title: A Critical Review of the Antipsychotic Effects of Cannabidiol: 30 Years of a Translational Investigation

abstract: Δ 9-tetrahydrocannabinol (Δ 9-THC) is the main compound of the Cannabis Sativa responsible for most of the effects of the plant. Another major constituent is cannabidiol (CBD), formerly regarded to be devoid of pharmacological activity. However, laboratory rodents and human studies have shown that this cannabinoid is able to prevent psychotic-like symptoms induced by high doses of Δ 9- THC. Subsequent studies have demonstrated that CBD has antipsychotic effects as observed using animal models and in healthy volunteers. Thus, this article provides a critical review of the research evaluating antipsychotic potential of this cannabinoid. CBD appears to have pharmacological profile similar to that of atypical antipsychotic drugs as seem using behavioral and neurochemical techniques in animal models. Additionally, CBD prevented human experimental psychosis and was effective in open case reports and clinical trials in patients with schizophrenia with a remarkable safety profile. Moreover, fMRI results strongly suggest that the antipsychotic effects of CBD in relation to the psychotomimetic effects of Δ 9-THC involve the striatum and temporal cortex that have been traditionally associated with psychosis. Although the mechanisms of the antipsychotic properties are still not fully understood, we propose a hypothesis that could have a heuristic value to inspire new studies. These results support the idea that CBD may be a future therapeutic option in psychosis, in general and in schizophrenia, in particular.


title: Kodu game lab: improving the motivation for learning programming concepts

abstract: Integrating games into classroom programs to enrich learning has become easier with the increased range of digital media and the greater access to cost effective computing. In this proposed research a series of exploratory studies is planned to see if student engagement could be enhanced by embedding games and game development into a regular classroom program. An integrated game development environment will be used to provide an exploratory context for creativity and games to engage students in learning programming concepts.


title: Sleep spindles in midday naps enhance learning in preschool children

abstract: Despite the fact that midday naps are characteristic of early childhood, very little is understood about the structure and function of these sleep bouts. Given that sleep benefits memory in young adults, it is possible that naps serve a similar function for young children. However, children transition from biphasic to monophasic sleep patterns in early childhood, eliminating the nap from their daily sleep schedule. As such, naps may contain mostly light sleep stages and serve little function for learning and memory during this transitional age. Lacking scientific understanding of the function of naps in early childhood, policy makers may eliminate preschool classroom nap opportunities due to increasing curriculum demands. Here we show evidence that classroom naps support learning in preschool children by enhancing memories acquired earlier in the day compared with equivalent intervals spent awake. This nap benefit is greatest for children who nap habitually, regardless of age. Performance losses when nap-deprived are not recovered during subsequent overnight sleep. Physiological recordings of naps support a role of sleep spindles in memory performance. These results suggest that distributed sleep is critical in early learning; when short-term memory stores are limited, memory consolidation must take place frequently.


title: Instance-level Human Parsing via Part Grouping Network

abstract: Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing multiple instances in a single pass. Several related works all follow the "parsing-by-detection" pipeline that heavily relies on separately trained detection models to localize instances and then performs human parsing for each instance sequentially. Nonetheless, two discrepant optimization targets of detection and parsing lead to suboptimal representation learning and error accumulation for final results. In this work, we make the first attempt to explore a detection-free Part Grouping Network (PGN) for efficiently parsing multiple people in an image in a single pass. Our PGN reformulates instance-level human parsing as two twinned sub-tasks that can be jointly learned and mutually refined via a unified network: 1) semantic part segmentation for assigning each pixel as a human part (e.g., face, arms); 2) instance-aware edge detection to group semantic parts into distinct person instances. Thus the shared intermediate representation would be endowed with capabilities in both characterizing fine-grained parts and inferring instance belongings of each part. Finally, a simple instance partition process is employed to get final results during inference. We conducted experiments on PASCAL-Person-Part dataset and our PGN outperforms all state-of-the-art methods. Furthermore, we show its superiority on a newly collected multi-person parsing dataset (CIHP) including 38,280 diverse images, which is the largest dataset so far and can facilitate more advanced human analysis. The CIHP benchmark and our source code are available at this http URL.


title: Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks

abstract: Cognitive radio technology has been proposed to improve spectrum efficiency by having the cognitive radios act as secondary users to opportunistically access under-utilized frequency bands. Spectrum sensing, as a key enabling functionality in cognitive radio networks, needs to reliably detect signals from licensed primary radios to avoid harmful interference. However, due to the effects of channel fading/shadowing, individual cognitive radios may not be able to reliably detect the existence of a primary radio. In this paper, we propose an optimal linear cooperation framework for spectrum sensing in order to accurately detect the weak primary signal. Within this framework, spectrum sensing is based on the linear combination of local statistics from individual cognitive radios. Our objective is to minimize the interference to the primary radio while meeting the requirement of opportunistic spectrum utilization. We formulate the sensing problem as a nonlinear optimization problem. By exploiting the inherent structures in the problem formulation, we develop efficient algorithms to solve for the optimal solutions. To further reduce the computational complexity and obtain solutions for more general cases, we finally propose a heuristic approach, where we instead optimize a modified deflection coefficient that characterizes the probability distribution function of the global test statistics at the fusion center. Simulation results illustrate significant cooperative gain achieved by the proposed strategies. The insights obtained in this paper are useful for the design of optimal spectrum sensing in cognitive radio networks.


title: Design of people counting system using MATLAB

abstract: Automatic counting of people, entering or exiting a region of interest, is very important for both business and security applications. To properly manage security in various places, it is of great importance to introduce video surveillance. This paper introduces an automatic people counting system which can count multiple people who interact in the region of interest, by using only one camera. The algorithm uses Viola Jones method of facial recognition to detect people. Using a single overhead mounted camera, the system counts the number of people going in to an observed area. Counting is performed by analyzing the image to detect faces. We have tested the performance of the system, achieving a correct people counting rate of 85%.


title: Blue sky ideas in artificial intelligence education from the EAAI 2017 new and future AI educator program

abstract: The 7th Symposium on Educational Advances in Artificial Intelligence (EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and Future AI Educator Program to support the training of early-career university faculty, secondary school faculty, and future educators (PhD candidates or postdocs who intend a career in academia). As part of the program, awardees were asked to address one of the following "blue sky" questions: ::: ::: 1. How could/should Artificial Intelligence (AI) courses incorporate ethics into the curriculum? ::: ::: 2. How could we teach AI topics at an early undergraduate or a secondary school level? ::: ::: 3. AI has the potential for broad impact to numerous disciplines. How could we make AI education more interdisciplinary, specifically to benefit non-engineering fields? ::: ::: This paper is a collection of their responses, intended to help motivate discussion around these issues in AI education.


Don't Get Fooled by Word Embeddings-Better Watch their Neighborhood


title: A Multi-Sensor and Parallel Processing SoC for Miniaturized Medical Instrumentation

abstract: We report VivoSoC , a system-on-chip realized in 130-nm CMOS for miniaturized medical instrumentation as used in mobile health devices or implantable telemetry systems for animal experiments. It features six neural stimulation channels and acquisition circuits for 9 $\times $ electrode-based recordings, 4-channel/32-LED photoplethysmography (PPG), bioimpedance, and temperature. A 34- $\mu \text{W}$ /MHz quad-core processing unit with sophisticated data, power, and clock management techniques enables on-chip feature extraction, which can dramatically reduce transmission or storage data rates in a system—crucial for overall power consumption and exemplarily demonstrated on preliminary work on implantable PPG-based vital signs monitoring.


title: Automatic Resolution of Ambiguous Terms Based on Machine Learning and Conceptual Relations in the UMLS

abstract: Motivation. The UMLS has been used in natural language processing applications such as information retrieval and information extraction systems. The mapping of free-text to UMLS concepts is important for these applications. To improve the mapping, we need a method to disambiguate terms that possess multiple UMLS concepts. In the general English domain, machine-learning techniques have been applied to sense-tagged corpora, in which senses (or concepts) of ambiguous terms have been annotated (mostly manually). Sense disambiguation classifiers are then derived to determine senses (or concepts) of those ambiguous terms automatically. However, manual annotation of a corpus is an expensive task. We propose an automatic method that constructs sense-tagged corpora for ambiguous terms in the UMLS using MEDLINE abstracts. ::: ::: Methods. For a term W that represents multiple UMLS concepts, a collection of MEDLINE abstracts that contain W is extracted. For each abstract in the collection, occurrences of concepts that have relations with W as defined in the UMLS are automatically identified. A sense-tagged corpus, in which senses of W are annotated, is then derived based on those identified concepts. The method was evaluated on a set of 35 frequently occurring ambiguous biomedical abbreviations using a gold standard set that was automatically derived. The quality of the derived sense-tagged corpus was measured using precision and recall. ::: ::: Results. The derived sense-tagged corpus had an overall precision of 92.9% and an overall recall of 47.4%. After removing rare senses and ignoring abbreviations with closely related senses, the overall precision was 96.8% and the overall recall was 50.6%. ::: ::: Conclusions. UMLS conceptual relations and MEDLINE abstracts can be used to automatically acquire knowledge needed for resolving ambiguity when mapping free-text to UMLS concepts.


