title: When Emotions Run High: A Critical Role for Context in the Unfolding of Dynamic, Real-Life Facial Affect

abstract: Although positive and negative affect are assumed to be highly distinct, recent work has shown that facial valence of positive and negative situations may be highly confusable, especially when the emotions are intense. However, previous work has relied exclusively on static images, portraying a single peak frame of the emotional display. Dynamic expressions, on the other hand, convey a far broader representation of the emotional reaction, but are they diagnostic of the situational valence? Participants (N = 245) watched videos portraying reactions to real-life highly positive situations and evaluated the affective valence of the target. Video information was controlled by: (a) truncating the movies after 5, 10, or 20 seconds from the start, and by (b) digitally manipulating the videos such that only the face was visible with no context, only the context was visible with no face, or the face appeared in context. Results indicate that during real-life intense positive situations, facial expressions alone were rated as negative and failed to convey diagnostic information about the positive situational valence even at the most extended presentation durations. By contrast, when contextual information appeared alone or with the face, participants accurately rated the target as feeling positive, and this positivity increased with extended viewing duration. These findings suggests poor coupling between facial valence and felt emotions, supporting the notion that when emotions run high, the diagnostic power of facial expressions is reduced. Conversely, the findings demonstrate an inherent role for contextual information in the recognition of real-life intense faces. (PsycINFO Database Record (c) 2019 APA, all rights reserved).


title: Forecasting market states

abstract: We propose a novel methodology to define, analyze and forecast market states. In our approach market states are identified by a reference sparse precision matrix and a vector of expectation values. In our procedure, each multivariate observation is associated with a given market state accordingly to a minimization of a penalized Mahalanobis distance. The procedure is made computationally very efficient and can be used with a large number of assets. We demonstrate that this procedure is successful at clustering different states of the markets in an unsupervised manner. In particular, we describe an experiment with one hundred log-returns and two states in which the methodology automatically associates states prevalently to pre- and post- crisis periods with one state gathering periods with average positive returns and the other state periods with average negative returns, therefore discovering spontaneously the common classification of `bull' and `bear' markets. In another experiment, with again one hundred log-returns and two states, we demonstrate that this procedure can be efficiently used to forecast off-sample future market states with significant prediction accuracy. This methodology opens the way to a range of applications in risk management and trading strategies in the context where the correlation structure plays a central role.


title: Virtual reality and neuropsychology: upgrading the current tools.

abstract: Background: Virtual reality (VR) is an evolving technology that has been applied in various aspects of medicine, including the treatment of phobia disorders, pain distraction interventions, surgical training, and medical education. These applications have served to demonstrate the various assets offered through the use of VR. Objective: To provide a background and rationale for the application of VR to neuropsychological assessment. Methods: A brief introduction to VR technology and a review of current ongoing neuropsychological research that integrates the use of this technology. Conclusions: VR offers numerous assets that may enhance current neuropsychological assessment protocols and address many of the


title: Topology matching for fully automatic similarity estimation of 3D shapes

abstract: There is a growing need to be able to accurately and efficiently search visual data sets, and in particular, 3D shape data sets. This paper proposes a novel technique, called Topology Matching, in which similarity between polyhedral models is quickly, accurately, and automatically calculated by comparing Multiresolutional Reeb Graphs (MRGs). The MRG thus operates well as a search key for 3D shape data sets. In particular, the MRG represents the skeletal and topological structure of a 3D shape at various levels of resolution. The MRG is constructed using a continuous function on the 3D shape, which may preferably be a function of geodesic distance because this function is invariant to translation and rotation and is also robust against changes in connectivities caused by a mesh simplification or subdivision. The similarity calculation between 3D shapes is processed using a coarse-to-fine strategy while preserving the consistency of the graph structures, which results in establishing a correspondence between the parts of objects. The similarity calculation is fast and efficient because it is not necessary to determine the particular pose of a 3D shape, such as a rotation, in advance. Topology Matching is particularly useful for interactively searching for a 3D object because the results of the search fit human intuition well.


title: Factors affecting continuity in the peer-to-peer lending industry

abstract: Due to the effect of high tech on customer relationships in the loans industry, the internet has brought significant changes in the way people borrow money with financial institutions. This study examines those systematic factors that have critical effects on customer satisfaction with firms in the peer-to-peer P2P loans industry. Consistent with the findings reported in earlier studies, this research also identified four types of switching costs that affects the P2P lending exercise, namely, procedure, financial, relationship, and risk. Two other factors, continuity and satisfaction, were found to be related to the first four factors. However, the four cost factors were shown to have a negative effect on continuity and satisfaction. In other words, customers will select to remain with a specific platform even when the satisfaction level is low because it is now very costly to switch to another vendor.


title: A simple algorithm for homeomorphic surface reconstruction

abstract: Abstract 1 Introduction The problem of computing a piecewise linear approximation to a surface from a set of sam- ple points on the surface has been a focus of research in solid modeling and graphics due to its many applications. The input to this sur- face reconstruction problem consists of the three dimensional coordinates of the sampled points. The crust algorithm of [1] reconstructs a surface with topological and geometric guarantees using the Voronoi diagram of the input point set. We present new observations that simplify both the algorithm and the proofs for the crust, and we give for the first time a proof that the crust is homeomorphic to the input surface. *Dept. of Computer Science, U. of Texas, Austin TX 78712. e-mail: amenta@cs.utexas.edu tDept, of Computer Science, U. of Texas, Austin, TX 78712. e-mail: sunghee@cs.utexas.edu SDept. of Computer and Information Science, Ohio State U., Columbus, OH 43210. e-mail: t amaldey¢ci s. ohio-state, edu §Dept. of Computer and Information Science, Ohio State U., Columbus, OH 43210. e-mail: leekha@cis, ohio-state, edu


title: Homa: A Receiver-Driven Low-Latency Transport Protocol Using Network Priorities (Complete Version)

abstract: Homa is a new transport protocol for datacenter networks. It provides exceptionally low latency, especially for workloads with a high volume of very short messages, and it also supports large messages and high network utilization. Homa uses in-network priority queues to ensure low latency for short messages; priority allocation is managed dynamically by each receiver and integrated with a receiver-driven flow control mechanism. Homa also uses controlled overcommitment of receiver downlinks to ensure efficient bandwidth utilization at high load. Our implementation of Homa delivers 99th percentile round-trip times less than 15 μs for short messages on a 10 Gbps network running at 80% load. These latencies are almost 100x lower than the best published measurements of an implementation. In simulations, Homa's latency is roughly equal to pFabric and significantly better than pHost, PIAS, and NDP for almost all message sizes and workloads. Homa can also sustain higher network loads than pFabric, pHost, or PIAS.


title: Guidelines to efficient smart home design for rapid AI prototyping: a case study

abstract: Advances in ubiquitous technology have moved us towards the dream of creating intelligent houses that can help human in their everyday life. The next step in the completion of this vision is to make major breakthroughs in artificial intelligence. In fact, it is the key component for allowing sensors and effectors to give useful services when it is appropriate. In consequence, researchers need to conduct more experiments in realistic setting (e.g. smart home). In order to face this challenge, many research teams try to build new experimental infrastructures without any background experience, guidance or even a real idea of their research needs and issues. Our team is composed of specialists in AI for cognitive assistance and has worked with four major smart home infrastructures. From that experience, we propose, in this paper, a set of guidelines for designing and implementing an efficient smart home architecture on both hardware and software perspective. This paper aims to be a major step toward the AI development (rapid prototyping) and smart home research. Moreover, we share our recent experience with the construction of a new smart home and clinical trials conducted at our laboratory with real Alzheimer's subjects.


title: From Actemes to Action: A Strongly-Supervised Representation for Detailed Action Understanding

abstract: This paper presents a novel approach for analyzing human actions in non-scripted, unconstrained video settings based on volumetric, x-y-t, patch classifiers, termed actemes. Unlike previous action-related work, the discovery of patch classifiers is posed as a strongly-supervised process. Specifically, key point labels (e.g., position) across space time are used in a data-driven training process to discover patches that are highly clustered in the space time key point configuration space. To support this process, a new human action dataset consisting of challenging consumer videos is introduced, where notably the action label, the 2D position of a set of key points and their visibilities are provided for each video frame. On a novel input video, each acteme is used in a sliding volume scheme to yield a set of sparse, non-overlapping detections. These detections provide the intermediate substrate for segmenting out the action. For action classification, the proposed representation shows significant improvement over state-of-the-art low-level features, while providing spatiotemporal localization as additional output, which sheds further light into detailed action understanding.


title: Interactive query refinement

abstract: We investigate the problem of refining SQL queries to satisfy cardinality constraints on the query result. This has applications to the many/few answers problems often faced by database users. We formalize the problem of query refinement and propose a framework to support it in a database system. We introduce an interactive model of refinement that incorporates user feedback to best capture user preferences. Our techniques are designed to handle queries having range and equality predicates on numerical and categorical attributes. We present an experimental evaluation of our framework implemented in an open source data manager and demonstrate the feasibility and practical utility of our approach.


