title: Cint: a RISC interpreter for the C programming language

abstract: Cint is an interpretation system for the C programming language. Like most interpretation systems, it provides "load and go" type execution as well as enhanced debugging and performance analysis tools. Cint consists of two phases--a translator and an interpreter. The translator compiles the source program into code for a virtual machine. The interpreter then loads and executes this code. While providing services similar to traditional interpreters, Cint differs from them in two important ways. First, the virtual machine languages used by many interpreters are quite large; machines with 100 to 200 operations are common. In contrast, Cint's virtual machine has only 63 operations. Second, to achieve acceptable execution speeds, interpreters are often implemented in the assembly language of the host machine. Cint, however, is written entirely in C and is therefore portable. In fact, it has been transported to four machines without modification. Despite the compact size of the virtual machine language and the high-level language implementation, Cint's execution speed is comparable to that of other interpreters. This paper describes the design of the virtual machine, the implementation of the interpreter, and the performance of the system.


title: Signed Link Analysis in Social Media Networks

abstract: Numerous real-world relations can be represented by signed networks with positive links (e.g., trust) and negative links (e.g., distrust). Link analysis plays a crucial role in understanding the link formation and can advance various tasks in social network analysis such as link prediction. The majority of existing works on link analysis have focused on unsigned social networks. The existence of negative links determines that properties and principles of signed networks are substantially distinct from those of unsigned networks, thus we need dedicated efforts on link analysis in signed social networks. In this paper, following social theories in link analysis in unsigned networks, we adopt three social science theories, namely Emotional Information, Diffusion of Innovations and Individual Personality, to guide the task of link analysis in signed networks.


title: Studying the Reporting Cells strategy in a realistic mobile environment

abstract: In this paper, we optimize the Reporting Cells Planning Problem in a realistic mobile network. To the best of our knowledge, this is the first work in the literature in which the Reporting Cells Planning Problem is studied in a realistic mobile environment. This problem is based on a mobile location management strategy where the network cells can be in two possible states: Reporting Cells and non-Reporting Cells. In this location management strategy, a mobile station only updates its location when moving to a new Reporting Cell, and it is free to move among non-Reporting Cells without updating its location. The Reporting Cells Planning Problem can be classified as a multiobjective optimization problem with two objective functions: minimize the number of location updates and minimize the number of paging messages. With the aim of finding the best possible set of non-dominated solutions, we have implemented a well-known multiobjective evolutionary algorithm: the Non-dominated Sorting Genetic Algorithm II (NSGAII). Experimental results show that our proposal is able to achieve good sets of non-dominated solutions and, at the same time, to improve the results obtained with other optimization techniques.


title: Motion-aware noise filtering for deblurring of noisy and blurry images

abstract: Image noise can present a serious problem in motion deblurring. While most state-of-the-art motion deblurring algorithms can deal with small levels of noise, in many cases such as low-light imaging, the noise is large enough in the blurred image that it cannot be handled effectively by these algorithms. In this paper, we propose a technique for jointly denoising and deblurring such images that elevates the performance of existing motion deblurring algorithms. Our method takes advantage of estimated motion blur kernels to improve denoising, by constraining the denoised image to be consistent with the estimated camera motion (i.e., no high frequency noise features that do not match the motion blur). This improved denoising then leads to higher quality blur kernel estimation and deblurring performance. The two operations are iterated in this manner to obtain results superior to suppressing noise effects through regularization in deblurring or by applying denoising as a preprocess. This is demonstrated in experiments both quantitatively and qualitatively using various image examples.


title: Step Detection Robust against the Dynamics of Smartphones

abstract: A novel algorithm is proposed for robust step detection irrespective of step mode and device pose in smartphone usage environments. The dynamics of smartphones are decoupled into a peak-valley relationship with adaptive magnitude and temporal thresholds. For extracted peaks and valleys in the magnitude of acceleration, a step is defined as consisting of a peak and its adjacent valley. Adaptive magnitude thresholds consisting of step average and step deviation are applied to suppress pseudo peaks or valleys that mostly occur during the transition among step modes or device poses. Adaptive temporal thresholds are applied to time intervals between peaks or valleys to consider the time-varying pace of human walking or running for the correct selection of peaks or valleys. From the experimental results, it can be seen that the proposed step detection algorithm shows more than 98.6% average accuracy for any combination of step mode and device pose and outperforms state-of-the-art algorithms.


Organizational Commitment, Job Redesign, Employee Empowerment and Intent to Quit among Survivors of Restructuring and Downsizing


title: Exploring the Perception of Influencers Vs. Traditional Celebrities: Are Social Media Stars a New Type of Endorser?

abstract: The growing popularity of social media networks enables individuals to acquire large audiences of up to several million people on these platforms. Companies are starting to recognize this potential especially for young target groups, and to hire these so-called 'influencers' as endorsers in social media; these individual influences sometimes even take the place of traditional celebrities in corporate advertising. The question arises whether there is a difference in perception between these two types of endorsers and if so, which moderators influence these perceptions. In this study we explore consumers' general perception of social media influencers compared to traditional celebrities. We conduct an online survey with 590 respondents who were asked to rate 14 influencers and traditional celebrities in pairwise comparisons regarding their similarity. We apply multidimensional scaling (MDS) and find substantial differences in perceptions between the two groups. Additionally, we apply property fitting with evaluations regarding six different characteristics determining endorser effectiveness. While in total traditional celebrities are evaluated more favorably, this difference diminishes for high levels of familiarity and even reverses for perceived trustworthiness and similarity to oneself. The results indicate that marketers should carefully distinguish between influencers and traditional celebrities for endorsements, as systematic differences in perceptions between the groups are likely to have an impact on endorser effectiveness. Further research is needed to investigate which moderators (e.g. advertising type, product category) influence the perception and subsequently the endorsement effectiveness of the two types of endorsers.


title: The building blocks of consensus

abstract: Consensus is an important building block for building replicated systems, and many consensus protocols have been proposed. In this paper, we investigate the building blocks of consensus protocols and use these building blocks to assemble a skeleton that can be configured to produce, among others, three well-known consensus protocols: Paxos, Chandra-Toueg, and Ben-Or. Although each of these protocols specifies only one quorum system explicitly, all also employ a second quorum system. We use the skeleton to implement a replicated service, allowing us to compare the performance of these consensus protocols under various workloads and failure scenarios.


title: Chlamydia trachomatis infections: screening, diagnosis, and management.

abstract: Chlamydia trachomatis is a gram-negative bacterium that infects the columnar epithelium of the cervix, urethra, and rectum, as well as nongenital sites such as the lungs and eyes. The bacterium is the cause of the most frequently reported sexually transmitted disease in the United States, which is responsible for more than 1 million infections annually. Most persons with this infection are asymptomatic. Untreated infection can result in serious complications such as pelvic inflammatory disease, infertility, and ectopic pregnancy in women, and epididymitis and orchitis in men. Men and women can experience chlamydia-induced reactive arthritis. Treatment of uncomplicated cases should include azithromycin or doxycycline. Screening is recommended in all women younger than 25 years, in all pregnant women, and in women who are at increased risk of infection. Screening is not currently recommended in men. In neonates and infants, the bacterium can cause conjunctivitis and pneumonia. Adults may also experience conjunctivitis caused by chlamydia. Trachoma is a recurrent ocular infection caused by chlamydia and is endemic in the developing world.


title: Sensor fusion for indoor navigation and tracking of automated guided vehicles

abstract: A novel sensor fusion approach between a wireless Ultra-Wideband (UWB) Indoor Positioning System (IPS) and an Inertial Navigation System (INS) is presented for real-time indoor navigation and tracking of Automated Guided Vehicles (AGVs) and Mobile Robots in factories and warehouses. AGV applications are particularly challenging since they require very low position errors and very high position update rates to be able to track the highly dynamic movements in real-time. The sensor fusion algorithm consists of two main blocks: the first is a delay compensation block that compensates the delayed results of position estimation coming from the IPS, while the second block is a multi-rate Extended Kalman Filter (EKF) that combines dynamic models of the movements, delay-compensated position measurements from the IPS and measurements coming from the INS. To validate the proposed sensor fusion approach, extensive tests have been performed on a real-time platform using a combination of an Ubisense UWB IPS and an INS based on two low-cost MEMS accelerometers and one gyroscope mounted on the Flanders Make Badminton Robot performing highly dynamic movements. The results show that the proposed sensor fusion approach is able to deliver a 1 kHz position update rate in real-time operation with a position error standard deviation of 3.7 cm for linear movements and 1.7 degrees for rotational movements. These are significant performance improvements compared to a stand-alone Ubisense UWB indoor positioning system or a standalone inertial navigation system.


title: Carcinoma in situ in a patient with erythroleukoplakia: challenges in the diagnosis and in the therapeutic choice Short title: Carcinoma in situ in erythroleukoplakia

abstract: A 41-year-old Caucasian male smoker presented a red and white plaque on the left jugal mucosa suggestive of oral erythroleukoplakia (OEL). Biopsy followed by microscopic examination revealed carcinoma in situ. Since OEL is considered a precancerous lesion with a high malignant transformation potential, the present case set out to discuss the challenges in diagnosing the condition and choosing the most appropriate treatment. The discussion deals with issues such as whether there is a limit for choosing either a radical or a more conservative approach to treatment and the importance of using a procedure that allows a microscopic examination of the entire lesion. Carcinoma in situ associated with OEL made the treatment planning even more complex in the present case. In order to avoid a mutilating approach, multiple excisions with safety margins were performed, and the patient was instructed not to smoke. A strict 12-month follow-up shows no signs of recurrence of the OEL.(AU)


title: Effect of mobile phone intervention for diabetes on glycaemic control: a meta-analysis.

