearest meanings do not always represent their actual meanings. This paper presents a method which computes the similarity between two sentences based on a comparison of their actual meanings. This is achieved by transforming an existing most-outstanding corpus-based measure into a knowledge-based measure, which is then integrated with word sense disambiguation. The experimental results on a standard data set show that the proposed method outperforms the baseline and the improvement achieved is statistically significant at 0.025 levels.


title: Forecasting Nike's sales using Facebook data

abstract: This paper tests whether accurate sales forecasts for Nike are possible from Facebook data and how events related to Nike affect the activity on Nike's Facebook pages. The paper draws from the AIDA sales framework (Awareness, Interest, Desire, and Action) from the domain of marketing and employs the method of social set analysis from the domain of computational social science to model sales from Big Social Data. The dataset consists of (a) selection of Nike's Facebook pages with the number of likes, comments, posts etc. that have been registered for each page per day and (b) business data in terms of quarterly global sales figures published in Nike's financial reports. An event study is also conducted using the Social Set Visualizer (SoSeVi). The findings suggest that Facebook data does have informational value. Some of the simple regression models have a high forecasting accuracy. The multiple regressions have a lower forecasting accuracy and cause analysis barriers due to data set characteristics such as perfect multicollinearity. The event study found abnormal activity around several Nike specific events but inferences about those activity spikes, whether they are purely event-related or coincidences, can only be determined after detailed case-by-case text analysis. Our findings help assess the informational value of Big Social Data for a company's marketing strategy, sales operations and supply chain.


title: Interaction primitives for human-robot cooperation tasks

abstract: To engage in cooperative activities with human ::: partners, robots have to possess basic interactive abilities ::: and skills. However, programming such interactive skills is a ::: challenging task, as each interaction partner can have different ::: timing or an alternative way of executing movements. In this ::: paper, we propose to learn interaction skills by observing how ::: two humans engage in a similar task. To this end, we introduce ::: a new representation called Interaction Primitives. Interaction ::: primitives build on the framework of dynamic motor primitives ::: (DMPs) by maintaining a distribution over the parameters of ::: the DMP. With this distribution, we can learn the inherent ::: correlations of cooperative activities which allow us to infer the ::: behavior of the partner and to participate in the cooperation. ::: We will provide algorithms for synchronizing and adapting the ::: behavior of humans and robots during joint physical activities.


title: Tracking objects with point clouds from vision and touch

abstract: We present an object-tracking framework that fuses point cloud information from an RGB-D camera with tactile information from a GelSight contact sensor. GelSight can be treated as a source of dense local geometric information, which we incorporate directly into a conventional point-cloud-based articulated object tracker based on signed-distance functions. Our implementation runs at 12 Hz using an online depth reconstruction algorithm for GelSight and a modified second-order update for the tracking algorithm. We present data from hardware experiments demonstrating that the addition of contact-based geometric information significantly improves the pose accuracy during contact, and provides robustness to occlusions of small objects by the robot's end effector.


title: Does telecommuting improve productivity?

abstract: Seeking solid evidence of demonstrable productivity gains.


title: MedLeaf: Mobile biodiversity informatics tool for mapping and identifying Indonesian medicinal Plants

abstract: We presents a mobile biodiversity informatics tools for identifying and mapping Indonesian medicinal plants. The system — called MedLeaf — has been developed as a prototype data resource for documenting, integrating, disseminating, and identifying of Indonesian medicinal plants. Identification of medicinal plant is done automatically based on digital image processing. Fuzzy Local Binary Pattern (LBP) and geometrical features are used to extract leaves features. Probabilistic Neural Network is used as classifier for discrimination. Data set consist of 85 species of Indonesian medicinal plants with 3,502 leaves digital images. Our results indicate that combination of leaves features outperform than using single features with accuracy 88.5%. The distribution of medicinal plants can be shown on mobile phone using GIS application. The application is essential to help people identify the medicinal plants and disseminate information of medicinal plants distribution in Indonesia.


title: A new method for fruits recognition system

abstract: Several fruit recognition techniques are developed based upon color and shape attributes. However, different fruit images may have similar or identical color and shape values. Hence, using color features and shape features analysis methods are still not robust and effective enough to identify and distinguish fruits images. A new fruit recognition system has been proposed, which combines three features analysis methods: color-based, shape-based and size-based in order to increase accuracy of recognition. The proposed method classifies and recognizes fruit images based on obtained feature values by using nearest neighbours classification. Consequently, our system shows the fruit name and a short description to user. The proposed fruit recognition system analysis classifies and identifies fruits successfully up to 90% accuracy. This system also serves as a useful tool in a variety of fields such as education, image retrieval and plantation science.