title: A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis

abstract: Gene regulatory networks (GRNs) provide a representation of relationships between regulators and their target genes. Several methods for GRN inference, both unsupervised and supervised, have been developed to date. Because regulatory relationships consistently reprogram in diverse tissues or under different conditions, GRNs inferred without specific biological contexts are of limited applicability. In this report, a machine learning approach is presented to predict GRNs specific to developing Arabidopsis thaliana embryos. We developed the Beacon GRN inference tool to predict GRNs occurring during seed development in Arabidopsis based on a support vector machine (SVM) model. We developed both global and local inference models and compared their performance, demonstrating that local models are generally superior for our application. Using both the expression levels of the genes expressed in developing embryos and prior known regulatory relationships, GRNs were predicted for specific embryonic developmental stages. The targets that are strongly positively correlated with their regulators are mostly expressed at the beginning of seed development. Potential direct targets were identified based on a match between the promoter regions of these inferred targets and the cis elements recognized by specific regulators. Our analysis also provides evidence for previously unknown inhibitory effects of three positive regulators of gene expression. The Beacon GRN inference tool provides a valuable model system for context-specific GRN inference and is freely available at https://github.com/BeaconProjectAtVirginiaTech/beacon_network_inference.git.


title: Searching RDF Graphs with SPARQL and Keywords

abstract: The proliferation of knowledge-sharing communities like Wikipedia and the advances in automated information extraction from Web pages enable the construction of large knowledge bases with facts about entities and their relationships. The facts can be represented in the RDF data model, as so-called subject-property-object triples, and can thus be queried by structured query languages like SPARQL. In principle, this allows precise querying in the database spirit. However, RDF data may be highly diverse and queries may return way too many results, so that ranking by informativeness measures is crucial to avoid overwhelming users. Moreover, as facts are extracted from textual contexts or have community-provided annotations, it can be beneficial to consider also keywords for formulating search requests. This paper gives an overview of recent and ongoing work on ranked retrieval of RDF data with keyword-augmented structured queries. The ranking method is based on statistical language models, the state-of-the-art paradigm in information retrieval. The paper develops a novel form of language models for the structured, but schema-less setting of RDF triples and extended SPARQL queries. 1 Motivation and Background Entity-Relationship graphs are receiving great attention for information management outside of mainstream database engines. In particular, the Semantic-Web data model RDF (Resource Description Format) is gaining popularity for applications on scientific data such as biological networks [14], social Web2.0 applications [4], large-scale knowledge bases such as DBpedia [2] or YAGO [13], and more generally, as a light-weight representation for the “Web of data” [5]. An RDF data collection consists of a set of subject-property-object triples, SPO triples for short. In ER terminology, an SPO triple corresponds to a pair of entities connected by a named relationship or to an entity connected to the value of a named attribute. As the object of a triple can in turn be the subject of other triples, we can also view the RDF data as a graph of typed nodes and typed edges where nodes correspond to entities and edges to relationships (viewing attributes as relations as well). Some of the existing RDF collections contain more than a billion triples. As a simple example that we will use throughout the paper, consider a Web portal on movies. Table 1 shows a few sample triples. The example illustrates a number of specific requirements that RDF data poses for querying: Copyright 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering


AutoCTF: Creating Diverse Pwnables via Automated Bug Injection


Making Better Use of the Crowd ∗


title: Towards Energy Awareness in Hadoop

abstract: With the rise in the use of data centers comprised of commodity clusters for data-intensive applications, the energy efficiency of these setups is becoming a paramount concern for data center operators. Moreover, applications developed for Hadoop framework, which has now become a de-facto implementation of the MapReduce framework, now comprise complex workflows that are managed by specialized workflow schedulers, such as Oozie. These schedulers assume cluster resources to be homogeneous and often consider data locality to be the only scheduling constraint. However, this is increasingly not the case in modern data centers. The addition of low-power computing devices and regular hardware upgrades have made heterogeneity the norm, in that clusters are now comprised of several logical sub-clusters each with its own performance and energy profile. In this paper we present eSched, a workflow scheduler that profiles the performance and the energy characteristics of applications on each hardware sub-cluster in a heterogeneous cluster in order to improve the application-resource match while ensuring energy efficiency and performance related Service Level Agreement (SLA) goals. eSched borrows from our earlier work, φSched, a hardware-aware scheduler, that improves the resource--application match to improve application performance. We evaluate eSched on three clusters with different hardware configurations and energy profiles, where each sub-cluster comprises of five homogeneous nodes. Our evaluation of eSched shows that application performance and power characteristics vary significantly across different hardware configurations. We show that the hardware-aware scheduling can perform 12.8% faster, while saving 21% more power than hardware oblivious scheduling for the studied applications.


title: Computational prediction of nucleosome positioning by calculating the relative fragment frequency index of nucleosomal sequences.

abstract: We developed an accurate method to predict nucleosome positioning from genome sequences by refining the previously developed method of Peckham et al. (2007). Here, we used the relative fragment frequency index we developed and a support vector machine to screen for nucleosomal and linker DNA sequences. Our twofold cross-validation revealed that the accuracy of our method based on the area under the receiver operating characteristic curve was 81%, whereas that of Peckham's method was 75% when both of two nucleosomal sequence data obtained from independent experiments were used for validation. We suggest that our method is more effective in predicting nucleosome positioning.


title: Heparin-Mimicking Sulfonic Acid Polymers as Multitarget Inhibitors of Human Immunodeficiency Virus Type 1 Tat and gp120 Proteins

abstract: Human immunodeficiency virus (HIV) Tat and gp120 intriguingly share the feature of being basic peptides that, once released by HIV + cells, bind to polyanionic heparan sulfate proteoglycans (HSPGs) on target uninfected cells, contributing to the onset of AIDS-associated pathologies. To identify multitarget anti-HIV prodrugs, we investigated the gp120 and Tat antagonist potentials of a series of polyanionic synthetic sulfonic acid polymers (SSAPs). Surface plasmon resonance revealed that SSAPs inhibit with a competitive mechanism of action the binding of Tat and gp120 to surface-immobilized heparin, an experimental condition that resembles binding to cellular HSPGs. Accordingly, SSAPs inhibited HSPG-dependent cell internalization and the transactivating activity of Tat. Little is known about the binding of free gp120 to target cells. Here, we identified two classes of gp120 receptors expressed on endothelial cells, one of which was consistent with an HSPG-binding, low-affinity/high-capacity receptor that is inhibited by free heparin. SSAPs inhibited the binding of free gp120 to endothelial cells, as well as its capacity to induce apoptosis in the same cells. In all the assays, poly(4-styrenesulfonic acid) (PSS) proved to be the most potent antagonist of Tat and gp120. Accordingly, PSS bound both proteins with high affinity. In conclusion, SSAPs represent an interesting class of compounds that bind both gp120 and Tat and inhibit their HSPG-dependent cell surface binding and pathological effects. As these activities contribute to both AIDS progression and associated pathologies, SSAPs can be considered prototypic molecules for the development of multitarget drugs for the treatment of HIV infection and AIDS-associated pathologies.


title: Network biology: understanding the cell's functional organization

abstract: A key aim of postgenomic biomedical research is to systematically catalogue all molecules and their interactions within a living cell. There is a clear need to understand how these molecules and the interactions between them determine the function of this enormously complex machinery, both in isolation and when surrounded by other cells. Rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize our view of biology and disease pathologies in the twenty-first century.


title: Introduction to Behavioral Addictions

abstract: Background: Several behaviors, besides psychoactive substance ingestion, produce short-term reward that may engender persistent behavior, despite knowledge of adverse consequences, i.e., diminished control over the behavior. These disorders have historically been conceptualized in several ways. One view posits these disorders as lying along an impulsive-compulsive spectrum, with some classified as impulse control disorders. An alternate, but not mutually exclusive, conceptualization considers the disorders as non-substance or “behavioral” addictions. Objectives: Inform the discussion on the relationship between psychoactive substance and behavioral addictions. Methods: We review data illustrating similarities and differences between impulse control disorders or behavioral addictions and substance addictions. This topic is particularly relevant to the optimal classification of these disorders in the forthcoming fifth edition of the American Psychiatric Association Diagnostic and Statistical Manual of Menta...


title: Fruit recognition based on convolution neural network

abstract: Computer vision is widely used at present. However, fruit recognition is still a problem for the stacked fruits on weighing scale because of complexity and overlap. In this paper, a fruit recognition algorithm based on convolution neural network(CNN) is proposed. At first the image regions are extracted using selective search algorithm, then the regions have been selected by means of an entropy of fruit images, and finally these regions are regarded as input of CNN neural network for training and recognition. The final decision is made based on a fusion of all region classifications using voting mechanism. In order to achieve the actual application in supermarket, we have considered the variety of fruit, stack of fruits, the changes of fruit number and position, and have made a multifarious training set of fruits. After the network has been trained with an optimal training set, it has obtained a remarkable recognition rates for the fruits stacked on a weighing scale.


title: Using Decision Trees to Improve Case-Based Learning

abstract: This paper shows that decision trees can be used to improve the performance of case- based learning (CBL) systems. We introduce a performance task for machine learning systems called semi-flexible prediction that lies between the classification task performed by decision tree algorithms and the flexible prediction task performed by conceptual clustering systems. In semi-flexible prediction, learning should improve prediction of a specific set of features known a priori rather than a single known feature (as in classification) or an arbitrary set of features (as in conceptual clustering). We describe one such task from natural language processing and present experiments that compare solutions to the problem using decision trees, CBL, and a hybrid approach that combines the two. In the hybrid approach, decision trees are used to specify the features to be included in k-nearest neighbor case retrieval. Results from the experiments show that the hybrid approach outperforms both the decision tree and case-based approaches as well as two case-based systems that incorporate expert knowledge into their case retrieval algorithms. Results clearly indicate that decision trees can be used to improve the performance of CBL systems and do so without reliance on potentially expensive expert knowledge.


