title: Integrating Statistical Prior Knowledge into Convolutional Neural Networks

abstract: In this work we show how to integrate prior statistical knowledge, obtained through principal components analysis (PCA), into a convolutional neural network in order to obtain robust predictions even when dealing with corrupted or noisy data. Our network architecture is trained end-to-end and includes a specifically designed layer which incorporates the dataset modes of variation discovered via PCA and produces predictions by linearly combining them. We also propose a mechanism to focus the attention of the CNN on specific regions of interest of the image in order to obtain refined predictions. We show that our method is effective in challenging segmentation and landmark localization tasks.


title: An experimental study on the applicability of evolutionary algorithms to craniofacial superimposition in forensic identification

abstract: Photographic supra-projection is a forensic process that aims to identify a missing person from a photograph and a skull found. One of the crucial tasks throughout all this process is the craniofacial superimposition which tries to find a good fit between a 3D model of the skull and the 2D photo of the face. This photographic supra-projection stage is usually carried out manually by forensic anthropologists. It is thus very time consuming and presents several difficulties. In this paper, we aim to demonstrate that real-coded evolutionary algorithms are suitable approaches to tackle craniofacial superimposition. To do so, we first formulate this complex task in forensic identification as a numerical optimization problem. Then, we adapt three different evolutionary algorithms to solve it: two variants of a real-coded genetic algorithm and the state of the art evolution strategy CMA-ES. We also consider an existing binary-coded genetic algorithm as a baseline. Results on several superimposition problems of real-world identification cases solved by the Physical Anthropology lab at the University of Granada (Spain) are considered to test our proposals.


Unsupervised corpus–wide claim detection


title: Mining Worse and Better Opinions. Unsupervised and Agnostic Aggregation of Online Reviews

abstract: In this paper, we propose a novel approach for aggregating online reviews, according to the opinions they express. Our methodology is unsupervised - due to the fact that it does not rely on pre-labeled reviews - and it is agnostic - since it does not make any assumption about the domain or the language of the review content. We measure the adherence of a review content to the domain terminology extracted from a review set. First, we demonstrate the informativeness of the adherence metric with respect to the score associated with a review. Then, we exploit the metric values to group reviews, according to the opinions they express. Our experimental campaign has been carried out on two large datasets collected from Booking and Amazon, respectively.


Herland , by Charlotte Perkins Gilman


title: Embedded Steer-by-Wire System Development

abstract: Model-based design enables the automatic generation of final-build software from models for high-volume automotive embedded systems. A software engineering framework is needed to support this. This paper presents a framework of processes, methods, and tools for the design of automotive embedded systems. A steer-bywire system serves as an example.


title: Underground Mine Communications: A Survey

abstract: After a recent series of unfortunate underground mining disasters, the vital importance of communications for underground mining is underlined one more time. Establishing reliable communication is a very difficult task for underground mining due to the extreme environmental conditions. Until now, no single communication system exists which can solve all of the problems and difficulties encountered in underground mine communications. However, combining research with previous experiences might help existing systems improve, if not completely solve all of the problems. In this survey, underground mine communication is investigated. Major issues which underground mine communication systems must take into account are discussed. Communication types, methods, and their significance are presented.


title: Semi-supervised Bootstrapping approach for Named Entity Recognition

abstract: The aim of Named Entity Recognition (NER) is to identify references of named entities in unstructured documents, and to classify them into pre-defined semantic categories. NER often aids from added background knowledge in the form of gazetteers. However using such a collection does not deal with name variants and cannot resolve ambiguities associated in identifying the entities in context and associating them with predefined categories. We present a semi-supervised NER approach that starts with identifying named entities with a small set of training data. Using the identified named entities, the word and the context features are used to define the pattern. This pattern of each named entity category is used as a seed pattern to identify the named entities in the test set. Pattern scoring and tuple value score enables the generation of the new patterns to identify the named entity categories. We have evaluated the proposed system for English language with the dataset of tagged (IEER) and untagged (CoNLL 2003) named entity corpus and for Tamil language with the documents from the FIRE corpus and yield an average f-measure of 75% for both the languages.


title: Learning to Hash for Indexing Big Data—A Survey

abstract: The explosive growth in big data has attracted much attention in designing efficient indexing and search methods recently. In many critical applications such as large-scale search and pattern matching, finding the nearest neighbors to a query is a fundamental research problem. However, the straightforward solution using exhaustive comparison is infeasible due to the prohibitive computational complexity and memory requirement. In response, Approximate Nearest Neighbor (ANN) search based on hashing techniques has become popular due to its promising performance in both efficiency and accuracy. Prior randomized hashing methods, e.g., Locality-Sensitive Hashing (LSH), explore data-independent hash functions with random projections or permutations. Although having elegant theoretic guarantees on the search quality in certain metric spaces, performance of randomized hashing has been shown insufficient in many real-world applications. As a remedy, new approaches incorporating data-driven learning methods in development of advanced hash functions have emerged. Such learning to hash methods exploit information such as data distributions or class labels when optimizing the hash codes or functions. Importantly, the learned hash codes are able to preserve the proximity of neighboring data in the original feature spaces in the hash code spaces. The goal of this paper is to provide readers with systematic understanding of insights, pros and cons of the emerging techniques. We provide a comprehensive survey of the learning to hash framework and representative techniques of various types, including unsupervised, semi-supervised, and supervised. In addition, we also summarize recent hashing approaches utilizing the deep learning models. Finally, we discuss the future direction and trends of research in this area.


title: Some design considerations for a mobile payment architecture

abstract: The rapid proliferation of mobile phones now provides an opportunity to harness their potential towards financial transactions. One form of such financial transactions is mobile payments. Our focus, in this paper, will be on certain design considerations for a mobile payment architecture.


title: Discriminative Metric Learning by Neighborhood Gerrymandering

abstract: We formulate the problem of metric learning for k nearest neighbor classification as a large margin structured prediction problem, with a latent variable representing the choice of neighbors and the task loss directly corresponding to classification error. We describe an efficient algorithm for exact loss augmented inference, and a fast gradient descent algorithm for learning in this model. The objective drives the metric to establish neighborhood boundaries that benefit the true class labels for the training points. Our approach, reminiscent of gerrymandering (redrawing of political boundaries to provide advantage to certain parties), is more direct in its handling of optimizing classification accuracy than those previously proposed. In experiments on a variety of data sets our method is shown to achieve excellent results compared to current state of the art in metric learning.


title: Factors Influencing Consumers' Online Purchase Intention: A Study among University Students in Malaysia

abstract: E-Commerce is one of the crucial trading methods worldwide. Hence, it is important to understand consumers’ online purchase intention. This research aims to examine factors that influence consumers’ online purchase intention among university students in Malaysia. Quantitative research approach has been adapted in this research by distributing online questionnaires to 250 Malaysian university students aged between 20-29 years old, who possess experience in online purchases. Findings of this research have discovered that trust, perceived usefulness and subjective norm are the significant factors in predicting online purchase intention. However, perceived ease of use and perceived enjoyment are not significant in predicting the variance in online purchase intention. The findings also revealed that subjective norm is the most significant predicting factor on online purchase intention among university students in Malaysia. Findings of this research will provide online marketers with a better understanding on online purchase intention which enable them to direct effective online marketing strategies.


title: Female genital appearance: "normality" unfolds.

abstract: OBJECTIVE ::: To describe variations in genital dimensions of normal women. ::: ::: ::: DESIGN ::: Observational cross-sectional study. ::: ::: ::: SETTING ::: Elizabeth Garrett Anderson Hospital, University College Hospital NHS Trust, London, UK. ::: ::: ::: POPULATION ::: Fifty premenopausal women having gynaecological procedures not involving the external genitalia under general anaesthetic. ::: ::: ::: METHODS ::: A cross sectional study using digital photography and measurements of the external genitalia. ::: ::: ::: MAIN OUTCOME MEASURES ::: Clitoral size, labial length and width, colour and rugosity, vaginal length, distance from clitoris to urethral orifice, distance from posterior fourchette to anterior anal margin. ::: ::: ::: RESULTS ::: A wide range of values were noted for each measurement. There was no statistically significant association with age, parity, ethnicity, hormonal use or history of sexual activity. ::: ::: ::: CONCLUSION ::: Women vary widely in genital dimensions. This information should be made available to women when considering surgical procedures on the genitals, decisions for which must be carefully considered between surgeon and woman.


title: DEPTA: An efficient technique for web data extraction and alignment

abstract: Many web databases contains the data in the form of structured, semi structured and in unstructured format. This paper studies the issue of extracting these data records from online web database. The main motto of this paper is to recognize the data region which contains the data records, divide these data records, mine the data value from them and keep these extracted record in a structured format. This arrangement of extracted data is useful for many application like knowledge discovery purpose etc. Existing system has some data records arrangement problem which does not arrange dynamically generated web data properly. The proposed system is based on identification of data records, extraction of data values and arranging these data values in a database. The proposed system uses the partial tree alignment method for giving the better alignment outcome.