Image steganography based on hamming code and edge detection.


title: An Experience Report at Teaching a Group Based Agile Software Development Project Course

abstract: Teaching group based Agile software development project courses is difficult. There are many aspects that need to be considered for a project to be successful such as a well defined scope, students working effectively together, and engaging with the customer. In this paper we present an experience report at teaching an Agile software development project course that involved teams developing web applications. The resources developed for the course and discussion about our experience will help inform others who also wish to teach group based software development courses.


title: A Discriminative Lexicon Model for Complex Morphology

abstract: This paper describes successful applications of discriminative lexicon models to the statistical machine translation (SMT) systems into morphologically complex languages. We extend the previous work on discriminatively trained lexicon models to include more contextual information in making lexical selection decisions by building a single global log-linear model of translation selection. In offline experiments, we show that the use of the expanded contextual information, including morphological and syntactic features, help better predict words in three target languages with complex morphology (Bulgarian, Czech and Korean). We also show that these improved lexical prediction models make a positive impact in the end-to-end SMT scenario from English to these languages.


title: Sparse local embeddings for extreme multi-label classification

abstract: The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches attempt to make training and prediction tractable by assuming that the training label matrix is low-rank and reducing the effective number of labels by projecting the high dimensional label vectors onto a low dimensional linear subspace. Still, leading embedding approaches have been unable to deliver high prediction accuracies, or scale to large problems as the low rank assumption is violated in most real world applications. ::: ::: In this paper we develop the SLEEC classifier to address both limitations. The main technical contribution in SLEEC is a formulation for learning a small ensemble of local distance preserving embeddings which can accurately predict infrequently occurring (tail) labels. This allows SLEEC to break free of the traditional low-rank assumption and boost classification accuracy by learning embeddings which preserve pairwise distances between only the nearest label vectors. ::: ::: We conducted extensive experiments on several real-world, as well as benchmark data sets and compared our method against state-of-the-art methods for extreme multi-label classification. Experiments reveal that SLEEC can make significantly more accurate predictions then the state-of-the-art methods including both embedding-based (by as much as 35%) as well as tree-based (by as much as 6%) methods. SLEEC can also scale efficiently to data sets with a million labels which are beyond the pale of leading embedding methods.


title: TimeSpace: activity-based temporal visualisation of personal information spaces

abstract: Users’ personal information spaces are characterized by their content, organisation, and ongoing user interaction with them. They are fluid entities, evolving over time, and supporting multiple user activities that may require different perspectives of the same underlying information structure. Increasing storage capacity of computing devices and ready access to networked resources puts users at risk of information overload, and presents increasing challenges in organising and accessing their information. The hierarchical model of information organisation currently dominates personal computing, and is realised for the user in interfaces that help to manage and access filestore hierarchies. Such a model provides limited inherent support for what users do—carry out a range of interleaved activities over time. In this paper, we describe the TimeSpace system, which provides perspectives on a user’s information resources based on activities and temporal attributes of the information. TimeSpace can be used alongside, or in place of, existing systems and models (such as the Microsoft Windows hierarchical file model). User interaction with an information space is non-intrusively observed and then represented automatically in TimeSpace. Visualisations provide overviews of user activity on multiple projects and detailed views of activity within particular projects, allowing navigation forward and backward in time. An observational study of use of the system revealed positive user views of the utility of temporal, activity-oriented workspaces in real world contexts alongside existing tools. Participants appreciated being offered a different perspective on their electronic information collection, one that visually shows the composition and development of their information space. They were interested in using the system for current and long-term work as well as for archiving information, as the visualisations provide a context for their work and give an overview of all their work in progress. The ideas embodied by the system and its visualisations show promise and raise a number of issues for further exploration. In future work, these ideas will be adapted and extended to support users in managing their information spaces across multiple personal devices, locations and time.


title: Botnet Research Survey

abstract: Botnets are emerging threat with hundreds of millions of computers infected. A study shows that about 40% of all computers connected to the internet in the world are infected bots and controlled by attackers. This article is a survey of recent advances in botnet research. The survey classifies the botnet research into three areas: understanding botnets, detecting and tracking botnets, and defending against botnets. While botnets are widespread, the research and solutions for botnets are still in their infancy. The paper also summarizes the existing research and proposes future directions for botnet research.


title: A Motion Blur Resilient Fiducial for Quadcopter Imaging

abstract: Fiducials are commonly placed in environments to provide a uniquely identifiable object in the scene. In quad copter applications, these fiducials are often used to evaluate planning algorithms given that ground truth positions can be detected from the quad copter's camera. Low cost quad copters, however, are subject to quick and unstable motions that can cause significant motion blur that severely affects the detection rate of existing fiducials. This problem motivated us to design a fiducial that is robust to motion blur. Our proposed design uses concentric circles with the observation that the direction perpendicular to the motion blur direction will be relatively unaffected by the blur. As a result, an appropriate fiducial code orthogonal to the blur direction can be recognized. Since the direction of motion blur is unknown, the circular design is good for all motion blur directions. We describe the design of binary fiducials, and also a detection algorithm. We show that our marker can significantly outperform existing fiducials in scenes captured with a quad copter.


Automatic Calibration and Removal of Distortion From Scenes of Structured Environments


title: The possession game? A comparative analysis of ball retention and team success in European and international football, 2007–2010

abstract: Abstract Possession is thought of as central to success in modern football, but questions remain about its impact on positive team outcomes (Bate, 1988; Hughes & Franks, 2005; Pollard & Reep, 1997; Stanhope, 2001). Recent studies (e.g. Bloomfield, Polman, & O'Donoghue, 2005; Carling, Williams, & Reilly, 2005; James, Mellallieu, & Holley, 2002; Jones, James, & Mellalieu, 2004; Lago, 2009; Lago & Martin, 2007; Lago-Penas & Dellal, 2010; Lago-Penas, Lago-Ballesteros, Dellal, & Gomez, 2010; Taylor, Mellalieu, & James, 2005; Tucker, Mellalieu, James, & Taylor, 2005) that have examined these questions have often been constrained by an exclusive focus on English or Spanish domestic play. Using data from five European leagues, UEFA and FIFA tournaments, the study found that while possession time and passing predicted aggregated team success in domestic league play, both variables were poor predictors at the individual match level once team quality and home advantage were accounted for. In league play, the effect ...


title: Exploiting Spatial Structure for Localizing Manipulated Image Regions

abstract: The advent of high-tech journaling tools facilitates an image to be manipulated in a way that can easily evade state-of-the-art image tampering detection approaches. The recent success of the deep learning approaches in different recognition tasks inspires us to develop a high confidence detection framework which can localize manipulated regions in an image. Unlike semantic object segmentation where all meaningful regions (objects) are segmented, the localization of image manipulation focuses only the possible tampered region which makes the problem even more challenging. In order to formulate the framework, we employ a hybrid CNN-LSTM model to capture discriminative features between manipulated and non-manipulated regions. One of the key properties of manipulated regions is that they exhibit discriminative features in boundaries shared with neighboring non-manipulated pixels. Our motivation is to learn the boundary discrepancy, i.e., the spatial structure, between manipulated and non-manipulated regions with the combination of LSTM and convolution layers. We perform end-to-end training of the network to learn the parameters through back-propagation given ground-truth mask information. The overall framework is capable of detecting different types of image manipulations, including copy-move, removal and splicing. Our model shows promising results in localizing manipulated regions, which is demonstrated through rigorous experimentation on three diverse datasets.


title: Comparing Attention-based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension

abstract: We propose a machine reading comprehension model based on the compare-aggregate framework with two-staged attention that achieves state-of-the-art results on the MovieQA question answering dataset. To investigate the limitations of our model as well as the behavioral difference between convolutional and recurrent neural networks, we generate adversarial examples to confuse the model and compare to human performance. Furthermore, we assess the generalizability of our model by analyzing its differences to human inference,


title: Tamper-proofing of electronic and printed text documents via robust hashing and data-hiding

abstract: In this paper, we deal with the problem of authentication and tamper-proofing of text documents that can be distributed in electronic or printed forms. We advocate the combination of robust text hashing and text data-hiding technologies as an efficient solution to this problem. First, we consider the problem of text data-hiding in the scope of the Gel'fand-Pinsker data-hiding framework. For illustration, two modern text data-hiding methods, namely color index modulation (CIM) and location index modulation (LIM), are explained. Second, we study two approaches to robust text hashing that are well suited for the considered problem. In particular, both approaches are compatible with CIM and LIM. The first approach makes use of optical character recognition (OCR) and a classical cryptographic message authentication code (MAC). The second approach is new and can be used in some scenarios where OCR does not produce consistent results. The experimental work compares both approaches and shows their robustness against typical intentional/unintentional document distortions including electronic format conversion, printing, scanning, photocopying, and faxing.


title: Re-place-ing space: the roles of place and space in collaborative systems

abstract: Many collaborative and communicative environments use notions of “space” and spatial organisation to facilitate and structure interaction. We argue that a focus on spatial models is misplaced. Drawing on understandings from architecture and urban design, as well as from our own research findings, we highlight the critical distinction between “space” and “place”. While designers use spatial models to support interaction, we show how it is actually a notion of “place” which frames interactive behaviour. This leads us to re-evaluate spatial systems, and discuss how “place”, rather than “space”, can support CSCW design.