abstract: AIMS ::: To assess the effect of mobile phone intervention on glycaemic control in diabetes self-management. ::: ::: ::: METHODS ::: We searched three electronic databases (PubMed, EMBASE and Cochrane Library) using the following terms: diabetes or diabetes mellitus and mobile phone or cellular phone, or text message. We also manually searched reference lists of relevant papers to identify additional studies. Clinical studies that used mobile phone intervention and reported changes in glycosylated haemoglobin (HbA(1c) ) values in patients with diabetes were reviewed. The study design, intervention methods, sample size and clinical outcomes were extracted from each trial. The results of the HbA(1c) change in the trials were pooled using meta-analysis methods. ::: ::: ::: RESULTS ::: A total of 22 trials were selected for the review. Meta-analysis among 1657 participants showed that mobile phone interventions for diabetes self-management reduced HbA(1c) values by a mean of 0.5% [6 mmol/mol; 95% confidence interval, 0.3-0.7% (4-8 mmol/mol)] over a median of 6 months follow-up duration. In subgroup analysis, 11 studies among Type 2 diabetes patients reported significantly greater reduction in HbA(1c) than studies among Type 1 diabetes patients [0.8 (9 mmol/mol) vs. 0.3% (3 mmol/mol); P=0.02]. The effect of mobile phone intervention did not significantly differ by other participant characteristics or intervention strategies. ::: ::: ::: CONCLUSIONS ::: Results pooled from the included trials provided strong evidence that mobile phone intervention led to statistically significant improvement in glycaemic control and self-management in diabetes care, especially for Type 2 diabetes patients.


title: A Novel Level-Shifter Circuit Design For Display Panel Driver

abstract: A novel level-shifter circuit for display panel driver is presented. A proposed level-shifter is for the high speed and high-voltage driving capability. In order to achieve this purpose, the proposed level-shifter restricts and separates the Vgs of the output driver's pull-up PMOS and pull-down NMOS with Zener diode. The Zener diode can be replaced by normal diode combination. And a speed-up PMOS transistor is introduced to reduce delay. The control signal of speed-up PMOS was designed by bootstrapping method to minimize the gate to source (Vgs) voltage to avoid Vgs breakdown.


title: Advanced synthetic aperture radar based on digital beamforming and waveform diversity

abstract: This paper introduces innovative SAR system concepts for the acquisition of high resolution radar images with wide swath coverage from spaceborne platforms. The new concepts rely on the combination of advanced multi-channel SAR front-end architectures with novel operational modes. The architectures differ regarding their implementation complexity and it is shown that even a low number of channels is already well suited to significantly improve the imaging performance and to overcome fundamental limitations inherent to classical SAR systems. The more advanced concepts employ a multidimensional encoding of the transmitted waveforms to further improve the performance and to enable a new class of hybrid SAR imaging modes that are well suited to satisfy hitherto incompatible user requirements for frequent monitoring and detailed mapping. Implementation specific issues will be discussed and examples demonstrate the potential of the new techniques for different remote sensing applications.


title: A Secure M-Commerce System based on credit card transaction

abstract: The SMoSS does not require extra restrictions and resources on cash flow.Credit card fraud can be effectively avoided.Our analysis shows that the SMoSS is indeed a feasible mobile shopping system.The SMoSS can still protect a mobile phone, even the mobile phone is lost.The SMoSS can ensure the communication privacy, integrity, and non-repudiation. Nowadays the demands for wireless Internet shopping are increasing. But credit card fraud has been serious, and SET and SSL have their own problems. To enhance the security of online shopping, in this paper, we propose a secure m-commerce scheme, called the Secure M-Commerce System (SMCS for short), with which users can create a safe credit-card transaction for Internet shopping. Basically, the SMCS coordinates the cash flow of a trading system and its credit card entities to effectively protect the issued transactions against different attacks and avoid information leakage. The proposed system also employs a Data Connection Core (DCC for short) to link the card-issuing bank and consumers before their wireless communication starts so as to significantly improve the security level of our m-commerce environment. Theoretical analysis shows that the SMCS is more secure than SET and SSL. The performance analysis indicates that the SMCS is indeed a feasible m-commerce system.


title: Author manuscript, published in "WWW- Demos (2011)" YAGO2: Exploring and Querying World Knowledge in Time, Space, Context, and Many Languages

abstract: We present YAGO2, an extension of the YAGO knowledge base with focus on temporal and spatial knowledge. It is automatically built from Wikipedia, GeoNames, and WordNet, and contains nearly 10 million entities and events, as well as 80 million facts representing general world knowledge. An enhanced data representation introduces time and location as first-class citizens. The wealth of spatio-temporal information in YAGO can be explored either graphically or through a special time- and space-aware query language.


title: Image Compression Technique for Color Images Using Wavelets

abstract: the compression and analysis of color images is becoming more popular in nowadays, the high quality color images are in demand, but the power resources and bandwidths are limited, this shows the requirement of effective color image compression algorithm which is suitable for human visual system. The existing algorithms are designed for gray scale visual information. In this paper we propose a wavelet method for color image compression. The Wavelet families are used to characterize the quality of image by calculating its parameters, which are like peak signal to noise ratio, entropy, energy retained and redundancy. The entropy may be calculated by using color histogram for the compressed image. The criteria are determined by using coding algorithm. Results show that Biorthogonal wavelet filter outperforms the orthogonal one in quality of compressed image whereas the orthogonal filter is more energy preserving technique.


title: Hardware in the loop model for irregular conditions in tension leveler applications

abstract: One of the main parts of the production line in a metal web processing applications is tension leveler. It contains unwinder, tension bridles and winder. Parameters setting of regulators which control the production line and their testing of control system are needed. The paper presents a simulation and 3D visualization model of tension leveler developed for purpose of testing the control strategy. Complete hardware installed on the production line (drives, motors, etc.) is emulated by Simulation Unit hardware and is connected with detailed mathematical model of the tension leveler. Control software developed for the application is tested on hardware-in-the-loop real time simulation. Main goal of the work presented in the paper is to design and test control strategy for regular and irregular conditions in the simulated process of web tension leveling, and to represent it as a 3D animation.


title: Cognitive Radio Design Challenges and Techniques

abstract: Cognitive radios are expected to communicate across two or three frequency decades by continually sensing the spectrum and identifying available channels. This paper describes the issues related to the design of wideband signal paths and the decades-wide synthesis of carrier frequencies. A new CMOS low-noise amplifier topology for the range of 50 MHz to 10 GHz is introduced that achieves a noise figure of 2.9 to 5.7 dB with a power dissipation of 22 mW. Several multi-decade carrier generation techniques are proposed and a CMOS prototype is presented that exhibits a phase noise of -94 to -120 dBc/Hz at 1-MHz offset while consuming 31 mW.


title: Enhancing security in the memory management unit

abstract: We propose an hardware solution to several security problems that are difficult to solve on classical processor architectures, like licensing, electronic commerce, or software privacy. The memory management unit which provides multitasking and virtual memory support is extended and given a third purpose: to supply strong hardware security support for the software layer. The principle of this enhanced device, that we call a Security Management Unit (or SMU), is based on ciphered program execution and access control. It is composed of a pipelined block ciphering/deciphering unit, an infernal permanent memory and logic control, whose interaction is explained in this paper.


title: Dual RNA-seq unveils noncoding RNA functions in host–pathogen interactions

abstract: Bacteria express many small RNAs for which the regulatory roles in pathogenesis have remained poorly understood due to a paucity of robust phenotypes in standard virulence assays. Here we use a generic 'dual RNA-seq' approach to profile RNA expression simultaneously in pathogen and host during Salmonella enterica serovar Typhimurium infection and reveal the molecular impact of bacterial riboregulators. We identify a PhoP-activated small RNA, PinT, which upon bacterial internalization temporally controls the expression of both invasion-associated effectors and virulence genes required for intracellular survival. This riboregulatory activity causes pervasive changes in coding and noncoding transcripts of the host. Interspecies correlation analysis links PinT to host cell JAK-STAT signalling, and we identify infection-specific alterations in multiple long noncoding RNAs. Our study provides a paradigm for a sensitive RNA-based analysis of intracellular bacterial pathogens and their hosts without physical separation, as well as a new discovery route for hidden functions of pathogen genes.


title: Cognition and Docition in OFDMA-Based Femtocell Networks

abstract: We address the coexistence problem between macrocell and femtocell systems by controlling the aggregated interference generated by multiple femtocell base stations at the macrocell receivers in a distributed fashion. We propose a solution based on intelligent and self-organized femtocells implementing a realtime multi-agent reinforcement learning technique, known as decentralized Q- learning. We compare this cognitive approach to a non-cognitive algorithm and to the well known iterative water- filling, showing the general superiority of our scheme in terms of (non-jeopardized) macrocell capacity. Furthermore, in distributed settings of such femtocell networks, the learning may be complex and slow due to mutually impacting decision making processes, which results in a non-stationary environment. We propose a timely solution -referred to as docition- to improve the learning process based on the concept of teaching and expert knowledge sharing in wireless environments. We demonstrate that such an approach improves the femtocells' learning ability and accuracy. We evaluate the docitive paradigm in the context of a 3GPP compliant OFDMA (Orthogonal Frequency Division Multiple Access) femtocell network modeled as a multi-agent system. We propose different docitive algorithms and we show their superiority to the well known paradigm of independent learning in terms of speed of convergence and precision.


