title: Augmented Reality, the Future of Contextual Mobile Learning.

abstract: Purpose ::: ::: ::: ::: ::: This study aims to show the relevance of augmented reality (AR) in mobile learning for the 21st century. With AR, any real-world environment can be augmented by providing users with accurate digital overlays. AR is a promising technology that has the potential to encourage learners to explore learning materials from a totally new perspective. Besides, the advancements made in information technology further broaden the scope for educational AR applications. Furthermore, the proliferation of wireless mobile devices such as smartphones and tablets is also introducing AR into the mobile domain. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: This discussion paper gives an insight of the different potential fields of application of AR and eventually proposes an AR application that will give a completely different learning experience for learners. This AR mobile application will not only provide learners with supplementary information but will also assist lecturers in their teaching process. There are certain concepts in computer science at the tertiary level that are at times difficult for learners to understand using the traditional classroom approach. Through this AR application developed, the learners are able to see what is happening and experience a different form of learning where the focus is more on “learning by doing” and on the ability of visualizing the complete set of steps involved for a particular operation. Finally what is proposed is a generic framework/process for the development of AR applications for learning purposes. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The AR application developed and tested has proved to be helpful in understanding complex concepts of computer science that average students have much difficulty in understanding. Through AR, learning has been brought to a new dimension where the students can easily visualize what is happening and easily understand complex concepts. This low-cost system that has been proposed can track and detect both markerless and marker-based images. A number of experiments have also been carried out to determine a set of best practices for the development and use of such AR applications. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: Learners have been able to have a more interactive and enriching learning experience through two-dimensional and three-dimensional digital augmentations. The AR mobile application has been enhancing the cognitive skills of learners through enabling them to scan images from printed materials with their smartphones. Then, informative digital augmentation has been overlaid in real time on the mobile screen with the image preview still in the background.


title: Relationship Between Context and Sensory Processing in Children With Autism

abstract: OBJECTIVE. The purpose of the study was to determine the relationship between sensory processing and context for children with autism. We examined home and school contexts using the Sensory Profile (Dunn, 1999) and the Sensory Profile School Companion (Dunn, 2006a) questionnaires. METHOD. Teachersof49studentswithautismcompletedtheSensoryProfileSchoolCompanion,andparents completed the Sensory Profile. We conducted correlational analyses using the avoiding and seeking quadrant scores from the School Companion and corresponding avoiding and seeking quadrant scores from the Sensory Profile. RESULTS. The avoiding quadrant score coefficient (.59) and the seeking quadrant score coefficient (.45) were statistically significant (p 5 .01) with good and fair correlations, respectively, suggesting that sensory processing patterns have both universal qualities and context-specific qualities in children with autism. CONCLUSION. Findings from this study provide initial evidence that sensory processing and context for children with autism are related. Brown, N. B., & Dunn, W. (2010). Relationship between context and sensory processing in children with autism. American


title: An iterative optimization approach for unified image segmentation and matting

abstract: Separating a foreground object from the background in a static image involves determining both full and partial pixel coverages, also known as extracting a matte. Previous approaches require the input image to be presegmented into three regions: foreground, background and unknown, which are called a trimap. Partial opacity values are then computed only for pixels inside the unknown region. This presegmentation based approach fails for images with large portions of semitransparent foreground where the trimap is difficult to create even manually. In this paper, we combine the segmentation and matting problem together and propose a unified optimization approach based on belief propagation. We iteratively estimate the opacity value for every pixel in the image, based on a small sample of foreground and background pixels marked by the user. Experimental results show that compared with previous approaches, our method is more efficient to extract high quality mattes for foregrounds with significant semitransparent regions


title: A genome-wide scan of selective sweeps in two broiler chicken lines divergently selected for abdominal fat content

abstract: BackgroundGenomic regions controlling abdominal fatness (AF) were studied in the Northeast Agricultural University broiler line divergently selected for AF. In this study, the chicken 60KSNP chip and extended haplotype homozygosity (EHH) test were used to detect genome-wide signatures of AF.ResultsA total of 5357 and 5593 core regions were detected in the lean and fat lines, and 51 and 57 reached a significant level (P<0.01), respectively. A number of genes in the significant core regions, including RB1, BBS7, MAOA, MAOB, EHBP1, LRP2BP, LRP1B, MYO7A, MYO9A and PRPSAP1, were detected. These genes may be important for AF deposition in chickens.ConclusionsWe provide a genome-wide map of selection signatures in the chicken genome, and make a contribution to the better understanding the mechanisms of selection for AF content in chickens. The selection for low AF in commercial breeding using this information will accelerate the breeding progress.


title: Obfuscator-LLVM -- Software Protection for the Masses

abstract: Software security with respect to reverse-engineering is a challenging discipline that has been researched for several years and which is still active. At the same time, this field is inherently practical, and thus of industrial relevance: indeed, protecting a piece of software against tampering, malicious modifications or reverse-engineering is a very difficult task. In this paper, we present and discuss a software obfuscation prototype tool based on the LLVM compilation suite. Our tool is built as different passes, where some of them have been open-sourced and are freely available, that work on the LLVM Intermediate Representation (IR) code. This approach brings several advantages, including the fact that it is language-agnostic and mostly independent of the target architecture. Our current prototype supports basic instruction substitutions, insertion of bogus control-flow constructs mixed with opaque predicates, control-flow flattening, procedures merging as well as a code tamper-proofing algorithm embedding code and data checksums directly in the control-flow flattening mechanism.


title: Video transcoding time prediction for proactive load balancing

abstract: In this paper, we present a method for predicting the transcoding time of videos given an input video stream and its transcoding parameters. Video transcoding time is treated as a random variable and is statistically predicted from past observations. Our proposed method predicts the transcoding time as a function of several parameters of the input and output video streams, and does not require any detailed information about the codec used. We show the effectiveness of our method via comparing the resulting predictions with the actual transcoding times on unseen video streams. Simulation results show that our prediction method enables a significantly better load balancing of transcoding jobs than classical load balancing methods.


title: Carbon monoxide in intensive care medicine—time to start the therapeutic application?!

abstract: Carbon monoxide (CO) is not only known as a toxic gas due to its characteristics as an odorless molecule and its rapid binding to haem-containing molecules, thus inhibiting the respiratory chain in cells resulting in hypoxia. For decades, scientists established evidence about its endogenously production in the breakdown of haem via haem-oxygenase (HO-1) and its physiological effects. Among these, the modulation of various systems inside the body are well described (e.g., anti-inflammatory, anti-oxidative, anti-apoptotic, and anti-proliferative). Carbon monoxide is able to modulate several extra- and intra-cellular signaling molecules leading to differentiated response according to the specific stimulus. With our growing understanding in the way CO exerts its effects, especially in the mitochondria and its intracellular pathways, it is tempting to speculate about a clinical application of this substance. Since HO-1 is not easy to induce, research focused on the application of the gaseous molecule CO by itself or the implementation of carbon monoxide releasing molecules (CO-RM) to deliver the molecule at a time- and dose dependently safe way to any target organ. After years of research in cellular systems and animal models, summing up data about safety issues as well as possible target to treat in various diseases, the first feasibility trials in humans were established. Up-to-date, safety issues have been cleared for low-dose carbon monoxide inhalation (up to 500 ppm), while there is no clinical data regarding the injection or intake of any kind of CO-RM so far. Current models of human research include sepsis, acute lung injury, and acute respiratory distress syndrome as well as acute kidney injury. Carbon monoxide is a most promising candidate in terms of a therapeutic agent to improve outbalanced organ conditions. In this paper, we summarized the current understanding of carbon monoxide’s biology and its possible organ targets to treating the critically ill patients in tomorrow’s ICU.


title: Beyond nociception: the imprecision hypothesis of chronic pain

abstract: It seemsthat despite extensive advances in multiple fields,we have made little ground. In this topical review, we put forwardanewhypothesisofchronicpainthatexplainsthemostcommonpainful disorders, such as chronic widespread pain, nonspecificback or neck pain, and fibromyalgia. Our hypothesis draws onalonghistoryoffundamentalresearchinassociativelearningandis based on 2 core assumptions (1) that pain can be considereda response, not just a stimulus, and (2) that encoding non-nociceptive information predictably coincident with nociceptiveinput underpins the response to subsequent similar events.Briefly, our hypothesis posits that the precision with whichmultisensoryinformation(temporal,proprioceptive,spatial)aboutthe painful event is encoded and represented in the brain willdetermine the degree to which the painful response willsubsequently generalize to similar events.Much of the literature on pain considers it analogous tonociception, activity in high-threshold afferent neurons and theircentral projections. However, a very large body of evidencedemonstrates that nociception is neither sufficient nor necessaryfor pain (see Refs. 4,11,22). Pain is now considered a consciousexperiencethatcanbe,andoftenis,associatedwithnociception,but it is always modulated by a myriad of neurobiological,environmental, and cognitive factors. Plasticity, or the alterationsin stimulus–response patterns that occur over time, is thought tobeimportantinchronicpainandcanoccurinatleast2ways.Oneis nonassociative when the organism’s response to a stimuluschanges by virtue of repeated exposure to that particularstimulus. Habituation is 1 example of nonassociative plasticity,such that the response to a given stimulus decreases.Sensitization is another example, such that the response toa given stimulus increases. Sensitization of spinal nociceptors isconsidered a key mechanism underpinning persistent pain afterprimary nerve trauma or dysfunction; repeated noxious stimula-tion leads to a shift in the stimulus–response pattern of the spinalnociceptor.Asecondarguablymorecomplexformofplasticityinvolvestheassociation of at least 2 stimuli, such is the typical case ofclassical (Pavlovian) conditioning or associative learning.