title: A Distributed PIR-based Approach for Estimating People Count in Office Environments

abstract: Office buildings are key energy consumers and thus require attention to achieve efficient operation. While individual office spaces are dynamically used, current building automation does not receive information on utilisation that could be used to adaptively adjust energy consumption. In this work, we propose an approach to estimate people count per office space using distributed strategically placed PIR sensors and algorithms that can process the distributed sensor information. We detail our sensing node and evaluate its performance in an office installation. A sensor model was subsequently used in a floor-wide simulation of realistic occupant behaviours to investigate two algorithms to estimate people count per office space. The occupant behaviour simulations confirmed that our estimation algorithms can accurately predict people count in different office use scenarios. The errors introduced by the PIR masking time after a detection can be partially compensated when using distributed sensor information. Our approach can be used for dynamic, occupancy-dependent lighting, climate, and appliances control of office spaces.


title: When a 12.86 % Mortality is More Dangerous than 24.14%: Implications for Risk Communication KIMIHIKO YAMAGISHI*

abstract: SUMMARY Participants assessed the riskiness of 11 well-known causes of death. Each participant was presented with an estimation of the number of deaths in the population due to that particular cause. The estimates were obtained from a previous study of naive participants’ intuitive estimations. For instance, based on the result of the previous study, the number of deaths due to cancer was presented as: ‘2,414 out of 10,000’, ‘1,286 out of 10,000’, ‘24.14 out of 100’ and ‘12.86 out of 100’. The estimates of deaths were presented in analogous ways for the remaining ten causes of death. It was hypothesized that the judged degree of riskiness is affected by the number of deaths, irrespective of the total possible number (such as 10,000 or 100). Results from Experiment 1 were consistent with this prediction. Participants rated cancer as riskier when it was described as ‘kills 1,286 out of 10,000 people’ than as ‘kills 24.14 out of 100 people’, and similar results were observed regarding the remaining 10 causes of death. Experiment 2 replicated this trend. Implications for risk communications are discussed. & 1997 John Wiley & Sons, Ltd.


title: Qualia: The Geometry of Integrated Information

abstract: According to the integrated information theory, the quantity of consciousness is the amount of integrated information generated by a complex of elements, and the quality of experience is specified by the informational relationships it generates. This paper outlines a framework for characterizing the informational relationships generated by such systems. Qualia space (Q) is a space having an axis for each possible state (activity pattern) of a complex. Within Q, each submechanism specifies a point corresponding to a repertoire of system states. Arrows between repertoires in Q define informational relationships. Together, these arrows specify a quale—a shape that completely and univocally characterizes the quality of a conscious experience. Φ— the height of this shape—is the quantity of consciousness associated with the experience. Entanglement measures how irreducible informational relationships are to their component relationships, specifying concepts and modes. Several corollaries follow from these premises. The quale is determined by both the mechanism and state of the system. Thus, two different systems having identical activity patterns may generate different qualia. Conversely, the same quale may be generated by two systems that differ in both activity and connectivity. Both active and inactive elements specify a quale, but elements that are inactivated do not. Also, the activation of an element affects experience by changing the shape of the quale. The subdivision of experience into modalities and submodalities corresponds to subshapes in Q. In principle, different aspects of experience may be classified as different shapes in Q, and the similarity between experiences reduces to similarities between shapes. Finally, specific qualities, such as the “redness” of red, while generated by a local mechanism, cannot be reduced to it, but require considering the entire quale. Ultimately, the present framework may offer a principled way for translating qualitative properties of experience into mathematics.


title: Unsupervised segmentation of natural . . .

abstract: In this paper, we cast natural-image segmentation as a problem of clustering texture features as multivariate mixed data. We model the distribution of the texture features using a mixture of Gaussian distributions. Unlike most existing clustering methods, we allow the mixture components to be degenerate or nearly-degenerate. We contend that this assumption is particularly important for mid-level image segmentation, where degeneracy is typically introduced by using a common feature representation for different textures in an image. We show that such a mixture distribution can be effectively segmented by a simple agglomerative clustering algorithm derived from a lossy data compression approach. Using either 2D texture filter banks or simple fixed-size windows to obtain texture features, the algorithm effectively segments an image by minimizing the overall coding length of the feature vectors. We conduct comprehensive experiments to measure the performance of the algorithm in terms of visual evaluation and a variety of quantitative indices for image segmentation. The algorithm compares favorably against other well-known image-segmentation methods on the Berkeley image database.


title: DIETOS: A recommender system for adaptive diet monitoring and personalized food suggestion

abstract: Nowadays there is a widespread diffusion of mobile applications for weight and diet management. Even though, the most popular apps are not usually experimented in clinical contexts, as well as apps are not supported by medical evidence. Further research is necessary to assess the effectiveness of apps for weight and diet management. Moreover, there are few examples of food recommender systems that provide to the users nutritional facts about suitable food choices and take into account individual physiological status and environmental situations. We propose DIETOS (DIET Organizer System), a recommender system for the adaptive delivery of nutrition contents to improve the quality of life of both healthy people and individuals affected by chronic diet-related diseases. The proposed system is able to build a user's health profile, and provides individualized nutritional recommendation according to the health profile. The profile is created through the use of dynamic real-time questionnaires prepared by medical doctors and compiled by the users. The health profile includes information about health status and eventual chronic diseases. The first prototype of the system (available online at http://www.easyanalysis.it/dietos), includes a catalogue of typical Calabrian foods compiled by nutrition specialists (Calabria is a region of the southern Italy). DIETOS can suggest not only the use of specific foods compatible with the health status, but also it may give dietary indications related to some specific pathologies or health conditions.


title: Adolescent Development and Juvenile Justice

abstract: Although justice system policy and practice cannot, and should not, be dictated solely by studies of adolescent development, the ways in which we respond to juvenile offending should be informed by the lessons of developmental science. This review begins with a brief overview of the history, rationale, and workings of the American juvenile justice system. Following this, I summarize findings from studies of brain, cognitive, and psychosocial development in adolescence that have implications for the treatment of juveniles in the justice system. The utility of developmental science in this context is illustrated by the application of these research findings to three fundamental issues in contemporary justice policy: the criminal culpability of adolescents, adolescents’ competence to stand trial, and the impact of punitive sanctions on adolescents’ development and behavior. Taken together, the lessons of developmental science offer strong support for the maintenance of a separate juvenile justice system in which adolescents are judged, tried, and sanctioned in developmentally appropriate ways.


Comparison of Visual and Logical Character Segmentation in Tesseract OCR Language Data for Indic Writing Scripts.


title: Deploying Secure Multi-Party Computation for Financial Data Analysis

abstract: We show how to collect and analyze financial data for a consortium of ICT companies using secret sharing and secure multi-party computation (MPC). This is the first time where the actual MPC computation on real data was done over the internet with computing nodes spread geographically apart. We describe the technical solution and present user feedback revealing that MPC techniques give sufficient assurance for data donors to submit their sensitive information.


title: Malware Lineage in the Wild

abstract: Malware lineage studies the evolutionary relationships among malware and has important applications for malware analysis. A persistent limitation of prior malware lineage approaches is to consider every input sample a separate malware version. This is problematic since a majority of malware are packed and the packing process produces many polymorphic variants (i.e., executables with different file hash) of the same malware version. Thus, many samples correspond to the same malware version and it is challenging to identify distinct malware versions from polymorphic variants. This problem does not manifest in prior malware lineage approaches because they work on synthetic malware, malware that are not packed, or packed malware for which unpackers are available. In this work, we propose a novel malware lineage approach that works on malware samples collected in the wild. Given a set of malware executables from the same family, for which no source code is available and which may be packed, our approach produces a lineage graph where nodes are versions of the family and edges describe the relationships between versions. To enable our malware lineage approach, we propose the first technique to identify the versions of a malware family and a scalable code indexing technique for determining shared functions between any pair of input samples. We have evaluated the accuracy of our approach on 13 open-source programs and have applied it to produce lineage graphs for 10 popular malware families. Our malware lineage graphs achieve on average a 26 times reduction from number of input samples to number of versions.


EMD based Visual Similarity for Detection


title: Halfway between 2D and Animal Models: Are 3D Cultures the Ideal Tool to Study Cancer-Microenvironment Interactions?

abstract: An area that has come to be of tremendous interest in tumor research in the last decade is the role of the microenvironment in the biology of neoplastic diseases. The tumor microenvironment (TME) comprises various cells that are collectively important for normal tissue homeostasis as well as tumor progression or regression. Seminal studies have demonstrated the role of the dialogue between cancer cells (at many sites) and the cellular component of the microenvironment in tumor progression, metastasis, and resistance to treatment. Using an appropriate system of microenvironment and tumor culture is the first step towards a better understanding of the complex interaction between cancer cells and their surroundings. Three-dimensional (3D) models have been widely described recently. However, while it is claimed that they can bridge the gap between in vitro and in vivo, it is sometimes hard to decipher their advantage or limitation compared to classical two-dimensional (2D) cultures, especially given the broad number of techniques used. We present here a comprehensive review of the different 3D methods developed recently, and, secondly, we discuss the pros and cons of 3D culture compared to 2D when studying interactions between cancer cells and their microenvironment.