The Manipulation and Role of Stereotypes in the Rush Hour Trilogy


title: Visual ego motion estimation in urban environments based on U-V disparity

abstract: The movement of the vehicle provides useful information for different applications, such as driver assistant systems or autonomous vehicles. This information can be known by means of a GPS, but there are some areas in urban environments where the signal is not available, as tunnels or streets with high buildings. A new method for 2D visual ego motion estimation in urban environments is presented in this paper. This method is based on a stereo-vision system where the feature road points are tracked frame to frame in order to estimate the movement of the vehicle, avoiding outliers from dynamic obstacles. The road profile is used to obtain the world coordinates of the feature points as a unique function of its left image coordinates. For these reasons it is only necessary to search feature points in the lower third of the left images. Moreover, the Kalman filter is used as a solution for filtering problem. That is, in some cases, it is necessary to filter raw data due to noise acquisition of time series. The results of the visual ego motion are compared with raw data from a GPS.


title: Analysis of a privacy-preserving PCA algorithm using random matrix theory

abstract: To generate useful summarization of data while maintaining privacy of sensitive information is a challenging task, especially in the big data era. The privacy-preserving principal component algorithm proposed in [1] is a promising approach when a low rank data summarization is desired. However, the analysis in [1] is limited to the case of a single principal component, which makes use of bounds on the vector-valued Bingham distribution in the unit sphere. By exploring the non-commutative structure of data matrices in the full Stiefel manifold, we extend the analysis to an arbitrary number of principal components. Our results are obtained by analyzing the asymptotic behavior of the matrix-variate Bingham distribution using tools from random matrix theory.


Unsupervised Text Summarization Using Sentence Embeddings


title: Digital Supply Chain : Survey of the Literature

abstract: Purpose- The aim of this research is to provide accumulated body of prior knowledge on digital supply chain (DSC) and provides opportunities for further research.Design/Methodology/approach-This st ...


title: Effects of plant growth promoting bacteria (PGPB) on yield, growth and nutrient contents of organically grown strawberry

abstract: Abstract The effects of plant growth promoting bacteria (PGPB) on the fruit yield, growth and nutrient element content of strawberry cv. Fern were investigated under organic growing conditions between 2006 and 2008. The experimental plot was a completely randomized design with 3 replicates. Three PGPB strains ( Pseudomonas BA-8, Bacillus OSU-142 and Bacillus M-3) were used alone or in combination as bio-fertilizer agent in the experiment. Data through 3 years showed that the use of PGPB significantly increased fruit yield, plant growth and leaf P and Zn contents. Root inoculation of M3 and floral and foliar spraying of OSU-142 and BA-8 bacteria stimulated plant growth resulting in significant yield increases. M3 + BA-8, BA-8 + OSU-142, M3, M3 + OSU-142 and BA-8 applications increased cumulative yield by 33.2%, 18.4%, 18.2%, 15.3% and 10.5%, respectively. Number of fruits per plant significantly increased by the applications of M3 + BA-8 (91.73) and M3 (81.58) compared with the control (68.66). In addition, P and Zn contents of strawberry leaves with bacterial inoculation significantly increased under organic growing conditions. Available P contents in soil were increased from 0.35 kg P 2 O 5 /da at the beginning of the study to 2.00, 1.97 and 1.82 kg P 2 O 5 /da by M3 + OSU-142, M3 + BA-8 and M3 + BA-8 + OSU-142 applications, respectively. Overall, the results of this study suggest that root inoculation of Bacillus M3 alone or in combination with spraying Bacillus OSU-142 or Pseudomonas BA-8 have the potential to increase the yield, growth and nutrition content of strawberry plant under organic growing conditions.


title: Synthetic aperture radar (SAR) imaging based on low-cost FMCW radar for mini-UAVs

abstract: Synthetic aperture radar (SAR) imaging by frequency modulated continuous wave (FMCW) processing produces 2D images of high resolution mapped areas. This paper presents a fully developed FMCW radar at 2.36 GHz that can fit in a mini-UAV with capabilities to acquire and store data. As a first evaluation of the system, we devised a rail SAR system, and processed data using range migration algorithm (RMA). The results are tested by imaging a trihedral and the performance of the entire system is discussed.


title: The increasing diversity of functions attributed to the SAFB family of RNA-/DNA-binding proteins

abstract: RNA-binding proteins play a central role in cellular metabolism by orchestrating the complex interactions of coding, structural and regulatory RNA species. The SAFB (scaffold attachment factor B) proteins (SAFB1, SAFB2 and SAFB-like transcriptional modulator, SLTM), which are highly conserved evolutionarily, were first identified on the basis of their ability to bind scaffold attachment region DNA elements, but attention has subsequently shifted to their RNA-binding and protein–protein interactions. Initial studies identified the involvement of these proteins in the cellular stress response and other aspects of gene regulation. More recently, the multifunctional capabilities of SAFB proteins have shown that they play crucial roles in DNA repair, processing of mRNA and regulatory RNA, as well as in interaction with chromatin-modifying complexes. With the advent of new techniques for identifying RNA-binding sites, enumeration of individual RNA targets has now begun. This review aims to summarise what is currently known about the functions of SAFB proteins.


title: Overview of Generative Software Development

abstract: System family engineering seeks to exploit the commonalities among systems from a given problem domain while managing the variabilities among them in a systematic way. In system family engineering, new system variants can be rapidly created based on a set of reusable assets (such as a common architecture, components, models, etc.). Generative software development aims at modeling and implementing system families in such a way that a given system can be automatically generated from a specification written in one or more textual or graphical domain-specific languages. This paper gives an overview of the basic concepts and ideas of generative software development including DSLs, domain and application engineering, generative domain models, networks of domains, and technology projections. The paper also discusses the relationship of generative software development to other emerging areas such as Model Driven Development and Aspect-Oriented Software Development.


title: SAUDI ARABIAN CULTURAL FACTORS AND PERSONALISED ELEARNING

abstract: With the support of the internet, many institutes in various countries have adopted the idea of introducing a web-based education system. In many countries, the government implements initiatives pushing eLearning solutions forward, such as in Europe (e.g, the UK, Malaysia and Singapore, but also in the Arab world (e.g., in Saudi Arabia and Kuwaiti). However, as a late comer to eLearning, Saudi Arabia still has to find the right balance between this globalisation of learning, and its cultural values. This study thus looks into the specific case of Saudi Arabia, to identify the cultural factors that influence acceptance of eLearning, including the more recently developed area of personalised elearning. This research identifies Saudi Arabian users’ cultural characteristics, by analysing Hofstede's cultural value dimensions, and their appropriateness for Saudi Arabian eLearning. Additionally, the study analyses how these factors influence higher education students' acceptance of the eLearning. The quantitative data from the students was collected by using an online questionnaire. In this study, the findings demonstrate that Saudi Arabian users’ cultural characteristics are similar to Hofstede’s 1980 analysis for the Arab world and can be specifically applied for Saudi personalised eLearning. In addition, the factors of Individualism vs. Collectivism and Uncertainty Avoidance were found to have a significant influence on a student’s perception of education. Hence, implementers of eLearning in Saudi Arabia need to be aware of these strongly influential factors and implement them in their learning solution.


title: Amygdala, medial prefrontal cortex, and hippocampal function in PTSD.

abstract: The last decade of neuroimaging research has yielded important information concerning the structure, neurochemistry, and function of the amygdala, medial prefrontal cortex, and hippocampus in posttraumatic stress disorder (PTSD). Neuroimaging research reviewed in this article reveals heightened amygdala responsivity in PTSD during symptomatic states and during the processing of trauma-unrelated affective information. Importantly, amygdala responsivity is positively associated with symptom severity in PTSD. In contrast, medial prefrontal cortex appears to be volumetrically smaller and is hyporesponsive during symptomatic states and the performance of emotional cognitive tasks in PTSD. Medial prefrontal cortex responsivity is inversely associated with PTSD symptom severity. Lastly, the reviewed research suggests diminished volumes, neuronal integrity, and functional integrity of the hippocampus in PTSD. Remaining research questions and related future directions are presented.


title: Challenges of medical image processing

abstract: In todays health care, imaging plays an important role throughout the entire clinical process from diagnostics and treatment planning to surgical procedures and follow up studies. Since most imaging modalities have gone directly digital, with continually increasing resolution, medical image processing has to face the challenges arising from large data volumes. In this paper, we discuss Kilo- to Terabyte challenges regarding (i) medical image management and image data mining, (ii) bioimaging, (iii) virtual reality in medical visualizations and (iv) neuroimaging. Due to the increasing amount of data, image processing and visualization algorithms have to be adjusted. Scalable algorithms and advanced parallelization techniques using graphical processing units have been developed. They are summarized in this paper. While such techniques are coping with the Kilo- to Terabyte challenge, the Petabyte level is already looming on the horizon. For this reason, medical image processing remains a vital field of research.