Handwritten digits recognition using OpenCV Machine


Diversity of CRISPR-Cas immune systems and molecular machines


title: Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment

abstract: In this paper we present a new algorithm for manifold learning and nonlinear dimension reduction. Based on a set of unorganized data points sampled with noise from the manifold, we represent the local geometry of the manifold using tangent spaces learned by fitting an affine subspace in a neighborhood of each data point. Those tangent spaces are aligned to give the internal global coordinates of the data points with respect to the underlying manifold by way of a partial eigendecomposition of the neighborhood connection matrix. We present a careful error analysis of our algorithm and show that the reconstruction errors are of second-order accuracy. Numerical experiments including 64-by-64 pixel face images are given to illustrate our algorithm.


title: A broadband CPW-to-waveguide transition using quasi-Yagi antenna

abstract: A novel CPW-to-waveguide transition utilizing a uniplanar quasi-Yagi antenna is presented. The X-band back-to-back transition demonstrates 33% bandwidth with return loss better than -10 dB. This transition should find a wide variety of applications due to its high MIC/MMIC compatibility and low cost.


title: Massive MIMO Unlicensed: A New Approach to Dynamic Spectrum Access

abstract: Nowadays, the demand for wireless mobile services is copious and will continue increasing in the near future. Mobile cellular operators are therefore looking at the unlicensed spectrum as an economical supplement to augment the capacity of their soon-to-be overloaded networks. The same unlicensed bands are luring Internet service providers, venue owners, and authorities into autonomously setting up and managing their high-performance private networks. In light of this exciting future, enhancing the coexistence between multiple unlicensed technologies becomes a pivotal issue. In this article, we present the fundamentals and the main challenges behind massive MIMO unlicensed, a new approach for technology coexistence in the unlicensed bands, that is envisioned to boost spectrum reuse for a plethora of use cases.


title: Synthetic aperture radar tomography sampling criteria and three-dimensional range migration algorithm with elevation digital spotlighting

abstract: Based on the general geometric model of multi-baseline Synthetic Aperture Radar Tomography (TomoSAR), the three-dimensional (3-D) sampling criteria, the analytic expression of the 3-D Point Spread Function (PSF) and the 3-D resolution are derived in the 3-D wavenumber domain in this paper. Considering the relationship between the observation geometry and the size of illuminated scenario, a 3-D Range Migration Algorithm with Elevation Digital Spotlighting (RMA-EDS) is proposed. With this algorithm 3-D images of the area of interest can be directly and accurately reconstructed in the 3-D space avoiding the complex operations of 3-D geometric correction. Finally, theoretical analyses and simulation results are presented to demonstrate the shift-varying property of the 3-D PSF and the spatial-varying property of the 3-D resolution and to demonstrate the validity of the 3-D RMA-EDS.


title: MyAdChoices: Bringing Transparency and Control to Online Advertising

abstract: The intrusiveness and the increasing invasiveness of online advertising have, in the last few years, raised serious concerns regarding user privacy and Web usability. As a reaction to these concerns, we have witnessed the emergence of a myriad of ad-blocking and antitracking tools, whose aim is to return control to users over advertising. The problem with these technologies, however, is that they are extremely limited and radical in their approach: users can only choose either to block or allow all ads. With around 200 million people regularly using these tools, the economic model of the Web—in which users get content free in return for allowing advertisers to show them ads—is at serious peril. In this article, we propose a smart Web technology that aims at bringing transparency to online advertising, so that users can make an informed and equitable decision regarding ad blocking. The proposed technology is implemented as a Web-browser extension and enables users to exert fine-grained control over advertising, thus providing them with certain guarantees in terms of privacy and browsing experience, while preserving the Internet economic model. Experimental results in a real environment demonstrate the suitability and feasibility of our approach, and provide preliminary findings on behavioral targeting from real user browsing profiles.


Gastrointestinal Helminthes Infesting Stray Dogs with Emphasis on Echinococcus Spp. in Nyala, South Darfur State, Sudan


title: Optimizing two-pass connected-component labeling algorithms

abstract: We present two optimization strategies to improve connected-component labeling algorithms. Taking together, they form an efficient two-pass labeling algorithm that is fast and theoretically optimal. The first optimization strategy reduces the number of neighboring pixels accessed through the use of a decision tree, and the second one streamlines the union-find algorithms used to track equivalent labels. We show that the first strategy reduces the average number of neighbors accessed by a factor of about 2. We prove our streamlined union-find algorithms have the same theoretical optimality as the more sophisticated ones in literature. This result generalizes an earlier one on using union-find in labeling algorithms by Fiorio and Gustedt (Theor Comput Sci 154(2):165–181, 1996). In tests, the new union-find algorithms improve a labeling algorithm by a factor of 4 or more. Through analyses and experiments, we demonstrate that our new two-pass labeling algorithm scales linearly with the number of pixels in the image, which is optimal in computational complexity theory. Furthermore, the new labeling algorithm outperforms the published labeling algorithms irrespective of test platforms. In comparing with the fastest known labeling algorithm for two-dimensional (2D) binary images called contour tracing algorithm, our new labeling algorithm is up to ten times faster than the contour tracing program distributed by the original authors.


title: A reliable routing protocol for VANET communications

abstract: Vehicular Ad-hoc Network (VANET) is an emerging new technology to enable communications among vehicles and nearby roadside infrastructures to provide intelligent transportation applications. In order to provide stable connections between vehicles, a reliable routing protocol is needed. Currently, there are several routing protocols designed for MANETs could be applied to VANETs. However, due to the unique characteristics of VANETs, the results are not encouraging. In this paper, we propose a new routing protocol named AODV-VANET, which incorporates the vehicles' movement information into the route discovery process based on Ad hoc On-Demand Distance Vector (AODV). A Total Weight of the Route is introduced to choose the best route together with an expiration time estimation to minimize the link breakages. With these modifications, the proposed protocol is able to achieve better routing performances.


title: High efficiency drive system with 3-level T-type inverter

abstract: In this paper an efficiency optimized variable speed drive is presented. It consists of the alternative 3-level T-type converter topology which is very efficient for low switching frequencies and a standard induction machine. The total system efficiency is optimized concerning the converter losses as well as fundamental and harmonic induction machine losses.


title: Text Classification for Data Loss Prevention

abstract: Businesses, governments, and individuals leak confidential information, both accidentally and maliciously, at tremendous cost in money, privacy, national security, and reputation. Several security software vendors now offer "data loss prevention" (DLP) solutions that use simple algorithms, such as keyword lists and hashing, which are too coarse to capture the features what makes sensitive documents secret. In this paper, we present automatic text classification algorithms for classifying enterprise documents as either sensitive or non-sensitive. We also introduce a novel training strategy, supplement and adjust, to create a classifier that has a low false discovery rate, even when presented with documents unrelated to the enterprise. We evaluated our algorithm on several corpora that we assembled from confidential documents published on WikiLeaks and other archives. Our classifier had a false negative rate of less than 3.0% and a false discovery rate of less than 1.0% on all our tests (i.e, in a real deployment, the classifier can identify more than 97% of information leaks while raising at most 1 false alarm every 100th time).


title: Scalable browsing for large collections: a case study

abstract: Phrase browsing techniques use phrases extracted automatically from a large information collection as a basis for browsing and accessing it. This paper describes a case study that uses an automatically constructed phrase hierarchy to facilitate browsing of an ordinary large Web site. Phrases are extracted from the full text using a novel combination of rudimentary syntactic processing and sequential grammar induction techniques. The interface is simple, robust and easy to use. To convey a feeling for the quality of the phrases that are generated automatically, a thesaurus used by the organization responsible for the Web site is studied and its degree of overlap with the phrases in the hierarchy is analyzed. Our ultimate goal is to amalgamate hierarchical phrase browsing and hierarchical thesaurus browsing: the latter provides an authoritative domain vocabulary and the former augments coverage in areas the thesaurus does not reach.


title: A Survey of Maneuvering Target Tracking - Part III: Measurement Models

abstract: This is the third part of a series of papers that provide a comprehensive survey of the techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. Part I [1] and Part II [2] deal with general target motion models and ballistic target motion models, respectively. This part surveys measurement models, including measurement model-based techniques, used in target tracking. Models in Cartesian, sensor measurement, their mixed, and other coordinates are covered. The stress is on more recent advances - topics that have received more attention recently are discussed in greater details.


title: Can There Ever Be Too Many Options? A Meta-Analytic Review of Choice Overload

abstract: The choice overload hypothesis states that an increase in the number of options to choose from may lead to adverse consequences such as a decrease in the motivation to choose or the satisfaction with the finally chosen option. A number of studies found strong instances of choice overload in the lab and in the field, but others found no such effects or found that more choices may instead facilitate choice and increase satisfaction. In a meta‐analysis of 63 conditions from 50 published and unpublished experiments (N = 5,036), we found a mean effect size of virtually zero but considerable variance between studies. While further analyses indicated several potentially important preconditions for choice overload, no sufficient conditions could be identified. However, some idiosyncratic moderators proposed in single studies may still explain when and why choice overload reliably occurs; we review these studies and identify possible directions for future research.


title: Detection of Lip Synchronization Artifacts

abstract: Over 10 billion hours of video are watched each month on the Internet, what, together with high definition television broadcasting and the rise in high quality video on demand makes the task of quality assessment a key one in the global multimedia market nowadays. Automating quality checking is currently based on finding major audiovisual artifacts. The Monitoring Of Audio Visual quality by key Indicators (MOAVI) subgroup of the Video Quality Experts Group (VQEG) is an open collaborative project for developing No-Reference models for monitoring audiovisual service quality. The purpose of this paper is to report the development of the audiovisual part of this project, which includes the detection of lip synchronization (also known as lip sync) artifacts.


title: (Re)Defining Gamification: A Process Approach

abstract: Gamification is a growing phenomenon of interest to both practitioners and researchers. There remains, however, uncertainty about the contours of the field. Defining gamification as "the process of making activities more game-like" focuses on the crucial space between the components that make up games and the holistic experience of gamefulness. It better fits real-world examples and connects gamification with the literature on persuasive design.