title: iTopicModel: Information Network-Integrated Topic Modeling

abstract: Document networks, i.e., networks associated with text information, are becoming increasingly popular due to the ubiquity of Web documents, blogs, and various kinds of online data. In this paper, we propose a novel topic modeling framework for document networks, which builds a unified generative topic model that is able to consider both text and structure information for documents. A graphical model is proposed to describe the generative model. On the top layer of this graphical model, we define a novel multivariate Markov Random Field for topic distribution random variables for each document, to model the dependency relationships among documents over the network structure. On the bottom layer, we follow the traditional topic model to model the generation of text for each document. A joint distribution function for both the text and structure of the documents is thus provided. A solution to estimate this topic model is given, by maximizing the log-likelihood of the joint probability. Some important practical issues in real applications are also discussed, including how to decide the topic number and how to choose a good network structure. We apply the model on two real datasets, DBLP and Cora, and the experiments show that this model is more effective in comparison with the state-of-the-art topic modeling algorithms.


title: What's up with whatsapp?: comparing mobile instant messaging behaviors with traditional SMS

abstract: With the advent of instant mobile messaging applications, traditional SMS is in danger of loosing it's reign as the king of mobile messaging. Applications like WhatsApp allow mobile users to send real-time text messages to individuals or groups of friends at no cost. While there is a vast body of research on traditional text messaging practices, little is understood about how and why people have adopted and appropriated instant mobile messaging applications. The goal of this work is to provide a deeper understanding of the motives and perceptions of a popular mobile messaging application called WhatsApp and to learn more about what this service offers above and beyond traditional SMS. To this end, we present insights from two studies an interview study and a large-scale survey highlighting that while WhatsApp offers benefits such as cost, sense of community and immediacy, SMS is still considered a more reliable, privacy preserving technology for mobile communication.


title: Mining Scientific Papers for Bibliometrics: a (very) Brief Survey of Methods and Tools

abstract: The Open Access movement in scientific publishing and search engines like Google Scholar have made scientific articles more broadly accessible. During the last decade, the availability of scientific papers in full text has become more and more widespread thanks to the growing number of publications on online platforms such as ArXiv and CiteSeer. The efforts to provide articles in machine-readable formats and the rise of Open Access publishing have resulted in a number of standardized formats for scientific papers (such as NLM-JATS, TEI, DocBook). Our aim is to stimulate research at the intersection of Bibliometrics and Computational Linguistics in order to study the ways Bibliometrics can benefit from large-scale text analytics and sense mining of scientific papers, thus exploring the interdisciplinarity of Bibliometrics and Natural Language Processing.


title: Towards an Artificially Empathic Conversational Agent for Mental Health Applications: System Design and User Perceptions

abstract: BACKGROUND ::: Conversational agents cannot yet express empathy in nuanced ways that account for the unique circumstances of the user. Agents that possess this faculty could be used to enhance digital mental health interventions. ::: ::: ::: OBJECTIVE ::: We sought to design a conversational agent that could express empathic support in ways that might approach, or even match, human capabilities. Another aim was to assess how users might appraise such a system. ::: ::: ::: METHODS ::: Our system used a corpus-based approach to simulate expressed empathy. Responses from an existing pool of online peer support data were repurposed by the agent and presented to the user. Information retrieval techniques and word embeddings were used to select historical responses that best matched a user's concerns. We collected ratings from 37,169 users to evaluate the system. Additionally, we conducted a controlled experiment (N=1284) to test whether the alleged source of a response (human or machine) might change user perceptions. ::: ::: ::: RESULTS ::: The majority of responses created by the agent (2986/3770, 79.20%) were deemed acceptable by users. However, users significantly preferred the efforts of their peers (P<.001). This effect was maintained in a controlled study (P=.02), even when the only difference in responses was whether they were framed as coming from a human or a machine. ::: ::: ::: CONCLUSIONS ::: Our system illustrates a novel way for machines to construct nuanced and personalized empathic utterances. However, the design had significant limitations and further research is needed to make this approach viable. Our controlled study suggests that even in ideal conditions, nonhuman agents may struggle to express empathy as well as humans. The ethical implications of empathic agents, as well as their potential iatrogenic effects, are also discussed.


title: A Cross-Correlation Technique for Vehicle Detections in Wireless Magnetic Sensor Network

abstract: Vehicle detections are an important research field and attract many researchers. Most research efforts have been focused on vehicle parking detection (VPD) in indoor parking lot. For on-street parking, strong noise disturbances affect detection accuracy. To deal with vehicle detections in on-street environment, we propose two vehicle detection algorithms based on a cross-correlation technique in wireless magnetic sensor networks. One is for VPD, and the other one is for vehicle speed estimation (VSE). The proposed VPD algorithm combines the state-machine detection and the cross-correlation detection. In the VSE, speed estimation is based on the calculation of the normalized cross correlation between the signals of two sensors along the road with a certain spacing. Experimental results show that the VPD has an accuracy of 99.65% for arrival and 99.44% for departure, while the VSE has an accuracy of 92%.


title: An inside job: Remote power analysis attacks on FPGAs

abstract: Hardware Trojans have gained increasing interest during the past few years. Undeniably, the detection of such malicious designs needs a deep understanding of how they can practically be built and developed. In this work we present a design methodology dedicated to FPGAs which allows measuring a fraction of the dynamic power consumption. More precisely, we develop internal sensors which are based on FPGA primitives, and transfer the internally-measured side-channel leakages outside. These are distributed and calibrated delay sensors which can indirectly measure voltage fluctuations due to power consumption. By means of a cryptographic core as a case study, we present different settings and parameters for our employed sensors. Using their side-channel measurements, we further exhibit practical key-recovery attacks confirming the applicability of the underlying measurement methodology. This opens a new door to integrate hardware Trojans in a) applications where the FPGA is remotely accessible and b) FPGA-based multi-user platforms where the reconfigurable resources are shared among different users. This type of Trojan is highly difficult to detect since there is no signal connection between targeted (cryptographic) core and the internally-deployed sensors.


Parking Lots Space Detection


title: Nuclear medicine begins with a boa constrictor.

abstract: In the beginning, a boa constrictor defecated in London and the subsequent development of nuclear medicine was inevitable. It took a little time, but the 139-yr chain of cause and effect that followed was inexorable (7). One June week in 1815 an exotic animal exhibi tion was held on the Strand in London. A young "animal chemist" named William Prout (we would now call him a clinical pathologist) attended this scientific event of the year. While he was viewing a boa constrictor recently captured in South America, the animal defecated and Prout was amazed by what he saw. The physiological incident was common place, but he was the only person alive who could recognize the material. Just a year earlier he had isolated the first pure sample of urea—but from the urine of patients with gout! Upon seeing the unusual feces, Prout sought out the animal caretaker and requested a sample. Grave robbers were an ongoing scandal in London in those days, but coprophilia was a new twist. The incredu lous animal caretaker crossed himself twice and cleaned out the cage. Prout hurried back to his sur gery (the British use of the term) with his unusual prize.


title: Proteomics in Domestic Animals: from Farm to Systems Biology

abstract: . Proteomics in Domestic Animals on a Farm to Systems Biology Perspective: Introductory Note . Considerations for Farm Animal Proteomic Experiments: An Introductory View Gel-Based Versus Non-gel-Based Approaches . Quantitative Gel Electrophoresis . Sample Preparation for 2DE Using Samples of Animal Origin . Gel-Free Proteomics . Proteomic Research in Farm Animal Serum and Plasma . Proteomic Research in Urine and Other Fluids . Colostrum Proteomics Research: A Complex Fluid with Multiple Physiological Functions . Proteomics in Milk and Dairy Products . Proteomics in Skeletal Muscle Research . Proteomics and the Characterization of Fatty Liver Metabolism in Early Lactation Dairy Cows . Proteomics Research in the Adipose Tissue . Proteomics and Mammary Gland Research in Dairy Species . Proteomics in Wool and Fibre Research . Proteomics of Meat Products . Proteomics in Fish and Aquaculture Research . The Use of Proteomics to Study Biomarkers of Stress and Welfare in Farm Animals . Bioinformatics Support for Farm Animal Proteomics . Peptidomics on Farm Animal Research . Studying the Animal Transcriptome: State of the Art and Challenges in the Context of Animal and Veterinary Sciences . NMR Metabolomics pari passu with Proteomics: Two Relevant Tools for Animal Sciences Combined . Omics and Systems Biology: Integration of Production and Omics Data in Systems Biology . Proteomics in Domestic Animals on a Farm to Systems Biology Perspective: Final Remarks and Future Prospects


title: A high throughput Gaussian noise generator

abstract: A digital additive white Gaussian noise (AWGN) generator has recently become a public focus with the increasing demand of hardware simulation in researches on communication discipline. Some successful ideas of software, such as the Box-Muller method, are then proposed and implemented into hardware platforms and the architecture design has attracted new studies. Then high-precision small-error Gaussian noise generators appeared in literatures from time to time. Considering that the academic applications prefer higher throughput and the data resolution are always limited by hardware resources, we propose to use a straightforward architecture based on a Gaussian lookup table in these cases. In this paper, such an architecture has been fully investigated and the corresponding FPGA implementation gives out a high throughput of 800 million noise samples per second.


title: Machine Learning Algorithms for Multi-Agent Systems

abstract: Multi-agent systems are rapidly used in a variety of domains, including robotics, distributed control, telecommunications, collaborative decision support systems, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must instead discover a solution on their own, using learning. The heart of the problem is how the agents will learn the environment independently and then how they will cooperate to establish the common task. This paper will attempt to answer these commonly asked questions from the machine learning perspective. In addition, this paper discusses the current gap and challenges in adoption of machine learning algorithms in multiagent systems.


