title: Animal-assisted therapy and loneliness in nursing homes: use of robotic versus living dogs.

abstract: Loneliness is a common problem in long-term care facilities (LTCF) and previous work has shown that animal-assisted therapy (AAT) can to some degree reverse loneliness. Here, we compared the ability of a living dog (Dog) and a robotic dog (AIBO) to treat loneliness in elderly patients living in LTCF. In comparison with a control group not receiving AAT, both the Dog and AIBO groups had statistically significant improvements in their levels of loneliness. As measured by a modified Lexington Attachment to Pets Scale (MLAPS), residents showed high levels of attachment to both the dog and AIBO. Subscale analysis showed that the AIBO group scored lower than the living dog on "animal rights/animal welfare" but not on "general attachment" or "people substituting." However, MLAPS measures did not correlate with changes in loneliness, showing that attachment was not the mechanism by which AAT decreases loneliness. We conclude that interactive robotic dogs can reduce loneliness in residents of LTCF and that residents become attached to these robots. However, level of attachment does not explain the decrease in loneliness associated with AAT conducted with either a living or robotic dog.


title: Image encryption using DNA complementary rule and chaotic maps

abstract: This paper proposes a novel confusion and diffusion method for image encryption. One innovation is to confuse the pixels by transforming the nucleotide into its base pair for random times, the other is to generate the new keys according to the plain image and the common keys, which can make the initial conditions of the chaotic maps change automatically in every encryption process. For any size of the original grayscale image, after being permuted the rows and columns respectively by the arrays generated by piecewise linear chaotic map (PWLCM), each pixel of the original image is encoded into four nucleotides by the deoxyribonucleic acid (DNA) coding, then each nucleotide is transformed into its base pair for random time(s) using the complementary rule, the times is generated by Chebyshev maps. Experiment results and security analysis show that the scheme can not only achieve good encryption result, but also the key space is large enough to resist against common attacks.


title: Common skin disorders of the penis

abstract: Summary ::: Diseases of the male genitalia range from infectious lesions to inflammatory and neoplastic conditions, including many genital manifestations of more general skin diseases. This review highlights the clinical features, diagnosis and treatment of the most common dermatoses of the male genitalia. Herpes genitalis and infections caused by human papillomavirus (HPV) are increasing, particularly in young sexually active people. Herpes simplex virus infection is the commonest infectious cause of genital ulceration, with evidence that many infections are asymptomatic. HPV infection may be latent, subclinical and clinical. The most common causal agents for condyloma acuminatum are low-risk HPV 6 and 11; high-risk HPV types 16 and 18 are associated with premalignant and malignant lesions. Treatment for genital warts remains unsatisfactory; recurrences are common. Imiquimod, a new topical immunotherapeutic agent, which induces interferon and other cytokines, has the potential to be a first-line therapy for genital warts. Scabies and pediculosis are transmitted by skin-to-skin contact and sexual transmission is common, with the penis and scrotum favourite locations for scabious lesions. Oral ivermectin, a highly active antiparasitic drug, is likely to be the treatment of choice, but until approval is granted it should be reserved for special forms of scabies. Common skin diseases, e.g. psoriasis and lichen planus, may have an atypical appearance in the genital area. The typical psoriatic scale is usually not apparent because of moisture and maceration. Allergic contact dermatitis of the genital area may result from condoms, lubricants, feminine hygiene deodorant spray and spermicides. More often, contact dermatitis is irritant, resulting from persistent moisture and maceration. Lichen sclerosus is a chronic inflammatory disease that occurs as atrophic white patches on the glans penis and foreskin. The penile form is a common cause of phimosis in uncircumcised men; involvement of the urethral meatus may lead to progressive meatal stenosis. Plasma cell balanitis is a benign, idiopathic condition presenting as a solitary, smooth, shiny, red-orange plaque of the glans and prepuce of a middle-aged to older man. Squamous cell carcinoma (SCC) in situ, e.g. erythroplasia of Queyrat and Bowen's disease, cannot be excluded clinically; their apparent clinical benignity may lead to lengthy periods of misdiagnosis and biopsy is required to confirm the diagnosis. SCC is the most common malignancy of the penis and the role of oncogenic HPV-types has been also established in SCC of the penis. Prevention of SCC of the penis presupposes an identification of risk factors, early detection of all pre-cancerous lesions and treatment of phimosis.


title: ShadowCrypt: Encrypted Web Applications for Everyone

abstract: A number of recent research and industry proposals discussed using encrypted data in web applications. We first present a systematization of the design space of web applications and highlight the advantages and limitations of current proposals. Next, we present ShadowCrypt, a previously unexplored design point that enables encrypted input/output without trusting any part of the web applications. ShadowCrypt allows users to transparently switch to encrypted input/output for text-based web applications. ShadowCrypt runs as a browser extension, replacing input elements in a page with secure, isolated shadow inputs and encrypted text with secure, isolated cleartext. ShadowCrypt's key innovation is the use of Shadow DOM, an upcoming primitive that allows low-overhead isolation of DOM trees. Evaluation results indicate that ShadowCrypt has low overhead and of practical use today. Finally, based on our experience with ShadowCrypt, we present a study of 17 popular web applications, across different domains, and the functionality impact and security advantages of encrypting the data they handle.


Intuitionistic fuzzy graph structures


title: Beamforming concepts for angular measurements in azimuth and elevation with 77 GHz lens based radar sensors

abstract: With the rapid development of highly integrated 77 GHz SiGe-Radar-MMICs, cost effective and reliable radar systems are feasible to significantly increase driving safety in all vehicle classes. New functions have to be implemented in these sensors to fulfill the demanding requirements in future advanced driver assistance systems. One criterion will be the exact measurement of angles in the azimuth and also in the elevation plane. A beamforming concept for angular measurements in both directions with one single sensor, using a combination of series fed arrays and a cylindrical dielectric lens, is presented in this paper.


title: An Analysis of Latch Comparator Offset Due to Load Capacitor Mismatch

abstract: This brief analyzes the effect of load capacitor mismatch on the offset of a regenerative latch comparator. Two analytical models are presented and compared with HSpice simulations. Our results indicate that in a typical 0.18-mum CMOS latch, a capacitive imbalance of only 1 fF can lead to offsets of several tens of millivolts


title: SAS Global Forum 2012 Applied Business Intelligence Paper 031-2012 Using SAS ® Enterprise BI and SAS ® Enterprise Miner TM to Reduce Student Attrition

abstract: The true supremacy of the SAS® Enterprise Business Intelligence Server is the ability to utilize the power of SAS® Analytics to deliver real-time information to end users, who usually do not understand statistics, but have the ability to make a difference if they have easy access to the analyzed data. This paper describes the process of using SAS® Enterprise Miner to develop a model to score university students based on their risk of attrition and deliver easy-to-understand results to university personnel using SAS® EBI.


title: A Deterministic Analysis for LRR

abstract: The recently proposed low-rank representation (LRR) method has been empirically shown to be useful in various tasks such as motion segmentation, image segmentation, saliency detection and face recognition. While potentially powerful, LRR depends heavily on the configuration of its key parameter, $\lambda$ . In realistic environments where the prior knowledge about data is lacking, however, it is still unknown how to choose $\lambda$ in a suitable way. Even more, there is a lack of rigorous analysis about the success conditions of the method, and thus the significance of LRR is a little bit vague. In this paper we therefore establish a theoretical analysis for LRR, striving for figuring out under which conditions LRR can be successful, and deriving a moderately good estimate to the key parameter $\lambda$ as well. Simulations on synthetic data points and experiments on real motion sequences verify our claims.


title: Hybrid FMCW-interferometry radar system in the 5.8 GHz ISM band for indoor precise position and motion detection

abstract: A hybrid FMCW-interferometry radar sensor is presented. The proposed measurement system incorporates the linear frequency-modulated continuous-wave (FMCW) mode and the continuous wave (CW) interferometry mode. The radar system works in 5.8 GHz ISM band with a bandwidth of 160 MHz. Equipped with a special signal processing method, the hybrid system is capable of detecting absolute distance as well as slow motion. Experiments show that the absolute position detection has an average accuracy of 1.65 cm, and the accuracy of relative motion tracking is better than 2 mm.


title: Realistic Design of a Floating Breakwater Design with Moonpools

abstract: In an attempt to obtain a 2D floating breakwater model with high performance in wave reflection, genetic algorithm (GA) was combined with boundary element method (BEM) in the previous study. The performance of the obtained model was verified with numerical relations as well as an experiment in towing tank. Moreover, its performance and characteristics in 3D case were also evaluated in the subsequent study. However, because the 3D model is formed by simply extruding the 2D shape in longitudinal direction, it only produces a model with uniform transversal shape which is considered to be less effective and efficient in terms of technical and economical points of view. Consequently, it is needed to modify the model to obtain a more realistic and efficient design without reducing significantly the high performance obtained previously. In the present study, several modifications of the original 3D model are performed which include placing moonpools inside the body. The performance and characteristics of the modified models in terms of wave elevations on the free surface are evaluated at various wavelengths by using higher order boundary element method (HOBEM). The accuracy of the computed results is confirmed with Haskind-Newman and energy conservation relations. From the modifications and evaluations of the models, it could be realized that the moonpools inside the body could be used to obtain a more realistic model without reducing the optimum performance of the original model shape.


title: The effects of introducing Tabata interval training and stability exercises to school children as a school-based intervention program

abstract: Background Physical activities during leisure time as well as school hours have changed over the past few years, with adolescents being less physically active and adopting a sedentary lifestyle. Ob ...