title: Using Smart Edge IoT Devices for Safer , Rapid Response With Industry IoT Control Operations This paper presents a model that combines the capabilities of smart IoT devices with control system gateways using real-time challenge response for secure control operations

abstract: The Internet of Things (IoT) offers the user seamless interoperability and connectivity between devices, systems, services, disparate networks, and in particular control systems. End users expect to connect quickly and transparently via any endpoint device, be it a phone, tablet, wearable, TV, car, or other system that is Internet enabled. Smart IoT devices can serve as versatile control system interfaces, allowing for rapid response and potentially ubiquitous access. However, these devices can present significant security challenges. Using these technologies for user access control at IoT endpoints is vulnerable to replay, man-in-the-middle, and denial-of-service attacks. This paper presents a model that combines the capabilities of smart IoT devices with control system gateways using real-time challenge response for secure control operations. The proposed solution using both endpoint and gateway devices employs a mix of compute, cryptography, signal/image processing, and communication capabilities for authentication and authorization functions. We propose a model that is more secure, scalable, and resilient with real-time performance as compared to traditional approaches. We also provide an example IoT architecture, by showing an overview of Intel's IoT framework for control system gateways currently deployed in control, manufacturing, transportation, and retail tracking environments.


title: Frequency reconfigurable antenna using PIN diodes

abstract: A compact frequency reconfigurable double slot antenna for wireless communication is presented. The proposed antenna consists of double slot with six RF PIN diodes placed at different position on the ground plane to achieve frequency reconfigurability. Based on the switching state of the PIN diode the antenna is capable of operating at eleven different frequency ranges. The radiation pattern of the antenna is nearly omnidirectional for all the frequency bands. The simulated return loss for all the frequency bands meets the optimum value. The overall dimension of the antenna is 30 × 25mm2 including ground plane. For different switching state, the antenna can operate in both single and dual band mode. Without changing the size of the antenna the antenna can operate in single band and dual band mode by changing the state of the RF PIN diode. The antenna parameters such as VSWR return loss and radiation pattern are simulated. The analyses are performed using Ansoft HFSS v13.0 software.


title: MQTT based home automation system using ESP8266

abstract: By the virtue of blooming automation industry and wireless connectivity, all the devices within the home can be connected. This improves the comfort, energy efficiency, indoor security, cost savings of the home. Small and constrained embedded devices are used to remotely monitor the conditions within home and control the home appliances. In such case, power consumption and network bandwidth become a major concern. We need a low power device that transmits messages through a less verbose protocol. Owing to the ubiquitous availability of WiFi, all the appliances within home can be connected through a common gateway. This paper presents an overview of a light weight Message Queuing Telemetry Transport (MQTT) protocol. In the prototype, we attempt to implement MQTT on ESP8266, a WiFi based development board. Sensors and actuators are connected to ESP8266 and a Mosquitto based MQTT broker is established for remote monitoring and control.


title: Comparison between dissipative snubber and passive regenerative snubber cells as applied to isolated DCM SEPIC converters

abstract: This paper presents the comparison between dissipative RCD and passive regenerative snubber cells for isolated SEPIC converters. The passive cell is intended to improve the converter’s efficiency by transferring the energy stored in the transformer leakage inductance to the converter output. The analysis for both snubbers are detailed including design guidelines. In order to validate the regenerative proposal and compare its feasibility, experimental verification is performed for both snubbers on a 100 W, 100 V input and 50 V output voltage SEPIC converter operating in DCM.


Aerial Informatics and Robotics Platform


title: The HEXACO model of personality and video game preferences

abstract: Abstract Personality dimensions are associated with preferences for various recreational activities. The present study examined whether personality dimensions differed in their associations with preferences for particular aspects of gaming experiences. We examined the unique associations that personality dimensions had with gaming preferences reported by 359 community members who were active gamers. The basic personality dimensions captured by the HEXACO model of personality were found to be associated with gaming preferences. For example, extraversion was found to have a moderate association with the socializer gaming preference (i.e., enjoyed interacting with others while playing) and a weak association with the daredevil gaming preference (i.e., enjoyed the thrill of taking risks in games). Discussion focuses on the implications of these results for understanding the connection between personality and preferences for gaming experiences.


title: Multi-Pointer Co-Attention Networks for Recommendation

abstract: Many recent state-of-the-art recommender systems such as D-ATT, TransNet and DeepCoNN exploit reviews for representation learning. This paper proposes a new neural architecture for recommendation with reviews. Our model operates on a multi-hierarchical paradigm and is based on the intuition that not all reviews are created equal, i.e., only a selected few are important. The importance, however, should be dynamically inferred depending on the current target. To this end, we propose a review-by-review pointer-based learning scheme that extracts important reviews from user and item reviews and subsequently matches them in a word-by-word fashion. This enables not only the most informative reviews to be utilized for prediction but also a deeper word-level interaction. Our pointer-based method operates with a gumbel-softmax based pointer mechanism that enables the incorporation of discrete vectors within differentiable neural architectures. Our pointer mechanism is co-attentive in nature, learning pointers which are co-dependent on user-item relationships. Finally, we propose a multi-pointer learning scheme that learns to combine multiple views of user-item interactions. We demonstrate the effectiveness of our proposed model via extensive experiments on 24 benchmark datasets from Amazon and Yelp. Empirical results show that our approach significantly outperforms existing state-of-the-art models, with up to 19% and 71% relative improvement when compared to TransNet and DeepCoNN respectively. We study the behavior of our multi-pointer learning mechanism, shedding light on 'evidence aggregation' patterns in review-based recommender systems.


title: Local Rule-Based Explanations of Black Box Decision Systems

abstract: The recent years have witnessed the rise of accurate but obscure decision systems which hide the logic of their internal decision processes to the users. The lack of explanations for the decisions of black box systems is a key ethical issue, and a limitation to the adoption of machine learning components in socially sensitive and safety-critical contexts. %Therefore, we need explanations that reveals the reasons why a predictor takes a certain decision. In this paper we focus on the problem of black box outcome explanation, i.e., explaining the reasons of the decision taken on a specific instance. We propose LORE, an agnostic method able to provide interpretable and faithful explanations. LORE first leans a local interpretable predictor on a synthetic neighborhood generated by a genetic algorithm. Then it derives from the logic of the local interpretable predictor a meaningful explanation consisting of: a decision rule, which explains the reasons of the decision; and a set of counterfactual rules, suggesting the changes in the instance's features that lead to a different outcome. Wide experiments show that LORE outperforms existing methods and baselines both in the quality of explanations and in the accuracy in mimicking the black box.


title: A network-driven methodology for sports ranking and prediction

abstract: Recent years have seen increasing interest in ranking elite athletes and teams in professional sports leagues, and in predicting the outcomes of games. In this work, we draw an analogy between this problem and one in the field of search engine optimization, namely, that of ranking webpages on the Internet. Motivated by the famous PageRank algorithm, our TeamRank methods define directed graphs of sports teams based on the observed outcomes of individual games, and use these networks to infer the importance of teams that determines their rankings. In evaluating these methods on data from recent seasons in the National Football League (NFL) and National Basketball Association (NBA), we find that they can predict the outcomes of games with up to 70% accuracy, and that they provide useful rankings of teams that cluster by league divisions. We also propose some extensions to TeamRank that consider overall team win records and shifts in momentum over time.


title: COMPUTER GRAPHICS forum Hermite Radial Basis Functions Implicits

abstract: The Hermite radial basis functions (HRBF) implicits reconstruct an implicit function which interpolates or approximates scattered multivariate Hermite data (i.e. unstructured points and their corresponding normals). Experiments suggest that HRBF implicits allow the reconstruction of surfaces rich in details and behave better than previous related methods under coarse and/or non-uniform samplings, even in the presence of close sheets. HRBF implicits theory unifies a recently introduced class of surface reconstruction methods based on radial basis functions (RBF), which incorporate normals directly in their problem formulation. Such class has the advantage of not depending on manufactured offset-points to ensure existence of a non-trivial implicit surface RBF interpolant. In fact, we show that HRBF implicits constitute a particular case of Hermite–Birkhoff interpolation with radial basis functions, whose main results we present here. This framework not only allows us to show connections between the present method and others but also enable us to enhance the flexibility of our method by ensuring well-posedness of an interesting combined interpolation/regularization approach.


title: Identifying patients at higher risk of pneumonia after lung resection.

abstract: BACKGROUND ::: Pneumonia is considered as one of the most common and serious complications after lung resection. The purpose of this study was to identify the risk factors associated with postoperative pneumonia (POP) after lung resection and to develop a scoring system to stratify patients with increased risk of POP. ::: ::: ::: METHODS ::: A retrospective review of a prospective database of patients between September 2014 and June 2016 was carried out. Logistic regression analysis was used to examine the risk factors for POP. Bootstrap resampling analysis was used for internal validation. Regression coefﬁcients were used to develop weighted risk scores for POP. ::: ::: ::: RESULTS ::: Results revealed that age ≥64 years, smoking (current or previous), high pathological stage, and extent of excision of more than one lobe as risk factors. Logistic regression analysis showed that the predictors of POP were as follows: age ≥64 years, smoking, extent of excision of more than one lobe. A weighted score based on these factors was developed which was follows: smoking (three points), age ≥64 years (four points), and extent of excision of more than one lobe (five points). POP score >5 points offered the best combination of sensitivity (64.7%) and speciﬁcity (83.3%), and an area under receiver operating characteristic (ROC) curve (AUC) of 0.830 [95% confidence interval (CI): 0.746-0.914]. ::: ::: ::: CONCLUSIONS ::: Patients with older age, smoking and extent of excision of more than one lobe have a higher risk for pneumonia after lung cancer surgery. Also, the scoring system helps to guide decision making of POP risk reduction.