Medical Imaging Process Accelerated in FPGA Hardware by 82x over Software Line of Reaction Estimation for a PET scanner Optimized using C to FPGA Methodology


title: Probabilistic Partial Canonical Correlation Analysis

abstract: Partial canonical correlation analysis (partial CCA) is a statistical method that estimates a pair of linear projections onto a low dimensional space, where the correlation between two multi-dimensional variables is maximized after eliminating the influence of a third variable. Partial CCA is known to be closely related to a causality measure between two time series. However, partial CCA requires the inverses of covariance matrices, so the calculation is not stable. This is particularly the case for high-dimensional data or small sample sizes. Additionally, we cannot estimate the optimal dimension of the subspace in the model. In this paper, we have addressed these problems by proposing a probabilistic interpretation of partial CCA and deriving a Bayesian estimation method based on the probabilistic model. Our numerical experiments demonstrated that our methods can stably estimate the model parameters, even in high dimensions or when there are a small number of samples.


title: Fighting Botnets with Cyber-Security Analytics: Dealing with Heterogeneous Cyber-Security Information in New Generation SIEMs

abstract: One of the cyber-threats with the highest impact nowadays, in terms of number of compromised systems and the impact they can have on the Internet at large, is commonly known as the botnet. In the ACDC (Advanced Cyber Defence Centre) project, partners from 14 European countries, including public administrations, private sector organizations and academia, are trying to achieve a sustainable victory over botnets. This paper presents how a new generation SIEM is being used in the ACDC project to leverage its scalability and enhanced analytic capabilities and produce advance cyber-intelligence from the heterogeneous and massive streams of data continuously produced in the cyber-security context, in combination with traditional security events and system logs. The paper describes a case study where this approach is being tested. In the case study, the SIEM has been adapted to cope, not only with traditional security events and system logs, but also with pre-analyzed information about cyber-threats and incidents reported by the tools of some of the ACDC partner organizations. The case study also tests the adoption of the standard XML-based format called STIX, developed by the Mitre Corporation in the USA, and its suitability as a common specification for exchanging cybersecurity information between a subset of ACDC tools, the Atos SL SIEM and the ACDC's centralized data clearing house (CCH).


title: Comparison of Instances Seletion Algorithms I. Algorithms Survey

abstract: Several methods were proposed to reduce the number of instances (vectors) in the learning set. Some of them extract only bad vectors while others try to remove as many instances as possible without significant degradation of the reduced dataset for learning. Several strategies to shrink training sets are compared here using different neural and machine learning classification algorithms. In part II (the accompanying paper) results on benchmarks databases have been presented.


title: Interactive design optimization of architectural layouts. Engineering Optimization (this issue

abstract: Many areas of design involve both quantifiable and subjective goals, preferences, and constraints. Aesthetic and other subjective aspects of design are typically ignored in optimization models because they are difficult to model with mathematics; however, they are extremely important in areas such as product design and architectural design. This article presents an interactive method for integrating mathematical optimization with human decision-making during conceptual design of architectural floorplan layouts. The optimization models and algorithms were presented in a previous article. Here, an object-oriented representation allows the designer to interact with physically relevant building objects during optimization. The designer's interaction causes the program to dynamically change the optimization representation on-the-fly by adding, deleting, and modifying objectives, constraints, and structural units. This work presents mathematical optimization as a tool to assist the designer in refining ill-defi...


title: BsRADseq: screening DNA methylation in natural populations of non‐model species

abstract: Epigenetic modifications are expected to occur at a much faster rate than genetic mutations, potentially causing isolated populations to stochastically drift apart, or if they are subjected to different selective regimes, to directionally diverge. A high level of genome-wide epigenetic divergence between individuals occupying distinct habitats is therefore predicted. Here, we introduce bisulfite-converted restriction site associated DNA sequencing (bsRADseq), an approach to quantify the level of DNA methylation differentiation across multiple individuals. This reduced representation method is flexible in the extent of DNA sequence interrogated. We showcase its applicability in three natural systems, each comprising individuals adapted to divergent environments: a diploid plant (Heliosperma, Caryophyllaceae), a tetraploid plant (Dactylorhiza, Orchidaceae) and an animal (Gasterosteusaculeatus, Gasterosteidae). We present a robust bioinformatic pipeline, combining tools for RAD locus assembly, SNP calling, bisulfite-converted read mapping and DNA methylation calling to analyse bsRADseq data with or without a reference genome. Importantly, our approach accurately distinguishes between SNPs and methylation polymorphism (SMPs). Although DNA methylation frequency between different positions of a genome varies widely, we find a surprisingly high consistency in the methylation profile between individuals thriving in divergent ecological conditions, particularly in Heliosperma. This constitutive stability points to significant molecular or developmental constraints acting on DNA methylation variation. Altogether, by combining the flexibility of RADseq with the accuracy of bisulfite sequencing in quantifying DNA methylation, the bsRADseq methodology and our bioinformatic pipeline open up the opportunity for genome-wide epigenetic investigations of evolutionary and ecological relevance in non-model species, independent of their genomic features.


A Distributed Recommendation Platform for Big Data.


title: A faster fixed-parameter approach to drawing binary tanglegrams

abstract: Given two binary phylogenetic trees covering the same n species, it is useful to compare them by drawing them with leaves arranged side-by-side. To facilitate comparison, we would like to arrange the trees to minimize the number of crossings k induced by connecting pairs of identical species. This is the NP-hard Tanglegram Layout problem. By providing a fast transformation to the Balanced Subgraph problem, we show that the problem admits an O(2 k n 4) algorithm, improving upon a previous fixed-parameter approach with running time O(c k n O(1)) where c ≈ 1000. We enhance a Balanced Subgraph implementation based on data reduction and iterative compression with improvements tailored towards these instances, and run experiments with real-world data to show the practical applicability of this approach. All practically relevant (k ≤ 1000) Tanglegram Layout instances can be solved exactly within seconds. Additionally, we provide a kernel-like bound by showing how to reduce the Balanced Subgraph instances for Tanglegram Layout on complete binary trees to a size of O(k logk).


title: Multimedia learning: Are we asking the right questions?

abstract: How can we help students to understand scientific explanations of cause-and-effect systems, such as how a pump works, how the human respiratory system works, or how lightning storms develop? One promising approach involves multimedia presentation of explanations in visual and verbal formats, such as presenting computer-generated animations synchronized with computer-generated narration or presenting illustrations next to corresponding text. In a review of eight studies concerning whether multimedia instruction is effective, there was consistent evidence for a multimedia effect: Students who received coordinated presentation of explanations in verbal and visual format (multiple representation group) generated a median of over 75% more creative solutions on problem-solving transfer tests than did students who received verbal explanations alone (single representation group). In a review of 10 studies; concerning when multimedia instruction is effective, there was consistent evidence for a contiguity effect: ...


title: Tiotropium in Early‐Stage Chronic Obstructive Pulmonary Disease

abstract: BACKGROUND ::: Patients with mild or moderate chronic obstructive pulmonary disease (COPD) rarely receive medications, because they have few symptoms. We hypothesized that long-term use of tiotropium would improve lung function and ameliorate the decline in lung function in patients with mild or moderate COPD. ::: ::: ::: METHODS ::: In a multicenter, randomized, double-blind, placebo-controlled trial that was conducted in China, we randomly assigned 841 patients with COPD of Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage 1 (mild) or 2 (moderate) severity to receive a once-daily inhaled dose (18 μg) of tiotropium (419 patients) or matching placebo (422) for 2 years. The primary end point was the between-group difference in the change from baseline to 24 months in the forced expiratory volume in 1 second (FEV1) before bronchodilator use. Secondary end points included the between-group difference in the change from baseline to 24 months in the FEV1 after bronchodilator use and the between-group difference in the annual decline in the FEV1 before and after bronchodilator use from day 30 to month 24. ::: ::: ::: RESULTS ::: Of 841 patients who underwent randomization, 388 patients in the tiotropium group and 383 in the placebo group were included in the full analysis set. The FEV1 in patients who received tiotropium was higher than in those who received placebo throughout the trial (ranges of mean differences, 127 to 169 ml before bronchodilator use and 71 to 133 ml after bronchodilator use; P<0.001 for all comparisons). There was no significant amelioration of the mean (±SE) annual decline in the FEV1 before bronchodilator use: the decline was 38±6 ml per year in the tiotropium group and 53±6 ml per year in the placebo group (difference, 15 ml per year; 95% confidence interval [CI], -1 to 31; P=0.06). In contrast, the annual decline in the FEV1 after bronchodilator use was significantly less in the tiotropium group than in the placebo group (29±5 ml per year vs. 51±6 ml per year; difference, 22 ml per year [95% CI, 6 to 37]; P=0.006). The incidence of adverse events was generally similar in the two groups. ::: ::: ::: CONCLUSIONS ::: Tiotropium resulted in a higher FEV1 than placebo at 24 months and ameliorated the annual decline in the FEV1 after bronchodilator use in patients with COPD of GOLD stage 1 or 2. (Funded by Boehringer Ingelheim and others; Tie-COPD ClinicalTrials.gov number, NCT01455129 .).