title: Making Passwords Secure and Usable

abstract: To date, system research has focused on designing security mechanisms to protect systems access although their usability has rarely been investigated. This paper reports a study in which users’ perceptions of password mechanisms were investigated through questionnaires and interviews. Analysis of the questionnaires shows that many users report problems, linked to the number of passwords and frequency of password use. In-depth analysis of the interview data revealed that the degree to which users conform to security mechanisms depends on their perception of security levels, information sensitivity and compatibility with work practices. Security mechanisms incompatible with these perceptions may be circumvented by users and thereby undermine system security overall.


title: Securify: Practical Security Analysis of Smart Contracts

abstract: Permissionless blockchains allow the execution of arbitrary programs (called smart contracts), enabling mutually untrusted entities to interact without relying on trusted third parties. Despite their potential, repeated security concerns have shaken the trust in handling billions of USD by smart contracts. ::: To address this problem, we present Securify, a security analyzer for Ethereum smart contracts that is scalable, fully automated, and able to prove contract behaviors as safe/unsafe with respect to a given property. Securify's analysis consists of two steps. First, it symbolically analyzes the contract's dependency graph to extract precise semantic information from the code. Then, it checks compliance and violation patterns that capture sufficient conditions for proving if a property holds or not. To enable extensibility, all patterns are specified in a designated domain-specific language. ::: Securify is publicly released, it has analyzed >18K contracts submitted by its users, and is regularly used to conduct security audits by experts. We present an extensive evaluation of Securify over real-world Ethereum smart contracts and demonstrate that it can effectively prove the correctness of smart contracts and discover critical violations.


title: Bayesian Nonparametric Modeling of Categorical Data for Information Fusion and Causal Inference

abstract: This paper presents a nonparametric regression model of categorical time series in the setting of conditional tensor factorization and Bayes network. The underlying algorithms are developed to provide a flexible and parsimonious representation for fusion of correlated information from heterogeneous sources, which can be used to improve the performance of prediction tasks and infer the causal relationship between key variables. The proposed method is first illustrated by numerical simulation and then validated with two real-world datasets: (1) experimental data, collected from a swirl-stabilized lean-premixed laboratory-scale combustor, for detection of thermoacoustic instabilities and (2) publicly available economics data for causal inference-making.


title: Quantifying Controversy in Social Media

abstract: Which topics spark the most heated debates in social media? Identifying these topics is a first step towards creating systems which pierce echo chambers. In this paper, we perform a systematic methodological study of controversy detection using social media network structure and content. Unlike previous work, rather than identifying controversy in a single hand-picked topic and use domain-specific knowledge, we focus on comparing topics in any domain. Our approach to quantifying controversy is a graph-based three-stage pipeline, which involves (i) building a conversation graph about a topic, which represents alignment of opinion among users; (ii) partitioning the conversation graph to identify potential sides of the controversy; and (iii)measuring the amount of controversy from characteristics of the~graph. We perform an extensive comparison of controversy measures, as well as graph building approaches and data sources. We use both controversial and non-controversial topics on Twitter, as well as other external datasets. We find that our new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy, and show that content features are vastly less helpful in this task.


title: Structural Deep Network Embedding

abstract: Network embedding is an important method to learn low-dimensional representations of vertexes in networks, aiming to capture and preserve the network structure. Almost all the existing network embedding methods adopt shallow models. However, since the underlying network structure is complex, shallow models cannot capture the highly non-linear network structure, resulting in sub-optimal network representations. Therefore, how to find a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem. To solve this problem, in this paper we propose a Structural Deep Network Embedding method, namely SDNE. More specifically, we first propose a semi-supervised deep model, which has multiple layers of non-linear functions, thereby being able to capture the highly non-linear network structure. Then we propose to exploit the first-order and second-order proximity jointly to preserve the network structure. The second-order proximity is used by the unsupervised component to capture the global network structure. While the first-order proximity is used as the supervised information in the supervised component to preserve the local network structure. By jointly optimizing them in the semi-supervised deep model, our method can preserve both the local and global network structure and is robust to sparse networks. Empirically, we conduct the experiments on five real-world networks, including a language network, a citation network and three social networks. The results show that compared to the baselines, our method can reconstruct the original network significantly better and achieves substantial gains in three applications, i.e. multi-label classification, link prediction and visualization.