title: Beyond clicks: dwell time for personalization

abstract: Many internet companies, such as Yahoo, Facebook, Google and Twitter, rely on content recommendation systems to deliver the most relevant content items to individual users through personalization. Delivering such personalized user experiences is believed to increase the long term engagement of users. While there has been a lot of progress in designing effective personalized recommender systems, by exploiting user interests and historical interaction data through implicit (item click) or explicit (item rating) feedback, directly optimizing for users' satisfaction with the system remains challenging. In this paper, we explore the idea of using item-level dwell time as a proxy to quantify how likely a content item is relevant to a particular user. We describe a novel method to compute accurate dwell time based on client-side and server-side logging and demonstrate how to normalize dwell time across different devices and contexts. In addition, we describe our experiments in incorporating dwell time into state-of-the-art learning to rank techniques and collaborative filtering models that obtain competitive performances in both offline and online settings.


title: User Profiling for Recommendation System

abstract: Recommendation system is a type of information filtering systems that recommend various objects from a vast variety and quantity of items which are of the user interest. This results in guiding an individual in personalized way to interesting or useful objects in a large space of possible options. Such systems also help many businesses to achieve more profits to sustain in their filed against their rivals. But looking at the amount of information which a business holds it becomes difficult to identify the items of user interest. Therefore personalization or user profiling is one of the challenging tasks that give access to user relevant information which can be used in solving the difficult task of classification and ranking items according to an individuals interest. Profiling can be done in various ways such assupervised or unsupervised, individual or group profiling, distributive or and non distributive profiling. Our focus in this paper will be on the dataset which we will use, we identify some interesting facts by using Weka Tool that can be used for recommending the items from dataset. Our aim is to present a novel technique to achieve user profiling in recommendation system.


title: Hybrid Recommender Systems: A Systematic Literature Review

abstract: Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. This systematic literature review presents the state of the art in hybrid recommender systems of the last decade. It is the first quantitative review work completely focused in hybrid recommenders. We address the most relevant problems considered and present the associated data mining and recommendation techniques used to overcome them. We also explore the hybridization classes each hybrid recommender belongs to, the application domains, the evaluation process and proposed future research directions. Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way. Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors. As most of the studies are evaluated by comparisons with similar methods using accuracy metrics, providing more credible and user oriented evaluations remains a typical challenge. Besides this, newer challenges were also identified such as responding to the variation of user context, evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid recommenders represent a good basis with which to respond accordingly by exploring newer opportunities such as contextualizing recommendations, involving parallel hybrid algorithms, processing larger datasets, etc.


title: Estimating the tensor of curvature of a surface from a polyhedral approximation

abstract: Estimating principal curvatures and principal directions of a surface from a polyhedral approximation with a large number of small faces, such as those produced by iso-surface construction algorithms, has become a basic step in many computer vision algorithms, particularly in those targeted at medical applications. We describe a method to estimate the tensor of curvature of a surface at the vertices of a polyhedral approximation. Principal curvatures and principal directions are obtained by computing in closed form the eigenvalues and eigenvectors of certain 3/spl times/3 symmetric matrices defined by integral formulas, and closely related to the matrix representation of the tensor of curvature. The resulting algorithm is linear, both in time and in space, as a function of the number of vertices and faces of the polyhedral surface. >


title: A NOTE ON ESTIMATES FOR THE SPECTRAL RADIUS OF A NONNEGATIVE MATRIX ∗

abstract: Utilizing the concept of Perron complement, a new estimate for the spectral radius of a nonnegative irreducible matrix is presented. A new matrix is derived that preserves the spec- tral radius while its minimum row sum increases and its maximum row sum decreases. Numerical examples are provided to illustrate the effectiveness of this approach.


title: Practical Matrix Completion and Corruption Recovery Using Proximal Alternating Robust Subspace Minimization

abstract: Low-rank matrix completion is a problem of immense practical importance. Recent works on the subject often use nuclear norm as a convex surrogate of the rank function. Despite its solid theoretical foundation, the convex version of the problem often fails to work satisfactorily in real-life applications. Real data often suffer from very few observations, with support not meeting the randomness requirements, ubiquitous presence of noise and potentially gross corruptions, sometimes with these simultaneously occurring. This paper proposes a Proximal Alternating Robust Subspace Minimization method to tackle the three problems. The proximal alternating scheme explicitly exploits the rank constraint on the completed matrix and uses the $$\ell _0$$ l 0 pseudo-norm directly in the corruption recovery step. We show that the proposed method for the non-convex and non-smooth model converges to a stationary point. Although it is not guaranteed to find the global optimal solution, in practice we find that our algorithm can typically arrive at a good local minimizer when it is supplied with a reasonably good starting point based on convex optimization. Extensive experiments with challenging synthetic and real data demonstrate that our algorithm succeeds in a much larger range of practical problems where convex optimization fails, and it also outperforms various state-of-the-art algorithms.


title: “Unexpected Item in the Bagging Area”: Anomaly Detection in X-Ray Security Images

abstract: The role of anomaly detection in X-ray security imaging, as a supplement to targeted threat detection, is described, and a taxonomy of anomaly types in this domain is presented. Algorithms are described for detecting appearance anomalies of shape, texture, and density, and semantic anomalies of object category presence. The anomalies are detected on the basis of representations extracted from a convolutional neural network pre-trained to identify object categories in photographs, from the final pooling layer for appearance anomalies, and from the logit layer for semantic anomalies. The distribution of representations in normal data is modeled using high-dimensional, full-covariance, Gaussians, and anomalies are scored according to their likelihood relative to those models. The algorithms are tested on X-ray parcel images using stream-of-commerce data as the normal class, and parcels with firearms present the examples of anomalies to be detected. Despite the representations being learned for photographic images and the varied contents of stream-of-commerce parcels, the system, trained on stream-of-commerce images only, is able to detect 90% of firearms as anomalies, while raising false alarms on 18% of stream-of-commerce.


title: Better to Be Frustrated than Bored: The Incidence, Persistence, and Impact of Learners ’ Cognitive-Affective States during Interactions with Three Different Computer-Based Learning Environments

abstract: We study the incidence (rate of occurrence), persistence (rate of reoccurrence immediately after occurrence), and impact (effect on behavior) of students' cognitive-affective states during their use of three different computer-based learning environments. Students' cognitive-affective states are studied using different populations (Philippines, USA), different methods (quantitative field observation, self-report), and different types of learning environments (dialogue tutor, problem-solving game, and problem-solving-based Intelligent Tutoring System). By varying the studies along these multiple factors, we can have greater confidence that findings which generalize across studies are robust. The incidence, persistence, and impact of boredom, frustration, confusion, engaged concentration, delight, and surprise were compared. We found that boredom was very persistent across learning environments and was associated with poorer learning and problem behaviors, such as gaming the system. Despite prior hypothesis to the contrary, frustration was less persistent, less associated with poorer learning, and did not appear to be an antecedent to gaming the system. Confusion and engaged concentration were the most common states within all three learning environments. Experiences of delight and surprise were rare. These findings suggest that significant effort should be put into detecting and responding to boredom and confusion, with a particular emphasis on developing pedagogical interventions to disrupt the ''vicious cycles'' which occur when a student becomes bored and remains bored for long periods of time.


title: A proposal for a Unified Process for ONtology building: UPON

abstract: Ontologies are the backbone of the Semantic Web, a semantic-aware version of the World Wide Web. To the end of making available large-scale, high quality domain ontologies, effective and usable methodologies are needed to facilitate the process of Ontology Building. Many of the methods proposed so far only partly refer to well-known and widely used standards from other areas, like software engineering and knowledge representation. In this paper we present UPON, a methodology for ontology building derived from the Unified Software Development Process. A comparative evaluation with other methodologies, as well as the results of its adoption in the context of the Athena Integrated Project, are also discussed.


