title: A Dual-Band Printed Electrically Small Antenna Covered by Two Capacitive Split-Ring Resonators

abstract: In this letter, we present a dual-band electrically small antenna (ESA) operating at 934 MHz and 1.55 GHz. The miniaturized radiation structure consists of a small ring and two concentric split-ring resonators on an FR-4 substrate. The resonant response of the proposed radiation structure is characterized by the coupling among two split-ring resonators and the small ring. A weak electric field interaction between two split-ring resonators is observed. This interaction provides an additional capacitance to further reduce the total dimension of the resonant electrical length. The calculated return loss of the proposed structure agrees well with the measured data. Measured radiation patterns are presented. The planar structure is promising for compact wireless devices and may find applications in wireless electrocardiograph sensors, multiband portable MIMO wireless systems, and RF energy-harvesting systems.


title: Cyberbullying Detection using Time Series Modeling

abstract: Cyber bullying is a new phenomenon resulting from the advance of new communication technologies including the Internet, cell phones and Personal Digital Assistants. It is a challenging bullying problem occurring in a new territory. Online bullying can be particularly damaging and upsetting because it's usually anonymous or hard to trace. In this paper, the proposed method is utilizing a dataset of real world conversations (i.e. Pairs of questions and answers between cyber predator and the victim), in which each predator question is manually annotated in terms of severity using a numeric label. We approach the issue as a sequential data modelling approach, in which the predator's questions are formulated using a Singular Value Decomposition representation. The motivation of this procedure is to study the accuracy of predicting the level of cyber bullying attack using classification methods and also to examine potential patterns between the lingustic style of each predator. More specifically, unlike previous approaches that consider a fixed window of a cyber-predator's questions within a dialogue, we exploit the whole question set and model it as a signal, whose magnitude depends on the degree of bullying content. Using feature weighting and dimensionality reduction techniques, each signal is straightforwardly parsed by a neural network that forecasts the level of insult within a question given a window between two and three previous questions. Throughout the time series modeling experiments, an interesting discovery was made. By applying SVD on the time series data and taking into account the second dimension (since the first is usually modeling trivial dependencies between instances and attributes) we observed that its plot was very similar to the plot of the class attribute. By applying a Dynamic Time Warping algorithm, the similarity of the aforementioned signals was proved to exist, providing an immediate indicator for the severity of cyber bullying within a given dialogue.


title: Neural representation for object recognition in inferotemporal cortex

abstract: We suggest that population representation of objects in inferotemporal cortex lie on a continuum between a purely structural, parts-based description and a purely holistic description. The intrinsic dimensionality of object representation is estimated to be around 100, perhaps with lower dimensionalities for object representations more toward the holistic end of the spectrum. Cognitive knowledge in the form of semantic information and task information feed back to inferotemporal cortex from perirhinal and prefrontal cortex respectively, providing high-level multimodal-based expectations that assist in the interpretation of object stimuli. Integration of object information across eye movements may also contribute to object recognition through a process of active vision.


title: Pharmaceutical significance of cellulose: A review

abstract: The amalgamation of polymer and pharmaceutical sciences led to the introduction of polymer in the design and development of drug delivery systems. Polymeric delivery systems are mainly intended to achieve controlled or sustained drug delivery. Polysaccharides fabricated into hydrophilic matrices remain popular biomaterials for controlled-release dosage forms and the most abundant naturally occurring biopolymer is cellulose; so hdroxypropylmethyl cellulose, hydrox- ypropyl cellulose, microcrystalline cellulose and hydroxyethyl cellulose can be used for production of time controlled delivery systems. Additionally microcrystalline cellulose, sodium carboxymethyl cellulose, hydroxypropylmethyl cellu- lose, hydroxyethyl cellulose as well as hydroxypropyl cellulose are used to coat tablets. Cellulose acetate phthalate and hydroxymethyl cellulose phthalate are also used for enteric coating of tablets. Targeting of drugs to the colon following oral administration has also been accomplished by using polysaccharides such as hdroxypropylmethyl cellulose and hydrox- ypropyl cellulose in hydrated form; also they act as binders that swell when hydrated by gastric media and delay absorption. This paper assembles the current knowledge on the structure and chemistry of cellulose, and in the development of innova- tive cellulose esters and ethers for pharmaceuticals.