title: Child Sexual Abuse, Links to Later Sexual Exploitation/High-Risk Sexual Behavior, and Prevention/Treatment Programs

abstract: This paper reviews the literature on the nature and incidence of child sexual abuse, explores the link between child sexual abuse and later sexual exploitation, and reviews the literature on prevention strategies and effective interventions in child sexual abuse services.


title: Improving the user experience using an intelligent Adaptive User Interface in mobile applications

abstract: nowadays, we witness an abundancy in mobile applications and a richness in their functionality. However, many of them were developed with insufficient attention when it comes to usability and user friendliness. As such, we may find one application's interface clogged with controls that hamper the user from using it properly or another's interface designed without taking care of universal usability. The result is that the user experience with different applications goes from being a total satisfaction to a total frustration. Very good care should be taken while designing mobile applications since the mobile phone are constrained with a limited screen size and a few interaction mechanisms. Adaptive User Interfaces (AUIs) were proposed in order to levitate the negative impact of these constraints and, as such, help improve the application's usability. In this paper, we propose a number of intelligent adaptive mechanisms capable of monitoring the user's behavior on a mobile phone and then adapting the interface accordingly. Our proposed mechanisms yield significant usability benefits and make the user's experience with this application more satisfying.


title: Central Control Over Distributed Routing

abstract: Centralizing routing decisions offers tremendous flexibility, but sacrifices the robustness of distributed protocols. In this paper, we present Fibbing, an architecture that achieves both flexibility and robustness through central control over distributed routing. Fibbing introduces fake nodes and links into an underlying link-state routing protocol, so that routers compute their own forwarding tables based on the augmented topology. Fibbing is expressive, and readily supports flexible load balancing, traffic engineering, and backup routes. Based on high-level forwarding requirements, the Fibbing controller computes a compact augmented topology and injects the fake components through standard routing-protocol messages. Fibbing works with any unmodified routers speaking OSPF. Our experiments also show that it can scale to large networks with many forwarding requirements, introduces minimal overhead, and quickly reacts to network and controller failures.


title: A flow based approach for SSH traffic detection

abstract: The basic objective of this work is to assess the utility of two supervised learning algorithms AdaBoost and RIPPER for classifying SSH traffic from log files without using features such as payload, IP addresses and source/destination ports. Pre-processing is applied to the traffic data to express as traffic flows. Results of 10-fold cross validation for each learning algorithm indicate that a detection rate of 99% and a false positive rate of 0.7% can be achieved using RIPPER. Moreover, promising preliminary results were obtained when RIPPER was employed to identify which service was running over SSH. Thus, it is possible to detect SSH traffic with high accuracy without using features such as payload, IP addresses and source/destination ports, where this represents a particularly useful characteristic when requiring generic, scalable solutions.


title: Emotional speech: Towards a new generation of databases

abstract: Research on speech and emotion is moving from a period of exploratory research into one where there is a prospect of substantial applications, notably in human-computer interaction. Progress in the area relies heavily on the development of appropriate databases. This paper addresses four main issues that need to be considered in developing databases of emotional speech: scope, naturalness, context and descriptors. The state of the art is reviewed. A good deal has been done to address the key issues, but there is still a long way to go. The paper shows how the challenge of developing appropriate databases is being addressed in three major recent projects--the Reading--Leeds project, the Belfast project and the CREST--ESP project. From these and other studies the paper draws together the tools and methods that have been developed, addresses the problems that arise and indicates the future directions for the development of emotional speech databases.


title: Development of PD-1 and PD-L1 inhibitors as a form of cancer immunotherapy: a comprehensive review of registration trials and future considerations

abstract: Early preclinical evidence provided the rationale for programmed cell death 1 (PD-1) and programmed death ligand 1 (PD-L1) blockade as a potential form of cancer immunotherapy given that activation of the PD-1/PD-L1 axis putatively served as a mechanism for tumor evasion of host tumor antigen-specific T-cell immunity. Early-phase studies investigating several humanized monoclonal IgG4 antibodies targeting PD-1 and PD-L1 in advanced solid tumors paved way for the development of the first PD-1 inhibitors, nivolumab and pembrolizumab, approved by the Food and Drug Administration (FDA) in 2014. The number of FDA-approved agents of this class is rapidly enlarging with indications for treatment spanning across a spectrum of malignancies. The purpose of this review is to highlight the clinical development of PD-1 and PD-L1 inhibitors in cancer therapy to date. In particular, we focus on detailing the registration trials that have led to FDA-approved indications of anti-PD-1 and anti-PD-L1 therapies in cancer. As the number of PD-1/PD-L1 inhibitors continues to grow, predictive biomarkers, mechanisms of resistance, hyperprogressors, treatment duration and treatment beyond progression, immune-related toxicities, and clinical trial design are key concepts in need of further consideration to optimize the anticancer potential of this class of immunotherapy.


title: From Higher Education to Open Education: Challenges in the Transformation of an Online Traditional Course

abstract: Open education has revolutionized the educational environment. Massive open online courses have become the main showcase for open educational resources, but the competition to establish a position in this movement has led to the rapid publication of many courses of arguable quality. This paper presents and analyzes the process of creating a small private online course that meets the requirements to be offered as a massive course. The starting point was an online higher education course that had previously been published as OpenCourseWare. In this paper, data were examined from 112 students enrolled in four different academic years, during which changes were progressively incorporated to transform the traditional course to an open course. The results show an improvement in students’ perceptions of resources and assessments and in their overall satisfaction. New assessment tools were developed and validated, without affecting the academic results. The process had positive effects on the instructional design of the final course, but some risks were identified when the course was adapted as a massive course. The final course provides useful input for future developments and a more effective student-focused methodology.


title: Parent, school, and peer factors related to U.S. adolescents’ diet and exercise

abstract: PURPOSE ::: Adolescent obesity is a significant public health problem in the United States. Developing interventions to prevent or improve adolescent obesity may positively affect physical and mental health across the lifespan. However, to ensure that interventions are effective, influential factors must first be identified. The purpose of this study was to examine associations among parent-, school-, and peer-related factors and diet and physical activity of the adolescents in the United States. ::: ::: ::: DESIGN AND METHODS ::: Using logistic regression analyses, cross-sectional data of 5,248 adolescents in grades 5-10 from 184 schools from the World Health Organization's 2009/2010 survey of Health Behavior in School-Aged Children (HBSC) for the United States were analyzed, including variables for parent, peer, and school factors. ::: ::: ::: RESULTS ::: Parent- and peer-related factors were associated with adolescents' diet and physical activity (PA). Peer factors were associated with time spent on PA, but not a parent or school factors. The type of school lunch program was the only school factor associated with diet; no school factors were associated with exercise. ::: ::: ::: PRACTICE IMPLICATIONS ::: Results of this secondary analysis extend prior work, which identified associations between parent- and peer factors and adolescents' diet and exercise. Findings will help identify targets for improving adolescents' diet and exercise behaviors.


title: A discrete particle swarm optimization method for feature selection in binary classification problems

abstract: This paper investigates the feature subset selection problem for the binary classification problem using logistic regression model. We developed a modified discrete particle swarm optimization (PSO) algorithm for the feature subset selection problem. This approach embodies an adaptive feature selection procedure which dynamically accounts for the relevance and dependence of the features included the feature subset. We compare the proposed methodology with the tabu search and scatter search algorithms using publicly available datasets. The results show that the proposed discrete PSO algorithm is competitive in terms of both classification accuracy and computational performance.


title: Automatic Estimation and Removal of Noise from a Single Image

abstract: Image denoising algorithms often assume an additive white Gaussian noise (AWGN) process that is independent of the actual RGB values. Such approaches cannot effectively remove color noise produced by today's CCD digital camera. In this paper, we propose a unified framework for two tasks: automatic estimation and removal of color noise from a single image using piecewise smooth image models. We introduce the noise level function (NLF), which is a continuous function describing the noise level as a function of image brightness. We then estimate an upper bound of the real NLF by fitting a lower envelope to the standard deviations of per-segment image variances. For denoising, the chrominance of color noise is significantly removed by projecting pixel values onto a line fit to the RGB values in each segment. Then, a Gaussian conditional random field (GCRF) is constructed to obtain the underlying clean image from the noisy input. Extensive experiments are conducted to test the proposed algorithm, which is shown to outperform state-of-the-art denoising algorithms.


title: The Watershed Transform: Definitions, Algorithms and Parallelization Strategies

abstract: The watershed transform is the method of choice for image segmentation in the field of mathematical morphology. We present a critical review of several definitions of the watershed transform and the associated sequential algorithms, and discuss various issues which often cause confusion in the literature. The need to distinguish between definition, algorithm specification and algorithm implementation is pointed out. Various examples are given which illustrate differences between watershed transforms based on different definitions and/or implementations. The second part of the paper surveys approaches for parallel implementation of sequential watershed algorithms.