title: Research Design: Creating Robust Approaches for the Social Sciences

abstract: PART ONE: INTRODUCTION - 'DESIGN' AS DISTINCT FROM METHODS What Is Research Design? Introducing Designs in the Cycle of Research PART TWO: THE PRELIMINARIES OF RESEARCH DESIGN Identifying Researchable Questions Warranting Research Claims The Nature of Causal Claims PART THREE: PUTTING IT ALL TOGETHER Identifying the Sample or Cases Comparing for Comparative Claims Matters of Timing and Sequence Evaluating Controlled Interventions PART FOUR: MORE ADVANCED CONSIDERATIONS Further Designs for Evaluation Challenges for Validity How Big Is a Difference? A Second Principle of Ethics PART FIVE: CONCLUSION Revisiting the Need for Robust Design


title: ITS2 RNA secondary structure analysis reveals close affinity between endophytic and pathogenic fungi: A case study in Fusarium species

abstract: Endophytes are microbes which colonize inner plant tissues without causing any disease symptoms. Many of the fungi isolated as endophytes show a close morphological resemblance to plant pathogenic fungi. It is commonly believed that pathogenic/non-pathogenic fungi become endophytic/pathogenic due to virulence loss/acquisition, respectively, but the molecular basis of such transformations and the shared characteristics are still to be elucidated. We have investigated the relationships between endophytes and pathogens based on internal transcribed spacer 2 (ITS2) sequence and secondary structure analyses in Fusarium as a model organism. We found that the ITS2 sequence-based phylogeny indicated close genetic proximity among endophytic and pathogenic strains of Fusarium species, suggesting that strains of this fungus can easily change between an endophytic and a necrotrophic lifestyle. We also observed a considerable discrepancy in the positions of bases in the ITS2 nucleotide sequences. RNA secondary structures of both endophytic and pathogenic forms of Fusarium were generated to distinguish between conserved and variable regions within the ITS2 sequence. The generated structures showed some structural similarities between the endophytic and pathogenic forms, with coincident variations in their respective junctions, hairpin loops, terminal loops and internal loops. Such findings suggest that Fusarium lifestyles are not stable but rather are dynamic and likely influenced by the genetic makeup of the fungal species, host factors and changing environment. Our research highlights the importance of the ITS2 sequence and its secondary structure as possible molecular markers to establish relationships and variations between the endophyte and pathogen lifestyle.


title: Cross-parameterization and compatible remeshing of 3D models

abstract: Many geometry processing applications, such as morphing, shape blending, transfer of texture or material properties, and fitting template meshes to scan data, require a bijective mapping between two or more models. This mapping, or cross-parameterization, typically needs to preserve the shape and features of the parameterized models, mapping legs to legs, ears to ears, and so on. Most of the applications also require the models to be represented by compatible meshes, i.e. meshes with identical connectivity, based on the cross-parameterization. In this paper we introduce novel methods for shape preserving cross-parameterization and compatible remeshing. Our cross-parameterization method computes a low-distortion bijective mapping between models that satisfies user prescribed constraints. Using this mapping, the remeshing algorithm preserves the user-defined feature vertex correspondence and the shape correlation between the models. The remeshing algorithm generates output meshes with significantly fewer elements compared to previous techniques, while accurately approximating the input geometry. As demonstrated by the examples, the compatible meshes we construct are ideally suitable for morphing and other geometry processing applications.


title: MDP and Machine Learning-Based Cost-Optimization of Dynamic Resource Allocation for Network Function Virtualization

abstract: The introduction of Network Functions Virtualization (NFV) enables service providers to offer software-defined network functions with elasticity and flexibility. Its core technique, dynamic allocation procedure of NFV components onto cloud resources requires rapid response to changes on-demand to remain cost and QoS effective. In this paper, Markov Decision Process (MDP) is applied to the NP-hard problem to dynamically allocate cloud resources for NFV components. In addition, Bayesian learning method is applied to monitor the historical resource usage in order to predict future resource reliability. Experimental results show that our proposed strategy outperforms related approaches.


title: Statistical learning and language acquisition

abstract: Human learners, including infants, are highly sensitive to structure in their environment. Statistical learning refers to the process of extracting this structure. A major question in language acquisition in the past few decades has been the extent to which infants use statistical learning mechanisms to acquire their native language. There have been many demonstrations showing infants' ability to extract structures in linguistic input, such as the transitional probability between adjacent elements. This paper reviews current research on how statistical learning contributes to language acquisition. Current research is extending the initial findings of infants' sensitivity to basic statistical information in many different directions, including investigating how infants represent regularities, learn about different levels of language, and integrate information across situations. These current directions emphasize studying statistical language learning in context: within language, within the infant learner, and within the environment as a whole. WIREs Cogn Sci 2010 1 906–914 ::: ::: For further resources related to this article, please visit the WIREs website


title: Big Data Mining with Parallel Computing : A Comparison of Distributed and MapReduce Methodologies

abstract: The performances of distributed and MapReduce methodologies over big datasets are compared.Particularly, mining accuracy and efficiency of these two methodologies are examined.The MapReduce based procedure by different numbers of nodes performs very stable.Moreover, the MapReduce procedure requires the least computational cost to process big datasets. Mining with big data or big data mining has become an active research area. It is very difficult using current methodologies and data mining software tools for a single personal computer to efficiently deal with very large datasets. The parallel and cloud computing platforms are considered a better solution for big data mining. The concept of parallel computing is based on dividing a large problem into smaller ones and each of them is carried out by one single processor individually. In addition, these processes are performed concurrently in a distributed and parallel manner. There are two common methodologies used to tackle the big data problem. The first one is the distributed procedure based on the data parallelism paradigm, where a given big dataset can be manually divided into n subsets, and n algorithms are respectively executed for the corresponding n subsets. The final result can be obtained from a combination of the outputs produced by the n algorithms. The second one is the MapReduce based procedure under the cloud computing platform. This procedure is composed of the map and reduce processes, in which the former performs filtering and sorting and the later performs a summary operation in order to produce the final result. In this paper, we aim to compare the performance differences between the distributed and MapReduce methodologies over large scale datasets in terms of mining accuracy and efficiency. The experiments are based on four large scale datasets, which are used for the data classification problems. The results show that the classification performances of the MapReduce based procedure are very stable no matter how many computer nodes are used, better than the baseline single machine and distributed procedures except for the class imbalance dataset. In addition, the MapReduce procedure requires the least computational cost to process these big datasets.


title: Review on fraud detection methods in credit card transactions

abstract: Cashless transactions such as online transactions, credit card transactions, and mobile wallet are becoming more popular in financial transactions nowadays. With increased number of such cashless transaction, number of fraudulent transactions are also increasing. Fraud can be distinguished by analyzing spending behavior of customers (users) from previous transaction data. If any deviation is noticed in spending behavior from available patterns, it is possibly of fraudulent transaction. To detect fraud behavior, bank and credit card companies are using various methods of data mining such as decision tree, rule based mining, neural network, fuzzy clustering approach, hidden markov model or hybrid approach of these methods. Any of these methods is applied to find out normal usage pattern of customers (users) based on their past activities. The objective of this paper is to provide comparative study of different techniques to detect fraud.


title: A new fast prototype selection method based on clustering

abstract: In supervised classification, a training set T is given to a classifier for classifying new prototypes. In practice, not all information in T is useful for classifiers, therefore, it is convenient to discard irrelevant prototypes from T. This process is known as prototype selection, which is an important task for classifiers since through this process the time for classification or training could be reduced. In this work, we propose a new fast prototype selection method for large datasets, based on clustering, which selects border prototypes and some interior prototypes. Experimental results showing the performance of our method and comparing accuracy and runtimes against other prototype selection methods are reported.


title: A Tune-up for Tor: Improving Security and Performance in the Tor Network

abstract: The Tor anonymous communication network uses selfreported bandwidth values to select routers for building tunnels. Since tunnels are allocated in proportion to this bandwidth, this allows a malicious router operator to attract tunnels for compromise. Since the metric used is insensitive to relative load, it does not adequately respond to changing conditions and hence produces unreliable performance, driving many users away. We propose an opportunistic bandwidth measurement algorithm to replace selfreported values and address both of these problems. We also propose a mechanisms to let users tune Tor performance to achieve higher performance or higher anonymity. Our mechanism effectively blends the traffic from users of different preferences, making partitioning attacks difficult. We implemented the opportunistic measurement and tunable performance extensions and examined their performance both analytically and in the real Tor network. Our results show that users can get dramatic increases in either performance or anonymity with little to no sacrifice in the other metric, or a more modest improvement in both. Our mechanisms are also invulnerable to the previously published low-resource attacks on Tor.


title: Optimal dimensioning of active cell balancing architectures

abstract: This paper presents an approach to optimal dimensioning of active cell balancing architectures, which are of increasing relevance in Electrical Energy Storages (EESs) for Electric Vehicles (EVs) or stationary applications such as smart grids. Active cell balancing equalizes the state of charge of cells within a battery pack via charge transfers, increasing the effective capacity and lifetime. While optimization approaches have been introduced into the design process of several aspects of EESs, active cell balancing architectures have, until now, not been systematically optimized in terms of their components. Therefore, this paper analyzes existing architectures to develop design metrics for energy dissipation, installation volume, and balancing current. Based on these design metrics, a methodology to efficiently obtain Pareto-optimal configurations for a wide range of inductors and transistors at different balancing currents is developed. Our methodology is then applied to a case study, optimizing two state-of-the-art architectures using realistic balancing algorithms. The results give evidence of the applicability of systematic optimization in the domain of cell balancing, leading to higher energy efficiencies with minimized installation space.


title: Low-Power Multiport SRAM With Cross-Point Write Word-Lines, Shared Write Bit-Lines, and Shared Write Row-Access Transistors

abstract: This brief proposes one-write-one-read (1W1R) and two-write-two-read (2W2R) multiport (MP) SRAMs for register file applications in nanoscale CMOS technology. The cell features a cross-point Write word-line structure to mitigate Write Half-Select disturb and improve the static noise margin (SNM). The Write bit-lines (WBLs) and Write row-access transistors are shared with adjacent bit-cells to reduce the cell transistor count and area. The scheme halves the number of WBL, thus reducing WBL leakage and power consumption. In addition, column-based virtual VSS control is employed for the Read stack to reduce the Read power consumption. Post-sim results show that the proposed scheme reduces both Write/Read current consumption by over 30% compared with the previous MP structure. The proposed scheme is demonstrated and validated by an 8-Kb 2W2R SRAM test chip fabricated in TSMC 40-nm CMOS technology.