See No Evil, Hear No Evil, Feel No Evil, Print No Evil? Malicious Fill Patterns Detection in Additive Manufacturing


title: Physical activity predicts gray matter volume in late adulthood: The Cardiovascular Health Study(e–Pub ahead of print)

abstract: Objectives:Physical activity (PA) has been hypothesized to spare gray matter volume in late adulthood, but longitudinal data testing an association has been lacking. Here we tested whether PA would be associated with greater gray matter volume after a 9-year follow-up, a threshold could be identified for the amount of walking necessary to spare gray matter volume, and greater gray matter volume associated with PA would be associated with a reduced risk for cognitive impairment 13 years after the PA evaluation. Methods:In 299 adults (mean age 78 years) from the Cardiovascular Health Cognition Study, we examined the association between gray matter volume, PA, and cognitive impairment. Physical activity was quantified as the number of blocks walked over 1 week. High-resolution brain scans were acquired 9 years after the PA assessment on cognitively normal adults. White matter hyperintensities, ventricular grade, and other health variables at baseline were used as covariates. Clinical adjudication for cognitive impairment occurred 13 years after baseline. Results:Walking amounts ranged from 0 to 300 blocks (mean 56.3; SD 69.7). Greater PA predicted greater volumes of frontal, occipital, entorhinal, and hippocampal regions 9 years later. Walking 72 blocks was necessary to detect increased gray matter volume but walking more than 72 blocks did not spare additional volume. Greater gray matter volume with PA reduced the risk for cognitive impairment 2-fold. Conclusion:Greater amounts of walking are associated with greater gray matter volume, which is in turn associated with a reduced risk of cognitive impairment. GLOSSARY3MSE = modified Mini-Mental State Examination; CHS-CS = Cardiovascular Health Study Cognition Study; DSST = Digit Symbol Substitution Test; GM = gray matter; MCI = mild cognitive impairment; OR = odds ratio; PA = physical activity; SPM = Statistical Parametric Mapping; TIV = total intracranial volume; VBM = voxel-based morphometry; WM = white matter.


title: Device and operation mechanism for non-beacon IEEE802.15.4/Zigbee nodes running on harvested energy

abstract: The power supply is one of the main constraints when operating Wireless Sensor Networks (WSN). The most obvious energy source for a WSN node is a rechargeable or non-rechargeable battery. Batteries seriously limit WSNs usage and are associated to the increasing cost of large networks. Low cost networks will be achieved when battery consumption becomes substantially reduced or eliminated. Within this scope, powering WSN devices using mains power, is not an advantage since node installation requires complex and time consuming actions. Replacing batteries is therefore an important topic even in environments with mains power availability. In this paper, a battery-less device running an IEEE802.15.4/Zigbee protocol stack on harvested energy is proposed. Its operation mechanism and required software adaptation to sustain this complex protocol are described. The protocol compliant node uses a contact-less scavenger system that is able to power a WSN node from mains power lines thus providing an inexpensive and easily installable device even for non-skilled users.


title: Opportunistic communications in WSN using UAV

abstract: This paper studies the opportunistic routing (OR) in unmanned aerial vehicle (UAV) assisted wireless sensor networks (WSNs). We consider the scenario where a UAV collects data from randomly deployed mobile sensors that are moving with different velocities along a predefined route. Due to the dynamic topology, mobile sensors have different opportunities to communicate with the UAV. This paper proposes the All Neighbors Opportunistic Routing (ANOR) and Highest Velocity Opportunistic Routing (HVOR) protocols. In essence, ANOR forwards packets to all neighbors and HVOR forwards them to one neighbor with highest velocity. HVOR is a new OR protocol which dynamically selects route on a pre-transmission basis in multi-hop network. HVOR helps the sensor which has little opportunity to communicate with the UAV to determine which sensor, among all the sensors that are within its range, is the forwarder. The selected node forwards the packet. As a result, in each hop, the packet moves to the sensor that has higher opportunity to communicate with the UAV. In addition, we focus on various performance metrics, including Packets Delivery Ratio (PDR), Routing Overhead Ratio (ROR), Average Latency (AL) and Average Hop Count (AHC), to evaluate the proposed algorithms and compare them with a Direct Communication (DC) protocol. Through extensive simulations, we have shown that both HVOR and ANOR algorithms work better than DC. Moreover, the HVOR algorithm outperforms the other two algorithms in terms of the average overhead.


title: The Quantum PCP Conjecture

abstract: The classical PCP theorem is arguably the most important achievement of classical complexity theory in the past quarter century. In recent years, researchers in quantum computational complexity have tried to identify approaches and develop tools that address the question: does a quantum version of the PCP theorem hold? The story of this study starts with classical complexity and takes unexpected turns providing fascinating vistas on the foundations of quantum mechanics, the global nature of entanglement and its topological properties, quantum error correction, information theory, and much more; it raises questions that touch upon some of the most fundamental issues at the heart of our understanding of quantum mechanics. At this point, the jury is still out as to whether or not such a theorem holds. This survey aims to provide a snapshot of the status in this ongoing story, tailored to a general theory-of-CS audience.


The mechanisms underlying the selective targeting of specific brain regions by different neurodegenerative diseases is one of the most intriguing mysteries in medicine


title: Measuring information quality of web sites: development of an instrument

abstract: Web sites have been extensively used to provide information to consumers. While practitioners and researchers have proposed different criteria for effective Web site design based on common sense, intuition, and rules-ofthumb, effective Web site design focusing on the quality of the information it provides has rarely been studied. In this research, we propose a framework and develop an instrument to measure the information quality of individual or personal Web sites. The theoretical foundation of this research is the information quality framework. The proposed framework and instrument were tested in an individual or personal Web site context.


title: Weighted cluster ensembles: Methods and analysis

abstract: Cluster ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature. Cluster ensembles can provide robust and stable solutions by leveraging the consensus across multiple clustering results, while averaging out emergent spurious structures that arise due to the various biases to which each participating algorithm is tuned. In this article, we address the problem of combining multiple weighted clusters that belong to different subspaces of the input space. We leverage the diversity of the input clusterings in order to generate a consensus partition that is superior to the participating ones. Since we are dealing with weighted clusters, our consensus functions make use of the weight vectors associated with the clusters. We demonstrate the effectiveness of our techniques by running experiments with several real datasets, including high-dimensional text data. Furthermore, we investigate in depth the issue of diversity and accuracy for our ensemble methods. Our analysis and experimental results show that the proposed techniques are capable of producing a partition that is as good as or better than the best individual clustering.


Learning Multi-faceted Knowledge Graph Embeddings for Natural Language Processing


title: The Air Traffic Flow Management Problem: An Integer Optimization Approach

abstract: In this paper, we present a new Integer Program (IP) for the Air Traffic Flow Management (ATFM) problem. The model we propose provides a complete representation of all the phases of each flights, i.e., the phase of taking-off, of cruising and of landing; suggesting all the actions to be implemented to achieve the goal of safe, efficient, and expeditious aircraft movement. The distinctive feature of the model is that it allows rerouting decisions. These decisions are formulated by means of "local" conditions, which allow us to represent such decisions in a very compact way by only introducing new constraints. Moreover, to strengthen the polyhedral structure of the underlying relaxation, we also present three classes of valid inequalities. ::: ::: We report short computational times (less than 15 minutes) on instances of the size of the US air traffic control system that make it realistic that our approach can be used as the main engine of managing air traffic in the US.


title: Distributed and Decentralized Voltage Control of Smart Distribution Networks: Models, Methods, and Future Research

abstract: The future grid is evolving into a smart distribution network that integrates multiple distributed energy resources ensuring at the same time reliable operation and increased power quality. In recent years, many research papers have addressed the voltage violation problems that arise from the high penetration of distributed generation. In view of the transition to active network management and the increase in the quantity of collected data, distributed control schemes have been proposed that use pervasive communications to deal with the complexity of smart grid. This paper reviews the recent publications on distributed and decentralized voltage control of smart distribution networks, summarizes their control models, and classifies the solution methodologies. Moreover, it comments on issues that should be addressed in the future and the perspectives of industry applications.


title: Fingerprint matching using an orientation-based minutia descriptor

abstract: We introduce a novel fingerprint representation scheme that relies on describing the orientation field of the fingerprint pattern with respect to each minutia detail. This representation allows the derivation of a similarity function between minutiae that is used to identify corresponding features and evaluate the resemblance between two fingerprint impressions. A fingerprint matching algorithm, based on the proposed representation, is developed and tested with a series of experiments conducted on two public domain collections of fingerprint images. The results reveal that our method can achieve good performance on these data collections and that it outperforms other alternative approaches implemented for comparison.


title: Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs

abstract: Link prediction, or predicting the likelihood of a link in a knowledge graph based on its existing state is a key research task. It differs from a traditional link prediction task in that the links in a knowledge graph are categorized into different predicates and the link prediction performance of different predicates in a knowledge graph generally varies widely. In this work, we propose a latent feature embedding based link prediction model which considers the prediction task for each predicate disjointly. To learn the model parameters it utilizes a Bayesian personalized ranking based optimization technique. Experimental results on large-scale knowledge bases such as YAGO2 show that our link prediction approach achieves substantially higher performance than several state-of-art approaches. We also show that for a given predicate the topological properties of the knowledge graph induced by the given predicate edges are key indicators of the link prediction performance of that predicate in the knowledge graph.