Creating a High-Quality Machine Translation System for a Low-Resource Language: Yiddish


title: PCPNET: Learning Local Shape Properties from Raw Point Clouds

abstract: In this paper, we propose PCPNet, a deep-learning based approach for estimating local 3D shape properties in point clouds. In contrast to the majority of prior techniques that concentrate on global or mid-level attributes, e.g., for shape classification or semantic labeling, we suggest a patch-based learning method, in which a series of local patches at multiple scales around each point is encoded in a structured manner. Our approach is especially well-adapted for estimating local shape properties such as normals (both unoriented and oriented) and curvature from raw point clouds in the presence of strong noise and multi-scale features. Our main contributions include both a novel multi-scale variant of the recently proposed PointNet architecture with emphasis on local shape information, and a series of novel applications in which we demonstrate how learning from training data arising from well-structured triangle meshes, and applying the trained model to noisy point clouds can produce superior results compared to specialized state-of-the-art techniques. Finally, we demonstrate the utility of our approach in the context of shape reconstruction, by showing how it can be used to extract normal orientation information from point clouds.


title: A survey of Internet public opinion mining

abstract: Conference Name:2014 2nd IEEE International Conference on Progress in Informatics and Computing, PIC 2014. Conference Address: Shanghai, China. Time:May 16, 2014 - May 18, 2014.


title: Reference Genome Anchoring of High-Density Markers for Association Mapping and Genomic Prediction in European Winter Wheat

abstract: In this study, we anchored genotyping-by-sequencing data to the International Wheat Genome Sequencing Consortium Reference Sequence v1.0 assembly to generate over 40,000 high quality single nucleotide polymorphism markers on a panel of 376 elite European winter wheat varieties released between 1946 and 2007. We compared association mapping and genomic prediction accuracy for a range of productivity traits with previous results based on lower density dominant DArT markers. The results demonstrate that the availability of RefSeq v1.0 supports higher precision trait mapping and provides the density of markers required to obtain accurate predictions of traits controlled by multiple small effect loci, including grain yield.


title: Evaluation of knowledge management tools using AHP

abstract: This paper presents an application of the analytic hierarchy process (AHP) used to select the most appropriate tool to support knowledge management (KM). This method adopts a multi-criteria approach that can be used to analyse and compare KM tools in the software market. The method is based on pairwise comparisons between several factors that affect the selection of the most appropriate KM tool. An AHP model is formulated and applied to a real case of assisting decision-makers in a leading communications company in Hong Kong to evaluate a suitable KM tool. We believe that the application shown can be of use to managers and that, because of its ease of implementation, others can benefit from this approach.


title: Design and Evaluation of a Quasi-Passive Knee Exoskeleton for Investigation of Motor Adaptation in Lower Extremity Joints

abstract: In this study, we describe the mechanical design and control scheme of a quasi-passive knee exoskeleton intended to investigate the biomechanical behavior of the knee joint during interaction with externally applied impedances. As the human knee behaves much like a linear spring during the stance phase of normal walking gait, the exoskeleton implements a spring across the knee in the weight acceptance (WA) phase of the gait while allowing free motion throughout the rest of the gait cycle, accomplished via an electromechanical clutch. The stiffness of the device is able to be varied by swapping springs, and the timing of engagement/disengagement changed to accommodate different loading profiles. After describing the design and control, we validate the mechanical performance and reliability of the exoskeleton through cyclic testing on a mechanical knee simulator. We then describe a preliminary experiment on three healthy adults to evaluate the functionality of the device on both left and right legs. The kinetic and kinematic analyses of these subjects show that the exoskeleton assistance can partially/fully replace the function of the knee joint and obtain nearly invariant moment and angle profiles for the hip and ankle joints, and the overall knee joint and exoskeleton complex under the applied moments of the exoskeleton versus the control condition, implying that the subjects undergo a considerable amount of motor adaptation in their lower extremities to the exoskeletal impedances, and encouraging more in-depth future experiments with the device.


title: Trust-based RPL for the Internet of Things

abstract: The Routing Protocol for Low-Power and Lossy Networks (RPL) is the standardized routing protocol for constrained environments such as 6LoWPAN networks, and is considered as the routing protocol of the Internet of Things (IoT). However, this protocol is subject to several internal and external attacks. In fact, RPL is facing many issues. Among these issues, trust management is a real challenge when deploying RPL. In this paper, we highlight and discuss the different issues of trust management in RPL. We consider that using only TPM (Trust Platform Module) to ensure trustworthiness between nodes is not sufficient. Indeed, an internal infected or selfish node could participate in constructing RPL topology. To overcome this issue, we propose to strengthen RPL by adding a new trustworthiness metric during RPL construction and maintenance. This metric represents the level of trust for each node in the network, and is calculated using selfishness, energy, and honesty components. It allows a node to decide whether or not to trust the other nodes during the construction of the topology.


title: Mental Juggling: When Does Multitasking Impair Reading Comprehension?

abstract: ABSTRACTThe present study investigated the conditions under which multitasking impairs reading comprehension. Participants read prose passages (the primary task), some of which required them to perform a secondary task. In Experiment 1, we compared two different types of secondary tasks (answering trivia questions and solving math problems). Reading comprehension was assessed using a multiple-choice test that measured both factual and conceptual knowledge. The results showed no observable detrimental effects associated with multitasking. In Experiment 2, the secondary task was a cognitive load task that required participants to remember a string of numbers while reading the passages. Performance on the reading comprehension test was lower in the cognitive load conditions relative to the no-load condition. The present study delineates the conditions under which multitasking can impair or have no effect on reading comprehension. These results further our understanding of our capacity to multitask and have p...


title: Identifying Depression on Twitter

abstract: Social media has recently emerged as a premier method to disseminate information online. Through these online networks, tens of millions of individuals communicate their thoughts, personal experiences, and social ideals. We therefore explore the potential of social media to predict, even prior to onset, Major Depressive Disorder (MDD) in online personas. We employ a crowdsourced method to compile a list of Twitter users who profess to being diagnosed with depression. Using up to a year of prior social media postings, we utilize a Bag of Words approach to quantify each tweet. Lastly, we leverage several statistical classifiers to provide estimates to the risk of depression. Our work posits a new methodology for constructing our classifier by treating social as a text-classification problem, rather than a behavioral one on social media platforms. By using a corpus of 2.5M tweets, we achieved an 81% accuracy rate in classification, with a precision score of .86. We believe that this method may be helpful in developing tools that estimate the risk of an individual being depressed, can be employed by physicians, concerned individuals, and healthcare agencies to aid in diagnosis, even possibly enabling those suffering from depression to be more proactive about recovering from their mental health.


title: An IoT based low cost air pollution monitoring system

abstract: A prototype for an Environmental Air Pollution Monitoring System for monitoring the concentrations of major air pollutant gases has been developed. The system uses low cost air-quality monitoring nodes comprises of low cost semiconductor gas sensor with Wi-Fi modules. This system measures concentrations of gases such as CO, CO 2 , SO 2 and NO 2 using semiconductor sensors. The sensors will gather the data of various environmental parameters and provide it to raspberry pi which act as a base station. Realization of data gathered by sensors is displayed on Raspberry pi 3 based Webserver. A MEAN stack is developed to display data over website. The fundamental aspect of proposed work is to provide low cost infrastructure to enable the data collection and dissemination to all stakeholders.


The Wisdom of the Crowd vs . Expert Evaluation : A Conceptualization of Evaluation Validity


title: Semi-Supervised Learning for Relation Extraction

abstract: This paper proposes a semi-supervised learning method for relation extraction. Given a small amount of labeled data and a large amount of unlabeled data, it first bootstraps a moderate number of weighted support vectors via SVM through a co-training procedure with random feature projection and then applies a label propagation (LP) algorithm via the bootstrapped support vectors. Evaluation on the ACE RDC 2003 corpus shows that our method outperforms the normal LP algorithm via all the available labeled data without SVM bootstrapping. Moreover, our method can largely reduce the computational burden. This suggests that our proposed method can integrate the advantages of both SVM bootstrapping and label propagation.


title: Quality of service based routing: a performance perspective

abstract: Recent studies provide evidence that Quality of Service (QoS) routing can provide increased network utilization compared to routing that is not sensitive to QoS requirements of traffic. However, there are still strong concerns about the increased cost of QoS routing, both in terms of more complex and frequent computations and increased routing protocol overhead. The main goals of this paper are to study these two cost components, and propose solutions that achieve good routing performance with reduced processing cost. First, we identify the parameters that determine the protocol traffic overhead, namely (a) policy for triggering updates, (b) sensitivity of this policy, and (c) clamp down timers that limit the rate of updates. Using simulation, we study the relative significance of these factors and investigate the relationship between routing performance and the amount of update traffic. In addition, we explore a range of design options to reduce the processing cost of QoS routing algorithms, and study their effect on routing performance. Based on the conclusions of these studies, we develop extensions to the basic QoS routing, that can achieve good routing performance with limited update generation rates. The paper also addresses the impact on the results of a number of secondary factors such as topology, high level admission control, and characteristics of network traffic.


title: Deep Learning is Robust to Massive Label Noise

abstract: Deep neural networks trained on large supervised datasets have led to impressive results in recent years. However, since well-annotated datasets can be prohibitively expensive and time-consuming to collect, recent work has explored the use of larger but noisy datasets that can be more easily obtained. In this paper, we investigate the behavior of deep neural networks on training sets with massively noisy labels. We show on multiple datasets such as MINST, CIFAR-10 and ImageNet that successful learning is possible even with an essentially arbitrary amount of noise. For example, on MNIST we find that accuracy of above 90 percent is still attainable even when the dataset has been diluted with 100 noisy examples for each clean example. Such behavior holds across multiple patterns of label noise, even when noisy labels are biased towards confusing classes. Further, we show how the required dataset size for successful training increases with higher label noise. Finally, we present simple actionable techniques for improving learning in the regime of high label noise.