title: A smooth spiral tool path for high speed machining of 2D pockets

abstract: We introduce a new algorithm for generating a spiral tool path for high-speed machining of pockets without islands. The pocket may be an arbitrary simply-connected 2D shape bounded by straight-line segments and circular arcs. The tool path is generated by interpolating growing disks placed on the medial axis of the pocket. It starts inside the pocket and spirals out to the pocket boundary. The tool path is guaranteed to be free of self-intersections and allows machining of the pocket without tool retractions. The start point of the spiral may be chosen freely by the user anywhere within the pocket. Most importantly, the spiral tool path complies with a user-specified maximum cutting width. The output of our algorithm is a G^1-continuous spiral path. However, in a post-processing step, a properly adapted variant of the recent ''PowerApx'' package [Heimlich M, Held M. Biarc approximation, simplification and smoothing of polygonal curves by means of Voronoi-based tolerance bands. International Journal of Computational Geometry & Applications 2008;18(3):221-50] can be used to boost the tool path to C^2-continuity. Our new algorithm was implemented and tested successfully on real-world data. We conclude our paper by an analysis of sample tool paths produced by our implementation.


title: Sexual Conflict in Human Mating

abstract: Despite interdependent reproductive fates that favor cooperation, males and females exhibit many psychological and behavioral footprints of sexually antagonistic coevolution. These include strategies of deception, sexual exploitation, and sexual infidelity as well as anti-exploitation defenses such as commitment skepticism and emotions such as sexual regret and jealousy. Sexual conflict pervades the mating arena prior to sexual consummation, after a mating relationship has formed, and in the aftermath of a breakup. It also permeates many other social relationships in forms such as daughter-guarding, conflict in opposite-sex friendships, and workplace sexual harassment. As such, sexual conflict constitutes not a narrow or occasional flashpoint but rather persistent threads that run through our intensely group-living species.


title: Effects of formative assessment in an augmented reality approach to conducting ubiquitous learning activities for architecture courses

abstract: Augmented reality (AR) is helpful in leading students to observe real-world learning targets with supports from online learning resources using mobile and wireless communication technologies. In this study, an AR-based learning system for an architecture course is proposed based on a formative assessment mechanism, which guides students to find answers on their own by giving hints when they fail to correctly answer questions. To evaluate the effectiveness of the proposed approach, an experiment has been conducted in the Museum of World Religions for a university architecture course. A total of 39 students were randomly assigned to an experimental group learning with the proposed approach and a control group learning with the conventional AR-based learning. The experimental results showed that the AR-based learning with the formative assessment mechanism significantly improved the students’ learning achievements and motivation, while also reducing their cognitive load.


title: Power-Assistive Finger Exoskeleton With a Palmar Opening at the Fingerpad

abstract: This paper presents a powered finger exoskeleton with an open fingerpad, named the Open Fingerpad eXoskeleton (OFX). The palmar opening at the fingerpad allows for direct contact between the user's fingerpad and objects in order to make use of the wearer's own tactile sensation for dexterous manipulation. Lateral side walls at the end of the OFX's index finger module are equipped with custom load cells for estimating the wearer's pinch grip force. A pneumatic cylinder generates assistance force, which is determined according to the estimated pinch grip force. The OFX transmits the assistance force directly to the objects without exerting pressure on the wearer's finger. The advantage of the OFX over an exoskeleton with a closed fingerpad was validated experimentally. During static and dynamic manipulation of a test object, the OFX exhibited a lower safety margin than the closed exoskeleton, indicating a higher ability to adjust the grip force within an appropriate range. Furthermore, the benefit of force assistance in reducing the muscular burden was observed in terms of muscle fatigue during a static pinch grip. The median frequency (MDF) of the surface electromyography (sEMG) signal from the first dorsal interosseous (FDI) muscle displayed a lower reduction rate for the assisted condition, indicating a lower accumulation rate of muscle fatigue.


title: Consumer participation and gender differences on companies' microblogs: A brand attachment process perspective

abstract: We examine consumer participation and gender differences on companies' microblogs.We find that participation is predicted by trust and commitment.Self-congruence and partner quality are found to affect trust and commitment.The effects of self-congruence and trust are stronger for male consumers.The effects of partner quality and commitment are stronger for female consumers. This paper presents a research model to explicate that the level of consumers' participation on companies' brand microblogs is influenced by their brand attachment process. That is, self-congruence and partner quality affect consumers' trust and commitment toward companies' brands, which in turn influence participation on brand microblogs. Further, we propose that gender has important moderating effects in our research model. We empirically test the research hypotheses through an online survey. The findings illustrate that self-congruence and partner quality have positive effects on trust and commitment. Trust affects commitment and participation, while participation is also influenced by commitment. More importantly, the effects of self-congruence on trust and commitment are found to be stronger for male consumers than females. In contrast, the effects of partner quality on trust and commitment are stronger for female consumers than males. Trust posits stronger effects on commitment and participation for males, while commitment has a stronger effect on participation for females. We believe that our findings contribute to the literature on consumer participation behavior and gender differences on brand microblogs. Companies can also apply our findings to strengthen their brand building and participation level of different consumers on their microblogs.


title: Big data in economics: evolution or revolution?

abstract: Abstract The Big Data Era creates a lot of exciting opportunities for new developments in economics and econometrics. At the same time, however, the analysis of large datasets poses difficultmethodological problems that should be addressed appropriately and are the subject of the present chapter. Introduction ‘ Big Data ’ has become a buzzword both in academic and in business and policy circles. It is used to cover a variety of data-driven phenomena that have very different implications for empirical methods. This chapter discusses some of these methodological challenges. In the simplest case, ‘ Big Data ’ means a large dataset that otherwise has a standard structure. For example, Chapter 13 describes how researchers are gaining increasing access to administrative datasets or business records covering entire populations rather than population samples. The size of these datasets allows for better controls and more precise estimates and is a bonus for researchers. This may raise challenges for data storage and handling, but does not raise any distinct methodological issues. However, ‘ Big Data ’ often means much more than just large versions of standard datasets. First, large numbers of units of observation often come with large numbers of variables, that is, large numbers of possible covariates. To illustrate with the same example, the possibility to link different administrative datasets increases the number of variables attached to each statistical unit. Likewise, business records typically contain all consumer interactions with the business. This can create a tension in the estimation between the objective of ‘letting the data speak’ and obtaining accurate (in a way to be specified later) coefficient estimates. Second, Big Data sets often have a very different structure from those we are used to in economics. This includes web search queries, real-time geolocational data or social media, to name a few. This type of data raises questions about how to structure and possibly re-aggregate them. The chapter starts with a description of the ‘ curse of dimensionality ’, which arises from the fact that both the number of units of observation and the number of variables associated with each unit are large. This feature is present in many of the Big Data applications of interest to economists. One extreme example of this problem occurs when there are more parameters to estimate than observations.


title: How does your password measure up? the effect of strength meters on password creation

abstract: To help users create stronger text-based passwords, many web sites have deployed password meters that provide visual feedback on password strength. Although these meters are in wide use, their effects on the security and usability of passwords have not been well studied. ::: ::: We present a 2,931-subject study of password creation in the presence of 14 password meters. We found that meters with a variety of visual appearances led users to create longer passwords. However, significant increases in resistance to a password-cracking algorithm were only achieved using meters that scored passwords stringently. These stringent meters also led participants to include more digits, symbols, and uppercase letters. ::: ::: Password meters also affected the act of password creation. Participants who saw stringent meters spent longer creating their password and were more likely to change their password while entering it, yet they were also more likely to find the password meter annoying. However, the most stringent meter and those without visual bars caused participants to place less importance on satisfying the meter. Participants who saw more lenient meters tried to fill the meter and were averse to choosing passwords a meter deemed "bad" or "poor." Our findings can serve as guidelines for administrators seeking to nudge users towards stronger passwords.


title: A Dual-Band Wilkinson Power Divider

abstract: A new scheme is proposed for the dual-band operation of the Wilkinson power divider/combiner. The dual band operation is achieved by attaching two central transmission line stubs to the conventional Wilkinson divider. It has simple structure and is suitable for distributed circuit implementation.


title: Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States

abstract: The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed several years. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may become an increasingly practical supplement to the ACS. Here, we present a method that estimates socioeconomic characteristics of regions spanning 200 US cities by using 50 million images of street scenes gathered with Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22 million automobiles in total (8% of all automobiles in the United States), were used to accurately estimate income, race, education, and voting patterns at the zip code and precinct level. (The average US precinct contains ∼1,000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographics may effectively complement labor-intensive approaches, with the potential to measure demographics with fine spatial resolution, in close to real time.


title: Computer generated animation of faces

abstract: This paper describes the representation, animation and data collection techniques that have been used to produce "realistic" computer generated half-tone animated sequences of a human face changing expression. It was determined that approximating the surface of a face with a polygonal skin containing approximately 250 polygons defined by about 400 vertices is sufficient to achieve a realistic face. Animation was accomplished using a cosine interpolation scheme to fill in the intermediate frames between expressions. This approach is good enough to produce realistic facial motion. The three-dimensional data used to describe the expressions of the face was obtained photogrammetrically using pairs of photographs.