title: DANNP: an efficient artificial neural network pruning tool

abstract: This work was supported by King Abdullah University of Science and Technology (KAUST) through the baseline fund BAS/1/1606-01-01 for Vladimir B. Bajic. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


title: Walking speed influences on gait cycle variability

abstract: Abstract The purpose of this study was to investigate the influence of walking speed on the amount and structure of the stride-to-stride fluctuations of the gait cycle. Based on previous findings for both walking [Hausdorff JM, Purdon PL, Peng CK, Ladin Z, Wei JY, Goldberger AL. Fractal dynamics of human gait: stability of long-range correlations in stride interval fluctuations. J Appl Physiol 1996;80:1448–57], and running [Jordan K, Challis JH, Newell KM. Long range correlations in the stride interval of running. Gait Posture 2006;24:120–5] it was hypothesized that the fractal nature of human locomotion is a reflection of the attractor dynamics of human locomotion. Female participants walked for 12 min trials at 80%, 90%, 100%, 110% and 120% of their preferred walking speed. Eight gait cycle variables were investigated: stride interval and length, step interval and length, and from the vertical ground reaction force profile the impulse, first and second peak forces, and the trough force. Detrended fluctuation analysis (DFA) revealed the presence of long range correlations in all gait cycle variables investigated. Speed related U-shaped functions occurred in five of the eight variables, with the minima of these curves falling between 100% and 110% of the preferred walking speed. These findings are consistent with those previously shown in running studies and support the hypothesis that reduced strength of long range correlations at preferred locomotion speeds is reflective of enhanced stability and adaptability at these speeds.


title: Cognitive Inference of Demographic Data by User Ratings

abstract: Cognitive inference of user demographics, such as gender and age, plays an important role in creating user profiles for adjusting marketing strategies and generating personalized recommendations because user demographic data is usually not available due to data privacy concerns. At present, users can readily express feedback regarding products or services that they have purchased. During this process, user demographics are concealed, but the data has never yet been successfully utilized to contribute to the cognitive inference of user demographics. In this paper, we investigate the inference power of user ratings data, and propose a simple yet general cognitive inference model, called rating to profile (R2P), to infer user demographics from user provided ratings. In particular, the proposed R2P model can achieve the following: 1. Correctly integrate user ratings into model training. 2.Infer multiple demographic attributes of users simultaneously, capturing the underlying relevance between different demographic attributes. 3. Train its two components, i.e. feature extractor and classifier, in an integrated manner under a supervised learning paradigm, which effectively helps to discover useful hidden patterns from highly sparse ratings data. We introduce how to incorporate user ratings data into the research field of cognitive inference of user demographic data, and detail the model development and optimization process for the proposed R2P. Extensive experiments are conducted on two real-world ratings datasets against various compared state-of-the-art methods, and the results from multiple aspects demonstrate that our proposed R2P model can significantly improve on the cognitive inference performance of user demographic data.


title: Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models

abstract: Abstract A method of speaker adaptation for continuous density hidden Markov models (HMMs) is presented. An initial speaker-independent system is adapted to improve the modelling of a new speaker by updating the HMM parameters. Statistics are gathered from the available adaptation data and used to calculate a linear regression-based transformation for the mean vectors. The transformation matrices are calculated to maximize the likelihood of the adaptation data and can be implemented using the forward–backward algorithm. By tying the transformations among a number of distributions, adaptation can be performed for distributions which are not represented in the training data. An important feature of the method is that arbitrary adaptation data can be used—no special enrolment sentences are needed. Experiments have been performed on the ARPA RM1 database using an HMM system with cross-word triphones and mixture Gaussian output distributions. Results show that adaptation can be performed using as little as 11 s of adaptation data, and that as more data is used the adaptation performance improves. For example, using 40 adaptation utterances, a 37% reduction in error from the speaker-independent system was achieved with supervised adaptation and a 32% reduction in unsupervised mode.


title: Lane-Change Detection Based on Vehicle-Trajectory Prediction

abstract: We propose a new detection method to predict a vehicle's trajectory and use it for detecting lane changes of surrounding vehicles. According to the previous research, more than 90% of the car crashes are caused by human errors, and lane changes are the main factor. Therefore, if a lane change can be detected before a vehicle crosses the centerline, accident rates will decrease. Previously reported detection methods have the problem of frequent false alarms caused by zigzag driving that can result in user distrust in driving safety support systems. Most cases of zigzag driving are caused by the abortion of a lane change due to the presence of adjacent vehicles on the next lane. Our approach reduces false alarms by considering the possibility of a crash with adjacent vehicles by applying trajectory prediction when the target vehicle attempts to change a lane, and it reflects the result of lane-change detection. We used a traffic dataset with more than 500 lane changes and confirmed that the proposed method can considerably improve the detection performance.


title: Black SDN for the Internet of Things

abstract: In this paper, we present Black SDN, a Software Defined Networking (SDN) architecture for secure Internet of Things (IoT) networking and communications. SDN architectures were developed to provide improved routing and networking performance for broadband networks by separating the control plain from the data plain. This basic SDN concept is amenable to IoT networks, however, the common SDN implementations designed for wired networks are not directly amenable to the distributed, ad hoc, low-power, mesh networks commonly found in IoT systems. SDN promises to improve the overall lifespan and performance of IoT networks. However, the SDN architecture changes the IoT network's communication patterns, allowing new types of attacks, and necessitating a new approach to securing the IoT network. Black SDN is a novel SDN-based secure networking architecture that secures both the meta-data and the payload within each layer of an IoT communication packet while utilizing the SDN centralized controller as a trusted third party for secure routing and optimized system performance management. We demonstrate through simulation the feasibility of Black SDN in networks where nodes are asleep most of their lives, and specifically examine a Black SDN IoT network based upon the IEEE 802.15.4 LR WPAN (Low Rate - Wireless Personal Area Network) protocol.


title: The role of perceived organizational support, distributive justice and motivation in reactions to new information technology

abstract: Despite tremendous investments in information technology (IT), many technological interventions in organizations fail because employees do not fully accept and use IT. The present study explored how perceived organizational support and distributive justice affect employee reactions to new IT from a motivational point of view. Self-determination theory was used to understand how different motivational styles, varying in degree of self-determination, mediate the relationship of perceived organizational support and distributive justice with reactions to new IT. Results showed that perceived organizational support and distributive justice were associated with intrinsic and identified motivation to use the IT, but only POS was related to enjoyment and acceptance of the IT. Intrinsic and extrinsic motivation were both associated with IT usage, but IT usage was associated with enjoyment and acceptance only when people were intrinsically motivated. Intrinsic motivation also mediated the effects of POS on enjoyment and acceptance. Moreover, intrinsically motivated users were less likely to use a paper-based appointment booking alternative than those who were not. Implications for managing IT implementations are discussed.


title: 3D printing for the design and fabrication of polymer-based gradient scaffolds

abstract: To accurately mimic the native tissue environment, tissue engineered scaffolds often need to have a highly controlled and varied display of three-dimensional (3D) architecture and geometrical cues. Additive manufacturing in tissue engineering has made possible the development of complex scaffolds that mimic the native tissue architectures. As such, architectural details that were previously unattainable or irreproducible can now be incorporated in an ordered and organized approach, further advancing the structural and chemical cues delivered to cells interacting with the scaffold. This control over the environment has given engineers the ability to unlock cellular machinery that is highly dependent upon the intricate heterogeneous environment of native tissue. Recent research into the incorporation of physical and chemical gradients within scaffolds indicates that integrating these features improves the function of a tissue engineered construct. This review covers recent advances on techniques to incorporate gradients into polymer scaffolds through additive manufacturing and evaluate the success of these techniques. As covered here, to best replicate different tissue types, one must be cognizant of the vastly different types of manufacturing techniques available to create these gradient scaffolds. We review the various types of additive manufacturing techniques that can be leveraged to fabricate scaffolds with heterogeneous properties and discuss methods to successfully characterize them. ::: ::: ::: STATEMENT OF SIGNIFICANCE ::: Additive manufacturing techniques have given tissue engineers the ability to precisely recapitulate the native architecture present within tissue. In addition, these techniques can be leveraged to create scaffolds with both physical and chemical gradients. This work offers insight into several techniques that can be used to generate graded scaffolds, depending on the desired gradient. Furthermore, it outlines methods to determine if the designed gradient was achieved. This review will help to condense the abundance of information that has been published on the creation and characterization of gradient scaffolds and to provide a single review discussing both methods for manufacturing gradient scaffolds and evaluating the establishment of a gradient.


title: A Reliable and Predictable Scratchpad-centric OS for Multi-core Embedded Systems

abstract: Abstract-The reliable use of multi-core platforms for designing safety-critical systems still represents an open challenge. Recently, the FAA [1] has formally expressed its concern towards the use of multi-core systems in avionics. The sharing of hardware resources introduces non-trivial timing dependencies between logically independent components (e.g. cores); additionally, the increase in size of circuitry, memory resources, and transistor density makes these platforms more susceptible to transient memory (soft) errors. This work addresses the problem of memory soft errors and their recovery at an OS/platform level on commercial multi-core systems. Proposed strategy considers the schedulability impact of recovery procedures on hard real-time workloads. Finally, the implementation of a SPM-centric OS with the proposed OS-level strategies was performed by using a commercially available multi-core platform. The design has been validated and evaluated using a combination of synthetic and realistic (EEMBC) benchmarks.