title: Speed of processing in the human visual system

abstract: How long does it take for the human visual system to process a complex natural image? Subjectively, recognition of familiar objects and scenes appears to be virtually instantaneous, but measuring this processing time experimentally has proved difficult. Behavioural measures such as reaction times can be used1, but these include not only visual processing but also the time required for response execution. However, event-related potentials (ERPs) can sometimes reveal signs of neural processing well before the motor output2. Here we use a go/no-go categorization task in which subjects have to decide whether a previously unseen photograph, flashed on for just 20 ms, contains an animal. ERP analysis revealed a frontal negativity specific to no-go trials that develops roughly 150 ms after stimulus onset. We conclude that the visual processing needed to perform this highly demanding task can be achieved in under 150 ms.


title: The accessible chromatin landscape of the human genome

abstract: An extensive map of human DNase I hypersensitive sites, markers of regulatory DNA, in 125 diverse cell and tissue types is described; integration of this information with other ENCODE-generated data sets identifies new relationships between chromatin accessibility, transcription, DNA methylation and regulatory factor occupancy patterns.


title: Multi Objective Segmentation for Vehicle License Plate Detection with Immune-based Classifier: A General Framework

abstract: Vehicle License Plate Recognition (VLPR) is an important system for harmonious traffic. Moreover this system is helpful in many fields and places as private and public entrances, parking lots, border control and theft control. This paper presents a new framework for Sudanese VLPR system. The proposed framework uses Multi Objective Particle Swarm Optimization (MOPSO) and Connected Component Analysis (CCA) to extract the license plate. Horizontal and vertical projection will be used for character segmentation and the final recognition stage is based on the Artificial Immune System (AIS). A new dataset that contains samples for the current shape of Sudanese license plates will be used for training and testing the proposes framework.


The Effects of Music Tempo on Concentration and Task Performance


title: Fast Compressive Spectral Clustering

abstract: Compressive spectral clustering (CSC) efficiently leverages graph filter and random sampling techniques to speed up clustering process. However, we find that CSC algorithm suffers from two main problems: i) The direct use of the dichotomy and eigencount techniques for estimating laplacian matrix’s k-th eigenvalue is expensive. ii) The computation of polynomial approximation repeats in each iteration for every cluster in the interpolation process, which occupies most of the computation time of CSC. To address these problems, we propose a new approach called FCSC for fast compressive spectral clustering. FCSC addresses the first problem by assuming that the eigenvalues approximately satisfy local uniform distribution, and addresses the second problem by recalculating the pairwise similarity between nodes with low-dimensional representation to reconstruct denoised laplacian matrix. The time complexity of reconstruction is linear with the number of non-zeros in laplacian matrix. As experimentally demonstrated on artificial and real-world datasets, our approach significantly reduces the computation time while preserving high clustering accuracy comparable to previous designs, verifying the effectiveness of FCSC.


title: Applications of self-interference cancellation in 5G and beyond

abstract: Self-interference cancellation invalidates a long-held fundamental assumption in wireless network design that radios can only operate in half duplex mode on the same channel. Beyond enabling true in-band full duplex, which effectively doubles spectral efficiency, self-interference cancellation tremendously simplifies spectrum management. Not only does it render entire ecosystems like TD-LTE obsolete, it enables future networks to leverage fragmented spectrum, a pressing global issue that will continue to worsen in 5G networks. Self-interference cancellation offers the potential to complement and sustain the evolution of 5G technologies toward denser heterogeneous networks and can be utilized in wireless communication systems in multiple ways, including increased link capacity, spectrum virtualization, any-division duplexing (ADD), novel relay solutions, and enhanced interference coordination. By virtue of its fundamental nature, self-interference cancellation will have a tremendous impact on 5G networks and beyond.