title: Partial duplication of the mandible, parotid aplasia and facial cleft: a rare developmental disorder.

abstract: Duplication deformity of the mandible is exceedingly rare. Its occurrence with congenital facial cleft and parotid gland aplasia has been rarely reported as 1 entity. We report such a case with detailed computed tomography (CT) description and provide a review of the literature on mandible duplication.


title: Data lifetime is a systems problem

abstract: As sensitive data lifetime (i.e. propagation and duration in memory) increases, so does the risk of exposure. Unfortunately, this issue has been largely overlooked in the design of most of today's operating systems, libraries, languages, etc. As a result, applications are likely to leave the sensitive data they handle (passwords, financial and military information, etc.) scattered widely over memory, leaked to disk, etc. and left there for an indeterminate period of time. This greatly increases the impact of a system compromise.Dealing with data lifetime issues is currently left to application developers, who largely overlook them. Security-aware developers who attempt to address them (e.g. cryptographic library writers) are stymied by the limitations of the operating systems, languages, etc. they rely on. We argue that data lifetime is a systems issue which must be recognized and addressed at all layers of the software stack.


title: Agile Software Development: Adaptive Systems Principles and Best Practices

abstract: Abstract Today's environments of increasing business change require software development methodologies that are more adaptable. This article examines how complex adaptive systems (CAS) theory can be used to increase our understanding of how agile software development practices can be used to develop this capability. A mapping of agile practices to CAS principles and three dimensions (product, process, and people) results in several recommendations for “best practices” in systems development.


title: Social-Aware Movie Recommendation via Multimodal Network Learning

abstract: With the rapid development of Internet movie industry social-aware movie recommendation systems (SMRs) have become a popular online web service that provide relevant movie recommendations to users. In this effort many existing movie recommendation approaches learn a user ranking model from user feedback with respect to the movie's content. Unfortunately this approach suffers from the sparsity problem inherent in SMR data. In the present work we address the sparsity problem by learning a multimodal network representation for ranking movie recommendations. We develop a heterogeneous SMR network for movie recommendation that exploits the textual description and movie-poster image of each movie as well as user ratings and social relationships. With this multimodal data we then present a heterogeneous information network learning framework called SMR-multimodal network representation learning (MNRL) for movie recommendation. To learn a ranking metric from the heterogeneous information network we also developed a multimodal neural network model. We evaluated this model on a large-scale dataset from a real world SMR Web site and we find that SMR-MNRL achieves better performance than other state-of-the-art solutions to the problem.


title: Self-Oscillating Fly-back Converter for Mobile Batteries Charging Applications

abstract: The self-oscillating flyback converter is a popular circuit for cost-sensitive applications due to its simplicity and low component count. This paper presents self-oscillating fly-back converter for mobile batteries charging applications. The converter is designed to step-down the high voltage input to an acceptable level from 220 VAC to 7VDC. By adding indictor and capacitor the power factor and input currant increased, and became 500mA, where the old input currant was 250mA, Diode was connected between Emitter and collector, so the output voltage was improved, and has less ripple, in addition to the soft switching was obtained, simulation model is developed by using MULTISIM software. The operation principle of this converter is described using some operating waveforms based on simulation results and a detailed circuit operation analysis. Analytical results are verified by laboratory experiment. With an input of 220 VAC, 500mA and an output of 7V DC, the efficiency of circuit was75%. Index Terms: Mobile battery charger, DC-DC converter, fly back converter, self-oscillating fly-back converter


title: Sequential Conformal Anomaly Detection in trajectories based on Hausdorff distance

abstract: Abnormal behaviour may indicate important objects and situations in e.g. surveillance applications. This paper is concerned with algorithms for automated anomaly detection in trajectory data. Based on the theory of Conformal prediction, we propose the Similarity based Nearest Neighbour Conformal Anomaly Detector (SNN-CAD) which is a parameter-light algorithm for on-line learning and anomaly detection with well-calibrated false alarm rate. The only design parameter in SNN-CAD is the dissimilarity measure. We propose two parameter-free dissimilarity measures based on Hausdorff distance for comparing multi-dimensional trajectories of arbitrary length. One of these measures is appropriate for sequential anomaly detection in incomplete trajectories. The proposed algorithms are evaluated using two public data sets. Results show that high sensitivity to labelled anomalies and low false alarm rate can be achieved without any parameter tuning.


Analysis of Roadway Traffic using Data Mining Techniques for providing safety Measures to Avoid Fatal Accidents


title: Spinal epidural lipomatosis associated with Paget's disease of bone

abstract: We present a case of epidural lipomatosis in a non-obese patient with no history of steroid use. On MRI, the lipomatous mass had an unusual appearance and was centered over thoracic vertebrae with Paget's disease. Decompressive laminectomy was effective in promoting resolution of the neurological deficits.


title: Enhancing the LexVec Distributed Word Representation Model Using Positional Contexts and External Memory

abstract: In this paper we take a state-of-the-art model for distributed word representation that explicitly factorizes the positive pointwise mutual information (PPMI) matrix using window sampling and negative sampling and address two of its shortcomings. We improve syntactic performance by using positional contexts, and solve the need to store the PPMI matrix in memory by working on aggregate data in external memory. The effectiveness of both modifications is shown using word similarity and analogy tasks.


The foundation of the concept of relevance


title: Developments in Snake Robot Modeling and Locomotion

abstract: Snake robots may one day play a crucial role in search and rescue operations and fire-fighting where it may either be too narrow or to dangerous for personnel to operate. Properties such as high terrainability, redundancy, and the possibility of complete sealing of the body of the robot, make snake robots very interesting for practical applications and hence as a research topic. During the last ten to fifteen years, the published literature on snake robots has increased vastly. However, no thorough review of the theory presented in this period regarding mathematical modeling techniques and locomotion of snake robots has been found. The purpose of this paper is to give such a review. Both purely kinematic models and models including dynamics are investigated. The choice of modeling method is linked to snake robot design characteristics and locomotion approach. Different approaches to biologically inspired locomotion are also discussed.


title: A CPW Fed XB

abstract: This paper presents the analysis of simple, compact, CPW- fed monopole aperture antenna with 10dB return loss (VSWR<;2) band from 8 GHz to 12GHz for X-band applications. The antenna includes ground plane, rectangular slot and a crossed rectangular exciting stub. The antenna is very simple and easy to fabricate. The proposed antenna is designed on an inexpensive FR4_Epoxy substrate with dielectric constant of er =4.4 and with the size of 30mm×25mm×1.5mm The fundamental parameters of the antenna such as bandwidth return loss, radiation pattern, and gain are obtained All justifies the acceptable antenna standards. Simulation tool, based on the finite element method (HFSS V 13.0) has been used to analyze and optimize the proposed antenna Antenna is applicable for radar and satellite communication system It covers complete X-band (8-12) GHz and having the efficiency of 97%.


title: Reentrant Origami-Based Metamaterials with Negative Poisson's Ratio and Bistability.

abstract: We investigate the unique mechanical properties of reentrant 3D origami structures based on the Tachi-Miura polyhedron (TMP). We explore the potential usage as mechanical metamaterials that exhibit tunable negative Poisson's ratio and structural bistability simultaneously. We show analytically and experimentally that the Poisson's ratio changes from positive to negative and vice versa during its folding motion. In addition, we verify the bistable mechanism of the reentrant 3D TMP under rigid origami configurations without relying on the buckling motions of planar origami surfaces. This study forms a foundation in designing and constructing TMP-based metamaterials in the form of bellowslike structures for engineering applications.


title: Polytomous logistic regression analysis could be applied more often in diagnostic research

abstract: Objective: Physicians commonly consider the presence of all differential diagnoses simultaneously. Polytomous logistic regression modeling allows for simultaneous estimation of the probability of multiple diagnoses. We discuss and (empirically) illustrate the value of this method for diagnostic research. Study Design and Setting: We used data from a study on the diagnosis of residual retroperitoneal mass histology in patients presenting with nonseminomatous testicular germ cell tumor. The differential diagnoses include benign tissue, mature teratoma, and viable cancer. Probabilities of each diagnosis were estimated with a polytomous logistic regression model and compared with the probabilities estimated from two consecutive dichotomous logistic regression models. Results: We provide interpretations of the odds ratios derived from the polytomous regression model and present a simple score chart to facilitate calculation of predicted probabilities from the polytomous model. For both modeling methods, we show the calibration plots and receiver operating characteristics curve (ROC) areas comparing each diagnostic outcome category with the other two. The ROC areas for benign tissue, mature teratoma, and viable cancer were similar for both modeling methods, 0.83 (95% confidence interval [CI] = 0.80-0.85) vs. 0.83 (95% CI = 0.80-0.85), 0.78 (95% CI = 0.75-0.81) vs. 0.78 (95% CI = 0.75-0.81), and 0.66 (95% CI = 0.61-0.71) vs. 0.64 (95% CI = 0.59-0.69), for polytomous and dichotomous regression models, respectively. Conclusion: Polytomous logistic regression is a useful technique to simultaneously model predicted probabilities of multiple diagnostic outcome categories. The performance of a polytomous prediction model can be assessed similarly to a dichotomous logistic regression model, and predictions by a polytomous model can be made with a user-friendly method. Because the simultaneous consideration of the presence of multiple (differential) conditions serves clinical practice better than consideration of the presence of only one target condition, polytomous logistic regression could be applied more often in diagnostic research.