title: A high-speed protection circuit for IGBTs subjected to hard-switching faults

abstract: This paper describes a high-speed protection circuit for insulated-gate bipolar transistors (IGBTs) subjected to hard-switching faults (HSFs). The reverse transfer capacitance depends on the collector–emitter voltage, and it produces a significant effect on the switching behavior not only under normal conditions but also under HSF conditions. A gate charge characteristic under HSF conditions differs from that under normal turn-on conditions. Hence, an HSF can be detected by monitoring both the gate–emitter voltage and the amount of gate charge during the turn-on transient period. IGBTs can be rapidly protected from destruction by using this method because a blanking time is unnecessary. Simulation and experiment verify the validity of the proposed high-speed protection circuit.


title: Symmetry reCAPTCHA

abstract: This paper is a reaction to the poor performance of symmetry detection algorithms on real-world images, benchmarked since CVPR 2011. Our systematic study reveals significant difference between human labeled (reflection and rotation) symmetries on photos and the output of computer vision algorithms on the same photo set. We exploit this human-machine symmetry perception gap by proposing a novel symmetry-based Turing test. By leveraging a comprehensive user interface, we collected more than 78,000 symmetry labels from 400 Amazon Mechanical Turk raters on 1,200 photos from the Microsoft COCO dataset. Using a set of ground-truth symmetries automatically generated from noisy human labels, the effectiveness of our work is evidenced by a separate test where over 96% success rate is achieved. We demonstrate statistically significant outcomes for using symmetry perception as a powerful, alternative, image-based reCAPTCHA.


title: Enterprise information extraction: recent developments and open challenges

abstract: Information extraction (IE) - the problem of extracting structured information from unstructured text - has become an increasingly important topic in recent years. A SIGMOD 2006 tutorial [3] outlined challenges and opportunities for the database community to advance the state of the art in information extraction, and posed the following grand challenge: "Can we build a System R for information extraction? Our tutorial gives an overview of progress the database community has made towards meeting this challenge. In particular, we start by discussing design requirements in building an enterprise IE system. We then survey recent technological advances towards addressing these requirements, broadly categorized as: (1) Languages for specifying extraction programs in a declarative way, thus allowing database-style performance optimizations; (2) Infrastructure needed to ensure scalability, and (3) Development support for enterprise IE systems. Finally, we outline several open challenges and opportunities for the database community to further advance the state of the art in enterprise IE systems. The tutorial is intended for students and researchers interested in information extraction and its applications, and assumes no prior knowledge of the area.


title: Delayed product differentiation: a design and manufacturing perspective

abstract: Delayed product differentiation (DPD) is a design concept for improving customer satisfaction and manufacturing performance. In this paper, a methodology for implementing the delayed product differentiation strategy in manufacturing is presented. Three design rules are suggested. The impact of delayed product differentiation strategy on the performance of a manufacturing system is quantified and incorporated in the product design. The problem of selecting designs to minimize the total differentiation and manufacturing cost is formulated and solved. The methodology presented in the paper is illustrated with examples.


title: Quantum arithmetic and numerical analysis using Repeat-Until-Success circuits

abstract: We develop a method for approximate synthesis of single–qubit rotations of the form e−if(φ1,...,φk)X that is based on the Repeat-Until-Success (RUS) framework for quantum circuit synthesis. We demonstrate how smooth computable functions f can be synthesized from two basic primitives. This synthesis approach constitutes a manifestly quantum form of arithmetic that differs greatly from the approaches commonly used in quantum algorithms. The key advantage of our approach is that it requires far fewer qubits than existing approaches: as a case in point, we show that using as few as 3 ancilla qubits, one can obtain RUS circuits for approximate multiplication and reciprocals. We also analyze the costs of performing multiplication and inversion on a quantum computer using conventional approaches and find that they can require too many qubits to execute on a small quantum computer, unlike our approach.


title: Synthesis and stabilization of complex behaviors through online trajectory optimization

abstract: We present an online trajectory optimization method and software platform applicable to complex humanoid robots performing challenging tasks such as getting up from an arbitrary pose on the ground and recovering from large disturbances using dexterous acrobatic maneuvers. The resulting behaviors, illustrated in the attached video, are computed only 7 × slower than real time, on a standard PC. The video also shows results on the acrobot problem, planar swimming and one-legged hopping. These simpler problems can already be solved in real time, without pre-computing anything.