title: Effect of a coteaching handwriting program for first graders: one-group pretest-posttest design.

abstract: We examined the effects of a cotaught handwriting and writing program on first-grade students grouped by low, average, and high baseline legibility. The program’s aim was to increase legibility, handwriting speed, writing fluency, and written expression in students with diverse learning needs. Thirty-six first-grade students in two classrooms participated in a 12-wk handwriting and writing program cotaught by teachers and an occupational therapist. Students were assessed at pretest, posttest, and 6-mo follow-up using the Evaluation Tool of Children’s Handwriting‐Manuscript (ETCH‐M) and the Woodcock‐Johnson Writing Fluency and Writing Samples tests. Students made large gains in ETCH‐M legibility (h 2 5 .74), speed (h 2 s 5 .52‐.65), Writing Fluency (h 2 5 .58), and Writing Samples (h 2 5 .59). Students with initially low legibility improved most in legibility; progress on the other tests was similar across low-, average-, and high-performing groups. This program appeared to benefit first-grade students with diverse learning needs and to increase handwriting legibility and speed and writing fluency.


title: Developing strategic measurement and improvement for the biopharmaceutical firm

abstract: Research highlights? This paper presents the use of the analytic hierarchy process (AHP) to prioritize all of the measures and strategies in a BSC framework. ? This case illustrates selection or design of the most appropriate and helpful measures of the BSC in the pharmaceutical firm in an emerging market. ? According to the AHP, assure sustainable shareholder value, intellectual property, maintain the asset quality, and customer relationship management are the prioritizing strategies for strategy execution. The balanced scorecard (BSC) provides an enterprise view of an organization's overall performance. It integrates financial measures with other key performance indicators around customer perspectives, internal business processes, and organizational growth, learning, and innovation. The strategy planning is a virtual necessity for business activities, and this paper presents the use of the analytic hierarchy process (AHP) to prioritize all of the measures and strategies in a BSC framework. This study has found related strategies and objectives from four perspectives of balanced scorecard. This case illustrates selection or design of the most appropriate and helpful measures of the BSC in the pharmaceutical firm in an emerging market.


title: Modelling Information Needs in Collaborative Search Conversations

abstract: The increase of voice-based interaction has changed the way people seek information, making search more conversational. Development of effective conversational approaches to search requires better understanding of how people express information needs in dialogue. This paper describes the creation and examination of over 32K spoken utterances collected during 34 hours of collaborative search tasks. The contribution of this work is three-fold. First, we propose a model of conversational information needs (CINs) based on a synthesis of relevant theories in Information Seeking and Retrieval. Second, we show several behavioural patterns of CINs based on the proposed model. Third, we identify effective feature groups that may be useful for detecting CINs categories from conversations. This paper concludes with a discussion of how these findings can facilitate advance of conversational search applications.


title: Mining sequential patterns

abstract: We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empirically evaluate their performance using synthetic data. Two of the proposed algorithms, AprioriSome and AprioriAll, have comparable performance, albeit AprioriSome performs a little better when the minimum number of customers that must support a sequential pattern is low. Scale-up experiments show that both AprioriSome and AprioriAll scale linearly with the number of customer transactions. They also have excellent scale-up properties with respect to the number of transactions per customer and the number of items in a transaction. >


title: Anomalies in Intertemporal Choice: Evidence and an Interpretation

abstract: Research on decision making under uncertainly has been strongly influenced by the documentation of numerous expected utility (EU) anomalies—behaviors that violate the expected utility axioms. The relative lack of progress on the closely related topic of intertemporal choice is partly due to the absence of an analogous set of discounted utility (DU) anomalies. We enumerate a set of DU anomalies analogous to the EU anomalies and propose a model that accounts for the anomalies, as well as other intertemporal choice phenomena incompatible with DU. We discuss implications for savings behavior, estimation of discount rates, and choice framing effects.


title: The dawn of Big Data - Hbase

abstract: HBase is a distributed column-oriented database built on top of HDFS. HBase is the Hadoop application to use when you require real-time read/write random access to very large datasets. HBase is a scalable data store targeted at random read and write access of (fairly-) structured data. It's modeled after Google's Big table and targeted to support large tables, on the order of billions of rows and millions of columns. It uses HDFS as the underlying file system and is designed to be fully distributed and highly available. Version 0.20 introduces significant performance improvement. Base's Table Input Format is designed to allow a Map Reduce program to operate on data stored in an HBase table. Table Output Format is for writing Map Reduce outputs into an HBase table. HBase has different storage characteristics than HDFS, such as the ability to do row updates and column indexing, so we can expect to see these features used by Hive in future releases. It is already possible to access HBase tables from Hive. This paper includes the step by step introduction to the HBase, Identify differences between apache HBase and a traditional RDBMS, The Problem with Relational Database Systems, Relation between the Hadoop and HBase, How an Apache HBase table is physically stored on disk. Later part of this paper introduces Map Reduce, HBase table and how Apache HBase Cells stores data, what happens to data when it is deleted. Last part explains difference between Big Data and HBase, Conclusion followed with the References.


title: Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering

abstract: The last several years have seen intensive interest in exploring neural-network-based models for machine comprehension (MC) and question answering (QA). In this paper, we approach the problems by closely modelling questions in a neural network framework. We first introduce syntactic information to help encode questions. We then view and model different types of questions and the information shared among them as an adaptation task and proposed adaptation models for them. On the Stanford Question Answering Dataset (SQuAD), we show that these approaches can help attain better results over a competitive baseline.


title: Phishing Detection Taxonomy for Mobile Device

abstract: Phishing is one of the social engineering attacks and currently ::: hit on mobile devices. Based on security report by Lookout [1], ::: 30% of Lookout users clicking on an unsafe link per year by ::: using mobile device. Few phishing detection techniques have ::: been applied on mobile device. However, review on phishing ::: detection technique on the detection technique redundant is still ::: need. This paper addresses the current trend phishing detection ::: for mobile device and identifies significant criterion to improve ::: phishing detection techniques on mobile device. Thus, existing ::: research on phishing detection technique for computer and ::: mobile device will be compared and analysed. Hence, outcome ::: of the analysis becomes a guideline in proposing generic ::: phishing detection taxonomy for mobile device.


title: Sequential hardware Trojan: Side-channel aware design and placement

abstract: Various design-for-security (DFS) approaches have been proposed earlier for detection of hardware Trojans, which are malicious insertions in Integrated Circuits (ICs). In this paper, we highlight our major findings in terms of innovative Trojan design that can easily evade existing Trojan detection approaches based on functional testing or side-channel analysis. In particular, we illustrate design and placement of sequential hardware Trojans, which are rarely activated/observed and incur ultralow delay/power overhead. We provide models, examples, theoretical analysis of effectiveness, and simulation as well as measurement results of impact of these Trojans in a hardened design. It is shown that efficient design and placement of sequential Trojan would incur extremely low side-channel (power, delay) signature and hence, can easily evade both post-silicon validation and DFS (e.g. ring oscillator based) approaches.


title: A timbre matching approach to enhance audio quality of psychoacoustic bass enhancement system

abstract: Small and flat loudspeakers usually result in poor low-frequency (or bass) responses. Conventional gain equalization does not help significantly and may even result in overdriving and distortion. A psychoacoustic approach has been found to be suitable in tricking the human ear to perceive the fundamental frequency from its higher harmonics. Past research efforts have generally focused on weighting the harmonics based on the loudness matching method, but no work on timbre matching has been carried out so far. In this paper, we propose a new timbre matching technique, which can improve the sound quality of the psychoacoustically enhanced bass. This approach adjusts the amplitude of harmonics to produce similar timbre as the original audio content. Objective and subjective tests are carried out to compare the audio quality of the psychoacoustic bass enhanced signal using the equal-loudness weighting and the timbre matching methods.


title: Memory-efficient on-the-fly voxelization of particle data

abstract: In this paper we present a novel GPU-friendly real-time voxelization technique for rendering homogeneous media that is defined by particles, e.g. fluids obtained from particle-based simulations such as Smoothed Particle Hydrodynamics (SPH). Our method computes view-adaptive binary voxelizations with on-the-fly compression of a tiled perspective voxel grid, achieving higher resolutions than previous approaches. It allows for interactive generation of realistic images, enabling advanced rendering techniques such as ray casting-based refraction and reflection, light scattering and absorption, and ambient occlusion. In contrast to previous methods, it does not rely on preprocessing such as expensive, and often coarse, scalar field conversion or mesh generation steps. Our method directly takes unsorted particle data as input. It can be further accelerated by identifying fully populated simulation cells during simulation. The extracted surface can be filtered to achieve smooth surface appearance.


title: Step-by-step guide to critiquing research. Part 2: qualitative research

abstract: As with a quantitative study, critical analysis of a qualitative study involves an in-depth review of how each step of the research was undertaken. Qualitative and quantitative studies are, however, fundamentally different approaches to research and therefore need to be considered differently with regard to critiquing. The different philosophical underpinnings of the various qualitative research methods generate discrete ways of reasoning and distinct terminology; however, there are also many similarities within these methods. Because of this and its subjective nature, qualitative research it is often regarded as more difficult to critique. Nevertheless, an evidenced-based profession such as nursing cannot accept research at face value, and nurses need to be able to determine the strengths and limitations of qualitative as well as quantitative research studies when reviewing the available literature on a topic.