title: Efficient Stixel-based object recognition

abstract: This paper presents a novel attention mechanism to improve stereo-vision based object recognition systems in terms of recognition performance and computational efficiency at the same time. We utilize the Stixel World, a compact medium-level 3D representation of the local environment, as an early focus-of-attention stage for subsequent system modules. In particular, the search space of computationally expensive pattern classifiers is significantly narrowed down. We explicitly couple the 3D Stixel representation with prior knowledge about the object class of interest, i.e. 3D geometry and symmetry, to precisely focus processing on well-defined local regions that are consistent with the environment model. Experiments are conducted on large real-world datasets captured from a moving vehicle in urban traffic. In case of vehicle recognition as an experimental testbed, we demonstrate that the proposed Stixel-based attention mechanism significantly reduces false positive rates at constant sensitivity levels by up to a factor of 8 over state-of-the-art. At the same time, computational costs are reduced by more than an order of magnitude.


title: Foot-based mobile interaction with games

abstract: Interaction with mobile applications is often awkward due to the limited and miniaturized input modalities available. This is especially problematic for games where the only incentive to use an application is the pleasure derived from the interaction. It is therefore interesting to examine novel forms of interaction in order to increase the "playability" of mobile games.In this paper we present a simple mobile gaming application on a standard Pocket PC PDA that employs computer vision (CV) as it's main interaction modality. Practical experience with the application demonstrates the feasibility of CV as a primary interaction modality and indicates the high potential of CV as an input modality for mobile devices in the future. Our approach exploits the video capabilities that are becoming ubiquitous on camera equipped smart-phones and PDAs to provide a fun solution for interaction tasks in games like "Pong", "Break-out" or soccer.


An Adaption of BIOASQ Question Answering dataset for Machine Reading systems by Manual Annotations of Answer Spans.


title: Ethical Analyses of Smart City Applications

abstract: When it comes to smart cities, one of the most important components is data. To enable smart city applications, data needs to be collected, stored, and processed to accomplish intelligent tasks. In this paper we discuss smart cities and the use of new and existing technologies to improve multiple aspects of these cities. There are also social and environmental aspects that have become important in smart cities that create concerns regarding ethics and ethical conduct. Thus we discuss various issues relating to the appropriate and ethical use of smart city applications and their data. Many smart city projects are being implemented and here we showcase several examples to provide context for our ethical analysis. Law enforcement, structure efficiency, utility efficiency, and traffic flow control applications are some areas that could have the most gains in smart cities; yet, they are the most pervasive as the applications performing these activities must collect and process the most private data about the citizens. The secure and ethical use of this data must be a top priority within every project. The paper also provides a list of challenges for smart city applications pertaining in some ways to ethics. These challenges are drawn from the studied examples of smart city projects to bring attention to ethical issues and raise awareness of the need to address and regulate such use of data.


Mining Pixels: Weakly Supervised Semantic Segmentation Using Image Labels


title: Three Dimensions of Business Intelligence Systems Use Behavior

abstract: In the Business Intelligence Systems (BIS) use context, the well-researched dimension of intensity of use is not broad enough to capture its meaningful use. The authors therefore investigate the whole spectrum of its use that encompasses effective utilization. The post-adoption research of Information System (IS) use is still under-researched; the study therefore brings novel insights to this area. The authors have conceptualized three dimensions of BIS use that capture the intensity of BIS use, the extent of BIS use, and BIS embeddedness. Confirmatory Factor Analysis (CFA) was used to determine convergent and discriminant validities proving construct validities, based on survey data gathered from BIS users in medium and large sized organizations. The results of our study provide sound evidence for apprehending these three constructs as different conceptualizations. This work adds to the literature by emphasizing the role of post-adoptive behavior that can lead to greater and effective utilization in the studied BIS environment, and potentially to a better organizational performance.


title: Actor-Critic Reinforcement Learning with Simultaneous Human Control and Feedback

abstract: This paper contributes a first study into how different human users deliver simultaneous control and feedback signals during human-robot interaction. As part of this work, we formalize and present a general interactive learning framework for online cooperation between humans and reinforcement learning agents. In many human-machine interaction settings, there is a growing gap between the degrees-of-freedom of complex semi-autonomous systems and the number of human control channels. Simple human control and feedback mechanisms are required to close this gap and allow for better collaboration between humans and machines on complex tasks. To better inform the design of concurrent control and feedback interfaces, we present experimental results from a human-robot collaborative domain wherein the human must simultaneously deliver both control and feedback signals to interactively train an actor-critic reinforcement learning robot. We compare three experimental conditions: 1) human delivered control signals, 2) reward-shaping feedback signals, and 3) simultaneous control and feedback. Our results suggest that subjects provide less feedback when simultaneously delivering feedback and control signals and that control signal quality is not significantly diminished. Our data suggest that subjects may also modify when and how they provide feedback. Through algorithmic development and tuning informed by this study, we expect semi-autonomous actions of robotic agents can be better shaped by human feedback, allowing for seamless collaboration and improved performance in difficult interactive domains.


title: Analysis of Scan Blindness in a Linearly Polarized Tapered-Slot Phased Array in Triangular Lattice—Performance Improvement With Parasitic Notches

abstract: Various types of scan blindness are analyzed in a single-polarized tapered-slot phased array with triangular lattice arrangement, and a scan blindness removal strategy is proposed. In the presented analysis, two types of scan blindness are observed which have not been previously reported. First, a special case of parallel plate cavity resonance is demonstrated to cause scan blindness at a fixed frequency for E-plane scanning. Second, another cause for E-plane scan blindness is extended from previous observations, by including the fundamental mode of a half-mode open waveguide. This type of leaky-wave-induced scan blindness is limited to triangular lattices and can occur at lower frequencies compared to other types of leaky-wave blindness. In the second part of this paper, parasitic notches are proposed as an effective measure to significantly improve the E-plane scanning characteristics of the considered array by reducing the magnitude of the leaky modes. As a beneficial side-effect, these parasitic notches are also proven to improve the H-plane scanning characteristics by suppressing resonances within the stripline substrate cavity of the individual tapered-slot elements. Numerical simulations and measurements performed on a 169-element array prototype validate the analysis and confirm the effectiveness of the proposed concept for scan blindness suppression.


title: A Jury of Her Peers: The Impact of the First Female Jurors on Criminal Convictions

abstract: This paper uses an original data set of more than 3000 cases from 1918 to 1926 in the Central Criminal Courts of London to study the effect of the Sex Disqualification (Removal) Act of 1919. Implemented in 1921, this Act made females eligible to serve on English juries, providing a novel setting for studying the impact of female representation on jury verdicts. Results based on a pre-post research design imply that the inclusion of females had little effect on overall conviction rates but resulted in a large and significant increase in convictions for sex offenses and on the conviction rate differential between violent crime cases with female versus male victims. The inclusion of women also increased the likelihood of juries being discharged without reaching a verdict on all charges and the average time taken to reach a verdict. A complementary analysis of cases in which the jury was carried over from a previous trial also implies that the inclusion of female jurors on the seated jury sharply increased conviction rates for violent crimes against women versus men.


title: Hidden Ciphertext Policy Attribute-Based Encryption Under Standard Assumptions

abstract: We propose two new ciphertext policy attribute-based encryption (CP-ABE) schemes where the access policy is defined by AND-gate with wildcard. In the first scheme, we present a new technique that uses only one group element to represent an attribute, while the existing ABE schemes of the same type need to use three different group elements to represent an attribute for the three possible values (namely, positive, negative, and wildcard). Our new technique leads to a new CP-ABE scheme with constant ciphertext size, which, however, cannot hide the access policy used for encryption. The main contribution of this paper is to propose a new CP-ABE scheme with the property of hidden access policy by extending the technique we used in the construction of our first scheme. In particular, we show a way to bridge ABE based on AND-gate with wildcard with inner product encryption and then use the latter to achieve the goal of hidden access policy. We prove that our second scheme is secure under the standard decisional linear and decisional bilinear Diffie-Hellman assumptions.


title: Shape-tailored local descriptors and their application to segmentation and tracking

abstract: We propose new dense descriptors for texture segmentation. Given a region of arbitrary shape in an image, these descriptors are formed from shape-dependent scale spaces of oriented gradients. These scale spaces are defined by Poisson-like partial differential equations. A key property of our new descriptors is that they do not aggregate image data across the boundary of the region, in contrast to existing descriptors based on aggregation of oriented gradients. As an example, we show how the descriptor can be incorporated in a Mumford-Shah energy for texture segmentation. We test our method on several challenging datasets for texture segmentation and textured object tracking. Experiments indicate that our descriptors lead to more accurate segmentation than non-shape dependent descriptors and the state-of-the-art in texture segmentation.


title: The Hexapodopter: A holonomic flying hexapod

abstract: This document introduces an air-land robotic platform called Hexapodopter, capable of ground locomotion and vertical take-off and landing (VTOL). For walking, each of the limbs feature three degrees of freedom (DoF); for flying, the thrust is provided by a set of motors and propellers mounted on every limb. Due to its mechanical design, the Hexapodopter can reconfigure the thrust vectors of propellers into a true holonomic configuration during flight; in other words, the vehicle is capable of controlling individually its 6 degrees of freedom on the air, without reorienting any of the thrust motors nor the body. In this document, we present a formal analysis of the holonomic properties of the vehicle, not found in related works, as well as a control architecture for the Hexapodopter, exploiting these properties, shown through numerical test results.