title: Do types of collaboration change citation? Collaboration and citation patterns of South African science publications

abstract: Bibliographic records are extensively used in the study of citations. Based on ISI data, this paper examines citation patterns of the publications of South African scientists in recent years. In particular, the focus of this paper is on citations as to the collaborative dimensions of South African scientists in their publications. The study reveals that the number of citations received by a publication varies not only according to the collaboration but also to the types of collaboration of the authors who are involved in its production. Furthermore, it emerges that the impact of citations on publications differs from discipline to discipline, and affiliating sector to sector, regardless of collaboration.


title: Computational learning theory: survey and selected bibliography

abstract: Permission to copy without fee all or part of this material ie granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notica is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. 24th ANNUAL ACM STOC 5/92/VICTORiA, B. C., CANADA G 1992 ACM ()-89791.51 2-7/92/0004/03~j -..$1 .~() the only textbook in the field is Natarajan’s [101]. Surveys by Laird [83] and Valiant [129] are valuable. Somewhat more peripheral are the European meetings on Analogical and Inductive Inference, AH, and the AI machine learning communit y’s annual International Conference on Machine Learning. In addition, the general AI meetings, AAAI and IJCAI, currently have a large number of papers devoted to learning, as do the neural net meetings.


title: Enabling external routing logic in ONOS with Intent Monitor and Reroute service

abstract: SDN intent-based networking allows programmers to specify high-level policies without worrying about the low-level configurations which are compiled by the controller. Our Intent Monitor and Reroute service (IMR) extends the capabilities of ONOS Intent Framework by allowing to both compile multiple intents together and to re-optimize their paths, according to the network state and their flow statistics, via off-platform applications (OPA). In this demo we show two examples of intent-based ONOS application whose network performance can be improved, with no modifications to their code, exploiting the IMR service.


title: Relation Extraction Based on Deep Learning

abstract: Extracting entities and their relations is to detect entity mentions and recognize the semantic relation between them. Most of traditional methods are feature-based and treat this task as a pipeline of two separated tasks, i.e., named entity recognition and relation classification. Due to deep neural network models can learn effective entities and relations features from the given sentences without complicated manual feature engineering. Deep learning methods have been viewed as one of the major driving force in the recent development of natural language processing. We will briefly review the previous works on deep learning and give a brief overview of recent progresses on relation extraction. We implement the models based on deep neural networks both in pipeline methods and joint methods and come to conclusion about the comparison.


title: Wireless Caching: Technical Misconceptions and Business Barriers

abstract: Caching is a hot research topic and poised to develop into a key technology for the upcoming 5G wireless networks. However, the successful implementation of caching techniques crucially depends on joint research developments in different scientific domains such as networking, information theory, machine learning, and wireless communications. Moreover, there are business barriers related to the complex interactions between the involved stakeholders: users, cellular operators, and Internet content providers. In this article we discuss several technical misconceptions with the aim of uncovering enabling research directions for caching in wireless systems. Ultimately, we make a speculative stakeholder analysis for wireless caching in 5G.


title: A fall detection system using low resolution infrared array sensor

abstract: Nowadays, aging society is a big problem and demand for monitoring systems is becoming higher. Under this circumstance, a fall is a main factor of accidents at home. From this point of view, we need to detect falls expeditiously and correctly. However, usual methods like using a video camera or a wearable device have some issues in privacy and convenience. In this paper, we propose a system of fall detection using a low resolution infrared array sensor. The proposed system uses this sensor with advantages of privacy protection (low resolution), low cost (cheap sensor), and convenience (small device). We propose four features and based on them, classify activities as either a fall or a non-fall using k-nearest neighbor (k-NN) algorithm. We show a proof-of-concept of our proposed system using a commercial-off-the-shelf (COTS) hardware. Results of experiments show the detection rate of higher than 94 % irrespective of training data contains object's data or not.


title: Learning CRFs using Graph Cuts

abstract: Many computer vision problems are naturally formulated as random fields, specifically MRFs or CRFs. The introduction of graph cuts has enabled efficient and optimal inference in associative random fields, greatly advancing applications such as segmentation, stereo reconstruction and many others. However, while fast inference is now widespread, parameter learning in random fields has remained an intractable problem. This paper shows how to apply fast inference algorithms, in particular graph cuts, to learn parameters of random fields with similar efficiency. We find optimal parameter values under standard regularized objective functions that ensure good generalization. Our algorithm enables learning of many parameters in reasonable time, and we explore further speedup techniques. We also discuss extensions to non-associative and multi-class problems. We evaluate the method on image segmentation and geometry recognition.


title: How Convincing is Mr. Data's Smile: Affective Expressions of Machines

abstract: Emotions should play an important role in the design of interfaces because people interact with machines as if they were social actors. This paper presents a literature review on affective expressions through speech, music and body language. It summarizes the quality and quantity of their parameters and successful examples of synthesis. Moreover, a model for the convincingness of affective expressions, based on Fogg and Hsiang Tseng (1999), was developed and tested. The empirical data did not support the original model and therefore this paper proposes a new model, which is based on appropriateness and intensity of the expressions. Furthermore, the experiment investigated if the type of emotion (happiness, sadness, anger, surprise, fear and disgust), knowledge about the source (human or machine), the level of abstraction (natural face, computer rendered face and matrix face) and medium of presentation (visual, audio/visual, audio) of an affective expression influences its convincingness and distinctness. Only the type of emotion and multimedia presentations had an effect on convincingness. The distinctness of an expression depends on the abstraction and the media through which it is presented.


title: Predicting Available Parking Slots on Critical and Regular Services by Exploiting a Range of Open Data

abstract: Looking for available parking slots has become a serious issue in contemporary urban mobility. The selection of suitable car parks could be influenced by multiple factors—e.g., the walking distance to destination, driving and waiting time, parking prices, availability, and accessibility—while the availability of unused parking slots might depend on parking location, events in the area, traffic flow, and weather conditions. This paper presents a set of metrics and techniques to predict the number of available parking slots in city garages with gates. With this aim, we have considered three different predictive techniques, while comparing different approaches. The comparison has been performed according to the data collected in a dozen of garages in the area of Florence by using Sii-Mobility National Research Project and Km4City infrastructure. The resulting solution has demonstrated that a Bayesian regularized neural network exploiting historical data, weather condition, and traffic flow data can offer a robust approach for the implementation of reliable and fast predictions of available slots in terms of flexibility and robustness to critical cases. The solution adopted in a Smart City Apps in the Florence area for sustainable mobility has been welcomed with broad appreciation or has been praised as successful.


title: APIComposer: Data-Driven Composition of REST APIs

abstract: More and more companies and governmental organizations are publishing data on the Web via REST APIs. The increasing number of REST APIs has promoted the creation of specialized applications aiming to combine and reuse different data sources to generate and deduce new information. However, creating such applications is a tedious and error-prone process since developers must invest much time in discovering the data model behind each candidate REST API, define the composition strategy, and manually implement such strategy. To facilitate this process, we propose an approach to automatically compose and orchestrate data-oriented REST APIs. For an initial set of REST APIs, we discover the data models, identify matching concepts, obtain a global model, and make the latter available on the Web as a global REST API. A prototype tool relying on OpenAPI for describing APIs and on OData for querying them is also provided.


title: Android Based Vehicle Anti-Theft Alarm and Tracking System in Hand-Held Communication Terminal

abstract: With the rapid development of China's automobile industry, the number of cars is increasing rapidly. Automobile has developed into an important means of transportation for people. However, while people are enjoying the convenience and speed brought by the vehicle travel, the theft of vehicles is causing more and more concern. Owing to the importance of vehicles safety, how to prevent vehicles from being stolen is becoming a hot topic in the field of artificial intelligence. In this paper, a vehicle anti-theft alarm and tracking system based on Android operating system in hand-held communication terminal is designed and developed. By using the built-in sensors of the terminal based on Android Operating System (OS), such as a smart phone, the abnormal information of a vehicle will be sent to the owner's smart phone through communication network. In this way, the long-distance remote alarm can be realized. In addition, the usage of the positioning function of GPS or communication base station makes vehicle location tracking into possible. Finally, a demo system is performed and it can effectively perform vehicle anti-theft alarm and tracking.


title: Enabling scalability-sensitive speculative parallelization for FSM computations

abstract: Finite state machines (FSMs) are the backbone of many applications, but are difficult to parallelize due to their inherent dependencies. Speculative FSM parallelization has shown promise on multicore machines with up to eight cores. However, as hardware parallelism grows (e.g., Xeon Phi has up to 288 logical cores), a fundamental question raises: How does the speculative FSM parallelization scale as the number of cores increases? Without answering this question, existing methods for speculative FSM parallelization simply choose to use all available cores, which might not only waste computing resources, but also result in suboptimal performance. In this work, we conduct a systematic scalability analysis for speculative FSM parallelization. Unlike many other parallelizations which can be modeled by the classic Amdahl's law or its simple extensions, speculative FSM parallelization scales unconventionally due to the non-deterministic nature of speculation and the cost variations of misspeculation. To address these challenges, this work introduces a spectrum of scalability models that are customized to the properties of specific FSMs and the underlying architecture. The models, for the first time, precisely capture the scalability of speculative parallelization for different FSM computations, and clearly show the existence of a "sweet spot" in terms of the number of cores employed by the speculative FSM parallelization to achieve the optimal performance. To make the scalability models practical, we develop S3, a s calability- s ensitive s peculation framework for FSM parallelization. For any given FSM, S3 can automatically characterize its properties and analyze its scalability, hence guide speculative parallelization towards the optimal performance and more efficient use of computing resources. Evaluations on different FSMs and architectures confirm the accuracy of the proposed models and show that S3 achieves significant speedup (up to 5X) and energy savings (up to 77%).