www.ACSIJ.org Software Architecture Viewpoint Models: A Short Survey


title: Matching People across Camera Views using Kernel Canonical Correlation Analysis

abstract: Matching people across views is still an open problem in computer vision and in video surveillance systems. In this paper we address the problem of person re-identification across disjoint cameras by proposing an efficient but robust kernel descriptor to encode the appearance of a person. The matching is then improved by applying a learning technique based on Kernel Canonical Correlation Analysis (KCCA) which finds a common subspace between the proposed descriptors extracted from disjoint cameras, projecting them into a new description space. This common description space is then used to identify a person from one camera to another with a standard nearest-neighbor voting method. We evaluate our approach on two publicly available datasets for re-identification (VIPeR and PRID), demonstrating that our method yields state-of-the-art performance with respect to recent techniques proposed for the re-identification task.


title: Modeling energy performance of C-RAN with optical transport in 5G network scenarios

abstract: The deployment of new 5G wireless interfaces based on massive multiantenna transmission and beamforming is expected to have a significant impact on the complexity and power consumption of the transport network. This paper analyzes the energy performance of four radio access network (RAN) architectures, each one utilizing a different option for splitting the baseband processing functions. The radio segment is based on Long-Term Evolution (LTE) and 5G radio access technologies. The transport segment is based on optical wavelength division multiplexing, where coherent and direct detection transmissions are considered. The energy consumption of each RAN architecture is weighted against i) the benefits for the radio segment as a function of the level of centralization of the baseband processing functions and ii) the power consumption levels needed to accommodate the capacity generated at each base station. Results show that, with LTE radio interfaces, the energy consumption of the transport network amounts to only a few percent of the overall network power consumption. As a result, fully centralized LTE radio architectures are a viable option, with energy savings of at least 27% compared with conventional distributed architectures. On the other hand, with advanced 5G radio interfaces, centralized architectures, if not carefully designed, might become impractical due to the excessive energy consumption of the transport network (i.e., as a result of the huge capacity to be accommodated). This aspect can be mitigated via a careful joint design of the radio and the transport network (i.e., leveraging on appropriate optical transmission techniques and compromising where needed on the radio network performance).


title: How does project size affect cost estimation error? Statistical artifacts and methodological challenges

abstract: Abstract Empirical studies differ in what they report as the underlying relation between project size and percent cost overrun. As a consequence, the studies also differ in their project management recommendations. We show that studies with a project size measure based on the actual cost systematically report an increase in percent cost overrun with increased project size, whereas studies with a project size measure based on the estimated cost report a decrease or no change in percent cost overrun with increased project size. The observed pattern is, we argue, to some extent a statistical artifact caused by imperfect correlation between the estimated and the actual cost. We conclude that the previous observational studies cannot be considered to provide reliable evidence in favor of an underlying project size related cost estimation bias. We discuss the limited, statistically robust evidence and the need for other types of studies.


title: Using ghost edges for classification in sparsely labeled networks

abstract: We address the problem of classification in partially labeled networks (a.k.a. within-network classification) where observed class labels are sparse. Techniques for statistical relational learning have been shown to perform well on network classification tasks by exploiting dependencies between class labels of neighboring nodes. However, relational classifiers can fail when unlabeled nodes have too few labeled neighbors to support learning (during training phase) and/or inference (during testing phase). This situation arises in real-world problems when observed labels are sparse. In this paper, we propose a novel approach to within-network classification that combines aspects of statistical relational learning and semi-supervised learning to improve classification performance in sparse networks. Our approach works by adding "ghost edges" to a network, which enable the flow of information from labeled to unlabeled nodes. Through experiments on real-world data sets, we demonstrate that our approach performs well across a range of conditions where existing approaches, such as collective classification and semi-supervised learning, fail. On all tasks, our approach improves area under the ROC curve (AUC) by up to 15 points over existing approaches. Furthermore, we demonstrate that our approach runs in time proportional to L • E, where L is the number of labeled nodes and E is the number of edges.