title: Automated text summarisation and evidence-based medicine: A survey of two domains

abstract: The practice of evidence-based medicine (EBM) urges medical practitioners to utilise the latest research evidence when making clinical decisions. Because of the massive and growing volume of published research on various medical topics, practitioners often find themselves overloaded with information. As such, natural language processing research has recently commenced exploring techniques for performing medical domain-specific automated text summarisation (ATS) techniques-- targeted towards the task of condensing large medical texts. However, the development of effective summarisation techniques for this task requires cross-domain knowledge. We present a survey of EBM, the domain-specific needs for EBM, automated summarisation techniques, and how they have been applied hitherto. We envision that this survey will serve as a first resource for the development of future operational text summarisation techniques for EBM.


title: Improving Quality at the Preanalytical Phase of Blood Sampling: Literature Review

abstract: Aim. This paper describes a literature review of studies on the effect of the preanalytical phase on the quality of laboratory results, and consequently on the quality of nursing. The aim was to describe quality errors in the preanalytical phase of blood sampling, in order to increase nurses’ awareness of preanalytical errors and to facilitate improvements in nursing practice associated with blood sampling. Background. The quality of the preanalytical phase plays a vital role in obtaining reliable test results, thus promoting patients’ health, diagnostics and facilitating analysis of the effectiveness of the treatment. Data sources. Medline and CINAHL. Review methods. A narrative literature research was carried out (1990-2010). The inclusion criteria were: 1) an original study or review; and 2) studies involving blood sampling. The selected papers were screened and irrelevant studies were excluded. By examining the references cited in the selected papers an additional five relevant studies were identified. Results. Twenty four papers met the inclusion criteria and fell into three categories. The preanalytical errors associated with blood sampling were related to: 1) preparing patients for the tests; 2) collecting samples; and 3) the effects of handling, storage and transportation of blood samples. All these three factors can influence the quality of laboratory results. Conclusion. The information provided by this study review may be used to improve and evaluate the quality of blood sampling praxis. Nurses should be aware of the preanalytical errors on blood sampling and their influence on laboratory results on account of patient safety. Also multi-professional co-operation with laboratory personnel in sampling could decrease failures in preanalytical phase.


title: Effect of peer support on prevention of postnatal depression among high risk women: multisite randomised controlled trial

abstract: OBJECTIVE ::: To evaluate the effectiveness of telephone based peer support in the prevention of postnatal depression. ::: ::: ::: DESIGN ::: Multisite randomised controlled trial. ::: ::: ::: SETTING ::: Seven health regions across Ontario, Canada. ::: ::: ::: PARTICIPANTS ::: 701 women in the first two weeks postpartum identified as high risk for postnatal depression with the Edinburgh postnatal depression scale and randomised with an internet based randomisation service. ::: ::: ::: INTERVENTION ::: Proactive individualised telephone based peer (mother to mother) support, initiated within 48-72 hours of randomisation, provided by a volunteer recruited from the community who had previously experienced and recovered from self reported postnatal depression and attended a four hour training session. ::: ::: ::: MAIN OUTCOME MEASURES ::: Edinburgh postnatal depression scale, structured clinical interview-depression, state-trait anxiety inventory, UCLA loneliness scale, and use of health services. ::: ::: ::: RESULTS ::: After web based screening of 21 470 women, 701 (72%) eligible mothers were recruited. A blinded research nurse followed up more than 85% by telephone, including 613 at 12 weeks and 600 at 24 weeks postpartum. At 12 weeks, 14% (40/297) of women in the intervention group and 25% (78/315) in the control group had an Edinburgh postnatal depression scale score >12 (chi(2)=12.5, P<0.001; number need to treat 8.8, 95% confidence interval 5.9 to 19.6; relative risk reduction 0.46, 95% confidence interval 0.24 to 0.62). There was a positive trend in favour of the intervention group for maternal anxiety but not loneliness or use of health services. For ethical reasons, participants identified with clinical depression at 12 weeks were referred for treatment, resulting in no differences between groups at 24 weeks. Of the 221 women in the intervention group who received and evaluated their experience of peer support, over 80% were satisfied and would recommend this support to a friend. ::: ::: ::: CONCLUSION ::: Telephone based peer support can be effective in preventing postnatal depression among women at high risk. ::: ::: ::: TRIAL REGISTRATION ::: ISRCTN 68337727.