title: A 0.3 pJ/access 8T data-aware SRAM utilizing column-based data encoding for ultra-low power applications

abstract: This paper proposes an 8T SRAMs utilizing a column-based data encoding scheme to reduce read and write power when there are similarities between consecutive data. It is useful in image processing applications where nearby pixels tend to have similar values. The proposed design has two modes of operation: normal and sequential modes. In the normal mode, it operates as a normal SRAM. In the sequential mode, bit-wise differences between consecutive data are written instead of the actual data. This leads to a much higher number of zeros in the array. Accordingly, a new data-aware bitline pre-charge scheme is proposed to minimize write power when writing a zero. A PVT-tracking reference voltage generator is also employed to compensate the read-bitline leakage for ultra-low voltage operation. On average, it offers more than 30% WBL switching power reduction. A 32Kb SRAM implemented in 65 nm CMOS process demonstrated successful operation down to 0.36 V. The total power consumption is 0.37 μW, corresponding to the maximum frequency of 0.25 MHz. Its minimum energy is 0.3 pJ/access achieved at 0.5 V.


title: PreventFHP: Detection and warning system for Forward Head Posture

abstract: One of the most common poor sitting postures is Forward Head Posture (FHP), which can lead to serious health problems such as chronic neck pain. To remedy this problem, we propose PreventFHP, an automated system that detects FHP and warns the user of it. Using an inexpensive commercial depth camera, our FHP detection algorithm provides very high detection accuracy. Upon the detection of FHP, PreventFHP delivers warning messages through two sensory modalities, audio and/or haptic. The efficacy of PreventFHP is demonstrated by a user study, which suggests PreventFHP's high potential for improving the sitting posture of computer-intensive workers. The user study also showed that the haptic warning was marginally better than the audio warning in terms of appropriateness as a warning method of FHP.


title: FlatFIT: Accelerated Incremental Sliding-Window Aggregation For Real-Time Analytics

abstract: Data stream processing is becoming essential in most current advanced scientific or business applications as data production rates are increasing. Different companies compete to efficiently ingest high velocity data and apply some form of computation in order to make better business decisions. In order to successfully compete in this environment, companies are focusing on the most recent data within a count or time-based window by continuously executing aggregate queries on it. Incremental sliding-window computation is commonly used to avoid the performance implications of re-evaluating the aggregate value of the window from scratch on every update. The state-of-the-art FlatFAT technique executes ACQs with high efficiency but it does not scale well with the increasing workloads. In this paper we propose a novel algorithm, FlatFIT, that accelerates such calculations by intelligently maintaining index structures, leading to higher reuse of intermediate calculations and thus exceptional scalability in systems with heavy workloads. Our theoretical analysis shows that FlatFIT is superior in both time and space complexities compared to FlatFAT, while maintaining the same query generality. Given a window of size n, FlatFIT achieves constant algorithmic complexity compared to O(log(n)) complexity of FlatFAT. We experimentally show that FlatFIT achieves up to a 17x throughput improvement over FlatFAT for the same input workload while using less memory.


title: On the body and passion of history and historiography

abstract: Using Michel Foucault's notion that ‘Society Must Be Defended’ as his starting point, Sande Cohen argues that disciplinary issues within ‘historical culture’ belong to a notion of ‘war’ more than training or representation and that the discipline of history is not that effective in helping along public, intellectual life.


title: Authority-based keyword search in databases

abstract: Our system applies authority-based ranking to keyword search in databases modeled as labeled graphs. Three ranking factors are used: the relevance to the query, the specificity and the importance of the result. All factors are handled using authority-flow techniques that exploit the link-structure of the data graph, in contrast to traditional Information Retrieval. We address the performance challenges in computing the authority flows in databases by using precomputation and exploiting the database schema if present. We conducted user surveys and performance experiments on multiple real and synthetic datasets, to assess the semantic meaningfulness and performance of our system.


title: A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms

abstract: This paper presents a quantitative comparison of several multi-view stereo reconstruction algorithms. Until now, the lack of suitable calibrated multi-view image datasets with known ground truth (3D shape models) has prevented such direct comparisons. In this paper, we first survey multi-view stereo algorithms and compare them qualitatively using a taxonomy that differentiates their key properties. We then describe our process for acquiring and calibrating multiview image datasets with high-accuracy ground truth and introduce our evaluation methodology. Finally, we present the results of our quantitative comparison of state-of-the-art multi-view stereo reconstruction algorithms on six benchmark datasets. The datasets, evaluation details, and instructions for submitting new models are available online at http://vision.middlebury.edu/mview.


title: Study on Maximum Receivable Distance for Radiated Emission of Information Technology Equipment Causing Information Leakage

abstract: This study has discussed the information leakage threat due to the radiated emission of information technology equipment (ITE). This paper focused on the theoretical estimation of the maximum receivable distance for the radiated emission causing the information leakage. The maximum receivable distance estimation formula was theoretically derived based on the reception capacity of a wideband receiver with a specific antenna, the limits of the electric field strength of the radiated emission of ITE regulated in CISPR 22 standard, and the attenuation by the distance in a free space for the radiated emission. In addition, the maximum receivable distance estimation in the considerable worst case was conducted by assuming some realistic parameters for a commercial high-performance wideband receiver and a commercial high-gain log-periodic antenna.


title: Immersive 3D modeling with Blender and off-the-shelf hardware

abstract: We present an immersive 3D modeling application with stereoscopic graphics, head tracking, and 3D input devices. The application was built in three weeks on top of Blender, an open source 3D modeling software, and relies solely on affordable, off-the-shelf hardware like PlayStation Move controllers. Our goal was to create an easy to use 3D modeling environment that employs both 2D and 3D interaction techniques and contains several modeling tools. We conducted a basic user study where novice and professional 3D artists created 3D models with our application. The study participants thought that the application was fun and intuitive to use, but accurate posing of objects was difficult. We also examined the participants' beliefs about future use of immersive technology in 3D modeling. The short implementation time of the application, its many features, and the 3D models created by the study participants set an example of what can be achieved with open source software and off-the-shelf hardware.


title: Insular cortex activity and the evocation of laughter

abstract: The insular cortex is fundamentally involved in the processing of interoceptive information. It has been postulated that the integrative monitoring of the bodily responses to environmental stimuli is crucial for the recognition and experience of emotions. Because emotional arousal is known to be closely coupled to functions of the anterior insula, we suspected laughter to be associated primarily with neuronal activity in this region. An anatomically constrained re-analysis of our imaging data pertaining to ticklish laughter, to inhibited ticklish laughter, and to voluntary laughter revealed regional differences in the levels of neuronal activity in the posterior and mid-/anterior portions of the insula. Ticklish laughter was associated specifically with right ventral anterior insular activity, which was not detected under the other two conditions. Hence, apparently, only laughter that is evoked as an emotional response bears the signature of autonomic arousal in the insular cortex. J. Comp. Neurol., 2015. © 2015 Wiley Periodicals, Inc.


title: From stakeholders analysis to cognitive mapping and Multi-Attribute Value Theory: An integrated approach for policy support

abstract: One of the fundamental features of policy processes in contemporary societies is complexity. It follows from the plurality of points of view actors adopt in their interventions, and from the plurality of criteria upon which they base their decisions. In this context, collaborative multicriteria decision processes seem to be appropriate to address part of the complexity challenge. This study discusses a decision support framework that guides policy makers in their strategic decisions by using a multi-method approach based on the integration of three tools, i.e., (i) stakeholders analysis, to identify the multiple interests involved in the process, (ii) cognitive mapping, to define the shared set of objectives for the analysis, and (iii) Multi-Attribute Value Theory, to measure the level of achievement of the previously defined objectives by the policy options under investigation. The integrated decision support framework has been tested on a real world project concerning the location of new parking areas in a UNESCO site in Southern Italy. The purpose of this study was to test the operability of an integrated analytical approach to support policy decisions by investigating the combined and synergistic effect of the three aforementioned tools. The ultimate objective was to propose policy recommendations for a sustainable parking area development strategy in the region under consideration. The obtained results illustrate the importance of integrated approaches for the development of accountable public decision processes and consensus policy alternatives. The proposed integrated methodological framework will, hopefully, stimulate the application of other collaborative decision processes in public policy making.