title: Automatic Classification of UML Class Diagrams from Images

abstract: Graphical modelling of various aspects of software and systems is a common part of software development. UML is the de-facto standard for various types of software models. To be able to research UML, academia needs to have a corpus of UML models. For building such a database, an automated system that has the ability to classify UML class diagram images would be very beneficial, since a large portion of UML class diagrams (UML CDs) is available as images on the Internet. In this study, we propose 23 image-features and investigate the use of these features for the purpose of classifying UML CD images. We analyse the performance of the features and assess their contribution based on their Information Gain Attribute Evaluation scores. We study specificity and sensitivity scores of six classification algorithms on a set of 1300 images. We found that 19 out of 23 introduced features can be considered as influential predictors for classifying UML CD images. Through the six algorithms, the prediction rate achieves nearly 96% correctness for UML-CD and 91% of correctness for non-UML CD.


title: Functional Analysis in Systems Engineering: Methodology and Applications

abstract: Functional Analysis is a fundamental tool of the design process to explore new concepts and define their architectures. When systems engineers design new products, they perform Functional Analysis to refine the new product's functional requirements, to map its functions to physical components, to guarantee that all necessary components are listed and that no unnecessary components are requested and to understand the relationships between the new product's components. The chapter begins with the definition of the role of Functional Analysis in conceptual design (section 2) and then proceeds with the description of a proposed methodology (section 3 and sub-sections 3.1, 3.2 and 3.3) and with the presentation of its applications (section 4 and sub-sections 4.1, 4.2 and 4.3) at subsystem, system and system of systems levels. Eventually some conclusions are drawn. The design process, in particular the design process of complex systems, can be split into three phases (Raymer, 1999): 1. Conceptual design. 2. Preliminary design. 3. Detail design. Even though Functional Analysis applies to every phase of the design process, it turns out to be particularly useful during conceptual design, when there is still a wide range of potentially feasible solutions for the future product. The precious role of Functional Analysis consists in individuating as many available options as possible, without forgetting any ideas that may offer significant advantages. In the remainder of the chapter we refer specifically to the application of Functional Analysis during the conceptual design phase to explore complex systems


Traffic Flow Prediction using Adaboost Algorithm with Random Forests as a Weak Learner


title: A Text Summarization Approach under the Influence of Textual Entailment

abstract: This paper presents how text summarization can be influenced by textual entailment. We show that if we use textual entailment recognition together with text summarization approach, we achieve good results for final summaries, obtaining an improvement of 6.78% with respect to the summarization approach only. We also compare the performance of this combined approach to two baselines (the one provided in DUC 2002 and ours based on word-frequency technique) and we discuss the preliminary results obtained in order to infer conclusions that can be useful for future research.


title: Understanding of website usability: Specifying and measuring constructs and their relationships

abstract: Developing a usable website is pivotal for e-business success. Researchers have devoted effort to develop metrics, guidelines and theories of website usability, yet there still is a lack of consensus on the multifaceted dimensions of website usability and lack of investigation of the nomological networks among website usability constructs. This study first investigated the common dimensions of website usability by integrating the findings of previous studies and a focus group study with website usability experts. Instruments to measure the constructs were developed and empirically validated. Then nomological networks between website usability constructs and between those constructs and online purchase intention and purchase were examined. Three field studies including two questionnaire surveys and a causal mapping analysis were conducted. The research identified ten website usability constructs with strong psychometric properties. A number of nomological networks between usability constructs were discovered, contributing to identification of sources of variances of purchase intention and purchase behavior. Findings of this study are expected to provide useful insights for practitioners to develop more usable websites and for researchers to better assess the effect of website usability on online customer behavior.


title: Distributed Q-Learning for Aggregated Interference Control in Cognitive Radio Networks

abstract: This paper deals with the problem of aggregated interference generated by multiple cognitive radios (CRs) at the receivers of primary (licensed) users. In particular, we consider a secondary CR system based on the IEEE 802.22 standard for wireless regional area networks (WRANs), and we model it as a multiagent system where the multiple agents are the different secondary base stations in charge of controlling the secondary cells. We propose a form of real-time multiagent reinforcement learning, which is known as decentralized Q-learning, to manage the aggregated interference generated by multiple WRAN systems. We consider both situations of complete and partial information about the environment. By directly interacting with the surrounding environment in a distributed fashion, the multiagent system is able to learn, in the first case, an efficient policy to solve the problem and, in the second case, a reasonably good suboptimal policy. Computational and memory requirement considerations are also presented, discussing two different options for uploading and processing the learning information. Simulation results, which are presented for both the upstream and downstream cases, reveal that the proposed approach is able to fulfill the primary-user interference constraints, without introducing signaling overhead in the system.


title: pH-sensitive poly(glutamic acid) grafted mesoporous silica nanoparticles for drug delivery.

abstract: pH-sensitive poly(L-glutamic acid) grafted mesoporous silica nanoparticles (MSN-PLGA) were prepared by the surface-initiated N-carboxyanhydride polymerization method. The resultant MSN-PLGA was well dispersed in aqueous medium and showed high drug loading efficiency, superior stability, and significantly higher drug release rates. The cumulative release of doxorubicin hydrochloride (DOX) from DOX-loaded MSN-PLGA (DOX@MSN-PLGA) was pH-dependent and the release rate was much higher at pH 5.5 than that at pH 7.4. The cytotoxicity results indicated that the blank MSN-PLGA was biocompatible and the DOX@MSN-PLGA had potent in vitro cytotoxicity effect similar to free DOX. Overall, these results demonstrate that MSN-PLGA is a promising platform to build pH controlled drug delivery systems for cancer therapy.


title: The Role of Strategic Information Systems (SIS) in Supporting and Achieving the Competitive Advantages (CA): An Empirical Study on Saudi Banking Sector

abstract: The purpose of this research paper is to identify the significant role of Strategic Information Systems (SIS) in supporting the Competitive Advantage (CA). It also explains its role on the dimensions that increase the competitive advantage which are the operational efficiency, information quality and innovation. In order to achieve the goal of this study and to collect the primary data, the researchers designed a survey, in the form of an electronic questionnaire. This survey instrument consisted of 20 questions. It was distributed to members of the study sample, which contains the managers at all levels, and the employees in the Saudi banking sector. The number of the participants included in the survey was 147. The results of this study revealed that there is a significant role of strategic information systems on increasing operational efficiency, improving the quality of information and promoting innovation. This in turn enabled the organizations to achieve higher levels of competitive advantages. The strategic information systems have deep consequences for organizations that adopt them; managers could achieve great and sustainable competitive advantages from such systems if carefully considered and developed. On the other hand, this study was conducted in the banking sector in KSA context. So, more research is needed in other sectors and in the context of other countries; to confirm and generalize the results. Finally, the paper’s primary value lies in its ability to provide the evidence that the strategic information systems play a significant role in supporting and achieving the competitive advantages in Saudi Arabia, particularly in the banking sector. Since there was a lack of such research in the Saudi context, this paper can provide a theoretical basis for future researchers as well as practical implications for managers.


title: Images

abstract: To study the adaptive anti-forensic approach for optimization of digital images comprises of the various quantization and compression techniques. The technology and the changing methods of capturing digital images and the methodologies used are very powerful to identify the forgery in the image processing. As every new leap in the technology brings along positive and negative responses, similar way the technological changes in the field of image processing has led to forgery of digital images. The advancement in the techniques of digital image is used for processing millions of images those are captured every second.


title: Erasing Lane Changes From Roads: A Design of Future Road Intersections

abstract: Imagine in the future that autonomous vehicles coordinated and guided by signal-free autonomous intersections are able to pass through the intersections immediately after the vehicles in the conflicting direction leave. Meanwhile, with the coordination of the autonomous intersections, autonomous vehicles on any approaching lane are able to turn onto any downstream lane, expecting that driving in such a road system, autonomous vehicles can reach their destinations without any on-road lane changes, and high traffic efficiency will be achieved as well as great traffic safety. To draw the picture in detail, this paper designs a signal-free autonomous intersection with all-direction turn lanes (ADTL) under the environment of autonomous vehicles, and proposes a conflict-avoidance-based approach to coordinate all approaching vehicles in different directions. Communicating with the approaching autonomous vehicles and utilizing the approach, the autonomous ADTL intersection is able to coordinate the approaching vehicles in all directions and guide them to safely and efficiently pass through the intersection. Two simulation scenarios are conducted in a road network with an isolated intersection composed of four three-lane arms. One scenario validates the collision-free design of the system, and the other shows that the designed ADTL intersection outperforms the conventional signal controlled intersection in terms of traffic efficiency, and is potentially better than the autonomous intersection with specific-direction turn lanes. The autonomous ADTL intersection can be an important basis for designing a future autonomous urban road traffic system.


title: Mask R-CNN

abstract: We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without bells and whistles, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code has been made available at: https://github.com/facebookresearch/Detectron .