title: N-opcode Analysis for Android Malware Classification and Categorization

abstract: Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated detection avoidance techniques employed by emerging malware families. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper we present an n-opcode analysis based approach that utilizes machine learning to classify and categorize Android malware. This approach enables automated feature discovery that eliminates the need for applying expert or domain knowledge to define the needed features. Our experiments on 2520 samples that were performed using up to 10-gram opcode features showed that an f-measure of 98% is achievable using this approach.


title: Supporting MOOC Instruction with Social Network Analysis

abstract: With an expansive and ubiquitously available gold mine of educational data, Massive Open Online courses (MOOCs) have become the an important foci of learning analytics research. In this paper, we investigate potential reasons as to why are these digitalized learning repositories being plagued with huge attrition rates. We analyze an ongoing online course offered in Coursera using a social network perspective, with an objective to identify students who are actively participating in course discussions and those who are potentially at a risk of dropping off. We additionally perform extensive forum analysis to visualize student's posting patterns longitudinally. Our results provide insights that can assist educational designers in establishing a pedagogical basis for decision-making while designing MOOCs. We infer prominent characteristics about the participation patterns of distinct groups of students in the networked learning community, and effectively discover important discussion threads. These methods can, despite the otherwise prohibitive number of students involved, allow an instructor to leverage forum behavior to identify opportunities for support.


title: Body Area Networks: A Survey

abstract: Advances in wireless communication technologies, such as wearable and implantable biosensors, along with recent developments in the embedded computing area are enabling the design, development, and implementation of body area networks. This class of networks is paving the way for the deployment of innovative healthcare monitoring applications. In the past few years, much of the research in the area of body area networks has focused on issues related to wireless sensor designs, sensor miniaturization, low-power sensor circuitry, signal processing, and communications protocols. In this paper, we present an overview of body area networks, and a discussion of BAN communications types and their related issues. We provide a detailed investigation of sensor devices, physical layer, data link layer, and radio technology aspects of BAN research. We also present a taxonomy of BAN projects that have been introduced/proposed to date. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make BANs truly ubiquitous for a wide range of applications.


title: Applying the Smart Grid Architecture Model SGAM to the EV Domain.