title: Computing with hardware neurons: spiking or classical? Perspectives of applied Spiking Neural Networks from the hardware side

abstract: While classical neural networks take a position of a leading method in the machine learning community, spiking neuromorphic systems bring attention and large projects in neuroscience. Spiking neural networks were shown to be able to substitute networks of classical neurons in applied tasks. This work explores recent hardware designs focusing on perspective applications (like convolutional neural networks) for both neuron types from the energy efficiency side to analyse whether there is a possibility for spiking neuromorphic hardware to grow up for a wider use. Our comparison shows that spiking hardware is at least on the same level of energy efficiency or even higher than non-spiking on a level of basic operations. However, on a system level, spiking systems are outmatched and consume much more energy due to inefficient data representation with a long series of spikes. If spike-driven applications, minimizing an amount of spikes, are developed, spiking neural systems may reach the energy efficiency level of classical neural systems. However, in the near future, both type of neuromorphic systems may benefit from emerging memory technologies, minimizing the energy consumption of computation and memory for both neuron types. That would make infrastructure and data transfer energy dominant on the system level. We expect that spiking neurons have some benefits, which would allow achieving better energy results. Still the problem of an amount of spikes will still be the major bottleneck for spiking hardware systems.


title: Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography

abstract: Ensemble methodology, which builds a classification model by integrating multiple classifiers, can be used for improving prediction performance. Researchers from various disciplines such as statistics, pattern recognition, and machine learning have seriously explored the use of ensemble methodology. This paper presents an updated survey of ensemble methods in classification tasks, while introducing a new taxonomy for characterizing them. The new taxonomy, presented from the algorithm designer's point of view, is based on five dimensions: inducer, combiner, diversity, size, and members' dependency. We also propose several selection criteria, presented from the practitioner's point of view, for choosing the most suitable ensemble method.


title: A meta-analytic examination of work and general locus of control.

abstract: The current meta-analysis examined the hypothesized consequences of work and general locus of control. As expected, work locus of control generally yielded stronger relationships with work-related criteria (e.g., job satisfaction, affective commitment, and burnout) than general locus of control. We also found some evidence that general locus of control yielded relatively stronger relationships with general criteria (e.g., life satisfaction, affective commitment, and burnout). Regression analysis found several unique effects for both work and general locus of control.


title: Combining Two Perspectives on Classifying Multimodal Data for Recognizing Speaker Traits

abstract: Human communication involves conveying messages both through verbal and non-verbal channels (facial expression, gestures, prosody, etc.). Nonetheless, the task of learning these patterns for a computer by combining cues from multiple modalities is challenging because it requires effective representation of the signals and also taking into consideration the complex interactions between them. From the machine learning perspective this presents a two-fold challenge: a) Modeling the intermodal variations and dependencies; b) Representing the data using an apt number of features, such that the necessary patterns are captured but at the same time allaying concerns such as over-fitting. In this work we attempt to address these aspects of multimodal recognition, in the context of recognizing two essential speaker traits, namely passion and credibility of online movie reviewers. We propose a novel ensemble classification approach that combines two different perspectives on classifying multimodal data. Each of these perspectives attempts to independently address the two-fold challenge. In the first, we combine the features from multiple modalities but assume inter-modality conditional independence. In the other one, we explicitly capture the correlation between the modalities but in a space of few dimensions and explore a novel clustering based kernel similarity approach for recognition. Additionally, this work investigates a recent technique for encoding text data that captures semantic similarity of verbal content and preserves word-ordering. The experimental results on a recent public dataset shows significant improvement of our approach over multiple baselines. Finally, we also analyze the most discriminative elements of a speaker's non-verbal behavior that contribute to his/her perceived credibility/passionateness.


title: Fast decorrelated neural network ensembles with random weights

abstract: Negative correlation learning (NCL) aims to produce ensembles with sound generalization capability through controlling the disagreement among base learners' outputs. Such a learning scheme is usually implemented by using feed-forward neural networks with error back-propagation algorithms (BPNNs). However, it suffers from slow convergence, local minima problem and model uncertainties caused by the initial weights and the setting of learning parameters. To achieve a better solution, this paper employs the random vector functional link (RVFL) networks as base components, and incorporates with the NCL strategy for building neural network ensembles. The basis functions of the base models are generated randomly and the parameters of the RVFL networks can be determined by solving a linear equation system. An analytical solution is derived for these parameters, where a cost function defined for NCL and the well-known least squares method are used. To examine the merits of our proposed algorithm, a comparative study is carried out with nine benchmark datasets. Results indicate that our approach outperforms other ensembling techniques on the testing datasets in terms of both effectiveness and efficiency.


title: Cooperative Resource Management in Cloud-Enabled Vehicular Networks

abstract: Cloud-enabled vehicular networks are a new paradigm to improve the quality of vehicular services, which have drawn considerable attention in industry and academia. In this paper, we consider the resource management and sharing problem for bandwidth and computing resources to support mobile applications in cloud-enabled vehicular networks. In such an environment, cloud service providers (SPs) can cooperate to form coalitions to share their idle resources with each other. We propose a coalition game model based on two-sided matching theory for cooperation among cloud SPs to share their idle resources. As a result, the resources can be better utilized, and the QoS for users can be improved. Numerical results indicate that our scheme can improve resource utilization and increase by 75% the QoS of the applications compared with that without cooperation. Moreover, the higher service cost of cooperation brings negative effect on coalition formation. The higher cooperation willingness of cloud SPs and the lower service cost support more service applications.


title: Validation of the MOS Social Support Survey 6-item (MOS-SSS-6) measure with two large population-based samples of Australian women

abstract: Purpose ::: This study aimed to validate a 6-item 1-factor global measure of social support developed from the Medical Outcomes Study Social Support Survey (MOS-SSS) for use in large epidemiological studies.


title: Extending Fitts' law to two-dimensional tasks

abstract: Fitts' law, a one-dimensional model of human movement, is commonly applied to two-dimensional target acquisition tasks on interactive computing systems. For rectangular targets, such as words, it is demonstrated that the model can break down and yield unrealistically low (even negative!) ratings for a task's index of difficulty (ID). The Shannon formulation is shown to partially correct this problem, since ID is always ≥ 0 bits. As well, two alternative interpretations “target width” are introduced that accommodate the two-dimensional nature of tasks. Results of an experiment are presented that show a significant improvement in the model's performance using the suggested changes.


title: Klamath Sibilant Degemination: Implications of a Recent Sound Change

abstract: This paper documents a recent sound change in Klamath of *ss > s which took place sometime between the late 1800s and the mid 1900s. A phonetic basis for this sound change is proposed, based on the intrinsic duration of short sibilants. This sound change not only explains the absence of geminate sibilants in modern Klamath but also the lack of derived distributive forms which, historically, differed only from nondistributives in the presence of a geminate sibilant.


title: More Than Words: The Effect of Emoticons in Electronic Negotiations

abstract: While affect plays a similar fundamental role in both, electronic and face-to-face negotiations, the expression of emotions in computer-mediated communication differs considerably from face-to-face settings. The aim of this experimental study is to analyze how the systematic use of emoticons – facilitated with software – affects negotiation behavior in alternative computer-mediated negotiation settings. With a 2 × 2 design comparing system-induced emoticon use with a text-only condition in synchronous chat or asynchronous e-mail mode we isolate effects of emoticons in these different communication settings. Results show that emoticons are used in different functions, i.e. mainly to supplement and support text messages and less often to mitigate its content. Furthermore, emoticon support increases the communication of positive affect in asynchronous negotiations while it decreases communication of negative affect and distributive negotiation behavior in synchronous negotiations. These findings propose that advancing communication quality via contextualization of affective information in negotiation support systems is promising.


title: AdaBoost on low-rank PSD matrices for metric learning

abstract: The problem of learning a proper distance or similarity metric arises in many applications such as content-based image retrieval. In this work, we propose a boosting algorithm, MetricBoost, to learn the distance metric that preserves the proximity relationships among object triplets: object i is more similar to object j than to object k. Metric-Boost constructs a positive semi-definite (PSD) matrix that parameterizes the distance metric by combining rank-one PSD matrices. Different options of weak models and combination coefficients are derived. Unlike existing proximity preserving metric learning which is generally not scalable, MetricBoost employs a bipartite strategy to dramatically reduce computation cost by decomposing proximity relationships over triplets into pair-wise constraints. Met-ricBoost outperforms the state-of-the-art on two real-world medical problems: 1. identifying and quantifying diffuse lung diseases; 2. colorectal polyp matching between different views, as well as on other benchmark datasets.


title: A Characterization of Chameleon Hash Functions and New, Efficient Designs

abstract: This paper shows that chameleon hash functions and Sigma protocols are equivalent. We provide a transform of any suitable Sigma protocol to a chameleon hash function, and also show that any chameleon hash function is the result of applying our transform to some suitable Sigma protocol. This enables us to unify previous designs of chameleon hash functions, seeing them all as emanating from a common paradigm, and also obtain new designs that are more efficient than previous ones. In particular, via a modified version of the Fiat---Shamir protocol, we obtain the fastest known chameleon hash function with a proof of security based on the standard factoring assumption. The increasing number of applications of chameleon hash functions, including on-line/off-line signing, chameleon signatures, designated-verifier signatures and conversion from weakly-secure to fully-secure signatures, make our work of contemporary interest.