title: Ego-net Community Mining Applied to Friend Suggestion

abstract: In this paper, we present a study of the community structure of ego-networks---the graphs representing the connections among the neighbors of a node---for several online social networks. Toward this goal, we design a new technique to efficiently build and cluster all the ego-nets of a graph in parallel (note that even just building the ego-nets efficiently is challenging on large networks). Our experimental findings are quite compelling: at a microscopic level it is easy to detect high quality communities. ::: ::: Leveraging on this fact we, then, develop new features for friend suggestion based on co-occurrences of two nodes in different ego-nets' communities. Our new features can be computed efficiently on very large scale graphs by just analyzing the neighborhood of each node. Furthermore, we prove formally on a stylized model, and by experimental analysis that this new similarity measure outperforms the classic local features employed for friend suggestions.


title: Fatigue life and frequency response of braided pneumatic actuators

abstract: Although braided pneumatic actuators are capable of producing remarkable forces compared to their weight, they have seen little mainstream use. One of the reasons is their relatively short fatigue lives at high pressure. By improving the manufacturing techniques, the actuator lifetime was extended by nearly an order of magnitude. Another concern is that their response times may be too long for the control of legged robots. However, the frequency response of these actuators was found to be similar to that of human muscle.


title: The contribution of data mining to information science

abstract: The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research.


title: Information Technology and Productivity: A Review of the Literature

abstract: In recent years, the relationship between information technology (IT) and productivity has become a source of debate. In the 1980s and early 1990s, empirical research generally did not significant productivity improvements associated with IT investments. More recently, as new data are identified and new methodologies are applied, several researchers have found evidence that IT is associated not only with improvements in productivity, but also in intermediate measures, consumer surplus, and economic growth. Nonetheless, new questions emerge even as old puzzles fade. This survey reviews the literature, identifies remaining questions, and concludes with recommendations for applications of traditional methodologies to new data sources, as well as alternative, broader metrics of welfare to assess and enhance the benefits of IT.


title: Bandwidth Enhancement of a Microstrip-Line-Fed Printed Wide-Slot Antenna With a Fractal-Shaped Slot

abstract: Microstrip-line-fed printed wide-slot antenna with a fractal-shaped slot for bandwidth enhancement is proposed and experimentally studied. By etching the wide slot as fractal shapes, it is experimentally found that the operating bandwidth can be significantly enhanced, and the relation between the bandwidth and the iteration order (IO) and iteration factor (IF) of the fractal shapes is experimentally studied. Experimental results indicate that the impedance bandwidth, defined by - 10 dB reflection coefficient, of the proposed fractal slot antenna can reach an operating bandwidth of 2.4 GHz at operating frequencies around 4 GHz, which is about 3.5 times that of a conventional microstrip-line-fed printed wide-slot antenna. It also achieved a 2-dB gain bandwidth of at least 1.59 GHz.


title: Compiler techniques for code compaction

abstract: In recent years there has been an increasing trend toward the incorpor ation of computers into a variety of devices where the amount of memory available is limited. This makes it desirable to try to reduce the size of applications where possible. This article explores the use of compiler techniques to accomplish code compaction to yield smaller executables. The main contribution of this article is to show that careful, aggressive, interprocedural optimization, together with procedural abstraction of repeated code fragments, can yield significantly better reductions in code size than previous approaches, which have generally focused on abstraction of repeated instruction sequences. We also show how “equivalent” code fragments can be detected and factored out using conventional compiler techniques, and without having to resort to purely linear treatments of code sequences as in suffix-tree-based approaches, thereby setting up a framework for code compaction that can be more flexible in its treatment of what code fragments are considered equivalent. Our ideas have been implemented in the form of a binary-rewriting tool that reduces the size of executables by about 30% on the average.


title: Identifing influential users in an online healthcare social network

abstract: As an important information portal, online healthcare forum are playing an increasingly crucial role in disseminating information and offering support to people. It connects people with the leading medical experts and others who have similar experiences. During an epidemic outbreak, such as H1N1, it is critical for the health department to understand how the public is responding to the ongoing pandemic, which has a great impact on the social stability. In this case, identifying influential users in the online healthcare forum and tracking the information spreading in such online community can be an effective way to understand the public reaction toward the disease. In this paper, we propose a framework to monitor and identify influential users from online healthcare forum. We first develop a mechanism to identify and construct social networks from the discussion board of an online healthcare forum. We propose the UserRank algorithm which combines link analysis and content analysis techniques to identify influential users. We have also conducted an experiment to evaluate our approach on the Swine Flu forum which is a sub-community of a popular online healthcare community, MedHelp (www.medhelp.org). Experimental results show that our technique outperforms PageRank, in-degree and out-degree centrality in identifying influential user from an online healthcare forum.


title: Text mining techniques for patent analysis

abstract: Patent documents contain important research results. However, they are lengthy and rich in technical terminology such that it takes a lot of human efforts for analyses. Automatic tools for assisting patent engineers or decision makers in patent analysis are in great demand. This paper describes a series of text mining techniques that conforms to the analytical process used by patent analysts. These techniques include text segmentation, summary extraction, feature selection, term association, cluster generation, topic identification, and information mapping. The issues of efficiency and effectiveness are considered in the design of these techniques. Some important features of the proposed methodology include a rigorous approach to verify the usefulness of segment extracts as the document surrogates, a corpus- and dictionary-free algorithm for keyphrase extraction, an efficient co-word analysis method that can be applied to large volume of patents, and an automatic procedure to create generic cluster titles for ease of result interpretation. Evaluation of these techniques was conducted. The results confirm that the machine-generated summaries do preserve more important content words than some other sections for classification. To demonstrate the feasibility, the proposed methodology was applied to a real-world patent set for domain analysis and mapping, which shows that our approach is more effective than existing classification systems. The attempt in this paper to automate the whole process not only helps create final patent maps for topic analyses, but also facilitates or improves other patent analysis tasks such as patent classification, organization, knowledge sharing, and prior art searches.


title: Original Article: A proposed model for data warehouse ETL processes

abstract: Extraction-transformation-loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, its cleansing, customization, reformatting, integration, and insertion into a data warehouse. Building the ETL process is potentially one of the biggest tasks of building a warehouse; it is complex, time consuming, and consumes most of data warehouse project's implementation efforts, costs, and resources. Building a data warehouse requires focusing closely on understanding three main areas: the source area, the destination area, and the mapping area (ETL processes). The source area has standard models such as entity relationship diagram, and the destination area has standard models such as star schema, but the mapping area has not a standard model till now. In spite of the importance of ETL processes, little research has been done in this area due to its complexity. There is a clear lack of a standard model that can be used to represent the ETL scenarios. In this paper we will try to navigate through the efforts done to conceptualize the ETL processes. Research in the field of modeling ETL processes can be categorized into three main approaches: Modeling based on mapping expressions and guidelines, modeling based on conceptual constructs, and modeling based on UML environment. These projects try to represent the main mapping activities at the conceptual level. Due to the variation and differences between the proposed solutions for the conceptual design of ETL processes and due to their limitations, this paper also will propose a model for conceptual design of ETL processes. The proposed model is built upon the enhancement of the models in the previous models to support some missing mapping features.


title: ebusiness@mit.edu or 617-253-7054The Spatial, Temporal, and Configurational Characteristics of Geographic Dispersion in Teams

abstract: As organizations operate across greater distances, scholars are increasingly interested in the work of geographically dispersed teams and the technologies that they use to communicate and coordinate their work. However, research has generally not specified the dimensions (spatial, temporal, or configurational) and degrees of team dispersion, nor has it articulated the theoretical connections between those dimensions and important team outcomes. This research essay expands upon previous field and lab studies of dispersed teamwork by presenting a new conceptualization of dispersion as a continuous, multidimensional construct, in which each dimension is theoretically linked with different outcomes. We illustrate this new conceptualization with a series of examples from real dispersed teams and present implications for research regarding technology use.


title: Feature preserving mesh generation from 3d point clouds

abstract: We address the problem of generating quality surface triangle meshes from 3D point clouds sampled on piecewise smooth surfaces. Using a feature detection process based on the covariance matrices of Voronoi cells, we ﬁrst ex- tract from the point cloud a set of sharp features. Our algorithm also runs on the input point cloud a reconstruction process, such as Poisson reconstruction, providing an implicit surface. A feature preserving variant of a Delaunay reﬁnement process is then used to generate a mesh approximating the implicit surface and containing a faithful representation of the extracted sharp edges. Such a mesh provides an enhanced trade-off between accuracy and mesh complexity. The whole process is robust to noise and made versatile through a small set of parameters which govern the mesh sizing, approximation error and shape of the elements. We demonstrate the effectiveness of our method on a variety of models including laser scanned datasets ranging from indoor to outdoor scenes.