abstract: Within this contribution, we will introduce the so called Smart Grid Architecture Model (SGAM) which has been developed in the European Commissions’ M/490 mandate. After having proved successful in dozens of projects and especially standardization, the SGAM can be considered as state of the art to document smart grid architectures on a high level. However, as electric mobility is a part of the overall Smart Grid, a better fit to this domain can be achieved with tailoring the SGAM to an Electric Mobility Architecture Model (EMAM). This paper outlines the transfer process, an overview on standards to be applied and a final recommendation how to use the EMAM in context with existing SGAM processes. A future smart grid is a complex system-of-a-systems or often also called a cyber-physical system. One particular important aspect in such grids is the growing need for using ICT for communication between the various components involved in the processes. Particular goals to be achieved by the smart grid may be related to aspects like the optimization and coordination of the various elements and their operation in the transmission as well as distribution grid. For instance for aspects of critical infrastructure protection, the level needed for the different protection scenarios of the components involved is rather ambitious and elevated. Also, the importance of the aspect of (system) availability and uptime for the electric power distribution system is undeniably high. Additionally, the dependability of the infrastructure as well as of its basic components is the focus of system and interface design at design-time, even before actual deployment. At design time, interoperability and interchangeability have to be taken into account to ensure a meaningful analysis of the technical and non-technical requirements. To achieve this goal, one particular way is to standardize technical solutions at both international and national level. To fulfil the interoperability requirements in a holistic architecture and to enable a smarter, ICTcontrolled transmission and distribution grid, the system openness as well as the necessary amount of data exchange between participating parties and components inside a smart grid ecosystem has to rely on an agreed set of concepts. This basically leads to standardization of data models, interfaces, processes and communication protocols. Without standardization (e.g. in terms of data models and interfaces), overall costs for integrating (distribution automation) components as well as applications would increase due to the large number of new interfaces and processes involved. After the first standardization initiatives were raised by both IEC and NIST, the very idea that standards do answer most questions became apparent. But non-interoperable standards can still occur. So the NIST framework and roadmap for interoperability as well as the European initiatives derived from the M/490 Smart Grid mandate focus on properly using, expanding and adopting so called IEC core standards as well as various related ones.


title: Clostridium difficile pilot study: effects of probiotic supplementation on the incidence of C. difficile diarrhoea

abstract: Colonic infection with Clostridium difficile , leading to pseudomembranous colitis, is a common complication of antibiotic therapy, especially in elderly patients. It has been suggested that non-pathogenic probiotic bacteria might prevent the development and recurrence of C. difficile infection. This double-blind, placebo-controlled study examines the role of probiotic administration in the prevention of C. difficile -associated diarrhoea (CDAD) in elderly patients receiving antibiotic therapy. Consecutive patients (150) receiving antibiotic therapy were randomised to receive either a probiotic containing both Lactobacillus and Bifidobacterium or placebo for 20 days. Upon admission to hospital, bowel habit was recorded and a faecal sample taken. Trial probiotic or placebo was taken within 72 h of prescription of antibiotics, and a second stool sample was taken in the event of development of diarrhoea during hospitalisation or after discharge. Of the randomised patients, 138 completed the study, 69 with probiotics in conjunction with antibiotics and 69 with antibiotics alone. On the basis of development of diarrhoea, the incidence of samples positive for C. difficile -associated toxins was 2.9% in the probiotic group compared with 7.25% in the placebo-control group. When samples from all patients were tested (rather than just those developing diarrhoea) 46% of probiotic patients were toxin-positive compared with 78% of the placebo group. [Int Microbiol 2004; 7(1):59–62]


title: Urdu Nasta’liq text recognition system based on multi-dimensional recurrent neural network and statistical features

abstract: Character recognition for cursive script like Arabic, handwritten English and French is a challenging task which becomes more complicated for Urdu Nasta'liq text due to complexity of this script over Arabic. Recurrent neural network (RNN) has proved excellent performance for English, French as well as cursive Arabic script due to sequence learning property. Most of the recent approaches perform segmentation-based character recognition, whereas, due to the complexity of the Nasta'liq script, segmentation error is quite high as compared to Arabic Naskh script. RNN has provided promising results in such scenarios. In this paper, we achieved high accuracy for Urdu Nasta'liq using statistical features and multi-dimensional long short-term memory. We present a robust feature extraction approach that extracts feature based on right-to-left sliding window. Results showed that selected features significantly reduce the label error. For evaluation purposes, we have used Urdu printed text images dataset and compared the proposed approach with the recent work. The system provided 94.97 % recognition accuracy for unconstrained printed Nasta'liq text lines and outperforms the state-of-the-art results.