title: ServerSwitch: A Programmable and High Performance Platform for Data Center Networks

abstract: As one of the fundamental infrastructures for cloud computing, data center networks (DCN) have recently been studied extensively. We currently use pure software-based systems, FPGA based platforms, e.g., NetFPGA, or OpenFlow switches, to implement and evaluate various DCN designs including topology design, control plane and routing, and congestion control. However, software-based approaches suffer from high CPU overhead and processing latency; FPGA based platforms are difficult to program and incur high cost; and OpenFlow focuses on control plane functions at present. ::: ::: In this paper, we design a ServerSwitch to address the above problems. ServerSwitch is motivated by the observation that commodity Ethernet switching chips are becoming programmable and that the PCI-E interface provides high throughput and low latency between the server CPU and I/O subsystem. ServerSwitch uses a commodity switching chip for various customized packet forwarding, and leverages the server CPU for control and data plane packet processing, due to the low latency and high throughput between the switching chip and server CPU. ::: ::: We have built our ServerSwitch at low cost. Our experiments demonstrate that ServerSwitch is fully programmable and achieves high performance. Specifically, we have implemented various forwarding schemes including source routing in hardware. Our in-network caching experiment showed high throughput and flexible data processing. Our QCN (Quantized Congestion Notification) implementation further demonstrated that ServerSwitch can react to network congestions in 23us.


title: Deep Residual Network for Sound Source Localization in the Time Domain

abstract: This study presents a system for sound source localization in time domain using a deep residual neural network. Data from the linear 8 channel microphone array with 3 cm spacing is used by the network for direction estimation. We propose to use the deep residual network for sound source localization considering the localization task as a classification task. This study describes the gathered dataset and developed architecture of the neural network. We will show the training process and its result in this study. The developed system was tested on validation part of the dataset and on new data capture in real time. The accuracy classification of 30 m sec sound frames is 99.2%. The standard deviation of sound source localization is 4{\deg}. The proposed method of sound source localization was tested inside of speech recognition pipeline. Its usage decreased word error rate by 1.14% in comparison with similar speech recognition pipeline using GCC-PHAT sound source localization.


title: Enhancing compressive sensing encryption in constrained devices using chaotic sequences

abstract: The rapid proliferation of IoT (Internet-of-Things) applications has raised major concerns regarding privacy and security issues. Typical IoT applications, like ehealth, smart transportation, etc., are often used to convey highly sensitive and personal information. The vast majority of the IoT applications heavily depend on miniature sensors that are usually battery operated; hence, energy efficiency is also a crucial aspect. In this respect, we use the Compressive Sensing (CS) principles to enable simultaneous data encryption and compression. We study CS from the security point of view, showing that it is highly vulnerable to Chosen Plaintext Attacks. We propose a scheme based on chaotic sequences that makes CS immune to these attacks. We also show that attackers are not able to correctly decrypt captured ciphertext. Moreover, we describe the implementation of this crypto-scheme in a real mote, presenting evaluation results, in terms of energy consumption and execution time.


title: Software Development Life Cycle AGILE vs Traditional Approaches

abstract: Software development life cycle is the most important element in software development. It depicts the necessary phases in software development. This paper reviews the modern SDLC which are traditional methods and agile methods and explains the advantages and disadvantages of both methodologies. It also suggests improvements for current agile development so that this lightweight SDLC could be adopted more in practice for organizational project management.


title: Incremental learning for ν -Support Vector Regression

abstract: The ? -Support Vector Regression ( ? -SVR) is an effective regression learning algorithm, which has the advantage of using a parameter ? on controlling the number of support vectors and adjusting the width of the tube automatically. However, compared to ? -Support Vector Classification ( ? -SVC) (Scholkopf et?al., 2000), ? -SVR introduces an additional linear term into its objective function. Thus, directly applying the accurate on-line ? -SVC algorithm (AONSVM) to ? -SVR will not generate an effective initial solution. It is the main challenge to design an incremental ? -SVR learning algorithm. To overcome this challenge, we propose a special procedure called initial adjustments in this paper. This procedure adjusts the weights of ? -SVC based on the Karush-Kuhn-Tucker (KKT) conditions to prepare an initial solution for the incremental learning. Combining the initial adjustments with the two steps of AONSVM produces an exact and effective incremental ? -SVR learning algorithm (INSVR). Theoretical analysis has proven the existence of the three key inverse matrices, which are the cornerstones of the three steps of INSVR (including the initial adjustments), respectively. The experiments on benchmark datasets demonstrate that INSVR can avoid the infeasible updating paths as far as possible, and successfully converges to the optimal solution. The results also show that INSVR is faster than batch ? -SVR algorithms with both cold and warm starts.


title: Mechanism design of an insulator cleaning robot for suspension insulator strings

abstract: Cleaning contaminated insulators is an effective way to protect power transmission lines from accidental power outages caused by flashovers due to wet polluted insulators. Traditional cleaning methods are risky, inefficient, expensive and labor-intensive. This paper presents an insulator cleaning robot mechanism which is mainly consisted of locomotion mechanism, cleaning mechanism and the frame. The structure is introduced and the principles of moving and cleaning are analyzed. The kinematic equations are established. Simulation results of the downward moving process prove the robot is able to move along the suspension insulator string and has such advantages as stability, large load carrying capacity and adaptability to dimensional variations of the insulator.


title: A multi-optimizer cooperative coevolution method for large scale optimization

abstract: Cooperative coevolution framework is an effective strategy to deal with large scale optimization problems. However, most studies on cooperative coevolution framework utilize the same optimizer for all subcomponents, which may not be effective enough. In this paper, we propose a novel multi-optimizer cooperative coevolution method for large scale optimization problems which randomly chooses an optimization strategy for each subcomponent independently. Four extensively used differential evolution algorithms are utilized as candidate optimizers. Two of them have good exploration properties while the other two have good exploitation properties. Experimental results utilizing differential grouping algorithm as decomposition strategy of the cooperative coevolution framework show that this multi-optimizer CC method performs better on most of the CEC'2010 large-scale global optimization (LSGO) benchmark functions than each single-optimizer CC framework where all of the subcomponents use the same optimizer. What is more, experimental results also show that this multi-optimizer CC method is suitable for not only fixed decomposition strategy (DG, XDG, and GDG) but also dynamic decomposition strategy (Delta Grouping).


title: Multimodal Deep Autoencoders for Control of a Mobile Robot

abstract: Robot navigation systems are typically engineered to suit certain platforms, sensing suites and environment types. In order to deploy a robot in an environment where its existing navigation system is insuffi�cient, the system must be modified manually, often at significant cost. In this paper we address this problem, proposing a system based on multimodal deep autoencoders that enables a robot to learn how to navigate by observing a dataset of sensor input and motor commands collected while being teleoperated by a human. Low-level features and cross modal correlations are learned and used in initialising two different architectures with three operating modes. During operation, these systems exploit the learned correlations in generating suitable control signals based only on the sensor information. In simulation these systems are shown to run indefinitely in simple environments, are capable of generalising to more complex environments and found to be ::: robust to significant amounts of sensor noise. The system has been deployed on a real robot and operated in a simple corridor for over 30 minutes without collision.


title: A weight-coded genetic algorithm for the capacitated arc routing problem

abstract: In this paper we present a weight coded genetic algorithm (GA) based approach to the capacitated arc routing problem (CARP). In comparison to metaheuristic algorithms, simple constructive heuristic algorithms often produce poor quality solutions to the CARP. Using a novel weight coding model in conjunction with a series of standard CARP heuristics, acting as a solution engine, we demonstrate how these simple heuristic procedures can be `duped' into producing better solutions to the CARP. The algorithm is tested on a set of problem instances drawn from the literature. Initial results for our GA show that it is possible to reliably produce an uplift in solution quality of between 7.3% and 14.3% above the standard heuristics, the GA identifying 47 optimum or best known solutions from the 57 problem instances tested.


title: Crowds: Anonymity for Web Transactions

abstract: In this paper we introduce a system called Crowds for protecting users' anonymity on the world-wide-web. Crowds, named for the notion of “blending into a crowd,” operates by grouping users into a large and geographically diverse group (crowd) that collectively issues requests on behalf of its members. Web servers are unable to learn the true source of a request because it is equally likely to have originated from any member of the crowd, and even collaborating crowd members cannot distinguish the originator of a request from a member who is merely forwarding the request on behalf of another. We describe the design, implementation, security, performance, and scalability of our system. Our security analysis introduces degrees of anonymity as an important tool for describing and proving anonymity properties.