Interactive Visual Analysis of Multi-faceted Scientific Data


title: Scattered Data Interpolation with Multilevel B-Splines

abstract: The paper describes a fast algorithm for scattered data interpolation and approximation. Multilevel B-splines are introduced to compute a C/sup 2/ continuous surface through a set of irregularly spaced points. The algorithm makes use of a coarse to fine hierarchy of control lattices to generate a sequence of bicubic B-spline functions whose sum approaches the desired interpolation function. Large performance gains are realized by using B-spline refinement to reduce the sum of these functions into one equivalent B-spline function. Experimental results demonstrate that high fidelity reconstruction is possible from a selected set of sparse and irregular samples.


title: Data quality: The other face of Big Data

abstract: In our Big Data era, data is being generated, collected and analyzed at an unprecedented scale, and data-driven decision making is sweeping through all aspects of society. Recent studies have shown that poor quality data is prevalent in large databases and on the Web. Since poor quality data can have serious consequences on the results of data analyses, the importance of veracity, the fourth ‘V’ of big data is increasingly being recognized. In this tutorial, we highlight the substantial challenges that the first three ‘V’s, volume, velocity and variety, bring to dealing with veracity in big data. Due to the sheer volume and velocity of data, one needs to understand and (possibly) repair erroneous data in a scalable and timely manner. With the variety of data, often from a diversity of sources, data quality rules cannot be specified a priori; one needs to let the “data to speak for itself” in order to discover the semantics of the data. This tutorial presents recent results that are relevant to big data quality management, focusing on the two major dimensions of (i) discovering quality issues from the data itself, and (ii) trading-off accuracy vs efficiency, and identifies a range of open problems for the community.


title: Dual-polarized Vivaldi array for X- and Ku-Band

abstract: A very lightweight, broad-band, dual polarized antenna array with 128 elements for the frequency range from 7 GHz to 18 GHz has been designed, manufactured and measured. The total gain at the center frequency was measured to be 20 dBi excluding feeding network losses.


Walden Two, with a New Introduction by the Author


title: Explainable Planning

abstract: As AI is increasingly being adopted into application solutions, the challenge of supporting interaction with humans is becoming more apparent. Partly this is to support integrated working styles, in which humans and intelligent systems cooperate in problem-solving, but also it is a necessary step in the process of building trust as humans migrate greater responsibility to such systems. The challenge is to find effective ways to communicate the foundations of AI-driven behaviour, when the algorithms that drive it are far from transparent to humans. In this paper we consider the opportunities that arise in AI planning, exploiting the model-based representations that form a familiar and common basis for communication with users, while acknowledging the gap between planning algorithms and human problem-solving.


title: Evaluation Criteria for Sparse Matrix Storage Formats

abstract: When authors present new storage formats for sparse matrices, they usually focus mainly on a single evaluation criterion, which is the performance of sparse matrix-vector multiplication (SpMV) in FLOPS. Though such an evaluation is essential, it does not allow to directly compare the presented format with its competitors. Moreover, in case that matrices are within an HPC application constructed in different formats, this criterion alone is not sufficient for the key decision whether or not to convert them into the presented format for the SpMV-based application phase. We establish ten evaluation criteria for sparse matrix storage formats, discuss their advantages and disadvantages, and provide general suggestions for format authors/evaluators to make their work more valuable for the HPC community.


title: Adaptive Human–Robot Interaction Control for Robots Driven by Series Elastic Actuators

abstract: Series elastic actuators (SEAs) are known to offer a range of advantages over stiff actuators for human-robot interaction, such as high force/torque fidelity, low impedance, and tolerance to shocks. While a variety of SEAs have been developed and implemented in initiatives that involve physical interactions with humans, relatively few control schemes were proposed to deal with the dynamic stability and uncertainties of robotic systems driven by SEAs, and the open issue of safety that resolves the conflicts of motion between the human and the robot has not been systematically addressed. In this paper, a novel continuous adaptive control method is proposed for SEA-driven robots used in human-robot interaction. The proposed method provides a unified formulation for both the robot-in-charge mode, where the robot plays a dominant role to follow a desired trajectory, and the human-in-charge mode, in which the human plays a dominant role to guide the movement of robot. Instead of designing multiple controllers and switching between them, both typical modes are integrated into a single controller, and the transition between two modes is smooth and stable. Therefore, the proposed controller is able to detect the human motion intention and guarantee the safe human-robot interaction. The dynamic stability of the closed-loop system is theoretically proven by using the Lyapunov method, with the consideration of uncertainties in both the robot dynamics and the actuator dynamics. Both simulation and experimental results are presented to illustrate the performance of the proposed controller.


title: Unsupervised Generative Modeling Using Matrix Product States

abstract: Generative modeling, which learns joint probability distribution from training data and generates samples according to it, is an important task in machine learning and artificial intelligence. Inspired by probabilistic interpretation of quantum physics, we propose a generative model using matrix product states, which is a tensor network originally proposed for describing (particularly one-dimensional) entangled quantum states. Our model enjoys efficient learning by utilizing the density matrix renormalization group method which allows dynamic adjusting dimensions of the tensors, and offers an efficient direct sampling approach, Zipper, for generative tasks. We apply our method to generative modeling of several standard datasets including the principled Bars and Stripes, random binary patterns and the MNIST handwritten digits, to illustrate ability of our model, and discuss features as well as drawbacks of our model over popular generative models such as Hopfield model, Boltzmann machines and generative adversarial networks. Our work shed light on many interesting directions for future exploration on the development of quantum-inspired algorithms for unsupervised machine learning, which is of possibility of being realized by a quantum device.


title: Relational Web Search

abstract: Facts are naturally organized in terms of entities, classes, and their relationships as in an entity-relationship diagram or a semantic network. Search engines have eschewed such structures because, in the past, their creation and processing have not been practical at Web scale. This paper introduces the extraction graph, a textual approximation to an entity-relationship graph, which is automatically extracted from Web pages. The extraction graph is an intermediate representation that is more informative than a mere page-hyperlink graph but far easier to construct than a semantic network. The paper also introduces TextRunner, a search engine that utilizes this representation to answer complex relational queries that are dicult to answer using today’s search engines or Web Information Extraction (IE) systems. The paper compares TextRunner to a state-of-the-art IE system on list searches, and nds that TextRunner is 40% more precise, with 11% better recall than the IE system. Our experiments, computed over a 90-million page corpus and a 227-million node extraction graph, show how TextRunner will scale to billions of pages.


title: Violent Video Games and Aggression: Causal Relationship or Byproduct of Family Violence and Intrinsic Violence Motivation?

abstract: Two studies examined the relationship between exposure to violent video games and aggression or violence in the laboratory and in real life. Study 1 participants were either randomized or allowed to choose to play a violent or nonviolent game. Although males were more aggressive than females, neither randomized exposure to violent-video-game conditions nor previous real-life exposure to violent video games caused any differences in aggression. Study 2 examined correlations between trait aggression, violent criminal acts, and exposure to both violent games and family violence. Results indicated that trait aggression, family violence, and male gender were predictive of violent crime, but exposure to violent games was not. Structural equation modeling suggested that family violence and innate aggression as predictors of violent crime were a better fit to the data than was exposure to video game violence. These results question the common belief that violent-video-game exposure causes violent acts.


title: The Use of Edmodo in Creating an Online Learning Community of Practice for Learning to Teach Science

abstract: This study aimed to create an online community of practice by creating a virtual classroom in the Edmodo application and ascertain the opinions of pre-service primary teachers about the effects of Edmodo on their learning to teach science and availability of Edmodo. The research used a case study, which is one method of descriptive research. During the implementation process, pre-service primary teachers used Edmodo to share activities they had designed that centred on the scientific concepts taught in primary science education programmes. They also shared their diary entries that outlined their experiences and views after they had practised their activities in a real classroom. 58 pre-service primary teachers participated in the study. The author developed a questionnaire and it included one closed-ended and six open-ended questions; the questionnaire was used as the data collection tool. The pre-service primary teachers used Edmodo for 12 weeks. Descriptive and content analysis methods were used to analyse the data obtained from the study. The results obtained from the data analysis showed that pre-service primary teachers generally had positive views about the use of Edmodo in teacher education programmes. Most pre-service primary teachers stated that Edmodo provides the possibility of sharing knowledge, experiences and views. However, some pre-service teachers stated that Edmodo has some limitations; for example, the fact that it requires the user to have internet access. As a result, it can be said that Edmodo can be used to create an online community of practice in teacher education programmes.


title: Improving classification using preprocessing and machine learning algorithms on NSL-KDD dataset

abstract: Classification is the category that consists of identification of class labels of records that are typically described by set of features in dataset. The paper describes a system that uses a set of data pre-processing activities which includes Feature Selection and Discretization. Feature selection and dimension reduction are common data mining approaches in large datasets. Here the high data dimensionality of the dataset due to its large feature set poses a significant challenge. In Pre-processing with the help of Feature selection algorithm the various required features are selected, these activities helps to improve the accuracy of the classifier. After this step various classifiers are used such as Naive Bayes, Hidden Naive Bayes and NBTree. The advantage of Hidden Naive Bayes is a data mining model that relaxes the Naive Bayes Method's conditional Independence assumption. Also the next Classifier used is NBTree which induces a hybrid of decision tree classifiers and Naive Bayes classifiers which significantly improves the accuracy of classifier and decreases the Error rate of the classifier. The output of the proposed method are checked for True positive, True negative, False positive, False negative. Based on these values the Accuracy and error rate of each classifier is computed.