title: BranchGAN: Branched Generative Adversarial Networks for Scale-Disentangled Learning and Synthesis of Images

abstract: We introduce BranchGAN, a novel training method that enables unconditioned generative adversarial networks (GANs) to learn image manifolds at multiple scales. The key novel feature of BranchGAN is that it is trained in multiple branches, progressively covering both the breadth and depth of the network, as resolutions of the training images increase to reveal finer-scale features. Specifically, each noise vector, as input to the generator network, is explicitly split into several sub-vectors, each corresponding to, and is trained to learn, image representations at a particular scale. During training, we progressively de-freeze the sub-vectors, one at a time, as a new set of higher-resolution images is employed for training and more network layers are added. A consequence of such an explicit sub-vector designation is that we can directly manipulate and even combine latent (sub-vector) codes which model different feature scales. Experiments demonstrate the effectiveness of our training method in scale-disentangled learning of image manifolds and synthesis, without any extra labels and without compromising quality of the synthesized high-resolution images. We further demonstrate three applications enabled or improved by BranchGAN.


title: Self-Compassion and Trauma Processing Outcomes Among Victims of Violence

abstract: Self-compassion entails being kind and understanding toward oneself in instances of psychological pain and holding painful thoughts and feelings in mindful awareness. Self-compassion has been associated with positive mental health outcomes and may have implications for post-traumatic processing. Sixty-three female trauma survivors completed measures of post-traumatic stress and self-compassion (baseline). One week later, they were randomly assigned to one of three processing conditions to undergo an induction: “analytic” (brooding, conceptualizing), “experiential” (self-reflective, mindful experiencing), or control. Following induction, participants completed a trauma-specific interview to discuss their trauma. Before induction (T1) and after the interview (T2), women completed measures of anxiety and affectivity. At baseline, greater self-compassion correlated with less post-traumatic stress symptoms. After the induction and trauma interview at T2, greater self-kindness and mindfulness correlated with less anxiety and negative affectivity in controls. In the analytic processing condition, greater self-kindness correlated with less negative affectivity, whereas mindfulness correlated with less anxiety and negative affectivity and greater positive affectivity at T2. In the experiential processing condition, greater self-kindness correlated with more anxiety and positive affectivity at T2 and greater common humanity correlated with more anxiety at T2. Results indicated that processing modes exert differential effects of self-compassion on trauma-related anxiety and affectivity. During conditions of experiential processing, self-kindness may allow for greater tolerance of anxiety. During conditions of analytic processing, mindfulness may decrease anxiety and negative affectivity by reducing the tendency to perseverate on negative internal experiences. Implications for research, theory, and practice are discussed.


title: Recalling Holistic Information for Semantic Segmentation

abstract: Semantic segmentation requires a detailed labeling of image pixels by object category. Information derived from local image patches is necessary to describe the detailed shape of individual objects. However, this information is ambiguous and can result in noisy labels. Global inference of image content can instead capture the general semantic concepts present. We advocate that high-recall holistic inference of image concepts provides valuable information for detailed pixel labeling. We build a two-stream neural network architecture that facilitates information flow from holistic information to local pixels, while keeping common image features shared among the low-level layers of both the holistic analysis and segmentation branches. We empirically evaluate our network on four standard semantic segmentation datasets. Our network obtains state-of-the-art performance on PASCAL-Context and NYUDv2, and ablation studies verify its effectiveness on ADE20K and SIFT-Flow.


title: Evolution of web site design patterns

abstract: The Web enables broad dissemination of information and services; however, the ways in which sites are designed can either facilitate or impede users' benefit from these resources. We present a longitudinal study of web site design from 2000 to 2003. We analyze over 150 quantitative measures of interface aspects (e.g., amount of text on pages, numbers and types of links, consistency, accessibility, etc.) for 22,000 pages and over 1,500 sites that received ratings from Internet professionals. We examine characteristics of highly rated sites and provide three perspectives on the evolution of web site design patterns: (1) descriptions of design patterns during each time period; (2) changes in design patterns across the three time periods; and (3) comparisons of design patterns to those that are recommended in the relevant literature (i.e., texts by recognized experts and user studies). We illustrate how design practices conform to or deviate from recommended practices and the consequent implications. We show that the most glaring deficiency of web sites, even for sites that are highly rated, is their inadequate accessibility, in particular for browser scripts, tables, and form elements.


title: Implementation and application of HDLC protocol based on FPGA in radar processing system

abstract: To meet the communications requirements of a wide band radar system, a HDLC controller based on FPGA was designed. The controller is set to idle mode when system reset or software reset occurs. The controller can be set to receiving or sending mode by software control. The special feature of the controller is especial low error rate and using very little hardware resource. Finally the controller was integrated into a wide band radar system, which proved the correctness and dependability of the controller.


title: Deep Learning for User Comment Moderation

abstract: Experimenting with a new dataset of 1.6M user comments from a Greek news portal and existing datasets of English Wikipedia comments, we show that an RNN outperforms the previous state of the art in moderation. A deep, classification-specific attention mechanism improves further the overall performance of the RNN. We also compare against a CNN and a word-list baseline, considering both fully automatic and semi-automatic moderation.


title: Understanding the Level of Compliance by South African Institutions to the Protection of Personal Information (POPI) Act

abstract: Privacy entails controlling the use and access to place, location and personal information. In South Africa, the first privacy legislation in the form of the Protection of Personal Information (POPI) Act was signed into law on 26 November 2013. The POPI Act promotes the protection of personal information by South African public and private institutions and specifies the minimum requirements in twelve chapters, which includes eight conditions for lawful processing of personal information. In 2012, CIBECS as part of their State of Business Data Protection in South Africa survey assessed, amongst other aspects, how prepared South African institutions were to comply with the then forthcoming protection of personal information legislation. Since that survey, the POPI Bill progressed to an Act and, more recently, in 2015 processes commenced to appoint the Information Regulator (in terms of the legislation), who would be responsible for enforcing the POPI Act. Due to the aforementioned developments and looming enforcement date associated with the POPI Act, this paper assesses the level of understanding of the POPI Act by participants from South African institutions as well as the current level of compliance to the POPI Act. Specifically, the current level of compliance to Condition Seven of the POPI Act, relating to the confidentiality and integrity of electronic personal information, is explored. Furthermore, a view is provided of the financial value associated with electronic personal information maintained as well as the potential impact a data breach of electronic personal information may have on an institution.


title: OpenRatSLAM: an open source brain-based SLAM system

abstract: RatSLAM is a navigation system based on the neural processes underlying navigation in the rodent brain, capable of operating with low resolution monocular image data. Seminal experiments using RatSLAM include mapping an entire suburb with a web camera and a long term robot delivery trial. This paper describes OpenRatSLAM, an open-source version of RatSLAM with bindings to the Robot Operating System framework to leverage advantages such as robot and sensor abstraction, networking, data playback, and visualization. OpenRatSLAM comprises connected ROS nodes to represent RatSLAM’s pose cells, experience map, and local view cells, as well as a fourth node that provides visual odometry estimates. The nodes are described with reference to the RatSLAM model and salient details of the ROS implementation such as topics, messages, parameters, class diagrams, sequence diagrams, and parameter tuning strategies. The performance of the system is demonstrated on three publicly available open-source datasets.


title: Regulation of guinea-pig intestinal peristalsis by endogenous endothelin acting at ETB receptors

abstract: Abstract Background & Aims: Endothelins are expressed in many enteric neurons of the gut. Because activation of endothelin ET A and ET B receptors is known to alter intestinal muscle activity, the effect of ET A and ET B receptor agonists and antagonists on propulsive peristalsis was examined. Methods: Repetitive peristalsis in fluid-perfused segments of the guinea pig isolated small intestine was elicited by a rise of the intraluminal pressure and recorded via the pressure changes generated by the peristaltic waves. Results: Endothelin 1 (0.3-10 nmol/L added to the organ bath) stimulated peristalsis as shown by a decrease in the pressure threshold at which peristaltic waves were triggered, whereas the endothelin analog sarafotoxin 6c (0.3-10 nmol/L) inhibited peristalsis as reflected by an increase in the pressure threshold. The ET A receptor antagonist BQ-123 (3 μmol/L) converted the properistaltic action of endothelin 1 to an antiperistaltic action, whereas the ET B receptor antagonist BQ-788 (3 μmol/L) prevented the antiperistaltic action of sarafotoxin 6c. BQ-788, but not BQ-123, facilitated peristalsis on its own. Additional experiments indicated that the properistaltic action of endothelin 1 is mediated by enteric neurons, whereas the peristaltic motor effects of sarafotoxin 6c and BQ-788 are caused by a direct action on the muscle. Conclusions: ET A receptor activation stimulates, whereas ET B receptor activation inhibits, intestinal peristalsis. The ability of BQ-788 to facilitate peristalsis per se points to a physiologic role of ET B receptors in peristaltic motor regulation. GASTROENTEROLOGY 2000;119:80-88


title: An empirical comparison of influence measurements for social network analysis

abstract: The studying of social influence can be used to understand and solve many complicated problems in social network analysis such as predicting influential users. This paper focuses on the problem of predicting influential users on social networks. We introduce a three-level hierarchy that classifies the influence measurements. The hierarchy categorizes the influence measurements by three folds, i.e., models, types and algorithms. Using this hierarchy, we classify the existing influence measurements. We further compare them based on an empirical analysis in terms of performance, accuracy and correlation using datasets from two different social networks to investigate the feasibility of influence measurements. Our results show that predicting influential users does not only depend on the influence measurements but also on the nature of social networks. Our goal is to introduce a standardized baseline for the problem of predicting influential users on social networks.