Toward Automated Story Generation with Markov Chain Monte Carlo Methods and Deep Neural Networks


title: Correlating Speaker Gestures in Political Debates with Audience Engagement Measured via EEG

abstract: We hypothesize that certain speaker gestures can convey significant information that are correlated to audience engagement. We propose gesture attributes, derived from speakers' tracked hand motions to automatically quantify these gestures from video. Then, we demonstrate a correlation between gesture attributes and an objective method of measuring audience engagement: electroencephalography (EEG) in the domain of political debates. We collect 47 minutes of EEG recordings from each of 20 subjects watching clips of the 2012 U.S. Presidential debates. The subjects are examined in aggregate and in subgroups according to gender and political affiliation. We find statistically significant correlations between gesture attributes (particularly extremal pose) and our feature of engagement derived from EEG both with and without audio. For some stratifications, the Spearman rank correlation reaches as high as rho = 0.283 with p


title: A comparison of the discrete and dimensional models of emotion in music

abstract: The primary aim of the present study was to systematically compare perceived emotions in music using two different theoretical frameworks: the discrete emotion model, and the dimensional model of affect. A secondary aim was to introduce a new, improved set of stimuli for the study of music-mediated emotions. A large pilot study established a set of 110 film music excerpts, half were moderately and highly representative examples of five discrete emotions (anger, fear, sadness, happiness and tenderness), and the other half moderate and high examples of the six extremes of three bipolar dimensions (valence, energy arousal and tension arousal). These excerpts were rated in a listening experiment by 116 non-musicians. All target emotions of highly representative examples in both conceptual sets were discriminated by self-ratings. Linear mapping techniques between the discrete and dimensional models revealed a high correspondence along two central dimensions that can be labelled as valence and arousal, and the three dimensions could be reduced to two without significantly reducing the goodness of fit. The major difference between the discrete and categorical models concerned the poorer resolution of the discrete model in characterizing emotionally ambiguous examples. The study offers systematically structured and rich stimulus material for exploring emotional processing.


title: Output torque regulation through series elastic actuation with torsion spring hysteresis

abstract: Series elastic actuation (SEA) has been widely used for output force/torque regulation, however, the nonlinearity introduced by its torsion spring hysteresis will greatly affect the quality of feedback control. Thus an experimental procedure is conducted for the modeling and identification of spring hysteresis effect, based on which a compensated control scheme is given. Experiment results show that the compensated control can improve the control performance compared with ordinary linear controller.


abstract: Big Data is an emerging technology. There are many applications using this concept and area of implementation is still larger. Different examples such as transport, healthcare, industry, and education. In recent years, the amount of data stored is developing rapidly. The main goal is to make a survey on “Classification model to predict the learners' academic performance using big data” to develop a model in order to predict students' semester grade result and also predict final year student campus placement. The ability to predict the student performance in education is very significant in educational environments. The stored database contains students' information to improve students' perspective and behavior. This paper shows Big Data trends and types of analyzes should be performed to improve education process.


title: Imaging in Patellofemoral Instability

abstract: Imaging in patellofemoral instability confirms the diagnosis and guides treatment. It is essential to the accurate diagnosis of the root causes and their adequate treatment. A structured approach must be used to identify the factors causing instability, and more than 1 imaging modality may provide similar information. This article revises the acute findings and the abnormalities present in the acute and the chronic settings.


title: A Low-Cost EEG System-Based Hybrid Brain-Computer Interface for Humanoid Robot Navigation and Recognition

abstract: This paper describes a hybrid brain-computer interface (BCI) technique that combines the P300 potential, the steady state visually evoked potential (SSVEP), and event related de-synchronization (ERD) to solve a complicated multi-task problem consisting of humanoid robot navigation and control along with object recognition using a low-cost BCI system. Our approach enables subjects to control the navigation and exploration of a humanoid robot and recognize a desired object among candidates. This study aims to demonstrate the possibility of a hybrid BCI based on a low-cost system for a realistic and complex task. It also shows that the use of a simple image processing technique, combined with BCI, can further aid in making these complex tasks simpler. An experimental scenario is proposed in which a subject remotely controls a humanoid robot in a properly sized maze. The subject sees what the surrogate robot sees through visual feedback and can navigate the surrogate robot. While navigating, the robot encounters objects located in the maze. It then recognizes if the encountered object is of interest to the subject. The subject communicates with the robot through SSVEP and ERD-based BCIs to navigate and explore with the robot, and P300-based BCI to allow the surrogate robot recognize their favorites. Using several evaluation metrics, the performances of five subjects navigating the robot were quite comparable to manual keyboard control. During object recognition mode, favorite objects were successfully selected from two to four choices. Subjects conducted humanoid navigation and recognition tasks as if they embodied the robot. Analysis of the data supports the potential usefulness of the proposed hybrid BCI system for extended applications. This work presents an important implication for the future work that a hybridization of simple BCI protocols provide extended controllability to carry out complicated tasks even with a low-cost system.


title: Automatic keyphrase extraction: An overview of the state of the art

abstract: Keyphrases are useful for a variety of tasks in information retrieval systems and natural language processing, such as text summarization, automatic indexing, clustering/classification, ontology learning and building and conceptualizing particular knowledge domains, etc. However, assigning these keyphrases manually is time consuming and expensive in term of human resources. Therefore, there is a need to automate the task of extracting keyphrases. A wide range of techniques of keyphrase extraction have been proposed, but they are still suffering from the low accuracy rate and poor performance. This paper presents a state of the art of automatic keyphrase extraction approaches to identify their strengths and weaknesses. We also discuss why some techniques perform better than others and how can we improve the task of automatic keyphrase extraction.


title: Interactive RGB-D SLAM on Mobile Devices

abstract: In this paper we present a new RGB-D SLAM system specifically designed for mobile platforms. Though the basic approach has already been proposed, many relevant changes are required to suit a user-centered mobile environment. In particular, our implementation tackles the strict memory constraints and limited computational power of a typical tablet device, thus delivering interactive usability without hindering effectiveness. Real-time 3D reconstruction is achieved by projecting measurements from aligned RGB-D keyframes, so to provide the user with instant feedback. We analyze quantitatively the accuracy vs. speed trade-off of diverse variants of the proposed pipeline, we estimate the amount of memory required to run the application and we also provide qualitative results dealing with reconstructions of indoor environments.


Swan, K. (2001). Virtual interactivity: design factors affecting student satisfaction and perceived learning in asynchronous online courses. Distance Education, 22, (2), 306-331. VITRUAL INTERACTIVITY: DESIGN FACTORS AFFECTING STUDENT SATISFACTION AND PERCEIVED LEARNING IN ASYNCHRONOUS ONLINE COURSE


title: A Comparative Study of Polar Code Constructions for the AWGN Channel

abstract: We present a comparative study of the performance of various polar code constructions in an additive white Gaussian noise (AWGN) channel. A polar code construction is any algorithm that selects $K$ best among $N$ possible polar bit-channels at the design signal-to-noise-ratio (design-SNR) in terms of bit error rate (BER). Optimal polar code construction is hard and therefore many suboptimal polar code constructions have been proposed at different computational complexities. Polar codes are also non-universal meaning the code changes significantly with the design-SNR. However, it is not known which construction algorithm at what design-SNR constructs the best polar codes. We first present a comprehensive survey of all the well-known polar code constructions along with their full implementations. We then propose a heuristic algorithm to find the best design-SNR for constructing best possible polar codes from a given construction algorithm. The proposed algorithm involves a search among several possible design-SNRs. We finally use our algorithm to perform a comparison of different construction algorithms using extensive simulations. We find that all polar code construction algorithms generate equally good polar codes in an AWGN channel, if the design-SNR is optimized.


title: Metamaterial-based tunable absorber in the infrared regime

abstract: In this paper, we present a design for a narrowband absorber based on metamaterials in the infrared wavelengths for wavelength-selective uncooled hyperspectral imaging systems. The proposed narrowband absorber integrated with microbolometer focal plane arrays has the potential to increase the detection sensitivity of the microbolometers. The design of the metamaterial unit cell consists of a resonant metallic ‘cross’ structure which has a resonance in the IR wavelengths and is placed on a dielectric substrate with a metal back plate. In order to achieve a very high absorption of the electromagnetic radiation, the designed metamaterial needs to have minimal transmission and reflection within its spectral response window. Minimal reflection is achieved through impedance matching of the metamaterial with the free space whereas zero transmission is ensured through the metal back plate. Moreover, for the purpose of hyperspectral imaging, the metamaterial structure is combined with a tunable electro-optic material, namely, liquid crystal. Tunability can be achieved upon applying a voltage across the combined liquid crystal and metamaterial structure thus bringing about a shift in the resonant frequency. In our simulated model, where losses of metal and dielectric substrate materials were taken into account, we noted more than ninety percent of absorption can be achieved in a narrow spectral window for the designed metamaterial structure.


title: A Reference Method for User Story Requirements in Agile Systems Development

abstract: User story literature nearly exclusively studies how to create, prioritise, or ensure the quality of a user story. Little is known about other activities and artefacts involved preceding or following these activities in systems development. Consequentially, software teams seeking to leverage user stories’ full potential do not know how to incorporate user stories into all aspects of their software development process. This paper constructs a reference method by examining and consolidating the user story life-cycle obtained in a multi-case study research project. Practitioners can take inspiration from this method to learn the improvements possible with user stories.


title: Perturbation expansions of stochastic wavefunctions for open quantum systems.

abstract: Based on the stochastic unravelling of the reduced density operator in the Feynman path integral formalism for an open quantum system in touch with harmonic environments, a new non-Markovian stochastic Schrödinger equation (NMSSE) has been established that allows for the systematic perturbation expansion in the system-bath coupling to arbitrary order. This NMSSE can be transformed in a facile manner into the other two NMSSEs, i.e., non-Markovian quantum state diffusion and time-dependent wavepacket diffusion method. Benchmarked by numerically exact results, we have conducted a comparative study of the proposed method in its lowest order approximation, with perturbative quantum master equations in the symmetric spin-boson model and the realistic Fenna-Matthews-Olson complex. It is found that our method outperforms the second-order time-convolutionless quantum master equation in the whole parameter regime and even far better than the fourth-order in the slow bath and high temperature cases. Besides, the method is applicable on an equal footing for any kind of spectral density function and is expected to be a powerful tool to explore the quantum dynamics of large-scale systems, benefiting from the wavefunction framework and the time-local appearance within a single stochastic trajectory.