title: Geometry-Based Camera Calibration Using Five-Point Correspondences From a Single Image

abstract: As an essential step in many computer vision tasks, camera calibration has been studied extensively. In this paper, we propose a novel calibration technique that, based on geometric analysis, camera parameters can be estimated effectively and accurately from just one view of only five corresponding points. Our core contribution is the geometric analysis for deriving the basic equations to realize camera calibration from four coplanar corresponding points and a fifth noncoplanar one. The position, orientation, and focal length of a zooming camera can be directly estimated with unique solution. The estimated parameters are further optimized by the bundle adjustment technique. The proposed calibration method is examined and evaluated on both computer simulated data and real images. The experimental results confirm the validity of the proposed method that camera parameters can be estimated with sufficient accuracy using just five-point correspondences from a single image, even in the presence of image noise.


title: A Frequency-Based Model for Limit Cycle and Spur Predictions in Bang-Bang All Digital PLL

abstract: In this work, a frequency-based model is presented to examine limit cycle and spurious behavior in a bang-bang all-digital phase locked loop (BB-ADPLL). The proposed model considers different type of nonlinearities such as quantization effects of the digital controlled oscillator (DCO), quantization effects of the bang-bang phase detector (BB-PD) in noiseless BB-ADPLLs by a proposed novel discrete-time model. In essence, the traditional phase-locked model is transformed into a frequency-locked topology equivalent to a sigma delta modulator (SDM) with a dc-input which represents frequency deviation in phase locked state. The frequency deviation must be introduced and placed correctly within the proposed model to enable the accurate prediction of limit cycles. Thanks to the SDM-like topology, traditional techniques used in the SDM nonlinear analysis such as the discrete describing function (DDF) and number theory can be applied to predict limit cycles in first and second-order BB-ADPLLs. The inherent DCO and reference phase noise can also be easily integrated into the proposed model to accurately predict their effect on the stability of the limit cycle. The results obtained from the proposed model show good agreement with time-domain simulations.


title: Memory for moving faces : Psychological and Neural Perspectives on the Role of Motion in Face Recognition

abstract: In the real world, faces are in constant motion. Recently, researchers have begun to consider how facial motion affects memory for faces. The authors offer a theoretical framework that synthesizes psychological findings on memory for moving faces. Three hypotheses about the possible roles of facial motion in memory are evaluated. In general, although facial motion is helpful for recognizing familiar/famous faces, its benefits are less certain with unfamiliar faces. Importantly, the implicit social signals provided by a moving face (e.g., gaze changes, expression, and facial speech) may mediate the effects of facial motion on recognition. Insights from the developmental literature, which highlight the significance of attention in the processing of social information from faces, are also discussed. Finally, a neural systems framework that considers both the processing of socially relevant motion information and static feature-based information is presented. This neural systems model provides a useful framew...


Detectors and descriptors for photogrammetric applications


title: Consumer Acceptance of Location Based Services in the Retail Environment

abstract: Smartphones have become a commodity item. In combination with their seemingly infinite extensions through mobile applications, they hold great economic potential for businesses. Location Based Services (LBS) take advantage of their portability by providing relevant information to the user regarding their location. Utilizing the user’s position to create personalized location-specific marketing messages enables businesses to yield value for their customers. The main objective of this paper is to identify factors, which influence the acceptance of LBS apps in the context of retail since there is a lack of research in this field. A qualitative research approach was chosen to investigate the relevant variables. Based on the conducted interviews, theories were derived and verified against further data retrievals. Similar to findings of previous research the factors ease of use (usability) and usefulness were confirmed as being crucial in forming consumers’ attitudes.


title: Evaluation of a culturally-adapted lifestyle intervention to treat elevated cardiometabolic risk of Latino adults in primary care (Vida Sana): A randomized controlled trial.