title: Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection

abstract: In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-)generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. In these approaches, auditory spectral features of the next short term frame are predicted from the previous frames by means of Long-Short Term Memory recurrent denoising autoencoders. The reconstruction error between the input and the output of the autoencoder is used as activation signal to detect novel events. There is no evidence of studies focused on comparing previous efforts to automatically recognize novel events from audio signals and giving a broad and in depth evaluation of recurrent neural network-based autoencoders. The present contribution aims to consistently evaluate our recent novel approaches to fill this white spot in the literature and provide insight by extensive evaluations carried out on three databases: A3Novelty, PASCAL CHiME, and PROMETHEUS. Besides providing an extensive analysis of novel and state-of-the-art methods, the article shows how RNN-based autoencoders outperform statistical approaches up to an absolute improvement of 16.4% average F-measure over the three databases.


title: Formalizing architectural refactorings as graph transformation systems

abstract: Architectural refactorings are an appropriate technique for the development and improvement of architectural specifications. However, these refactorings are often applied manually. This paper presents a mapping of an architectural specification language to a hypergraph-based data structure. Thus, architectural refactorings can be formalized as hypergraph transformation rules and can be applied automatically.


title: RISK ANALYSIS APPLIED TO PETROLEUM EXPLORATION AND PRODUCTION: AN OVERVIEW

abstract: Abstract During the past decades, there have been some significant improvements in risk analysis applied to petroleum exploration and production. This special issue is dedicated to show some contributions and developments of risk analysis applied to petroleum exploration, field appraisal and development, production forecast under uncertainty, decision-making process, portfolio management, and real options approach. A brief overview is presented in this paper in order to introduce the universe of risk analysis, followed by a summary of the main contributions for this special edition and discussion and implication of the main trends in risk analysis.


title: From User Goals to Service Discovery and Composition

abstract: Goals are often used to represent stakeholder's objectives. The intentionality inherited by a goal drives stakeholders to pursuit the fulfillment of their goals either by themselves or by delegating this fulfillment to third parties. In Service-Oriented Computing, service client's requirements are commonly expressed in terms of inputs, outputs, pre-conditions and effects, also known as IOPE. End-users, i.e., human service clients, may have difficulties to express such requirements as they would have to deal with technical issues such as the request's language, and the type, format and coding of the IOPE. This paper presents the core concepts of the Goal-Based Service Ontology (GSO) that relates goals and services. By grounding GSO in a well-founded ontology we aim at clarifying the semantics for a set of relevant domain concepts that can support specialists in defining application ontologies based on goals and services.


title: Dimension Reduction in Text Classification with Support Vector Machines

abstract: Support vector machines (SVMs) have been recognized as one of the most successful classification methods for many applications including text classification. Even though the learning ability and computational complexity of training in support vector machines may be independent of the dimension of the feature space, reducing computational complexity is an essential issue to efficiently handle a large number of terms in practical applications of text classification. In this paper, we adopt novel dimension reduction methods to reduce the dimension of the document vectors dramatically. We also introduce decision functions for the centroid-based classification algorithm and support vector classifiers to handle the classification problem where a document may belong to multiple classes. Our substantial experimental results show that with several dimension reduction methods that are designed particularly for clustered data, higher efficiency for both training and testing can be achieved without sacrificing prediction accuracy of text classification even when the dimension of the input space is significantly reduced.


title: SELF-EFFICACY AND ACHIEVEMENT GOALS AS PREDICTORS OF HIGH-SCHOOL STUDENTS' ACADEMIC PERFORMANCE

abstract: Summary: It has long been recognized that students’ school performance is determined not only by their cogniƟ ve abiliƟ es, but even more importantly by their moƟ vaƟ on, achievement goals and perceived self-effi cacy. The present study explored the relaƟ onship between academic self-effi cacy, school achievement and four achievement goals of high-school students. The obtained results indicated a signifi cant eff ect of age on students’ grade point average as well as signifi cant eff ects of gender on students’ performance in the CroaƟ an language, their grade point average, self-effi cacy and three achievement goals (mastery, performance and social relaƟ ons). Furthermore, the obtained results indicated a high correlaƟ on between self-effi cacy and mastery goal orientaƟ on, while self-effi cacy was once again idenƟ fi ed as the most important predictor of school performance in all researched areas.


title: Drivers for Transactional B2C M-Commerce Adoption: Extended Theory of Planned Behavior

abstract: Although voluntary individual adoption of information technology is well studied in the literature, further theoretical development is needed to account for the specific characteristics of the mobile commerce artifact. In this study, we augment the theory of planned behavior to include evaluative variables that are not fully captured in attitude and to enhance the specificity of the model to mobile commerce. More specifically, we develop, operationalize and empirically test a model for explaining the adoption intention of transactional B2C mobile commerce. The model is empirically tested with mobile device users who have not adopted mobile commerce yet. The empirical results provide strong support for the theoretical model, shedding light on the significance and relative importance of specific adoption factors. The theoretical and empirical implications of these results are discussed.


Has Marijuana Legalization Increased Marijuana Use Among US Youth?


title: A Deep Learning Framework for Short-term Power Load Forecasting

abstract: The scheduling and operation of power system becomes prominently complex and uncertain, especially with the penetration of distributed power. Load forecasting matters to the effective operation of power system. This paper proposes a novel deep learning framework to forecast the short-term grid load. First, the load data is processed by Box-Cox transformation, and two parameters (electricity price and temperature) are investigated. Then, to quantify the tail-dependence of power load on the two parameters, parametric Copula models are fitted and the threshold of peak load are computed. Next, a deep belief network is built to forecast the hourly load of the power grid. One year grid load data collected from an urbanized area in Texas, United States is utilized in the case studies. Short-term load forecasting are examined in four seasons independently. Day-ahead and week-ahead load forecasting experiments are conducted in each season using the proposed framework. The proposed framework is compared with classical neural networks, support vector regression machine, extreme learning machine, and classical deep belief networks. The load forecasting performances are assessed by mean absolute percentage error, root mean square error, and hit rate. Computational results confirm the effectiveness of the proposed data-driven deep learning framework. The prediction accuracies of both day-ahead forecasting and week-ahead forecasting demonstrate that the proposed framework outperforms the tested algorithms.


title: Incremental heuristic search in AI

abstract: Incremental search reuses information from previous searches to find solutions to a series of similar search problems potentially faster than is possible by solving each search problem from scratch. This is important because many AI systems have to adapt their plans continuously to changes in (their knowledge of) the world. In this article, we give an overview of incremental search, focusing on LIFELONG PLANNING A*, and outline some of its possible applications in AI.


title: Impact of malaria morbidity on gross domestic product in Uganda

abstract: Background ::: The burden of malaria is a key challenge to both human and economic development in malaria endemic countries. The impact of malaria can be categorized from three dimensions, namely: health, social and economic. The objective of this study was to estimate the impact of malaria morbidity on gross domestic product (GDP) of Uganda.


title: A 40-Gb/s Transimpedance Amplifier in 0.18-$\mu$m CMOS Technology

abstract: A 40-Gb/s transimpedance amplifier (TIA) is realized in 0.18-mum CMOS technology. From the measured S-parameters, a transimpedance gain of 51 dBOmega and a 3-dB bandwidth up to 30.5 GHz were observed. A bandwidth enhancement technique, pi-type inductor peaking (PIP), is proposed to achieve a bandwidth enhancement ratio (BWER) of 3.31. In addition, the PIP topology used at the input stage decreases the noise current as the operation frequency increases. Under a 1.8 V supply voltage, the TIA consumes 60.1 mW with a chip area of 1.17 X 0.46 mm2. The proposed CMOS TIA presents a gain-bandwidth product per DC power figure of merit (GBP/Pde) of 180.1 GHzOmega/mW.


title: Revealing Event Saliency in Unconstrained Video Collection

abstract: Recent progresses in multimedia event detection have enabled us to find videos about a predefined event from a large-scale video collection. Research towards more intrinsic unsupervised video understanding is an interesting but understudied field. Specifically, given a collection of videos sharing a common event of interest, the goal is to discover the salient fragments, i.e., the curt video fragments that can concisely portray the underlying event of interest, from each video. To explore this novel direction, this paper proposes an unsupervised event saliency revealing framework. It first extracts features from multiple modalities to represent each shot in the given video collection. Then, these shots are clustered to build the cluster-level event saliency revealing framework, which explores useful information cues (i.e., the intra-cluster prior, inter-cluster discriminability, and inter-cluster smoothness) by a concise optimization model. Compared with the existing methods, our approach could highlight the intrinsic stimulus of the unseen event within a video in an unsupervised fashion. Thus, it could potentially benefit to a wide range of multimedia tasks like video browsing, understanding, and search. To quantitatively verify the proposed method, we systematically compare the method to a number of baseline methods on the TRECVID benchmarks. Experimental results have demonstrated its effectiveness and efficiency.


title: Think different: The merits of unconscious thought in preference development and decision making

abstract: The role of unconscious and conscious thought in decision making was investigated in 5 experiments. Because of the low processing capacity of consciousness, conscious thought was hypothesized to be maladaptive when making complex decisions. Conversely, unconscious thought was expected to be highly effective. In Experiments 1‐3, participants were presented with a complex decision problem in which they had to choose between various alternatives, each with multiple attributes. Some participants had to make a decision immediately after being presented with the options. In the conscious thought condition, participants could think about the decision for a few minutes. In the unconscious thought condition, participants were distracted for a few minutes and then indicated their decision. Throughout the experiments, unconscious thinkers made the best decisions. Additional evidence obtained in Experiments 4 and 5 suggests that unconscious thought leads to clearer, more polarized, and more integrated representations in memory.