Early socio–emotional development: Contingency perception and the social–biofeedback model


title: Accounting misstatements following lawsuits against auditors

abstract: This study investigates whether an auditor's experience of litigation in the recent past affects subsequent financial reporting quality. At the audit firm level, we find accounting misstatements occur significantly less (more) often after audit firms are sued (not sued). At the audit office level, the negative association between past litigation and future misstatements is stronger for offices who were directly implicated in the litigation than for the non-accused offices of sued audit firms. Therefore, the litigation experiences of both audit firms and audit offices are incrementally significant predictors of future financial reporting quality.


title: Speaker-Invariant Training via Adversarial Learning

abstract: We propose a novel adversarial multi-task learning scheme, aiming at actively curtailing the inter-talker feature variability while maximizing its senone discriminability so as to enhance the performance of a deep neural network (DNN) based ASR system. We call the scheme speaker-invariant training (SIT). In SIT, a DNN acoustic model and a speaker classifier network are jointly optimized to minimize the senone (tied triphone state) classification loss, and simultaneously mini-maximize the speaker classification loss. A speaker-invariant and senone-discriminative deep feature is learned through this adversarial multi-task learning. With SIT, a canonical DNN acoustic model with significantly reduced variance in its output probabilities is learned with no explicit speaker-independent (SI) transformations or speaker-specific representations used in training or testing. Evaluated on the CHiME-3 dataset, the SIT achieves 4.99% relative word error rate (WER) improvement over the conventional SI acoustic model. With additional unsupervised speaker adaptation, the speaker-adapted (SA) SIT model achieves 4.86% relative WER gain over the SA SI acoustic model.


title: A novel IoT platform for the era of connected cars

abstract: The race among manufacturers to build convenient, safe, and autonomous Connected Cars by applying the latest digital technologies, and ultimately a completely self-driving vehicle, is already underway. One of the cornerstones of such vehicles is the continuous ingestion of massive amounts of data from a wide variety of hardware components, including sensors, onboard cameras, and further external sources. Cloud computing and big data processing are ideal candidates and already proven technologies to store and process the heterogeneous, rapidly growing, and large-scale data sets. The cloud may act as a kind of central hub or as an Internet of Things (IoT) back-end where the sensor and the other available data can be gathered while also offering an elastic platform where the vast amount of data can be processed, analyzed and distributed real-time. In our paper we detail the evolution of a cloud-based, scalable IoT back-end framework and services built on top for handling and processing vehicular data in various use case scenarios: CAN data collection, remote device flashing, Eco-driving, weather report and forecast. The first version is an Infrastructure-as-a-Service (IaaS) solution with a reference implementation deployed on an OpenNebula based cloud. The second iteration runs on a private Platform-as-a-Service (PaaS) cloud built on the Cloud Foundry platform within the premises of an automotive supplier company. Both variants have been successfully evaluated and validated with benchmarks.


title: Single-Phase Single-Switch Vienna Rectifier as Electric Vehicle PFC Battery Charger

abstract: This paper presents a single-phase single-switch bridgeless power factor corrector (PFC) boost Vienna rectifier as an onboard electric vehicle (EV) battery charger. The presented topology generates 3-level voltage waveform at the input which eliminates harmonic contents significantly while using small filters. The reduced number of parts used in this topology makes it appealing for EV industries to develop this PFC rectifier as a compact battery charger. Modelling of the single- phase Vienna rectifier is performed and a cascaded PI controller is designed to regulate the DC bus voltage at 400V to charge EV batteries as well as drawing low harmonic grid current and ensuring unity power factor operation of the converter. Simulations are done to validate the good dynamic performance of the single-switch rectifier and adopted controller in generating regulated and low ripple DC voltage from AC grid to supply EV batteries.


title: Compassion fatigue: Psychotherapists' chronic lack of self care

abstract: Psychotherapists who work with the chronic illness tend to disregard their own self-care needs when focusing on the needs of clients. The article discusses the concept of compassion fatigue, a form of caregiver burnout among psychotherapists and contrasts it with simple burnout and countertransference. It includes a multi-factor model of compassion fatigue that emphasizes the costs of caring, empathy, and emotional investment in helping the suffering. The model suggests that psychotherapists that limiting compassion stress, dealing with traumatic memories, and more effectively managing case loads are effective ways of avoiding compassion fatigue. The model also suggests that, to limit compassion stress, psychotherapists with chronic illness need to development methods for both enhancing satisfaction and learning to separate from the work emotionally and physically in order to feel renewed. A case study illustrates how to help someone with compassion fatigue. © 2002 Wiley Periodicals, Inc. J Clin Psychol/In Session 58: 1433–1441, 2002.


title: A graph theoretic-based heuristic algorithm for responsive supply chain network design with direct and indirect shipment

abstract: The configuration of the supply chain network has a strong influence on the overall performance of the supply chain. A well designed supply chain network provides a proper platform for efficient and effective supply chain management. The supply chain network should be designed in the way that could meet the customer needs with an efficient cost. This paper studies the responsive, multi-stage supply chain network design (SCND) problem under two conditions: (1) when direct shipment is allowed and (2) when direct shipment is prohibited. First, two mixed integer programming models are proposed for multi-stage, responsive SCND problem under two abovementioned conditions. Then, to escape from the complexity of mixed integer mathematical programming models, graph theoretic approach is used to study the structure of the SCND problems and it is proven that both of SCND problems considered in this paper could be modeled by a bipartite graph. Finally, since such network design problems belong to the class of NP-hard problems, a novel heuristic solution method is developed based on a new solution representation method derived from graph theoretic view to the structure of the studied problem. To assess the performance of the proposed heuristic solution method, the associated results are compared to the exact solutions obtained by a commercial.


Feature Learning for Accurate Time Prediction in Congested Healthcare Systems


title: C# interpreter and unity 3D for educational programming games

abstract: In this paper we present an approach to creating educational programming computer games. We combine the CSI interpreter with Unity 3D to enable a player to enter program statements that can alter the game world while it is being played. We also create wrapper methods that encapsulate complex Unity C# tasks into easy to use helper functions. The results from our initial experimentation show that the technique offers a promising approach to educational programming games.


title: A Framework for Recommendation of Highly Popular News Lacking Social Feedback

abstract: Social media is rapidly becoming the main source of news consumption for users, raising significant challenges to news aggregation and recommendation tasks. One of these challenges concerns the recommendation of very recent news. To tackle this problem, approaches to the prediction of news popularity have been proposed. In this paper, we study the task of predicting news popularity upon their publication, when social feedback is unavailable or scarce, and to use such predictions to produce news rankings. Unlike previous work, we focus on accurately predicting highly popular news. Such cases are rare, causing known issues for standard prediction models and evaluation metrics. To overcome such issues we propose the use of resampling strategies to bias learners towards these rare cases of highly popular news, and a utility-based framework for evaluating their performance. An experimental evaluation is performed using real-world data to test our proposal in distinct scenarios. Results show that our proposed approaches improve the ability of predicting and recommending highly popular news upon publication, in comparison to previous work.


title: Self-Regulation, Individual Characteristics and Cyberloafing

abstract: Cyberloafinga form of counter-productive work behaviour has been found to be affected by several personality and contextual factors. An important relationship that has not been adequately explored in the past is how cyberloafing behaviours could be conceptualized as a failure to self-regulate. In this paper, we examine the impact of this lack self-regulation on cyberloafing. We also examine the effects of individual characteristics (self-efficacy, conscientiousness and achievement orientation) in moderating the relationship between self-regulation and cyberloafing. Results showed that all the individual characteristics are significant moderators. Implications are discussed.


title: Cannabis and Pain: A Clinical Review

abstract: Abstract Introduction: Cannabis has been used for medical purposes across the world for centuries. As states and countries implement medical and recreational cannabis policies, increasing numbers of people are using cannabis pharmacotherapy for pain. There is a theoretical rationale for cannabis' efficacy for pain management, although the subjective pain relief from cannabis may not match objective measurements of analgesia. As more patients turn to cannabis for pain relief, there is a need for additional scientific evidence to evaluate this increase. Materials and Methods: Research for this review was performed in the PubMed/National Library of Medicine database. Discussion: Preclinical studies demonstrate a narrow therapeutic window for cannabis as pharmacotherapy for pain; the body of clinical evidence for this indication is not as extensive. A recent meta-analysis of clinical trials of cannabis and cannabinoids for pain found modest evidence supporting the use of cannabinoid pharmacotherapy for pain. ...