title: Initial Results on Deep Learning for Joint Channel Equalization and Decoding

abstract: Historically, most of the channel encoding and decoding algorithms have been designed to deal with and evaluated under the additive white Gaussian noise (AWGN) channel. However, in the reality, the channel is far more complicated than the AWGN assumption. Traditionally, qualizers are employed to combat the channel effects and frequency-selective fading of wireless channels. In this article, we take the advantage of deep learning approaches to handle the various channel distortions, by proposing an end-to-end approach. To train the model efficiently, the training data is obtained by simulation where the encoding process and the channel effects are viewed as a complete black box. This method can also be applied to time- varying channels for simultaneously channel estimation and symbol detection. Simulation results show that the deep learning based decoders have the ability to learn the complicated encoder function and address various channel effects. Furthermore, the deep learning based method provides an end-to-end approach, leading to a better performance in the channels with various distortions. This article represents the first step for a universal framework of information recovering from a large range of channel codes and channels.


title: Weakly Supervised Image Classification with Coarse and Fine Labels

abstract: We consider image classification in a weakly supervised scenario where the training data are annotated at different levels of abstractions. A subset of the training data are annotated with coarse labels (e.g. wolf, dog), while the rest of the training data are annotated with fine labels (e.g. breeds of wolves and dogs). Each coarse label corresponds to a superclass of several fine labels. Our goal is to learn a model that can classify a new image into one of the fine classes. We investigate how the coarsely labeled data can help improve the fine label classification. Since it is usually much easier to collect data with coarse labels than those with fine labels, the problem setup considered in this paper can benefit a wide range of real-world applications. We propose a model based on convolutional neural networks (CNNs) to address this problem. We demonstrate the effectiveness of the proposed model on several benchmark datasets. Our model significantly outperforms the naive approach that discards the extra coarsely labeled data.


title: Sensing and Mining Urban Qualities in Smart Cities

abstract: The emergence of the Internet of Things in Smart Cities questions how the future citizens will perceive their predominant living and working environments and what quality of living they can experience within it, for instance the level of everyday stress. However, perception and experienced stress levels are challenging metrics to measure and are even more challenging to correlate with an underlying causal-effectual relationship in such stimulus abundant environments. The Internet of Things, enabled by several pervasive and ubiquitous devices such as smart phones and smart sensors, can provide real-time contextual information that can be used by advanced data science methodologies to generate new insights about urban qualities in Smart Cities and how they can be improved. The goal of this study is to show the predominant factors, which influence perceptual qualities of inhabitants in a Smart City equipped with sensing capabilities by the Internet of Things. To serve this goal, a novel data collection process for Smart Cities is introduced that involves (i) environmental data, such noise, dust, illuminance, temperature, relative humidity, (ii) location/mobility data, such as GNSS and citizens density detected via WiFi, and (iii) perceptual social data collected by citizens' responses in smart phones. These fine-grained real-time data can provide invaluable insights about the spatial correlations of the sensor measurements as well as the spatial and citizens' similarity illustrated. The data analysis illustrated reveals significant links between stress level and environmental changes observed.


title: Fuzzy logic and approximate reasoning

abstract: The term fuzzy logic is used in this paper to describe an imprecise logical system, FL, in which the truth-values are fuzzy subsets of the unit interval with linguistic labels such as true, false, not true, very true, quite true, not very true and not very false, etc. The truth-value set, ℐ, of FL is assumed to be generated by a context-free grammar, with a semantic rule providing a means of computing the meaning of each linguistic truth-value in ℐ as a fuzzy subset of [0, 1].


title: Adaptive figure-ground classification

abstract: We propose an adaptive figure-ground classification algorithm to automatically extract a foreground region using a user-provided bounding-box. The image is first over-segmented with an adaptive mean-shift algorithm, from which background and foreground priors are estimated. The remaining patches are iteratively assigned based on their distances to the priors, with the foreground prior being updated online. A large set of candidate segmentations are obtained by changing the initial foreground prior. The best candidate is determined by a score function that evaluates the segmentation quality. Rather than using a single distance function or score function, we generate multiple hypothesis segmentations from different combinations of distance measures and score functions. The final segmentation is then automatically obtained with a voting or weighted combination scheme from the multiple hypotheses. Experiments indicate that our method performs at or above the current state-of-the-art on several datasets, with particular success on challenging scenes that contain irregular or multiple-connected foregrounds. In addition, this improvement in accuracy is achieved with low computational cost.


title: Design of integrated transformer and inductor for high frequency dual active bridge GaN Charger for PHEV

abstract: This paper discusses the design of high power density transformer and inductor for the high frequency dual active bridge (DAB) GaN charger. Because the charger operates at 500 kHz, the inductance needed to achieve ZVS for the DAB converter is reduced to as low as 3µH. As a result, it is possible to utilize the leakage inductor as the series inductor of DAB converter. To create such amount of leakage inductance, certain space between primary and secondary winding is allocated to store the leakage flux energy. The designed transformer is above 99.2% efficiency while delivering 3.3kW. The power density of the designed transformer is 6.3 times of the lumped transformer and inductor in 50 kHz Si Charger. The detailed design procedure and loss analysis are discussed.


title: FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces

abstract: With recent advances in computer vision and graphics, it is now possible to generate videos with extremely realistic synthetic faces, even in real time. Countless applications are possible, some of which raise a legitimate alarm, calling for reliable detectors of fake videos. In fact, distinguishing between original and manipulated video can be a challenge for humans and computers alike, especially when the videos are compressed or have low resolution, as it often happens on social networks. Research on the detection of face manipulations has been seriously hampered by the lack of adequate datasets. To this end, we introduce a novel face manipulation dataset of about half a million edited images (from over 1000 videos). The manipulations have been generated with a state-of-the-art face editing approach. It exceeds all existing video manipulation datasets by at least an order of magnitude. Using our new dataset, we introduce benchmarks for classical image forensic tasks, including classification and segmentation, considering videos compressed at various quality levels. In addition, we introduce a benchmark evaluation for creating indistinguishable forgeries with known ground truth; for instance with generative refinement models.


title: Flexible 16 Antenna Array for Microwave Breast Cancer Detection

abstract: Radar-based microwave imaging has been widely studied for breast cancer detection in recent times. Sensing dielectric property differences of tissues has been studied over a wide frequency band for this application. We design single- and dual-polarization antennas for wireless ultrawideband breast cancer detection systems using an inhomogeneous multilayer model of the human breast. Antennas made from flexible materials are more easily adapted to wearable applications. Miniaturized flexible monopole and spiral antennas on a 50-μm Kapton polyimide are designed, using a high-frequency structure simulator, to be in contact with biological breast tissues. The proposed antennas are designed to operate in a frequency range of 2–4 GHz (with reflection coefficient (S11) below –10 dB). Measurements show that the flexible antennas have good impedance matching when in different positions with different curvature around the breast. Our miniaturized flexible antennas are 20 mm × 20 mm. Furthermore, two flexible conformal 4 × 4 ultrawideband antenna arrays (single and dual polarization), in a format similar to that of a bra, were developed for a radar-based breast cancer detection system. By using a reflector for the arrays, the penetration of the propagated electromagnetic waves from the antennas into the breast can be improved by factors of 3.3 and 2.6, respectively.


title: MECHANISMS AND PRACTICAL CONSIDERATIONS INVOLVED IN PLANT GROWTH PROMOTION BY RHIZOBACTERIA

abstract: Rhizobacteria are capable of stimulating plant growth through a variety of mechanisms that include improvement of plant nutrition, production and regulation of phytohormones, and suppression of disease causing organisms. While considerable research has demonstrated their potential utility, the successful application of plant growth promoting rhizobacteria (PGPR) in the field has been limited by a lack of knowledge of ecological factors that determine their survival and activity in the plant rhizosphere. To be effective, PGPR must maintain a critical population density of active cells. Inoculation with PGPR strains can temporarily enhance the population size, but inoculants often have poor survival and compete with indigenous bacteria for available growth substrates. PGPR often have more than one mechanism for enhancing plant growth and experimental evidence suggests that the plant growth stimulation is the net result of multiple mechanisms of action that may be activated simultaneously. The aim of this review is to describe PGPR modes of action and discuss practical considerations for PGPR use in agriculture.


title: On the Power of Attribute-based Communication

abstract: In open systems exhibiting adaptation, behaviors can arise as side effects of intensive components interaction. Finding ways to understand and design these systems, is a difficult but important endeavor. To tackle these issues, we present AbC, a calculus for attribute-based communication. An AbC system consists of a set of parallel agents each of which is equipped with a set of attributes. Communication takes place in an implicit multicast fashion, and interactions among agents are dynamically established by taking into account "connections" as determined by predicates over the attributes of agents. First, the syntax and the semantics of the calculus are presented, then expressiveness and effectiveness of AbC are demonstrated both in terms of modeling scenarios featuring collaboration, reconfiguration, and adaptation and of the possibility of encoding channel-based interactions and other interaction patterns. Behavioral equivalences for AbC are introduced for establishing formal relationships between different descriptions of the same system.


title: Primary mediastinal tumors. Part 1: tumors of the anterior mediastinum.