title: Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention

abstract: Transactions through the web are now a progressive mechanism to access an ever-increasing range of services over more and more diverse environments. The internet provides many opportunities for companies to provide personalized online services to their customers, but the quality and novelty of some web services may adversely affect the appeal and user gratification. In the future, prediction of the consumer intention needs to be a main focus for selecting the web services for an application. The aim of this study is to predict online consumer repurchase intentions; to accomplish this objective a hybrid approach is chosen with a combination of machine learning techniques and artificial bee colony (ABC) algorithm being used. The study starts with identification of consumer characteristics for repurchase intention, followed by determining the feature selection of consumer characteristics and shopping mall attributes (with >0.1 threshold value) for the prediction model using ABC. Finally, validation using k-fold cross has been employed to measure the best classification model robustness. The classification models, viz. decision trees (C5.0), AdaBoost, random forest, support vector machine and neural network, are utilized to predict consumer purchase intention. Performance evaluation of identified models on training–testing partitions (70–30%) of the data set shows that the AdaBoost method outperforms other classification models, with sensitivity and accuracy of 0.95 and 97.58%, respectively, on testing the data set. Examining the consumer repurchase intentions by considering both shopping mall and consumer characteristics makes this study unique.


title: Low-latency visual odometry using event-based feature tracks

abstract: New vision sensors, such as the Dynamic and Active-pixel Vision sensor (DAVIS), incorporate a conventional camera and an event-based sensor in the same pixel array. These sensors have great potential for robotics because they allow us to combine the benefits of conventional cameras with those of event-based sensors: low latency, high temporal resolution, and high dynamic range. However, new algorithms are required to exploit the sensor characteristics and cope with its unconventional output, which consists of a stream of asynchronous brightness changes (called “events”) and synchronous grayscale frames. In this paper, we present a low-latency visual odometry algorithm for the DAVIS sensor using event-based feature tracks. Features are first detected in the grayscale frames and then tracked asynchronously using the stream of events. The features are then fed to an event-based visual odometry algorithm that tightly interleaves robust pose optimization and probabilistic mapping. We show that our method successfully tracks the 6-DOF motion of the sensor in natural scenes. This is the first work on event-based visual odometry with the DAVIS sensor using feature tracks.


title: The principles and practice of probabilistic programming

abstract: Probabilities describe degrees of belief, and probabilistic inference describes rational reasoning under uncertainty. It is no wonder, then, that probabilistic models have exploded onto the scene of modern artificial intelligence, cognitive science, and applied statistics: these are all sciences of inference under uncertainty. But as probabilistic models have become more sophisticated, the tools to formally describe them and to perform probabilistic inference have wrestled with new complexity. Just as programming beyond the simplest algorithms requires tools for abstraction and composition, complex probabilistic modeling requires new progress in model representation—probabilistic programming languages. These languages provide compositional means for describing complex probability distributions; implementations of these languages provide generic inference engines: tools for performing efficient probabilistic inference over an arbitrary program. In their simplest form, probabilistic programming languages extend a well-specified deterministic programming language with primitive constructs for random choice. This is a relatively old idea, with foundational work by Giry, Kozen, Jones, Moggi, SahebDjahromi, Plotkin, and others [see e.g. 7]. Yet it has seen a resurgence thanks to new tools for probabilistic inference and new complexity of probabilistic modeling applications. There are a number of recent probabilistic programming languages [e.g. 8, 9, 11–17], embodying different tradeoffs in expressivity, efficiency, and perspicuity. We will focus on the probabilistic programming language Church [6] for simplicity, but the design of probabilistic languages to best support complex model representation and efficient inference is an active and important topic. Church extends (the purely functional subset of) Scheme with elementary random primitives, such as flip (a bernoulli), multinomial, and gaussian. In addition, Church includes language constructs that simplify modeling. For instance, mem, a higher-order procedure that memoizes its input function, is useful for describing persistent random properties and lazy model construction. (Interestingly, memoization has a semantic effect in probabilistic languages.) If we view the semantics of the underlying deterministic language as a map from programs to executions of the program, the semantics of the probabilistic language will be a map from programs to distributions over executions. When the program halts


The use of web 2.0 technologies in tourism industry: A conceptual model


title: Community Interaction and Conflict on the Web

abstract: Users organize themselves into communities on web platforms. These communities can interact with one another, often leading to conflicts and toxic interactions. However, little is known about the mechanisms of interactions between communities and how they impact users. Here we study intercommunity interactions across 36,000 communities on Reddit, examining cases where users of one community are mobilized by negative sentiment to comment in another community. We show that such conflicts tend to be initiated by a handful of communities---less than 1% of communities start 74% of conflicts. While conflicts tend to be initiated by highly active community members, they are carried out by significantly less active members. We find that conflicts are marked by formation of echo chambers, where users primarily talk to other users from their own community. In the long-term, conflicts have adverse effects and reduce the overall activity of users in the targeted communities. Our analysis of user interactions also suggests strategies for mitigating the negative impact of conflicts---such as increasing direct engagement between attackers and defenders. Further, we accurately predict whether a conflict will occur by creating a novel LSTM model that combines graph embeddings, user, community, and text features. This model can be used toreate early-warning systems for community moderators to prevent conflicts. Altogether, this work presents a data-driven view of community interactions and conflict, and paves the way towards healthier online communities.


title: A deep learning approach for detecting and correcting highlights in endoscopic images

abstract: The image of an object changes dramatically depending on the lightning conditions surrounding that object. Shadows, reflections and highlights can make the object very difficult to be recognized for an automatic system. Additionally, images used in medical applications, such as endoscopic images and videos contain a large amount of such reflective components. This can pose an extra difficulty for experts to analyze such type of videos and images. It can then be useful to detect — and possibly correct — the locations where those highlights happen. In this work we designed a Convolutional Neural Network for that task. We trained such a network using a dataset that contains groundtruth highlights showing that those reflective elements can be learnt and thus located and extracted. We then used that trained network to localize and correct the highlights in endoscopic images from the El Salvador Atlas Gastrointestinal videos obtaining promising results.


title: Shared Task on Sentiment Analysis in Indian Languages SAIL Tweets - An Overview

abstract: Sentiment Analysis in Twitter has been considered as a vital task for a decade from various academic and commercial perspectives. Several works have been performed on Twitter sentiment analysis or opinion mining for English in contrast to the Indian languages. Here, we summarize the objectives and evaluation of the sentiment analysis task in tweets for three Indian languages namely Bengali, Hindi and Tamil. This is the first attempt to sentiment analysis task in the context of Indian language tweets. The main objective of this task was to classify the tweets into positive, negative, and neutral polarity. For training and testing purpose, the tweets from each language were provided. Each of the participating teams was asked to submit two systems, constrained and unconstrained systems for each of the languages. We ranked the systems based on the accuracy of the systems. Total of six teams submitted the results and the maximum accuracy achieved for Bengali, Hindi, and Tamil are 43.2i¾?%, 55.67i¾?%, and 39.28i¾?% respectively.


title: Bipartite Consensus of Discrete-Time Double-Integrator Multi-Agent Systems with Measurement Noise

abstract: The effects of measurement noise are investigated in the context of bipartite consensus of multi-agent systems. In the system setting, discrete-time double-integrator dynamics are assumed for the agent, and measurement noise is present for the agent receiving the state information from its neighbors. Time-varying stochastic bipartite consensus protocols are designed in order to lessen the harmful effects of the noise. Consequently, the state transition matrix of the closed-loop system is analyzed, and sufficient and necessary conditions for the proposed protocol to be a mean square bipartite consensus protocol are given with the help of linear transformation and algebraic graph theory. It is proven that the signed digraph to be structurally balanced and having a spanning tree are the weakest communication assumptions for ensuring bipartite consensus. In particular, the proposed protocol is a mean square bipartite average consensus one if the signed digraph is also weight balanced.


title: Object localization in weakly labeled data using regularized attention networks

abstract: We consider the problem of weakly supervised object localization. For an object of interest (e.g. “car”), an image is weakly labeled when its label only indicates the presence/absence of this object, but not the exact location of the object in the image. Given a collection of weakly labeled images for an object, our goal is to localize the object of interest in each image. We propose a novel architecture called the regularized attention network for this problem. Our work builds upon the attention network proposed in [1]. We extend the standard attention network by incorporating a regularization term that encourages the attention scores of object proposals to mimic the scoring distribution of a strong fully supervised object detector. Despite of the simplicity of our approach, our proposed architecture achieves the state-of-the-art results on several benchmark datasets.


title: CTP-WUR: The collection tree protocol in wake-up radio WSNs for critical applications

abstract: Allowing the nodes of a wireless sensor network (WSN) to turn their radio off periodically noticeably increases network lifetime. Duty cycling, however, does not eliminate idle listening, comes at the price of longer latencies and obtains lifetimes that are still insufficient for many critical applications. Using a wake-up receiver (WUR) allows actual communications on the main radio only for transmission or reception, virtually eliminating node idling. However, the range of current WUR prototypes is still significantly shorter than that of the main radio, which can challenge the use of existing WSN protocols in WUR-based networks. In this paper we present an approach to mitigate this limitation of wake-up-based networks. In particular, we show that the Collection Tree Protocol (CTP), a standard protocol for data gathering in WSNs, suitably redefined to work on WUR-endowed nodes, achieves lifetimes of several decades. This constitutes a remarkable improvement over duty cycle-based solutions, where CTP makes the network lasts only a handful of months. At the same time, our WUR-based approach obtains data latencies comparable to those obtained by keeping the main radio always on.


title: Joint scheduling of processing and Shuffle phases in MapReduce systems

abstract: MapReduce has emerged as an important paradigm for processing data in large data centers. MapReduce is a three phase algorithm comprising of Map, Shuffle and Reduce phases. Due to its widespread deployment, there have been several recent papers outlining practical schemes to improve the performance of MapReduce systems. All these efforts focus on one of the three phases to obtain performance improvement. In this paper, we consider the problem of jointly scheduling all three phases of the MapReduce process with a view of understanding the theoretical complexity of the joint scheduling and working towards practical heuristics for scheduling the tasks. We give guaranteed approximation algorithms and outline several heuristics to solve the joint scheduling problem.