title: Camera-on-rails: automated computation of constrained camera paths

abstract: When creating real or computer graphics movies, the questions of how to layout elements on the screen, together with how to move the cameras in the scene are crucial to properly conveying the events composing a narrative. Though there is a range of techniques to automatically compute camera paths in virtual environments, none have seriously considered the problem of generating realistic camera motions even for simple scenes. Among possible cinematographic devices, real cinematographers often rely on camera rails to create smooth camera motions which viewers are familiar with. Following this practice, in this paper we propose a method for generating virtual camera rails and computing smooth camera motions on these rails. Our technique analyzes characters motion and user-defined framing properties to compute rough camera motions which are further refined using constrained-optimization techniques. Comparisons with recent techniques demonstrate the benefits of our approach and opens interesting perspectives in terms of creative support tools for animators and cinematographers.


title: Low-capacitance ESD protection design for high-speed I/O interfaces in a 130-nm CMOS process

abstract: Electrostatic discharge (ESD) protection design for high-speed input/output (I/O) interface circuits in a 130-nm CMOS process is presented in this paper. First, the ESD protection diodes with different dimensions were designed and fabricated to evaluate their ESD levels and parasitic effects in gigahertz frequency band. With the knowledge of the dependence of device dimensions on ESD robustness and the parasitic capacitance, whole-chip ESD protection scheme were designed for the general receiver and transmitter interface circuits. Besides, an ESD protection scheme is proposed to improve the ESD robustness under the positive-to-VSS (PS-mode) ESD test, which is the most critical ESD-test pin combination. With a silicon-controlled rectifier (SCR) between the I/O pad and VSS, the clamping voltage along the PS-mode ESD current path can be reduced, so the PS-mode ESD level can be improved. Besides, the parasitic P-well/N-well diode in the SCR can provide the NS-mode ESD current path. Thus, SCR is the most promising ESD protection device in ESD protection design with low-capacitance consideration. The ESD protection scheme presented in this paper has been practically applied to an IC product with 2.5-Gb/s high-speed front-end interface.


title: Emerging trends in informal sector recycling in developing and transition countries.

abstract: Optimistic estimates suggest that only 30-70% of waste generated in cities of developing countries is collected for disposal. As a result, uncollected waste is often disposed of into open dumps, along the streets or into water bodies. Quite often, this practice induces environmental degradation and public health risks. Notwithstanding, such practices also make waste materials readily available for itinerant waste pickers. These 'scavengers' as they are called, therefore perceive waste as a resource, for income generation. Literature suggests that Informal Sector Recycling (ISR) activity can bring other benefits such as, economic growth, litter control and resources conservation. This paper critically reviews trends in ISR activities in selected developing and transition countries. ISR often survives in very hostile social and physical environments largely because of negative Government and public attitude. Rather than being stigmatised, the sector should be recognised as an important element for achievement of sustainable waste management in developing countries. One solution to this problem could be the integration of ISR into the formal waste management system. To achieve ISR integration, this paper highlights six crucial aspects from literature: social acceptance, political will, mobilisation of cooperatives, partnerships with private enterprises, management and technical skills, as well as legal protection measures. It is important to note that not every country will have the wherewithal to achieve social inclusion and so the level of integration must be 'flexible'. In addition, the structure of the ISR should not be based on a 'universal' model but should instead take into account local contexts and conditions.


title: Graph It! How to Make, Read, and Interpret Graphs

abstract: This text is a guide to achieving the graphic literacy necessary for courses on research methods in psychology and statistics. Illustrated with over 80 samples, the book teaches how to recognize and describe data relationships depicted in graph form, using a background consistent with courses on research methods.


Efficient Feature Selection Technique for Network Intrusion Detection System Using Discrete Differential Evolution and Decision


title: Automatic detection and recognition of traffic signs

abstract: Automatic detection of road sign is a challenging but demanding job. A new approach namely automatic detection and recognition of traffic signs (ADRTS) considering color segmentation, moment invariants, and neural networks has been proposed in this paper. Experimental result proves the superior performance in the detection and recognition of road signs. Computational time complexity is also quite low that makes it applicable for the real time system.


title: TIPS: context-aware implicit user identification using touch screen in uncontrolled environments

abstract: Due to the dramatical increase in popularity of mobile devices in the last decade, more sensitive user information is stored and accessed on these devices everyday. However, most existing technologies for user authentication only cover the login stage or only work in restricted controlled environments or GUIs in the post login stage. In this work, we present TIPS, a Touch based Identity Protection Service that implicitly and unobtrusively authenticates users in the background by continuously analyzing touch screen gestures in the context of a running application. To the best of our knowledge, this is the first work to incorporate contextual app information to improve user authentication. We evaluate TIPS over data collected from 23 phone owners and deployed it to 13 of them with 100 guest users. TIPS can achieve over 90% accuracy in real-life naturalistic conditions within a small amount of computational overhead and 6% of battery usage.


title: Center of gravity balance approach based on CPG algorithm for locomotion control of a quadruped robot

abstract: The research area of bio-inspired control methods for multi-legged robots and reptile robots has made significant development with the use of central pattern generators (CPGs) in recent years. However, there are still many problems to be solved to learn clearly the structure of CPG to adapt it to different frameworks and applications. One is that the exiting CPGs are almost only can be used to a special kind of robots. In this article, we present an improved method of constructing CPGs. The phase difference between the neurons which constitute the CPGs can be set to a random numeric number to fulfill different frameworks and applications. And also we use this kind of CPGs to successfully solve the problem which the quadruped robot can't lift it hind limbs from the ground during a walk gait locomotion.


title: A Rule-Based Framework of Metadata Extraction from Scientific Papers

abstract: Most scientific documents on the web are unstructured or semi-structured, and the automatic document metadata extraction process becomes an important task. This paper describes a framework for automatic metadata extraction from scientific papers. Based on a spatial and visual knowledge principle, our system can extract title, authors and abstract from scientific papers. We utilize format information such as font size and position to guide the metadata extraction process. The experiment results show that our system achieves a high accuracy in header metadata extraction which can effectively assist the automatic index creation for digital libraries.


Next-generation Intrusion Detection Expert System ( NIDES ) A Summary


title: Design Methodology of LLC Resonant Converters for Electric Vehicle Battery Chargers

abstract: In this paper, an inductor–inductor–capacitor (LLC) resonant dc–dc converter design procedure for an onboard lithium-ion battery charger of a plug-in hybrid electric vehicle (PHEV) is presented. Unlike traditional resistive load applications, the characteristic of a battery load is nonlinear and highly related to the charging profiles. Based on the features of an LLC converter and the characteristics of the charging profiles, the design considerations are studied thoroughly. The worst-case conditions for primary-side zero-voltage switching (ZVS) operation are analytically identified based on fundamental harmonic approximation when a constant maximum power (CMP) charging profile is implemented. Then, the worst-case operating point is used as the design targeted point to ensure soft-switching operation globally. To avoid the inaccuracy of fundamental harmonic approximation approach in the below-resonance region, the design constraints are derived based on a specific operation mode analysis. Finally, a step-by-step design methodology is proposed and validated through experiments on a prototype converting 400 V from the input to an output voltage range of 250–450 V at 3.3 kW with a peak efficiency of 98.2%.


title: Enabling User-Policy-Confined VM Migration in Trusted Cloud Computing

abstract: The trusted cloud environment model, based on the trusted computing technology, provides cloud users the capabilities to remotely attest the trustworthiness of cloud service. But unlike physical machine, the virtual machine is migratory among cloud nodes constantly which makes it hard for cloud users to guarantee the Virtual Machine (VM) is running in a trusted cloud node and its migration is consistent with user's policy. Since the migration operation is entirely transparent to cloud users, it is hard for them to launch the remote attestation with the existing remote attestation scheme. To guarantee the migration of VMs is consistent with user's policy, we propose a User-Policy-Confined VM Migration Protocol (UVMP), which provides a new attestation scheme that addresses the limitation and enables VM migration to be controllable and verifiable by cloud users by introducing the Ciphertext-Policy AttributeBased Encryption(CP-ABE) into attestation field. To demonstrate that UVMP is practical, we incorporate it in the Xen opensource virtualization platform and implement UTVMS: UserPolicy-Confined Trusted VM Migration System. Our evaluation suggests that the attestation scheme cannot lead to the obvious performance on the VM migration by analyzing the migration latency. The attestation scheme can provide greater flexibility for cloud users to control their own VM migration.