title: Tetherin is an exosomal tether

abstract: Cells generally communicate with each other over short distances by direct contact, and over long distances by releasing chemicals such as hormones. But there is also a third way that is less well understood – small capsules or “vesicles” called exosomes can transfer molecules from one cell to another. Exosomes are involved in the immune response and have been linked to a number of diseases, including cancer and neurodegeneration. However, scientists are still trying to understand how exosomes are made, what they contain and how they are released from cells. A common set of cells used in laboratory studies are known as HeLa cells. These cells are the descendants of cancerous cells taken from a patient called Henrietta Lacks in 1951. When treated with a particular drug, HeLa cells produce vesicles that look like exosomes. Yet instead of moving freely like other exosomes, these structures stick together in clusters. This raises questions – are these cancer cell vesicles truly exosomes? And if so, why and how are they tethered to the cell? Using electron microscopy and biochemical tests, Edgar et al. confirm that the unusual vesicles produced by HeLa cells are exosomes. As well as sharing characteristics with other exosomes, the vesicles also show similarities with viruses like HIV, which attach themselves to cell surfaces and each other using a protein called tetherin. Using a technique called gene editing to remove tetherin from HeLa cells allowed the exosomes in the cluster to move apart. Further investigation revealed that some cells in the immune system also produce exosome clusters and that these clusters also contain tetherin. Edgar et al. propose that cells control whether exosomes are involved in short-range or long-range communication by controlling the amount of tetherin they produce. So far, studies into the roles that exosomes play in the body have been hampered by a lack of experimental tools. The study by Edgar et al. opens up new methods of investigation by providing ways of altering the number of exosomes released from a cell. This should help to clarify what exosomes do and how they work in a wide range of different cell types.


title: NISLVMP: Improved Virtual Machine-Based Software Protection

abstract: The VM (Virtual Machine)-based software protection technique provides an effective solution to protect software, making it extremely difficult to analyze and crack. This technique has become the research focus of software protection. In this paper, we introduce the general design ideas of this technique. However, there exist some vulnerabilities in the design. We introduce these vulnerabilities in detail and come up with some improvements to mitigate them. We design and develop a VM-based protection system, named NISLVMP, and carry out some experiments with it. The results show that the improvements are effective.


title: On approximately fair allocations of indivisible goods

abstract: We study the problem of fairly allocating a set of indivisible goods to a set of people from an algorithmic perspective. fair division has been a central topic in the economic literature and several concepts of fairness have been suggested. The criterion that we focus on is envy-freeness. In our model, a monotone utility function is associated with every player specifying the value of each subset of the goods for the player. An allocation is envy-free if every player prefers her own share than the share of any other player. When the goods are divisible, envy-free allocations always exist. In the presence of indivisibilities, we show that there exist allocations in which the envy is bounded by the maximum marginal utility, and present a simple algorithm for computing such allocations. We then look at the optimization problem of finding an allocation with minimum possible envy. In the general case the problem is not solvable or approximable in polynomial time unless P = NP. We consider natural special cases (e.g.additive utilities) which are closely related to a class of job scheduling problems. Approximation algorithms as well as inapproximability results are obtained. Finally we investigate the problem of designing truthful mechanisms for producing allocations with bounded envy.


title: Dual-channel DNN-based speech enhancement for smartphones

abstract: Speech communications in real-world scenarios need high performance enhancement algorithms to address the distortions that can degrade the intelligibility and quality of the speech signal. Current portable devices usually integrate multiple microphones that can conveniently be exploited to improve the signal quality. In this paper we present a dual-microphone speech enhancement approach suitable for smartphones with primary (front) and reference (back) microphones. Our proposal is based on the use of deep neural networks which are able to obtain a non-linear mapping function between noisy and clean speech signals. We explore two different architectures: a feedforward deep neural network (DNN) with temporal context and a gated recurrent unit (GRU) recurrent neural network (RNN). The proposed system is evaluated under different acoustic conditions in close- and far-talk device positions. A comparison with other single- and dual-channel approaches shows that our proposal obtains the best performance in terms of perceptual quality.


title: Antimicrobial Peptides from Plants

abstract: Plant antimicrobial peptides (AMPs) have evolved differently from AMPs from other life forms. They are generally rich in cysteine residues which form multiple disulfides. In turn, the disulfides cross-braced plant AMPs as cystine-rich peptides to confer them with extraordinary high chemical, thermal and proteolytic stability. The cystine-rich or commonly known as cysteine-rich peptides (CRPs) of plant AMPs are classified into families based on their sequence similarity, cysteine motifs that determine their distinctive disulfide bond patterns and tertiary structure fold. Cystine-rich plant AMP families include thionins, defensins, hevein-like peptides, knottin-type peptides (linear and cyclic), lipid transfer proteins, α-hairpinin and snakins family. In addition, there are AMPs which are rich in other amino acids. The ability of plant AMPs to organize into specific families with conserved structural folds that enable sequence variation of non-Cys residues encased in the same scaffold within a particular family to play multiple functions. Furthermore, the ability of plant AMPs to tolerate hypervariable sequences using a conserved scaffold provides diversity to recognize different targets by varying the sequence of the non-cysteine residues. These properties bode well for developing plant AMPs as potential therapeutics and for protection of crops through transgenic methods. This review provides an overview of the major families of plant AMPs, including their structures, functions, and putative mechanisms.


title: Optimizing Java Bytecode using the Soot Framework: Is it Feasible?

abstract: This paper presents Soot, a framework for optimizing Java™ bytecode. The framework is implementedin Java and supports three intermediate representations for representing Java bytecode: Baf, a streamlined representation of Java's stack-based bytecode; Jimple, a typed three-address intermediate representation suitable for optimization; and Grimp, an aggregated version of Jimple. ::: ::: Our approach to class file optimization is to first convert the stack-based bytecode into Jimple, a three-address form more amenable to traditional program optimization, and then convert the optimized Jimple back to bytecode. ::: ::: In order to demonstrate that our approach is feasible, we present experimental results showing the effects of processing class files through our framework. In particular, we study the techniques necessary to effectively translate Jimple back to bytecode, without losing performance. Finally, we demonstrate that class file optimization can be quite effective by showing the results of some basic optimizations using our framework. Our experiments were done on ten benchmarks, including seven SPECjvm98 benchmarks, and were executedon five different Java virtual machine implementations.


title: Mathematical modeling of evolution. Solved and open problems

abstract: Evolution is a highly complex multilevel process and mathematical modeling of evolutionary phenomenon requires proper abstraction and radical reduction to essential features. Examples are natural selection, Mendel’s laws of inheritance, optimization by mutation and selection, and neutral evolution. An attempt is made to describe the roots of evolutionary theory in mathematical terms. Evolution can be studied in vitro outside cells with polynucleotide molecules. Replication and mutation are visualized as chemical reactions that can be resolved, analyzed, and modeled at the molecular level, and straightforward extension eventually results in a theory of evolution based upon biochemical kinetics. Error propagation in replication commonly results in an error threshold that provides an upper bound for mutation rates. Appearance and sharpness of the error threshold depend on the fitness landscape, being the distribution of fitness values in genotype or sequence space. In molecular terms, fitness landscapes are the results of two consecutive mappings from sequences into structures and from structures into the (nonnegative) real numbers. Some properties of genotype–phenotype maps are illustrated well by means of sequence–structure relations of RNA molecules. Neutrality in the sense that many RNA sequences form the same (coarse grained) structure is one of these properties, and characteristic for such mappings. Evolution cannot be fully understood without considering fluctuations—each mutant originates form a single copy, after all. The existence of neutral sets of genotypes called neutral networks, in particular, necessitates stochastic modeling, which is introduced here by simulation of molecular evolution in a kind of flowreactor.


Congruence and Work Centrality on Organizational Citizenship Behavior


title: Speckle reduction in SAR images via Shearlet transform-based thresholding

abstract: The quality of synthetic aperture radar (SAR) images generally suffers from speckle noise, which damages the radiometric resolution of SAR images. A new speckle reduction method is proposed by thresholding the speckled SAR image coefficients in the Shearlet transform domain. As a powerful multiscale image representation tool, Shearlet tranform enables better preservation of significant detail information in despeckled results. Compared with the wavelet transform, Shearlet transform is more suitable for image restoration applications where detail preservation is highly demanding. Experimental results on real X-band amplitude and intensity SAR images demonstrate the efficacy of the proposed method. A quantitative analysis is also given to show the superiority of the Shearlet-based thresholding method over the translation-invariant wavelet-based counterpart.


title: Real-Time Detection and Understanding of Isolated Protruded Fingers

abstract: Making sense of hand gestures in applications such as novel user interfaces is an active area of research. Many current methods use complex 3D model of non-rigid hands and involved paradigms in solving the general problem. In this paper we look at a subproblem of sign language alphabet recognition where gestures are made with protruded fingers. In contrast to the more intricate schemes, we propose a simple, fast classification algorithm using two dimensional projection of Euler angles. Not only is our scheme novel, it lends itself to a real time implementation.


title: Pathogen recognition by the innate immune system.

abstract: Microbial infection initiates complex interactions between the pathogen and the host. Pathogens express several signature molecules, known as pathogen-associated molecular patterns (PAMPs), which are essential for survival and pathogenicity. PAMPs are sensed by evolutionarily conserved, germline-encoded host sensors known as pathogen recognition receptors (PRRs). Recognition of PAMPs by PRRs rapidly triggers an array of anti-microbial immune responses through the induction of various inflammatory cytokines, chemokines and type I interferons. These responses also initiate the development of pathogen-specific, long-lasting adaptive immunity through B and T lymphocytes. Several families of PRRs, including Toll-like receptors (TLRs), RIG-I-like receptors (RLRs), NOD-like receptors (NLRs), and DNA receptors (cytosolic sensors for DNA), are known to play a crucial role in host defense. In this review, we comprehensively review the recent progress in the field of PAMP recognition by PRRs and the signaling pathways activated by PRRs.


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