abstract: UNLABELLED ::: Latinos bear a disproportionate burden of the dual pandemic of obesity and diabetes. However, successful interventions addressing this disparity through primary care are lacking. To address this gap, the 5-year Vida Sana (Healthy Life) study tests a culturally adapted and technology-enhanced group-based Diabetes Prevention Program intervention in a randomized controlled trial with overweight/obese Latino adults who have metabolic syndrome and/or pre-diabetes. Eligible, consenting patients (n=186) from a large community-based multispecialty group practice in Northern California will be randomly assigned to receive the culturally-adapted intervention or usual care. The RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework guided the planned evaluations. The primary aim is to determine the effectiveness of the intervention (the "E" in RE-AIM). We hypothesize that the intervention will lead to a greater mean reduction in weight at 24months (primary endpoint) vs. usual care. Secondary outcomes will include measures of cardiometabolic risk factors (e.g., blood pressure), psychosocial well-being (e.g., health-related quality of life), and behavior change (e.g., physical activity). The secondary aim is to evaluate the other RE-AIM dimensions using mixed methods: reach (e.g., participation rate of the target population), adoption (e.g., participating clinic and provider characteristics), implementation (e.g., intervention fidelity), and maintenance (e.g., sustainability in the practice setting). These findings have real word applicability with value to clinicians, patients, and other decision makers considering effective diabetes prevention programs for primary care that would support the millions of Latino adults who experience a disproportionate burden of diabetes. ::: ::: ::: TRIAL REGISTRATION ::: NCT02459691.


title: Embodied cognition and its applications: a brief review

abstract: Embodied cognition is a research program comprising an array of methods from diverse theoretical fields (e.g., philosophy, neuroscience, psychology, etc.) held together by the key assumption that the body functions as a constituent of the mind rather than a passive perceiver and actor serving the mind. With a longstanding tradition in continental and pragmatic philosophy and a recent explosion in theoretical and empirical research in psychology and cognitive science, the embodied cognition research program is now ready to be formally translated into an applied approach for clinical, sport, education, social, media and health settings. This brief review sets the scene for this special edition by outlining philosophies and theory underpinning the embodied cognition research program and briefly reviewing accounts of embodied cognition that form themes running through the articles included in this special edition. Finally, we provide some examples of existing interventions, therapies and practices that utilise body–mind principles common to embodied cognition, though under other descriptive methodological titles. We suggest that embracing and integrating these interventions, therapies and practices under “applied embodied cognition” will encourage interdisciplinary discussion, thereby helping to move the field forward.


title: High-Quality, Semi-Analytical Volume Rendering for AMR Data

abstract: This paper presents a pipeline for high quality volume rendering of adaptive mesh refinement (AMR) datasets. We introduce a new method allowing high quality visualization of hexahedral cells in this context; this method avoids artifacts like discontinuities in the isosurfaces. To achieve this, we choose the number and placement of sampling points over the cast rays according to the analytical properties of the reconstructed signal inside each cell. We extend our method to handle volume shading of such cells. We propose an interpolation scheme that guarantees continuity between adjacent cells of different AMR levels. We introduce an efficient hybrid CPU-GPU mesh traversal technique. We present an implementation of our AMR visualization method on current graphics hardware, and show results demonstrating both the quality and performance of our method.


title: Reading Twice for Natural Language Understanding

abstract: Despite the recent success of neural networks in tasks involving natural language understanding (NLU) there has only been limited progress in some of the fundamental challenges of NLU, such as the disambiguation of the meaning and function of words in context. This work approaches this problem by incorporating contextual information into word representations prior to processing the task at hand. To this end we propose a general-purpose reading architecture that is employed prior to a task-specific NLU model. It is responsible for refining context-agnostic word representations with contextual information and lends itself to the introduction of additional, context-relevant information from external knowledge sources. We demonstrate that previously non-competitive models benefit dramatically from employing contextual representations, closing the gap between general-purpose reading architectures and the state-of-the-art performance obtained with fine-tuned, task-specific architectures. Apart from our empirical results we present a comprehensive analysis of the computed representations which gives insights into the kind of information added during the refinement process.