title: Breaking SIMD shackles with an exposed flexible microarchitecture and the access execute PDG

abstract: Modern microprocessors exploit data level parallelism through in-core data-parallel accelerators in the form of short vector ISA extensions such as SSE/AVX and NEON. Although these ISA extensions have existed for decades, compilers do not generate good quality, high-performance vectorized code without significant programmer intervention and manual optimization. The fundamental problem is that the architecture is too rigid, which overly complicates the compiler's role and simultaneously restricts the types of codes that the compiler can profitably map to these data-parallel accelerators. We take a fundamentally new approach that first makes the architecture more flexible and exposes this flexibility to the compiler. Counter-intuitively, increasing the complexity of the accelerator's interface to the compiler enables a more robust and efficient system that supports many types of codes. This system also enables the performance of auto-acceleration to be comparable to that of manually-optimized implementations. To address the challenges of compiling for flexible accelerators, we propose a variant of Program Dependence Graph called the Access Execute Program Dependence Graph to capture spatio-temporal aspects of memory accesses and computations. We implement a compiler that uses this representation and evaluate it by considering both a suite of kernels developed and tuned for SSE, and "challenge" data-parallel applications, the Parboil benchmarks. We show that our compiler, which targets the DySER accelerator, provides high-quality code for the kernels and full applications, commonly reaching within 30% of manually-optimized and out-performs compiler-produced SSE code by 1.8 times.


title: Design Research in Information Systems: Theory and Practice

abstract: The study of Information Systems (IS) design is an essential part of the education of IS students and professionals. The purpose of this book is to provide a thorough reference on Design Science Research (DSR), and it comes from two authors closely identified with DSR Alan Hevner and Samir Chatterjee. As founders of the Design Science Research in Information Systems and Technology (DESRIST) annual conference, and as leading educators and researchers in the field, these authors, along with several invited contributors , are uniquely qualified to create this easy-to-read, easy-to-understand, and easy-to-apply text/reference. Suitable for graduate courses in IS, computer science, software engineering, engineering design and other design-oriented fields, it can be used as a core text or a reference for doctoral seminars in DSR. IS faculty and researchers will find much of value here as well. It requires no extensive background in design and can be appreciated by practitioners working in IS or technology design. Its 18 chapters are all individually referenced, and two appendices provide a reprint of the seminal 2004 MISQ paper by Hevner, March, Park, and Ram, as well as a list of exemplar papers in Design Science. The book provides a thorough introduction to DSR, a look at DSR in IS, examinations of DSR frameworks and design theory, and a look at the key principles of DSR in IS. Other chapters look at design for software-intensive systems, people and design, the past and present of software designs, evaluation methods, focus-group use, design creativity, and a design language for knowledge management systems. Later chapters explore integrating action research with design research, design science in management disciplines, a critical realist perspective of DSR in IS, a taxonomic look at design of emerging digital services, the dissemination of DSR, and, finally, a look at the future for DSR in IS.


title: Generating Test Data from OCL Constraints with Search Techniques

abstract: Model-based testing (MBT) aims at automated, scalable, and systematic testing solutions for complex industrial software systems. To increase chances of adoption in industrial contexts, software systems can be modeled using well-established standards such as the Unified Modeling Language (UML) and the Object Constraint Language (OCL). Given that test data generation is one of the major challenges to automate MBT, we focus on test data generation from OCL constraints in this paper. This endeavor is all the more challenging given the numerous OCL constructs and operations that are designed to facilitate the definition of constraints. Though search-based software testing has been applied to test data generation for white-box testing (e.g., branch coverage), its application to the MBT of industrial software systems has been limited. In this paper, we propose a set of search heuristics targeted to OCL constraints to guide test data generation and automate MBT in industrial applications. We evaluate these heuristics for three search algorithms: Genetic Algorithm, (1+1) Evolutionary Algorithm, and Alternating Variable Method. We empirically evaluate our heuristics using complex artificial problems, followed by empirical analyses of the feasibility of our approach on one industrial system in the context of robustness testing. Our approach is also compared with the most widely referenced OCL solver (UMLtoCSP) in the literature and shows to be significantly more efficient.


title: Active Inference, Belief Propagation, and the Bethe Approximation

abstract: When modeling goal-directed behavior in the presence of various sources of uncertainty, planning can be described as an inference process. A solution to the problem of planning as inference was previously proposed in the active inference framework in the form of an approximate inference scheme based on variational free energy. However, this approximate scheme was based on the mean-field approximation, which assumes statistical independence of hidden variables and is known to show overconfidence and may converge to local minima of the free energy. To better capture the spatiotemporal properties of an environment, we reformulated the approximate inference process using the so-called Bethe approximation. Importantly, the Bethe approximation allows for representation of pairwise statistical dependencies. Under these assumptions, the minimizer of the variational free energy corresponds to the belief propagation algorithm, commonly used in machine learning. To illustrate the differences between the mean-field approximation and the Bethe approximation, we have simulated agent behavior in a simple goal-reaching task with different types of uncertainties. Overall, the Bethe agent achieves higher success rates in reaching goal states. We relate the better performance of the Bethe agent to more accurate predictions about the consequences of its own actions. Consequently, active inference based on the Bethe approximation extends the application range of active inference to more complex behavioral tasks.


title: Image encryption by blockwise pixel shuffling using Modified Fisher Yates shuffle and pseudorandom permutations

abstract: Present paper proposed an image encryption technique using Fisher Yates shuffling along with pseudorandom permutation. Hazra et al [1] proposed a technique that encrypts general file. The technique, however, produced an encrypted file whose size was double than that of the plain text file. The said procedure was not implemented for image encryption as well. In this paper, a modification has been suggested such that the process can be applied for grayscale image encryption and the size of the encrypted image increases marginally. The proposed scheme has been subjected to various forms of analysis like histograms, information entropy, horizontal and vertical correlation coefficients and the results obtained suggests that the modified technique can be used for secure image encryptions.


title: SmartSync: An Integrated Real-Time Structural Health Monitoring and Structural Identification System for Tall Buildings

abstract: AbstractThis study introduces a unique prototype system for structural health monitoring (SHM), SmartSync, which uses the building’s existing Internet backbone as a system of virtual instrumentation cables to permit modular and largely plug-and-play deployments. Within this framework, data streams from distributed heterogeneous sensors are pushed through network interfaces in real time and seamlessly synchronized and aggregated by a centralized server, which performs basic data acquisition, event triggering, and database management while also providing an interface for data visualization and analysis that can be securely accessed. The system enables a scalable approach to monitoring tall and complex structures that can readily interface a variety of sensors and data formats (analog and digital) and can even accommodate variable sampling rates. This study overviews the SmartSync system, its installation/operation in the world’s tallest building, Burj Khalifa, and proof-of-concept in triggering under dual e...


title: Prediction of DNA-binding propensity of proteins by the ball-histogram method using automatic template search

abstract: We contribute a novel, ball-histogram approach to DNA-binding propensity prediction of proteins. Unlike state-of-the-art methods based on constructing an ad-hoc set of features describing physicochemical properties of the proteins, the ball-histogram technique enables a systematic, Monte-Carlo exploration of the spatial distribution of amino acids complying with automatically selected properties. This exploration yields a model for the prediction of DNA binding propensity. We validate our method in prediction experiments, improving on state-of-the-art accuracies. Moreover, our method also provides interpretable features involving spatial distributions of selected amino acids.


Gender Stereotypes : An Analysis of Popular Films and TV


title: FRED: A Hosted Data Flow Platform for the IoT

abstract: IoT developers need to integrate a variety of protocols, backend components and services; they often need to pre and post-process data as well as react to changes in the real world. Data flow programming tools have been introduced in a number of related domains to provide a flexible, but easy to use visual programming environment for rapid development. The open source Node-RED system provides such a tool for IoT applications, but is limited to executing a single flow file in a single thread. In this paper we describe the design of our system called the Front-End for Node-RED (FRED) that manages multiple instances of Node-RED for logged in users, allowing Node-RED to be used as a cloud-hosted data flow mashup tool for the IoT. We present some examples of how some of our 1800+ users are using FRED for IoT mashups, and some of the challenged we faced in implementing the FRED system.


title: Technological Discontinuities and Complementary Assets: A Longitudinal Study of Industry and Firm Performance

abstract: We suggest that the type of complementary assets (generic versus specialized) needed to commercialize a new technology is critical in determining the industry- and firm-level performance implications of a competence-destroying technological discontinuity. At the industry level, we hypothesize that incumbent industry performance declines if the new technology can be commercialized through generic complementary assets, whereas incumbent industry performance improves if the new technology can be commercialized through specialized complementary assets. At the firm level, we posit that an incumbent firm's financial strength has a stronger positive impact on firm performance in the postdiscontinuity time period if the new technology can be commercialized through generic complementary assets. We hypothesize, however, that an incumbent firm's R&D capability has a stronger positive impact on firm performance in the postdiscontinuity time period if the new technology can be commercialized through specialized complementary assets. Drawing on multi-industry, time series, and panel data over a 26-year period to analyze pre- and postdiscontinuity industry and firm performance, we find broad support for our theoretical model.


title: Access Control Models for Virtual Object Communication in Cloud-Enabled IoT

abstract: The Internet of Things (IoT) is the latest evolution of the Internet, encompassing an enormous number of connected physical "things." The access-control oriented (ACO) architecture was recently proposed for cloud-enabled IoT, with virtual objects (VOs) and cloud services in the middle layers. A central aspect of ACO is to control communication among VOs. This paper develops operational and administrative access control models for this purpose, assuming topic-based publishsubscribe interaction among VOs. Operational models are developed using (i) access control lists for topics and capabilities for virtual objects and (ii) attribute-based access control, and it is argued that role-based access control is not suitable for this purpose. Administrative models for these two operational models are developed using (i) access control lists, (ii) role-based access control, and (iii) attribute-based access control. A use case illustrates the details of these access control models for VO communication, and their differences. An assessment of these models with respect to security and privacy preserving objectives of IoT is also provided.