title: "Roles for the Boys?": Mining Cast Lists for Gender and Role Distributions over Time

abstract: Film and television play an important role in popular culture. However studies that require watching and annotating video are time-consuming and expensive to run at scale. We explore information mined from media database cast lists to explore the evolution of different roles over time. We focus on the gender distribution of those roles and how this changes over time. Finally, we compare real-life census gender distributions to our web-mediated onscreen gender data. We propose these methodologies are a useful adjunct to traditional analysis that allow researchers to explore the relationship between online and onscreen gender depictions.


title: A Fast Symbolic Image Indexing and Retrieval Method Based On TSR and Linear Hashing

abstract: In recent years, image databases have grown faster; hence there are a real need for fast indexing and retrieval methods in image databases. In this paper, we proposed an approach for fast image indexing and retrieval in symbolic image databases using triangular spatial relations (TSR). The indexing data structure is based on a new introduced structure and hash function. To obtain the time complexity O(1); the linear hashing was used that has constant load factor. The experimental results were great.


title: An efficient maximum power point tracking controller for photovoltaic systems using new boost converter design and improved control algorithm

abstract: This paper presents an efficient maximum power point tracking (MPPT) controller for a standalone photovoltaic (PV) generation system. To achieve an efficient MPPT controller, a new boost converter design and an improved MPPT algorithm are incorporated. In the proposed boost converter design, a passive regenerative snubber circuit is included to absorb the energy of stray inductance so as to reduce the IGBT switching losses. As for the improved MPPT algorithm, it is based on the curve fitting method which attempts to predict the power-voltage characteristic curve by a fourth order polynomial function. The predicted P-V curve strongly depends on the cell temperature and therefore the ambient temperature and solar radiation are used to track the maximum power point (MPP) of the PV module. Experimental results are given to verify the validity and performance of the MPPT algorithm which is embedded in a prototype MPPT controller. The experimental results showed that the proposed MPPT controller successfully tracked the MPP by giving an average tracking efficiency of 89.2%


title: AR-ABAC: A New Attribute Based Access Control Model Supporting Attribute-Rules for Cloud Computing

abstract: One of the most important challenges that have threatened cloud computing and caused its slow adoption is security. Since clouds have diverse groups of users with different sets of security requirements, restricting the users' accesses and protecting information from unauthorized accesses have become the most difficult tasks. To address these critical challenges, in this paper we first formalize Attribute Based Access Control (ABAC) and propose a new access control model, called Attribute-Rule ABAC (AR-ABAC), for cloud computing to meet critical access control requirements in clouds. Our model supports the attribute-rules that deal with the association between users and objects, as well as the capability for accessing objects based on their sensitivity levels. The attribute-rules specify an agreement that determines what kind of attributes should be used and the number of attributes considered for making access decisions. In addition, our model ensures secure resource sharing among potential untrusted tenants and supports different access permissions to the same user at the same session.


title: The Not-so-Staggering Effect of Staggered Animated Transitions on Visual Tracking

abstract: Interactive visual applications often rely on animation to transition from one display state to another. There are multiple animation techniques to choose from, and it is not always clear which should produce the best visual correspondences between display elements. One major factor is whether the animation relies on staggering-an incremental delay in start times across the moving elements. It has been suggested that staggering may reduce occlusion, while also reducing display complexity and producing less overwhelming animations, though no empirical evidence has demonstrated these advantages. Work in perceptual psychology does show that reducing occlusion, and reducing inter-object proximity (crowding) more generally, improves performance in multiple object tracking. We ran simulations confirming that staggering can in some cases reduce crowding in animated transitions involving dot clouds (as found in, e.g., animated 2D scatterplots). We empirically evaluated the effect of two staggering techniques on tracking tasks, focusing on cases that should most favour staggering. We found that introducing staggering has a negligible, or even negative, impact on multiple object tracking performance. The potential benefits of staggering may be outweighed by strong costs: a loss of common-motion grouping information about which objects travel in similar paths, and less predictability about when any specific object would begin to move. Staggering may be beneficial in some conditions, but they have yet to be demonstrated. The present results are a significant step toward a better understanding of animation pacing, and provide direction for further research.


title: Is psychology suffering from a replication crisis? What does “failure to replicate” really mean?

abstract: Psychology has recently been viewed as facing a replication crisis because efforts to replicate past study findings frequently do not show the same result. Often, the first study showed a statistically significant result but the replication does not. Questions then arise about whether the first study results were false positives, and whether the replication study correctly indicates that there is truly no effect after all. This article suggests these so-called failures to replicate may not be failures at all, but rather are the result of low statistical power in single replication studies, and the result of failure to appreciate the need for multiple replications in order to have enough power to identify true effects. We provide examples of these power problems and suggest some solutions using Bayesian statistics and meta-analysis. Although the need for multiple replication studies may frustrate those who would prefer quick answers to psychology's alleged crisis, the large sample sizes typically needed to provide firm evidence will almost always require concerted efforts from multiple investigators. As a result, it remains to be seen how many of the recently claimed failures to replicate will be supported or instead may turn out to be artifacts of inadequate sample sizes and single study replications.


title: FAST PATTERN MATCHING IN STRINGS*

abstract: An algorithm is presented which finds all occurrences of one given string within another, in running time proportional to the sum of the lengths of the strings. The constant of proportionality is low enough to make this algorithm of practical use, and the procedure can also be extended to deal with some more general pattern-matching problems. A theoretical application of the algorithm shows that the set of concatenations of even palindromes, i.e., the language $\{\alpha \alpha ^R\}^*$, can be recognized in linear time. Other algorithms which run even faster on the average are also considered.


title: DPPG: A Dynamic Password Policy Generation System

abstract: To keep password users from creating simple and common passwords, major websites and applications provide a password-strength measure, namely a password checker. While critical requirements for a password checker to be stringent have prevailed in the study of password security, we show that regardless of the stringency, such static checkers can leak information and actually help the adversary enhance the performance of their attacks. To address this weakness, we propose and devise the Dynamic Password Policy Generator , namely DPPG , to be an effective and usable alternative to the existing password strength checker. DPPG aims to enforce an evenly-distributed password space and generate dynamic policies for users to create passwords that are diverse and that contribute to the overall security of the password database. Since DPPG is modular and can function with different underlying metrics for policy generation, we further introduce a diversity-based password security metric that evaluates the security of a password database in terms of password space and distribution. The metric is useful as a countermeasure to well-crafted offline cracking algorithms and theoretically illustrates why DPPG works well.


title: Multi-criteria recommendation schemes based on factorization machines

abstract: Traditional collaborative filtering (CF) recommendation algorithms usually use a single rating to recommend items to users, which works well in terms of predictive accuracy. However, recent research on multi-criteria recommender has shown that multi-criteria ratings are of great value to improving recommendation performance. In this paper, we present novel multi-criteria recommendation schemes which leverage multi-criteria ratings and codebook cluster information derived from user-item-criteria ratings matrix to enhance recommendation quality. Particularly, we utilize Factorization Machines (FMs) to integrate the codebook clusters information on individual criteria, which contains users’ preferences on different criteria of items, to extend user-item-criteria interaction feature vectors and make an overall rating prediction. A set of experiments on a real-world datasets show that our approach outperforms both FMs-based single-rating recommendation algorithms in which the clusters information of users or items are based on an overall rating, as well as three existing state-of-the-art multi-criteria recommendation algorithms even in case where data are under high sparsity.


title: A man-in-the-middle attack on UMTS

abstract: In this paper we present a man-in-the-middle attack on the Universal Mobile Telecommunication Standard (UMTS), one of the newly emerging 3G mobile technologies. The attack allows an intruder to impersonate a valid GSM base station to a UMTS subscriber regardless of the fact that UMTS authentication and key agreement are used. As a result, an intruder can eavesdrop on all mobile-station-initiated traffic.Since the UMTS standard requires mutual authentication between the mobile station and the network, so far UMTS networks were considered to be secure against man-in-the-middle attacks. The network authentication defined in the UMTS standard depends on both the validity of the authentication token and the integrity protection of the subsequent security mode command.We show that both of these mechanisms are necessary in order to prevent a man-in-the middle attack. As a consequence we show that an attacker can mount an impersonation attack since GSM base stations do not support integrity protection. Possible victims to our attack are all mobile stations that support the UTRAN and the GSM air interface simultaneously. In particular, this is the case for most of the equipment used during the transition phase from 2G (GSM) to 3G (UMTS) technology.