title: Design Space Exploration of Split-Path Data Driven Dynamic Full Adder

abstract: This paper presents the design, the analysis and the complete characterization of a novel split-path Data Driven Dynamic (sp-D3L) full adder cell in IBM’s 65 nm CMOS process. The split path D3L design style derived from standard D3L allows the design of high speed dynamic circuits without the power overhead of the clock tree while providing significantly higher performance than the D3L due to reduced capacitance at the pre-charge node. To demonstrate the performance benefits of the new split-path dynamic approach, we present comparison of the proposed adder with conventional static and dynamic adder cells. All the adder circuits were characterized for speed, power, area, noise margins, supply voltage scaling as well as fan-out capabilities. To evaluate the combined impact of load driven by the adder and load presented by the adder to the driving circuit, a combined fan-infan-out analysis with varying loads was also performed. Monte Carlo simulations were performed to evaluate the reliability of the adder design against random process, voltage and temperature variations. To compare with state of the art, we also performed a comparison of our proposed adder with several low power as well as high performance adders proposed recently in literature. Furthermore, to simulate the behavior of the adder in data path elements, we built ripple carry adders of varying lengths using the proposed adder. The new design was found to achieve from 16% to 27% performance advantages over its static and dynamic counterparts at nominal supply voltage. With supply voltage scaled from 1 V to 0.8 V, the adder shows 12%, 34% and 39% PDP advantage over domino, static and conventional D3L designs respectively. Fan-out analysis showed the adder to perform with 11% to 41% better PDP than the others at worst case FO32 loading.


title: Performance Evaluation of Darshan 3.0.0 on the Cray XC30

abstract: Abstract—Darshan is a lightweight I/O characterization tool used to gather and summarize salient I/O workload statistics from HPC applications. Darshan was designed to minimize any possible perturbations of an application’s performance, leading it to be enabled by default on a number of production HPC systems. For each file accessed by a given application, Darshan records the count and types of I/O operations performed, histograms of access sizes, cumulative timers on the amount of time spent doing I/O, and other statistical data. This type of data has proved invaluable in understanding and improving the I/O performance of HPC applications. Darshan 3.0.0 is the new modularized version of the traditional Darshan library and file format, allowing users to easily add more in-depth I/O characterization data to Darshan logs. In this work we perform an empirical evaluation of Darshan 3.0.0 to ensure that it continues to meet performance expectations for broad deployment. In particular, we evaluate the imposed overhead on instrumented I/O operations, time taken to shut down and generate corresponding Darshan log files, and resultant log file sizes for different workloads. These performance results are compared to results of Darshan 2.3.0 on the Edison XC30 system at NERSC to determine whether themore » new version is lightweight enough to run full-time on production HPC systems. Our evaluation shows that Darshan has limited impact on application I/O performance and can fully generate a corresponding log file for most application workloads in under two seconds.« less


title: RaBit EscAPE: a board game for computational thinking

abstract: Computational thinking (CT) is increasingly seen as a core literacy skill for the modern world on par with the longestablished skills of reading, writing, and arithmetic. To promote the learning of CT at a young age we capitalized on children's interest in play. We designed RabBit EscApe, a board game that challenges children, ages 610, to orient tangible, magnetized manipulatives to complete or create paths. We also ran an informal study to investigate the effectiveness of the game in fostering children's problemsolving capacity during collaborative game play. We used the results to inform our instructional interaction design that we think will better support the learning activities and help children hone the involved CT skills. Overall, we believe in the power of such games to challenge children to grow their understanding of CT in a focused and engaging activity.


title: Wavelet Packet Multi-layer Perceptron for Chaotic Time Series Prediction : Effects of Weight Initialization

abstract: We train the wavelet packet multi-layer perceptron neural network (WP-MLP) by backpropagation for time series prediction. Weights in the backpropagation algorithm are usually initialized with small random values. If the random initial weights happen to be far from a good solution or they are near a poor local optimum, training may take a long time or get trap in the local optimum. Proper weights initialization will place the weights close to a good solution with reduced training time and increased the possibility of reaching a good solution. In this paper, we investigate the effect of weight initialization on WP-MLP using two clustering algorithms. We test the initialization methods on WP-MLP with the sunspots and Mackey-Glass benchmark time series. We show that with proper weight initialization, better prediction performance can be attained.


title: Experiential Narrative in Game Environments

abstract: This paper explores the contentious notion of experiential narrative and proposes the first step in a narrative framework for game environment. It argues for a shift in emphasis from story-telling, the dominant mode of narrative in literature and cinema, to story generation. To this effect the paper forwards a perspective on experiential narrative that is grounded in the specific qualities of the game. This avoids the over-generalization that tends to accompany discussions of experiential narrative while retaining the cognitive dimension in play.