title: Organization's structure based on competing value approach and merger strategy

abstract: Organization is a collection of people who are formally united and agreed to fulfill the same goals. The organization effectiveness using the competing values approach is divided into 4 quadrants. To fulfill goals the organization needs strategy. The merger strategy used to grow the organization externally. Strategies affecting the structure. The methods of this paper using literature review (secondary data). This paper describes the structure (complexity, formalization and centralization) of the new organization (merger) based on the competing values approach which in the entrepreneurial stage (first stage of the organization life cycle). The conclusion will be the basic hypotheses to be deeply investigated in the next research of contextual organization empirical study.


title: An interdisciplinary course on artificial intelligence designed for a liberal arts curriculum

abstract: This article introduces a course the author recently designed; and which was first taught in Fall 2006. The course provides an introduction to the field of artificial intelligence (AI) focused at non-majors as part of a liberal arts curriculum. The course provides an overview of the foundational science and philosophy at the heart of AI from an interdisciplinary perspective. The course largely focuses on the diverse applications to many fields of endeavor, and the impact AI is beginning to have on areas of society as well as the potential future impact. The course draws together topics from diverse fields including computer science, philosophy, psychology, game theory, among others. One of the goals of the course is to engage students from disciplines outside the computing sciences, while drawing on the strengths of their individual backgrounds. A secondary objective is to raise awareness among students outside of the computing sciences that there is more to computer science than programming and mathematics (e.g., that some CS topics are highly interdisciplinary). A key teaching element of this course is that of utilizing prior knowledge for discussion context.


title: Sexuality and Gender Role in Autism Spectrum Disorder: A Case Control Study

abstract: The ‘extreme male brain theory of autism’ describes an extreme male pattern of cognitive traits defined as strong systemising abilities paired with empathising weaknesses in autism spectrum disorder. However, beyond these cognitive traits, clinical observations have suggested an ambiguous gender-typed pattern regarding several sexually dimorphic traits. ::: ::: The aim of the present study was to investigate if patterns of non-cognitive sexually dimorphic traits differed between the autism spectrum disorder and control groups. Fifty adults with autism spectrum disorder and intelligence within the normal range, and 53 neurotypical controls responded to questions on gender role, self-perceived gender typicality and gender identity, as well as sexuality. Measures used were a Swedish modification of the Bem Sex Role Inventory and questions on sexuality and gender designed for the purpose of this study. Our results showed that one common gender role emerged in the autism spectrum disorder group. Masculinity (e.g. assertiveness, leadership and competitiveness) was weaker in the autism spectrum disorder group than in the controls, across men and women. Self-perceived gender typicality did not differ between the groups but tomboyism and bisexuality were overrepresented amongst women with autism spectrum disorder. Lower libido was reported amongst both male and female participants with autism spectrum disorder compared with controls. We conclude that the extreme male patterns of cognitive functions in the autistic brain do not seem to extend to gender role and sexuality. A gender-atypical pattern for these types of characteristics is suggested in autism spectrum disorder.


title: An Improved Submodule Differential Power Processing-Based PV System With Flexible Multi-MPPT Control

abstract: With inhomogeneous irradiance on photovoltaic (PV) units significantly deteriorating the effectiveness of solar energy harvest, distributed maximum power point tracking (DMPPT) concept is considered as an effective solution against resultant mismatch. A comparative analysis of full power processing and differential power processing structures, as two main branches of DMPPT solution, is provided first. Advantages and disadvantages of widely used structures are analyzed and compared from different aspects. To harvest more power and realize better utilization of PV units, this paper presents a submodule level differential power processing PV system with a flexible double stage time-sharing maximum power point tracking (MPPT) control strategy. The proposal can achieve the accurate maximum power points (MPPs) of all PV submodules regardless of mismatch cases with only one adaptive MPPT controller, facilitating system integration and largely reducing system cost and conversion loss. The efficacy of the scheme is analyzed using Simulink and validated by experimental results.


title: A Sensor System for Automatic Detection of Food Intake Through Non-Invasive Monitoring of Chewing

abstract: Objective and automatic sensor systems to monitor ingestive behavior of individuals arise as a potential solution to replace inaccurate method of self-report. This paper presents a simple sensor system and related signal processing and pattern recognition methodologies to detect periods of food intake based on non-invasive monitoring of chewing. A piezoelectric strain gauge sensor was used to capture movement of the lower jaw from 20 volunteers during periods of quiet sitting, talking and food consumption. These signals were segmented into non-overlapping epochs of fixed length and processed to extract a set of 250 time and frequency domain features for each epoch. A forward feature selection procedure was implemented to choose the most relevant features, identifying from 4 to 11 features most critical for food intake detection. Support vector machine classifiers were trained to create food intake detection models. Twenty-fold cross-validation demonstrated per-epoch classification accuracy of 80.98% and a fine time resolution of 30 s. The simplicity of the chewing strain sensor may result in a less intrusive and simpler way to detect food intake. The proposed methodology could lead to the development of a wearable sensor system to assess eating behaviors of individuals.


title: Edge Agreement: Graph-Theoretic Performance Bounds and Passivity Analysis

abstract: This work explores the properties of the edge variant of the graph Laplacian in the context of the edge agreement problem. We show that the edge Laplacian, and its corresponding agreement protocol, provides a useful perspective on the well-known node agreement, or the consensus algorithm. Specifically, the dynamics induced by the edge Laplacian facilitates a better understanding of the role of certain subgraphs, e.g., cycles and spanning trees, in the original agreement problem. Using the edge Laplacian, we proceed to examine graph-theoretic characterizations of the H2 and H∞ performance for the agreement protocol. These results are subsequently applied in the contexts of optimal sensor placement for consensus-based applications. Finally, the edge Laplacian is employed to provide new insights into the nonlinear extension of linear agreement to agents with passive dynamics.


title: Cortisol increase in empathic stress is modulated by emotional closeness and observation modality

abstract: Summary Stress disorders are among the most commonly occurring of all mental disorders. In this context, the question arises whether the stress inevitably unfolding around us has the potential to ‘‘contaminate’’ and compromise us. In the current multi-center study, we investigate the existence of such empathic stress (defined as a full-blown physiological stress response that arises solely by observingatargetundergoastressfulsituation),andwhetherempathicstresspermeatestothecoreof the stress system, the hypothalamic-pituitary-adrenal (HPA) axis. Additionally, we investigate whether empathic stress responses may be modulated by the familiarity between observer and target (partners vs. strangers), the modality of observation (real-life vs. virtual) and observer sex (female vs. male). Participants were tested in dyads, paired with a loved one or a stranger of the opposite sex. While the target of the dyad (n = 151) was exposed to a psychosocial stressor, the observer (n = 211) watched through a one-way mirror or via live video transmission. Overall, 26% of the observers displayed physiologically significant cortisol increases. This empathic stress was more pronounced in intimate observer-target dyads (40%) and during the real-life representation of the stressor (30%). Empathic stress was further modulated by interindividual differences in empathy measures. Despite the higher prevalence of empathic stress in the partner and real-life observation conditions, significantcortisolresponsesalsoemergedinstrangers(10%)andthevirtualobservationmodality(24%).The occurrence of empathic stress down to the level of HPA-axis activation, in some cases even in total strangers and when only virtually witnessing another’s distress, may have important implications for the development of stress-related diseases.


title: POSTER: A Behavioral Biometric Authentication Framework on Smartphones

abstract: To protect smartphones from unauthorized access, the user has the option to activate authentication mechanisms : PIN, Password, or Pattern. Unfortunately, these mechanisms are vulnerable to shoulder-surfing, smudge and snooping attacks. Even the traditional biometric based systems such as fingerprint or face, also could be bypassed. In order to protect smartphones data against these sort of attacks, we propose a behavioral biometric authentication framework that leverages the user's behavioral patterns such as touchscreen actions, keystroke, application used and sensor data to authenticate smartphone users. To evaluate the framework, we conducted a field study in which we instrumented the Android OS and collected data from 52 participants during 30-day period. We present the prototype of our framework and we are working on its components to select the best features set that can be used to build different modalities to authenticate users on different contexts. To this end, we developed only one modality, a gesture authentication modality, which authenticate smartphone users based on touch gesture. We evaluated this authentication modality on about 3 million gesture samples based on two schemes, classification scheme with EER~0.004, and anomaly detection scheme with EER~0.10.


title: Gait Recognition Based on Normal Distance Maps

abstract: Gait is a commonly used biometric for human recognition. Its main advantage relies on its ability to identify people at distances at which other biometrics fail. In this paper, we develop a new approach for gait recognition that combines the distance transform with curvatures of local contours. We call our gait feature template the normal distance map. Our method encodes both body shapes and boundary curvatures into a novel feature descriptor that is more robust than existing gait representations. We evaluate our approach on the widely used and challenging USF and CASIA-B datasets. Furthermore, we evaluate it on the OU-ISIR gait dataset, the largest one available in the literature, to obtain statistically reliable results. We verify our approach is significantly superior to the current state-of-the-art under most conditions.