title: Unsupervised outlier detection for time series by entropy and dynamic time warping

abstract: In the last decade, outlier detection for temporal data has received much attention from data mining and machine learning communities. While other works have addressed this problem by two-way approaches (similarity and clustering), we propose in this paper an embedded technique dealing with both methods simultaneously. We reformulate the task of outlier detection as a weighted clustering problem based on entropy and dynamic time warping for time series. The outliers are then detected by an optimization problem of a new proposed cost function adapted to this kind of data. Finally, we provide some experimental results for validating our proposal and comparing it with other methods of detection.


title: Neurobiology of resilience

abstract: The authors review our understanding of the biological basis of resilience to stress. The review examines findings from both humans and animals and also discusses how this knowledge can help guide treatment for stress-related disorders.


title: The place for ubiquitous computing in schools: lessons learned from a school-based intervention for youth physical activity

abstract: With rising concerns about obesity and sedentary lifestyles in youth, there has been an increasing interest in understanding how pervasive and ubiquitous computing technologies can catalyze positive health behaviors in children and teens. School-based interventions seem like a natural choice, and ubiquitous computing technologies hold much promise for these interventions. Yet the literature contains little guidance for how to approach school-based ubicomp deployments. Grounded in our analysis of a large-scale US school-based intervention for promoting youth physical activity, we present an approach to the design and evaluation of school-based ubicomp that treats the school as a social institution. We show how the school regulates students' daily lives, drawing from work in the sociology of schools to create a framing for planning, executing and analyzing school-based ubicomp deployments. These insights will assist other researchers and designers engaging in deployments of ubiquitous computing systems in settings with established institutional structures.


title: Geometric Analogue of Holographic Reduced Representation

abstract: Abstract Holographic reduced representations (HRRs) are distributed representations of cognitive structures based on superpositions of convolution-bound n -tuples. Restricting HRRs to n -tuples consisting of ±1, one reinterprets the variable binding as a representation of the additive group of binary n -tuples with addition modulo 2. Since convolutions are not defined for vectors, the HRRs cannot be directly associated with geometric structures. Geometric analogues of HRRs are obtained if one considers a projective representation of the same group in the space of blades (geometric products of basis vectors) associated with an arbitrary n -dimensional Euclidean (or pseudo-Euclidean) space. Switching to matrix representations of Clifford algebras, one can always turn a geometric analogue of an HRR into a form of matrix distributed representation. In typical applications the resulting matrices are sparse, so that the matrix representation is less efficient than the representation directly employing the rules of geometric algebra. A yet more efficient procedure is based on ‘projected products’, a hierarchy of geometrically meaningful n -tuple multiplication rules obtained by combining geometric products with projections on relevant multivector subspaces. In terms of dimensionality the geometric analogues of HRRs are in between holographic and tensor-product representations.


title: Liquidity risk of corporate bond returns: conditional approach

abstract: We study the exposure of the US corporate bond returns to liquidity shocks of stocks and Treasury bonds over the period 1973 - 2007 in a regime - switching model. In one regime, liquidity shocks have mostly insignificant effects on bond prices, whereas in another regime, a rise in illiquidity produces significant but conflicting effects: Prices of investment-grade bonds rise while prices of speculative-grade (junk) bonds fall substantially (relative to the market). Relating the probability of these regimes to macroeconomic conditions we find that the second regime can be predicted by economic conditions that are characterized as "stress." These effects, which are robust to controlling for other systematic risks (term and default), suggest the existence of time-varying liquidity risk of corporate bond returns conditional on episodes of flight to liquidity. Our model can predict the out-of-sample bond returns for the stress years 2008 - 2009. We find a similar pattern for stocks classified by high or low book-to-market ratio, where again, liquidity shocks play a special role in periods characterized by adverse economic conditions.


title: Scaling agile methods to regulated environments: An industry case study

abstract: Agile development methods are growing in popularity with a recent survey reporting that more than 80% of organizations now following an agile approach. Agile methods were seen initially as best suited to small, co-located teams developing non-critical systems. The first two constraining characteristics (small and co-located teams) have been addressed as research has emerged describing successful agile adoption involving large teams and distributed contexts. However, the applicability of agile methods for developing safety-critical systems in regulated environments has not yet been demonstrated unequivocally, and very little rigorous research exists in this area. Some of the essential characteristics of agile approaches appear to be incompatible with the constraints imposed by regulated environments. In this study we identify these tension points and illustrate through a detailed case study how an agile approach was implemented successfully in a regulated environment. Among the interesting concepts to emerge from the research are the notions of continuous compliance and living traceability.


title: Power Factor Correction in Modified SEPIC Fed Switched Reluctance Motor Drives

abstract: This paper presents a dual output power factor correction (PFC) converter for switched reluctance motor (SRM) drives. Here, a modified single-ended primary inductance converter (SEPIC) is proposed for a wide range of speed control and to achieve PFC at ac mains. The switching frequency of the converter is considered to be 20 kHz. The converter is designed to operate in the discontinuous conduction mode (DCM) of operation, which reduces the size of the output inductor. The DCM mode to obtain PFC also reduces the sensor requirement as compared to the continuous conduction mode (CCM), where two voltage sensors and one current sensor add to the drive cost. The drive is proposed for universal ac mains (90–270 V ac). Simulated and experimental results are demonstrated to validate the performance of the drive during different operating conditions. The input current total harmonic distortion is reduced to comply with an IEC 61000-3-2 standard.


title: Cross versus Within-Company Cost Estimation Studies: A Systematic Review

abstract: The objective of this paper is to determine under what circumstances individual organizations would be able to rely on cross-company-based estimation models. We performed a systematic review of studies that compared predictions from cross-company models with predictions from within-company models based on analysis of project data. Ten papers compared cross-company and within-company estimation models; however, only seven presented independent results. Of those seven, three found that cross-company models were not significantly different from within-company models, and four found that cross-company models were significantly worse than within-company models. Experimental procedures used by the studies differed making it impossible to undertake formal meta-analysis of the results. The main trend distinguishing study results was that studies with small within-company data sets (i.e., $20 projects) that used leave-one-out cross validation all found that the within-company model was significantly different (better) from the cross-company model. The results of this review are inconclusive. It is clear that some organizations would be ill-served by cross-company models whereas others would benefit. Further studies are needed, but they must be independent (i.e., based on different data bases or at least different single company data sets) and should address specific hypotheses concerning the conditions that would favor cross-company or within-company models. In addition, experimenters need to standardize their experimental procedures to enable formal meta-analysis, and recommendations are made in Section 3.


title: Soil-moisture Retention Characteristics of Earth Materials in the Weathering Profile over a Porphyritic Biotite Granite

abstract: Problem statement: Three broad morphological zones and several sub-zones were differentiated within the weathering profile based on variations in earth materials and the degree of preservation of original bedrock minerals, textures and structures. Approach: The topmost Zone I (Grade 6) is some 12 m thick and comprises completely weathered bedrock materials (mainly a stiff, clayey sand) with indistinct relict bedrock textures. Results: The intermediate Zone II (Grades 3, 4 and 5) is some 30 m thick and consists of in situ, moderately to highly weathered bedrock materials (mostly friable, gravelly silty sands and coreboulders) that indistinctly to distinctly preserve the original bedrock minerals, textures and structures; the degree of preservation increasing with depth. The bottom Zone III (Grade 2) consists of continuous bedrock with effects of weathering only along and between structural discontinuity planes; the earth materials of zones I and II thus representing the residual soil over bedrock. Constant volume samples show a general increase in porosity, silt and clay fractions up the weathering profile, but a corresponding decrease in the sand and gravel fractions with sub-zone IC having the highest density and lowest porosity. Soil-moisture retention curves, determined with the pressure plate method, show increasing suctions from 0-4.19 pF (0 to -1,500 kPa) with decreasing moisture contents of 35-18% for sub-zone IC samples, 32-7% for sub-zone IIB samples and some 25-4% for sub-zone IID samples. Conclusion/Recommendations: These soil-moisture retention variations reflect textural differences and point to the inherent role of suction in the unsaturated earth materials. Differences in the volumes of calculated drainage and storage pores indicate that the earth materials of sub-zone IC will have the lowest rates of infiltration and percolation, whilst those of sub- zone IID will have the fastest rates and those from sub-zone IIB, intermediate rates.


Efficient Exploration in Monte Carlo Tree Search using Human Action Abstractions


title: Self-organization and entropy reduction in a living cell

abstract: Abstract In this paper we discuss the entropy and information aspects of a living cell. Particular attention is paid to the information gain on assembling and maintaining a living state. Numerical estimates of the information and entropy reduction are given and discussed in the context of the cell's metabolic activity. We discuss a solution to an apparent paradox that there is less information content in DNA than in the proteins that are assembled based on the genetic code encrypted in DNA. When energy input required for protein synthesis is accounted for, the paradox is clearly resolved. Finally, differences between biological information and instruction are discussed.