title: ANSI: a swarm intelligence-based unicast routing protocol for hybrid ad hoc networks

abstract: We present a hybrid routing protocol for both pure and hybrid ad hoc networks which uses the mechanisms of swarm intelligence to select next hops. Our protocol, Ad hoc Networking with Swarm Intelligence (ANSI), is a congestion-aware routing protocol, which, owing to the self-organizing mechanisms of swarm intelligence, is able to collect more information about the local network and make more effective routing decisions than traditional MANET protocols. Once routes are found, ANSI maintains routes along a path from source to destination effectively by using swarm intelligence techniques, and is able to gauge the slow deterioration of a link and restore a path along newer links as and when necessary. ANSI is thus more responsive to topological fluctuations. ANSI is designed to work over hybrid ad hoc networks: ad hoc networks which consist of both lower-capability, mobile wireless devices and higher-capability, wireless devices which may or may not be mobile. In addition, ANSI works with multiple interfaces and with both wired and wireless interfaces.Our simulation study compared ANSI with AODV on both hybrid and pure ad hoc network scenarios using both TCP and UDP data flows. The results show that ANSI is able to achieve better results (in terms of packet delivery, number of packets sent, end-to-end delay, and jitter) as compared to AODV in most simulation scenarios. In addition, ANSI achieves this performance with fewer route errors as compared to AODV. Lastly, ANSI is able to perform more consistently, considering the lower variation (measured as the width of the confidence intervals) of the observed values in the results of the experiments. We show that ANSI's performance is aided by both its superior handling of routing information and also its congestion awareness properties, though we see that congestion awareness in ANSI comes at a price.


title: Left–right coding of past and future in language: The mental timeline during sentence processing

abstract: The metaphoric mapping theory suggests that abstract concepts, like time, are represented in terms of concrete dimensions such as space. This theory receives support from several lines of research ranging from psychophysics to linguistics and cultural studies; especially strong support comes from recent response time studies. These studies have reported congruency effects between the dimensions of time and space indicating that time evokes spatial representations that may facilitate or impede responses to words with a temporal connotation. The present paper reports the results of three linguistic experiments that examined this congruency effect when participants processed past- and future-related sentences. Response time was shorter when past-related sentences required a left-hand response and future-related sentences a right-hand response than when this mapping of time onto response hand was reversed (Experiment 1). This result suggests that participants can form time-space associations during the processing of sentences and thus this result is consistent with the view that time is mentally represented from left to right. The activation of these time-space associations, however, appears to be non-automatic as shown by the results of Experiments 2 and 3 when participants were asked to perform a non-temporal meaning discrimination task.


title: Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics

abstract: Today, it is not clear how the impact of research on other areas of society than science should be measured. While peer review and bibliometrics have become standard methods for measuring the impact of research in science, there is not yet an accepted framework within which to measure societal impact. Alternative metrics (called altmetrics to distinguish them from bibliometrics) are considered an interesting option for assessing the societal impact of research, as they offer new ways to measure (public) engagement with research output. Altmetrics is a term to describe web-based metrics for the impact of publications and other scholarly material by using data from social media platforms (e.g. Twitter or Mendeley). This overview of studies explores the potential of altmetrics for measuring societal impact. It deals with the definition and classification of altmetrics. Furthermore, their benefits and disadvantages for measuring impact are discussed.


LogP: a practical model of parallel computation


Parameterizing Homographies CMU-RI-TR-06-11


title: Explaining Impossible High-Level Robot Behaviors

abstract: A key challenge in robotics is the generation of controllers for autonomous, high-level robot behaviors comprising nontrivial sequences of actions, including reactive and repeated tasks. When constructing controllers to fulfill such tasks, it is often not known a priori whether the intended behavior is even feasible; plans are modified on the fly to deal with failures that occur during execution, often still without guaranteeing correct behavior. Recently, formal methods have emerged as a powerful tool to automatically generate autonomous robot controllers that guarantee desired behaviors expressed by a class of temporal logic specifications. However, when the specification cannot be fulfilled, these approaches do not provide the user with a source of failure, making the troubleshooting of specifications an unstructured and time-consuming process. This paper describes an algorithm to automatically analyze an unsynthesizable specification in order to identify causes of failure. It also introduces an interactive game to explore possible causes of unsynthesizability, in which the user attempts to fulfill the robot specification against an adversarial environment. The proposed algorithm and game are implemented as features within the LTLMoP toolkit for robot mission planning.


title: What Is Coefficient Alpha? An Examination of Theory and Applications

abstract: Coefficient alpha (Cronbach, 1951) is certainly one of the most important and pervasive statistics in research involving test construction and use. A review of the Social Sciences Citations Index for the literature from 1966 to 1990 revealed that Cronbach's (1951) article had been cited approximately 60 times per year and in a total of 278 different journals. In addition to the areas of psychology in which one may expect to see alpha used, such as educational, industrial, social, clinical, child, community, and abnormal psychology, this list of journals included representatives from experimental psychology, sociology, statistics, medicine, counseling, nursing, economics, political science, criminology, gerontology, broadcasting, anthropology, and accounting. In spite of its widespread use, however, there is some confusion as to the true meaning and proper interpretation of the statistic. In this article I address this confusion in two ways. First, a theoretical discussion of alpha is presented. This includes some of the many statements that have been made about alpha and an attempt to integrate these statements. Second, I take a more practical approach in which the interpretation of alpha is examined by observing the changes in alpha as the number of items and interitem correlations are manipulated.


Analysis of depth perception in smart phone HMDs


title: From Knowledge Graph Embedding to Ontology Embedding: Region Based Representations of Relational Structures.

abstract: Recent years have witnessed the enormous success of low-dimensional vector space representations of knowledge graphs to predict missing facts or find erroneous ones. Currently, however, it is not yet well-understood how ontological knowledge, e.g. given as a set of (existential) rules, can be embedded in a principled way. To address this shortcoming, in this paper we introduce a framework based on convex regions, which can faithfully incorporate ontological knowledge into the vector space embedding. Our technical contribution is two-fold. First, we show that some of the most popular existing embedding approaches are not capable of modelling even very simple types of rules. Second, we show that our framework can represent ontologies that are expressed using so-called quasi-chained existential rules in an exact way, such that any set of facts which is induced using that vector space embedding is logically consistent and deductively closed with respect to the input ontology.


title: Enhanced text steganography by changing word's spelling

abstract: Digitization of electronic signals has brought a stir in the telecommunication field. From copper wire to fiber optics, technology has gone through a mammoth change rendering a variety of communication media available for use by people, at the cheapest of rates possible, to communicate in a nearly perfect error-free environment. Consumers can now freely exchange or distribute digital contents at any time and anywhere in the world. The situation where a perfect replica of digital content can easily be made has raised serious security concerns prompting for the protection of rights of intellectual ownership and apprehension of unauthorized tampering of digital information for mala fide usage. On the other hand, the way multimedia contents are being tempered with has also made it an important subject of technological research. This paper suggests an enhanced method for secretly exchanging real time or off line information by using differently spelled words in British and American Language. The concept has been implemented via software applications developed in Visual Basic 6 programming language.


title: The Tomaco Hybrid Matching Framework for SAWSDL Semantic Web Services

abstract: This work aims to advance Web Service retrieval, also known as Matching, in two directions. First, it introduces a matching algorithm for SAWSDL, which adapts and extends known concepts with novel strategies. Effective logic-based and syntactic strategies are introduced and combined in a novel hybrid strategy, targeting an envisioned well-defined, real-world scenario for matching. The algorithm is evaluated in a universal environment for matching algorithms, SME2, in an objective, reproducible manner. Evaluation ranks Tomaco high amongst state of the art, especially for early recall levels (first in macro-averaging precision, up to 0.7 recall). Secondly, this work introduces the Tomaco web application, which aims to promote wide-spread adoption of Semantic Web Services while targeting the lack of user-friendly applications in this field, by integrating a variety of configurable matching algorithms proposed in this paper. It, finally, allows discovery of both existing and user-contributed service collections and ontologies, serving also as a service registry.


title: Emotional intelligence: An integrative meta-analysis and cascading model.

abstract: Research and valid practice in emotional intelligence (EI) have been impeded by lack of theoretical clarity regarding (a) the relative roles of emotion perception, emotion understanding, and emotion regulation facets in explaining job performance; (b) conceptual redundancy of EI with cognitive intelligence and Big Five personality; and (c) application of the EI label to 2 distinct sets of constructs (i.e., ability-based EI and mixed-based EI). In the current article, the authors propose and then test a theoretical model that integrates these factors. They specify a progressive (cascading) pattern among ability-based EI facets, in which emotion perception must causally precede emotion understanding, which in turn precedes conscious emotion regulation and job performance. The sequential elements in this progressive model are believed to selectively reflect Conscientiousness, cognitive ability, and Neuroticism, respectively. “Mixed-based” measures of EI are expected to explain variance in job performance beyond cognitive ability and personality. The cascading model of EI is empirically confirmed via meta-analytic data, although relationships between ability-based EI and job performance are shown to be inconsistent (i.e., EI positively predicts performance for high emotional labor jobs and negatively predicts performance for low emotional labor jobs). Gender and race differences in EI are also meta-analyzed. Implications for linking the EI fad in personnel selection to established psychological theory are discussed.


title: New types of acetate-oxidizing, sulfate-reducing Desulfobacter species, D. hydrogenophilus sp. nov., D. latus sp. nov., and D. curvatus sp. nov.