GEC into the future: Where are we going and how do we get there?


title: Stair descent in the simple hexapod 'RHex'

abstract: We present the first controller that allows our small hexapod robot, RHex, to descend a wide variety of regular sized, "real world" stairs. After selecting one of two sets of trajectories, depending on the slope of the stairs, our open-loop, clock-driven controllers require no further operator input nor task level feedback. Energetics for stair descent is captured via specific resistance values and compared to stair ascent and other behaviors. Even though the algorithms developed and validated in this paper were developed for a particular robot, the basic motion strategies, and the phase relationships between the contralateral leg pairs are likely applicable to other hexapod robots of similar size as well.


title: Modeling and simulation of a pacing engine for proactive campaigns in contact center environment

abstract: Proactive contact campaigns play a growing role in modern contact centers. Their traditional usage as a telemarketing tool now is widely extended to different types of service notifications. In this paper we describe the most common model of a proactive telephone contact campaign, suggest several methods of its pacing algorithm and present simulation results.


title: The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Minima and Regularization Effects

abstract: Understanding the behavior of stochastic gradient descent (SGD) in the context of deep neural networks has raised lots of concerns recently. Along this line, we theoretically study a general form of gradient based optimization dynamics with unbiased noise, which unifies SGD and standard Langevin dynamics. Through investigating this general optimization dynamics, we analyze the behavior of SGD on escaping from minima and its regularization effects. A novel indicator is derived to characterize the efficiency of escaping from minima through measuring the alignment of noise covariance and the curvature of loss function. Based on this indicator, two conditions are established to show which type of noise structure is superior to isotropic noise in term of escaping efficiency. We further show that the anisotropic noise in SGD satisfies the two conditions, and thus helps to escape from sharp and poor minima effectively, towards more stable and flat minima that typically generalize well. We verify our understanding through comparing this anisotropic diffusion with full gradient descent plus isotropic diffusion (i.e. Langevin dynamics) and other types of position-dependent noise.


title: Automatic Detection of User Abilities through the SmartAbility Framework

abstract: This paper presents a proposed smartphone application for the unique SmartAbility Framework that ::: supports interaction with technology for people with reduced physical ability, through focusing on ::: the actions that they can perform independently. The Framework is a culmination of knowledge ::: obtained through previously conducted technology feasibility trials and controlled usability ::: evaluations involving the user community. The Framework is an example of ability-based design that ::: focuses on the abilities of users instead of their disabilities. The paper includes a summary of ::: Versions 1 and 2 of the Framework, including the results of a two-phased validation approach, ::: conducted at the UK Mobility Roadshow and via a focus group of domain experts. A holistic model ::: developed by adapting the House of Quality (HoQ) matrix of the Quality Function Deployment (QFD) ::: approach is also described. A systematic literature review of sensor technologies built into smart ::: devices establishes the capabilities of sensors in the Android and iOS operating systems. The review ::: defines a set of inclusion and exclusion criteria, as well as search terms used to elicit literature from ::: online repositories. The key contribution is the mapping of ability-based sensor technologies onto ::: the Framework, to enable the future implementation of a smartphone application. Through the ::: exploitation of the SmartAbility application, the Framework will increase technology amongst people ::: with reduced physical ability and provide a promotional tool for assistive technology manufacturers.


title: Enhancement of oxidation rate of a-Si nanoparticles during dehydrogenation

abstract: Oxidation of amorphous silicon (a-Si) nanoparticles grown by plasma-enhanced chemical vapor deposition were investigated. Their hydrogen content has a great influence on the oxidation rate at low temperature. When the mass gain is recorded during a heating ramp in dry air, an oxidation process at low temperature is identified with an onset around 250 °C. This temperature onset is similar to that of hydrogen desorption. It is shown that the oxygen uptake during this process almost equals the number of hydrogen atoms present in the nanoparticles. To explain this correlation, we propose that oxidation at low temperature is triggered by the process of hydrogen desorption.


title: A load balancing mechanism for multiple SDN controllers based on load informing strategy

abstract: Software defined networking (SDN) is currently regarded as one of the most promising paradigms of future Internet. Although the availability and scalability that a single and centralized controller suffers from could be alleviated by using multiple controllers, there lacks a flexible mechanism to balance load among controllers. This paper proposes a load balancing mechanism based on a load informing strategy for multiple distributed controllers. With the mechanism, a controller can make load balancing decision locally as rapidly as possible. Experiments based on floodlight show that our mechanism can balance the load of each controller dynamically and reduce the time of load balancing.


The Security Evaluation of Time Stamping Schemes: The Present Situation and Studies


title: Multimodal Popularity Prediction of Brand-related Social Media Posts

abstract: Brand-related user posts on social networks are growing at a staggering rate, where users express their opinions about brands by sharing multimodal posts. However, while some posts become popular, others are ignored. In this paper, we present an approach for identifying what aspects of posts determine their popularity. We hypothesize that brand-related posts may be popular due to several cues related to factual information, sentiment, vividness and entertainment parameters about the brand. We call the ensemble of cues engagement parameters. In our approach, we propose to use these parameters for predicting brand-related user post popularity. Experiments on a collection of fast food brand-related user posts crawled from Instagram show that: visual and textual features are complementary in predicting the popularity of a post; predicting popularity using our proposed engagement parameters is more accurate than predicting popularity directly from visual and textual features; and our proposed approach makes it possible to understand what drives post popularity in general as well as isolate the brand specific drivers.


title: Assessing the Refactoring of Brain Methods

abstract: Code smells are a popular mechanism for identifying structural design problems in software systems. Several tools have emerged to support the detection of code smells and propose some refactorings. However, existing tools do not guarantee that a smell will be automatically fixed by means of refactorings. This article presents Bandago, an automated approach to fix a specific type of code smell called Brain Method. A Brain Method centralizes the intelligence of a class and manifests itself as a long and complex method that is difficult to understand and maintain by developers. For each Brain Method, Bandago recommends several refactoring solutions to remove the smell using a search strategy based on simulated annealing. Our approach has been evaluated with several open-source Java applications, and the results show that Bandago can automatically fix more than 60% of Brain Methods. Furthermore, we conducted a survey with 35 industrial developers that showed evidence about the usefulness of the refactorings proposed by Bandago. Also, we compared the performance of the Bandago against that of a third-party refactoring tool.


title: Direct Sparse Visual-Inertial Odometry Using Dynamic Marginalization

abstract: We present VI-DSO, a novel approach for visual-inertial odometry, which jointly estimates camera poses and sparse scene geometry by minimizing photometric and IMU measurement errors in a combined energy functional. The visual part of the system performs a bundle-adjustment like optimization on a sparse set of points, but unlike key-point based systems it directly minimizes a photometric error. This makes it possible for the system to track not only corners, but any pixels with large enough intensity gradients. IMU information is accumulated between several frames using measurement preintegration, and is inserted into the optimization as an additional constraint between keyframes. We explicitly include scale and gravity direction into our model and jointly optimize them together with other variables such as poses. As the scale is often not immediately observable using IMU data this allows us to initialize our visual-inertial system with an arbitrary scale instead of having to delay the initialization until everything is observable. We perform partial marginalization of old variables so that updates can be computed in a reasonable time. In order to keep the system consistent we propose a novel strategy which we call"dynamic marginalization". This technique allows us to use partial marginalization even in cases where the initial scale estimate is far from the optimum. We evaluate our method on the challenging EuRoC dataset, showing that VI-DSO outperforms the state of the art.


title: Observing and participating in social interactions: Action perception and action control across the autistic spectrum

abstract: Autism is a developmental condition, characterized by difficulties of social interaction and communication, as well as restricted interests and repetitive behaviors. Although several important conceptions have shed light on specific facets, there is still no consensus about a universal yet specific theory in terms of its underlying mechanisms. While some theories have exclusively focused on sensory aspects, others have emphasized social difficulties. However, sensory and social processes in autism might be interconnected to a higher degree than what has been traditionally thought. We propose that a mismatch in sensory abilities across individuals can lead to difficulties on a social, i.e. interpersonal level and vice versa. In this article, we, therefore, selectively review evidence indicating an interrelationship between perceptual and social difficulties in autism. Additionally, we link this body of research with studies, which investigate the mechanisms of action control in social contexts. By doing so, we highlight that autistic traits are also crucially related to differences in integration, anticipation and automatic responding to social cues, rather than a mere inability to register and learn from social cues. Importantly, such differences may only manifest themselves in sufficiently complex situations, such as real-life social interactions, where such processes are inextricably linked.


Identifying and Visualization Refactoring Candidates for Extract Method using Program Dependence Graph