title: Symbolic heuristic search value iteration for factored POMDPs

abstract: We propose Symbolic heuristic search value iteration (Symbolic HSVI) algorithm, which extends the heuristic search value iteration (HSVI) algorithm in order to handle factored partially observable Markov decision processes (factored POMDPs). The idea is to use algebraic decision diagrams (ADDs) for compactly representing the problem itself and all the relevant intermediate computation results in the algorithm. We leverage Symbolic Perseus for computing the lower bound of the optimal value function using ADD operators, and provide a novel ADD-based procedure for computing the upper bound. Experiments on a number of standard factored POMDP problems show that we can achieve an order of magnitude improvement in performance over previously proposed algorithms.


School of Computing 2008-0101 Referring Expression Generation Challenge 2008 DIT System Description


title: Structure prediction of stem bromelain from pineapples (Ananas comosus) using procaricain enzyme as a modelling template

abstract: Bromelain is a general name for a family of sulfhydryl that can be found in the proteolytic enzyme group from pineapples (Ananas Comosus). In this study focuses on the prediction of three dimensional structure (3D) of bromelain using homology modelling by MODELLER. The amino acid sequence of bromelain was obtained from the NCBI database and the analysis was done using various bioinformatics tools. 1PCI was used as the target templates for bromelain structure prediction. The 1PCI was found to be the most suitable template for the prediction of the bromelain structure.


title: On Using Mobility to Propagate Malware

abstract: Mobility can be exploited to spread malware among wireless nodes. In this paper, we present an analytical model for estimating the evolution of infections spanning multiple network domains that host mobile nodes. We validate the accuracy of the proposed model by comparing its predictions to simulations driven by realistic mobility patterns. Our results show that such a mobile infection requires less than a day to infect the majority of a mobile population with thousands of wireless nodes spanning hundreds of network domains. Moreover, if mobile nodes are allowed to infect nodes within the same domain that are connected to the wired network, then an even smaller number of mobile nodes can inflict comparable damage in similar time frames. Unfortunately, these infections generate negligible activity at global malware monitoring stations (e.g., network telescopes and honeypots), which contributes to their stealthiness. By observing the infection's spatial evolution we show that popular domains are infected during the early stages of the infection. This observation is likely to be useful in designing countermeasures against mobile infections. By placing monitors in approximately 10% of the most visited domains, we can detect the mobile worm before it reaches a majority of the population. Finally, we elucidate why simply placing telescopes in just the popular domains is not sufficient for early detection.


title: Death in head-down position in a heavily intoxicated obese man.

abstract: Dying in a head-down position is rare, and autopsy may reveal no morphological findings which can sufficiently explain the cause of death. The authors describe a case of positional asphyxia of a 78-year-old man, found dead hanging in head-down position inside a blackberry bush. The subject was suspended by his left leg and wedged inside a thick blackberry bush, while his right leg was free and inflected. Investigation revealed that the elderly man had last been seen after lunch in the early afternoon (about 2.00 p.m.), two days before being found. The subject had no pre-existing physical or mental condition which might have explained his death. He was 164 cm tall and weighed 90 kg (BMI: 33.4). External examination of the body revealed abrasions on the head and multiple superficial scratch marks on the hands; no external signs of violence were observed. The deceased's head was cyanotic and revealed marked petechial haemorrhages of the conjunctiva. Rigor mortis was fully developed and reddish fixed livor was observed on the face, neck and upper chest. The distribution of livor was consistent with the position of the body at the scene. Autopsy revealed marked pulmonary and cerebral oedema; the liver showed fatty vacuolization with a mild increase of connective tissue and thickening of the walls of the central veins and centrilobular sinusoids. The heart was enlarged, particularly the left ventricle, with a slight, calcified, diffuse and increased thickness of the aortic and coronary arteries. The remains of meat and vegetables in the initial phase of digestion were found inside the stomach, and there was a strong smell of alcohol. The blood alcohol concentration, ascertained by GC (Gas Chromatography), was 2.10 g/l. Toxicological assays for addictive drugs (cocaine, heroin, THC) yielded negative results. There were no features suggesting that the deceased had been the victim of an assault. The authors of this case report illustrate the main pathophysiological and diagnostic features of positional asphyxia and possible relevant co-causes of death, with an emphasis on the combined role of acute alcoholic intoxication and obesity in the hastened demise of a subject in the head-down position.