abstract: Sulfate-reducing bacteria with oval to rod-shaped cells (strains AcRS1, AcRS2) and vibrio-shaped cells (strains AcRM3, AcRM4, AcRM5) differing by size were isolated from anaerobic marine sediment with acetate as the only electron donor. A vibrio-shaped type (strain AcKo) was also isolated from freshwater sediment. Two strains (AcRS1, AcRM3) used ethanol and pyruvate in addition to acetate, and one strain (AcRS1) grew autotrophically with H2, sulfate and CO2. Higher fatty acids or lactate were never utilized. All isolates were able to grow in ammonia-free medium in the presence of N2. Nitrogenase activity under such conditions was demonstrated by the acetylene reduction test. The facultatively lithoautotrophic strain (AcRS1), a strain (AcRS2) with unusually large cells (2×5 μm), and a vibrio-shaped strain (AcRM3) are described as new Desulfobacter species, D. hydrogenophilus, D. latus, and D. curvatus, respectively.


title: Channel Estimation Techniques Based on Pilot Arrangement in OFDM Systems

abstract: Channel estimation techniques for OFDM systems based on a pilot arrangement are investigated. Channel estimation based on a comb type pilot arrangement is studied through different algorithms for both estimating the channel at pilot frequencies and interpolating the channel. Channel estimation at pilot frequencies is based on LS and LMS methods while channel interpolation is done using linear interpolation, second order interpolation, low-pass interpolation, spline cubic interpolation, and time domain interpolation. Time-domain interpolation is obtained by passing to the time domain by means of IDFT (inverse discrete Fourier transform), zero padding and going back to the frequency domain by DFT (discrete Fourier transform). In addition, channel estimation based on a block type pilot arrangement is performed by sending pilots in every sub-channel and using this estimation for a specific number of following symbols. We have also implemented a decision feedback equalizer for all sub-channels followed by periodic block-type pilots. We have compared the performances of all schemes by measuring bit error rates with 16QAM, QPSK, DQPSK and BPSK as modulation schemes, and multipath Rayleigh fading and AR based fading channels as channel models.


title: A Dexterous System for Laryngeal Surgery Multi-Backbone Bending Snake-like Slaves for Teleoperated Dexterous Surgical Tool Manipulation

abstract: This work presents a design overview of a novel high DoF (degrees-of-freedom) system being developed for minimally invasive surgery of the throat. The system is designed to allow remote operation of 2-3 tools with high tip dexterity to enable suturing and soft-tissue manipulation while using the patient's mouth as the only entry port. The slave is a 34 DoF unit equipped with three snake-like distal dexterity units for surgical tool manipulation. Each of these units is a multi-backbone snakelike mechanism equipped with a detachable milli parallel manipulator allowing interchangeable tools to be used. The paper presents the outline of the kinematic analysis of the snake-like units and proposes one possible actuation redundancy resolution to allow further downsize scalability while reducing the risk of buckling of the primary backbone of the snake-like units. Finally, The work presents a first early experiment with a prototype of the snake-like unit.


title: Anorexia on Tumblr: A Characterization Study

abstract: Eating disorders, such as anorexia nervosa are a major health concern affecting many young individuals. Given the extensive adoption of social media technologies in the anorexia affected demographic, we study behavioral characteristics of this population focusing on the social media Tumblr. Aligned with observations in prior literature, we find the presence of two prominent anorexia related communities on Tumblr -- pro-anorexia and pro-recovery. Empirical analyses on several thousand Tumblr posts show use of the site as a media-rich platform replete with triggering content for enacting anorexia as a lifestyle choice. Through use of common pro-anorexia tags, the pro-recovery community however attempts to "permeate" into the pro-anorexia community to educate them of the health risks of anorexia. Further, the communities exhibit distinctive affective, social, cognitive, and linguistic style markers. Compared with recover- ing anorexics, pro-anorexics express greater negative affect, higher cognitive impairment, and greater feelings of social isolation and self-harm. We also observe that these characteristics may be used in a predictive setting to detect anorexia content with 80% accuracy. Based on our findings, clinical implications of detecting anorexia related content on social media are discussed.


title: An Improved Indoor Robot Human-Following Navigation Model Using Depth Camera, Active IR Marker and Proximity Sensors Fusion

abstract: Creating a navigation system for autonomous companion robots has always been a difficult process, which must contend with a dynamically changing environment, which is populated by a myriad of obstructions and an unspecific number of people, other than the intended person, to follow. This study documents the implementation of an indoor autonomous robot navigation model, based on multi-sensor fusion, using Microsoft Robotics Developer Studio 4 (MRDS). The model relies on a depth camera, a limited array of proximity sensors and an active IR marker tracking system. This allows the robot to lock onto the correct target for human-following, while approximating the best starting direction to begin maneuvering around obstacles for minimum required motion. The system is implemented according to a navigation algorithm that transforms the data from all three types of sensors into tendency arrays and fuses them to determine whether to take a leftward or rightward route around an encountered obstacle. The decision process considers visible short, medium and long-range obstructions and the current position of the target person. The system is implemented using MRDS and its functional test performance is presented over a series of Virtual Simulation Environment scenarios, greenlighting further extensive benchmark simulations.


title: Automatic chemical design using a data-driven continuous representation of molecules

abstract: We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This generative model allows efficient search and optimization through open-ended spaces of chemical compounds. We train deep neural networks on hundreds of thousands of existing chemical structures to construct two coupled functions: an encoder and a decoder. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to the discrete representation from this latent space. Continuous representations allow us to automatically generate novel chemical structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chemical structures, or interpolating between molecules. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compounds. We demonstrate our method in the design of drug-like molecules as well as organic light-emitting diodes.


title: New occupational risk factors for chronic renal failure

abstract: Occupational pollutants may have a role in development of chronic renal failure (CRF). Most epidemiological studies have been cross-sectional, limited to certain renal diagnoses, or concentrated on early transient renal effects. In a case-control study, we examined the association between CRF and occupational exposure. Occupational histories of 272 men and women with CRF (of all types) were compared with those of 272 controls matched for age, sex, and region of residence. Exposures were assessed and degree and frequency were scored independently by three industrial hygienists unaware of case/control status. Significantly increased risks of CRF were found for exposure to lead (odds ratio 2.11 [95% CI 1.23-4.36]), copper (2.54 [1.16-5.53]), chromium (2.77 [1.21-6.33]), tin (3.72 [1.22-11.3]), mercury (5.13 [1.02-25.7]), welding fumes (2.06 [1.05-4.04]), silicon-containing compounds (2.51 [1.37-4.60]), grain dust (2.96 [1.24-7.04]), and oxygenated hydrocarbons (5.45 [1.84-16.2]). The frequencies of various occupational exposures were high among patients with diabetic nephropathy. This epidemiological study confirms previously identified risk factors and suggests that additional occupational exposures, for which there is some other experimental evidence, may be important in the development of CRF. The role of grain dust and the association between occupational exposure and diabetic nephropathy merit further investigation.


THE SKIN SURFACE AND FRICTION.


title: Genetic interchange of Dreissena polymorpha populations across a canal

abstract: The zebra mussel Dreissena polymorpha, has been invading western Central Europe since the early 19th century, coming from the Pontocaspian by two routes, each interconnected by large rivers and canals constructed at that time. By 1992 these two invasion populations had been given the opportunity to mix extensively across the newly built Main–Danube canal. Different hypotheses about the dynamics of genetic interchange have been postulated, ranging from a stable intergradation zone to complete mixing. We analysed the allozyme variation at nine loci in 14 or 17 populations, respectively, across the canal on two occasions: 2½ years and 4½ years after the opening. At the first sampling a strong genetic differentiation at three loci was found between the two invasion lines. This differentiation declined during the two-year period. The main effect was an assimilation of the Main populations according to the gene frequencies of the Danube populations, which suggest a swamping of the Main populations by Danube-type larvae. This inference is congruent with the overall water flow regime across the canal, i.e. active pumping from the Danube into the Main drainage system. The observed interchange processes allow some inferences to be made about the selective significance of the highly heterozygous allozyme loci in D. polymorpha.


title: RU-93-44 hep-th/9309145 SOME SPECULATIONS ABOUT BLACK HOLE ENTROPY IN STRING THEORY

abstract: The classical Bekenstein entropy of a black hole is argued to arise from configurations of strings with ends which are frozen on the horizon. Quantum corrections to this entropy are probably finite unlike the case in quantum field theory. Finally it is speculated that all black holes are single string states. The level density of strings is of the right order of magnitude to reproduce the Bekenstein entropy.


title: A Four-Plate Compact Capacitive Coupler Design and LCL-Compensated Topology for Capacitive Power Transfer in Electric Vehicle Charging Application

abstract: This paper proposes a four-plate compact capacitive coupler and its circuit model for large air-gap distance capacitive power transfer (CPT). The four plates are arranged vertically, instead of horizontally, to save space in the electric vehicle charging application. The two plates that are on the same side are placed close to each other to maintain a large coupling capacitance, and they are of different sizes to maintain the coupling between the primary and secondary sides. The circuit model of the coupler is presented, considering all six coupling capacitors. The LCL compensation topology is used to resonate with the coupler and provide high voltage on the plates to transfer high power. The circuit model of the coupler is simplified to design the parameters of the compensation circuit. Finite-element analysis is employed to simulate the coupling capacitance and design the dimensions of the coupler. The circuit performance is simulated in LTspice to design the specific parameter values. A prototype of the CPT system was designed and constructed with the proposed vertical plate structure. The prototype achieved an efficiency of 85.87% at 1.88-kW output power with a 150-mm air-gap distance.