abstract: Primary anterior mediastinal neoplasms comprise a diverse group of tumors and account for 50% of all mediastinal masses. Thymomas are most common and can be locally invasive and associated with parathymic syndromes. Thymic carcinomas and thymic carcinoids are rare malignancies with a propensity for local invasion and distant metastases. Thymolipomas are benign thymic tumors. The mediastinal germ cell tumors are a heterogeneous group of benign and malignant neoplasms. Mediastinal lymphangiomas are rare tumors and predominantly occur in young children. In contrast, mediastinal goiters are relatively common in adults. Mediastinal parathyroid adenomas are an uncommon cause of persistent hyperparathyroidism and rarely cause a discernible mass. The clinical, radiologic, and therapeutic aspects of the most common masses are reviewed.


title: The Mozart Effect: Evidence for the Arousal Hypothesis

abstract: This study investigated the effect of music listening for performance on a 25-question portion of the analytical section of the Graduate Record Exam by 72 undergraduate students (M age 21.9 yr.). Five levels of an auditor condition were based on Mozart Piano Sonata No. 3 (K. 281), Movement I (Allegro); a rhythm excerpt; a melody excerpt; traffic sounds; and silence. Participants were randomly assigned to one of the stimuli. After a 5-min., 43-sec. (length of the first Allegro movement) listening period, participants answered the questions. Analysis indicated participants achieved significantly higher mean scores after all auditory conditions than those in the silent condition. No statistically significant pairwise mean difference appeared between scores for the auditory conditions. Findings were interpreted in terms of an arousal framework, suggesting the higher means in all auditory conditions may reflect immediate exposure to auditory stimuli.


title: Network coding-aware data aggregation for a distributed Wireless Sensor Network

abstract: Wireless Sensor Networks (WSNs) are immensely deployed for monitoring information like humidity, temperature and soil fertility. As the sensor nodes sense data and floods them in the network, the network traffic increases and may result in congestion which leads to broadcast storm problem. This paper addresses the utility of network coding to optimize data aggregation and to decrease the number of transmitted messages in a WSN. A coding-aware deployment strategy is presented which create opportunity for network coding at aggregate sensor nodes. Our deployment strategy leads to a topology which supports many-to-many network flows (multiple sources and multiple sink network flow). It also gives protection against multiple failures in the network. Furthermore, we propose a heuristic that restricts unnecessary transmissions.


Anomaly Detection in Networks with Changing Trends


title: Kolmogorov learning for feedforward networks

abstract: We present a learning algorithm for feedforward neural networks that is based on Kolmogorov theorem concerning composition of a n-dimensional continuous function from a one-dimensional continuous functions. A thorough analysis of the algorithm time complexity is presented for its serial and parallel implementation.


title: Accuracy of dementia diagnosis—a direct comparison between radiologists and a computerized method

abstract: There has been recent interest in the application of machine learning techniques to neuroimaging-based diagnosis. These methods promise fully automated, standard PC-based clinical decisions, unbiased by variable radiological expertise. We recently used support vector machines (SVMs) to separate sporadic Alzheimer's disease from normal ageing and from fronto-temporal lobar degeneration (FTLD). In this study, we compare the results to those obtained by radiologists. A binary diagnostic classification was made by six radiologists with different levels of experience on the same scans and information that had been previously analysed with SVM. SVMs correctly classified 95% (sensitivity/specificity: 95/95) of sporadic Alzheimer's disease and controls into their respective groups. Radiologists correctly classified 65-95% (median 89%; sensitivity/specificity: 88/90) of scans. SVM correctly classified another set of sporadic Alzheimer's disease in 93% (sensitivity/specificity: 100/86) of cases, whereas radiologists ranged between 80% and 90% (median 83%; sensitivity/specificity: 80/85). SVMs were better at separating patients with sporadic Alzheimer's disease from those with FTLD (SVM 89%; sensitivity/specificity: 83/95; compared to radiological range from 63% to 83%; median 71%; sensitivity/specificity: 64/76). Radiologists were always accurate when they reported a high degree of diagnostic confidence. The results show that well-trained neuroradiologists classify typical Alzheimer's disease-associated scans comparable to SVMs. However, SVMs require no expert knowledge and trained SVMs can readily be exchanged between centres for use in diagnostic classification. These results are encouraging and indicate a role for computerized diagnostic methods in clinical practice.


Stock Market Forecasting Using Machine Learning Algorithms


title: 4-channel, 40 Gb/s front-end amplifier for parallel optical receiver in 0.18 μm CMOS

abstract: This paper proposed a 4-channel parallel 40 Gb/s front-end amplifier (FEA) in optical receiver for parallel optical transmission system. A novel enhancement type regulated cascade (ETRGC) configuration with an active inductor is originated in this paper for the transimpedance amplifier to significantly increase the bandwidth. The technique of three-order interleaving active feedback expands the bandwidth of the gain stage of transimpedance amplifier and limiting amplifier. Experimental results show that the output swing is 210 mV (Vpp) when the input voltage varies from 5 mV to 500 mV. The power consumption of the 4-channel parallel 40 Gb/s front-end amplifier (FEA) is 370 mW with 1.8 V power supply and the chip area is 650 μm×1300 μm.


title: On evaluating embedding models for knowledge base completion

abstract: Knowledge graph embedding models have recently received significant attention in the literature. These models learn latent semantic representations for the entities and relations in a given knowledge base; the representations can be used to infer missing knowledge. In this paper, we study the question of how well recent embedding models perform for the task of knowledge base completion, i.e., the task of inferring new facts from an incomplete knowledge base. We argue that the entity ranking protocol, which is currently used to evaluate knowledge graph embedding models, is not suitable to answer this question since only a subset of the model predictions are evaluated. We propose an alternative entity-pair ranking protocol that considers all model predictions as a whole and is thus more suitable to the task. We conducted an experimental study on standard datasets and found that the performance of popular embeddings models was unsatisfactory under the new protocol, even on datasets that are generally considered to be too easy. Moreover, we found that a simple rule-based model often provided superior performance. Our findings suggest that there is a need for more research into embedding models as well as their training strategies for the task of knowledge base completion.


title: A Drift Detection Method Based on Active Learning

abstract: Several real-world prediction problems are subject to changes over time due to their dynamic nature. These changes, named concept drift, usually lead to immediate and disastrous loss in classifier's performance. In order to cope with such a serious problem, drift detection methods have been proposed in the literature. However, current methods cannot be widely used since they are based either on performance monitoring or on fully labeled data, or even both. Focusing on overcoming these drawbacks, in this work we propose using density variation of the most significant instances as an explicit unsupervised trigger for concept drift detection. Here, density variation is based on Active Learning, and it is calculated from virtual margins projected onto the input space according to classifier confidence. In order to investigate the performance of the proposed method, we have carried out experiments on six databases, precisely four synthetic and two real databases focusing on setting up all parameters involved in our method and on comparing it to three baselines, including two supervised drift detectors and one Active Learning-based strategy. The obtained results show that our method, when compared to the supervised baselines, reached better recognition rates in the majority of the investigated databases, while keeping similar or higher detection rates. In terms of the Active Learning-based strategies comparison, our method outperformed the baseline taking into account both recognition and detection rates, even though the baseline employed much less labeled samples. Therefore, the proposed method established a better trade-off between amount of labeled samples and detection capability, as well as recognition rate.


title: Extracting geometric models of muscles from the visible human data

abstract: Muscle simulation should be an important component of human modelling, but to date there have been few attempts to demonstrate muscle structures in an anatomically-correct way. This paper proposes a feature-based approach to muscle modelling which attempts to provide models for human musculature based on the real anatomical structures. These models provide a good visual description and form a sound basis for further developments towards medically-accurate simulation of human bodies. To allow for the wide variety of muscle shapes encountered in the body, the geometric models are based on muscle features identified from radiological data. These results are realistic models with correct anatomical structures.


title: Lying in all Honesty: Capturing Truth in Women's Confessional Memoir

abstract: This paper explores the slippery nature of illness and diagnosis in Lauren Slater’s memoir, Lying: a Metaphorical Memoir (2000). Speaking from the shadowy intersection of childhood and adolescence, Slater’s narrator, Lauren, uses the metaphor of epilepsy to describe her own predilection for exaggeration. In exploiting the fallibility of the first-person narrator, Slater insists on the legitimacy of metaphor in accounts of childhood illness that are more concerned with narrative truth than historical accuracy. The result of this playfulness and general misrule is that Slater writes herself into a double bind: on one side, she is the child narrator who inadvertently misrepresents events and misdirects readers, and on the other side, she is the untrustworthy author who employs metaphor as a licence to lie.


title: Design and Implementation of Fuzzy Controller on FPGA

abstract: Fuzzy Logic Controller (FLC) systems have emerged as one of the most promising areas for Industrial Applications. The highly growth of fuzzy logic applications led to the need of finding efficient way to hardware implementation. Field Programmable Gate Array (FPGA) is the most important tool for hardware implementation due to low consumption of energy, high speed of operation and large capacity of data storage. In this paper, instead of an introduction to fuzzy logic control methodology, we have demonstrated the implementation of a FLC through the use of the Very high speed integrated circuits Hardware Description Language (VHDL) code. FLC is designed for an armature control DC motor speed control. VHDL has been used to develop FLC on FPGA. A Sugeno type FLC structure has been used to obtain the controller output. The controller algorithm developed synthesized, simulated and implemented on FPGA Spartan 3E xc3s500e-4fg320 board.