title: Learning Differentially Private Language Models Without Losing Accuracy

abstract: We demonstrate that it is possible to train large recurrent language models with user-level differential privacy guarantees without sacrificing predictive accuracy. Our work builds on recent advances in the training of deep networks on user-partitioned data and privacy accounting for stochastic gradient descent. In particular, we add user-level privacy protection to the federated averaging algorithm, which makes "large step" updates from user-level data. Our work demonstrates that given a dataset with a sufficiently large number of users (a requirement easily met by even small internet-scale datasets), achieving differential privacy comes at the cost of increased computation, rather than in decreased utility as in most prior work. We find that private LSTM language models are quantitatively and qualitatively similar to un-noised models when trained on a large dataset.


title: Real-time plane segmentation using RGB-D cameras

abstract: Real-time 3D perception of the surrounding environment is a crucial precondition for the reliable and safe application of mobile service robots in domestic environments. Using a RGB-D camera, we present a system for acquiring and processing 3D (semantic) information at frame rates of up to 30Hz that allows a mobile robot to reliably detect obstacles and segment graspable objects and supporting surfaces as well as the overall scene geometry. Using integral images, we compute local surface normals. The points are then clustered, segmented, and classified in both normal space and spherical coordinates. The system is tested in different setups in a real household environment. ::: ::: The results show that the system is capable of reliably detecting obstacles at high frame rates, even in case of obstacles that move fast or do not considerably stick out of the ground. The segmentation of all planes in the 3D data even allows for correcting characteristic measurement errors and for reconstructing the original scene geometry in far ranges.


title: Role Transition During RN-to-FNP Education

abstract: This study examines the role transition that occurs during RN-to-family nurse practitioner (FNP) education, described in an earlier qualitative study that identified a role transition framework of influential positive forces and obstacles. The purposes of the study were to validate the educational phase of the original framework and explore other role transition issues. The study used a descriptive correlational design by asking all FNPs in two rural western states to participate by answering a questionnaire. Findings evidenced a stronger level of agreement with the positive forces than with the obstacles. In addition, two significant relationships were found between the positive forces and obstacles and personal life circumstances. These included personal support systems, which were significant for those who had to travel to class, and personal sacrifices, which were significant for those who had children at home. Further research will focus on testing across nurse practitioner specialties nationally.


title: 5G-Enabled Tactile Internet

abstract: The long-term ambition of the Tactile Internet is to enable a democratization of skill, and how it is being delivered globally. An integral part of this is to be able to transmit touch in perceived real-time, which is enabled by suitable robotics and haptics equipment at the edges, along with an unprecedented communications network. The fifth generation (5G) mobile communications systems will underpin this emerging Internet at the wireless edge. This paper presents the most important technology concepts, which lay at the intersection of the larger Tactile Internet and the emerging 5G systems. The paper outlines the key technical requirements and architectural approaches for the Tactile Internet, pertaining to wireless access protocols, radio resource management aspects, next generation core networking capabilities, edge-cloud, and edge-AI capabilities. The paper also highlights the economic impact of the Tactile Internet as well as a major shift in business models for the traditional telecommunications ecosystem.


Deep Learning for Natural Language Sequence Labelling Applied to Epigenomics


title: Design of an arm exoskeleton with scapula motion for shoulder rehabilitation

abstract: The evolution of an arm exoskeleton design for treating shoulder pathology is examined. Tradeoffs between various kinematics configurations are explored, and a device with five active degrees of freedom is proposed. Two rapid-prototype designs were built and fitted to several subjects to verify the kinematic design and determine passive link adjustments. Control modes are developed for exercise therapy and functional rehabilitation, and a distributed software architecture that incorporates computer safety monitoring is described. Although intended primarily for therapy, the exoskeleton is also used to monitor progress in strength, range of motion, and functional task performance


title: Video In Sentences Out

abstract: We present a system that produces sentential descriptions of video: who did what to whom, and where and how they did it. Action class is rendered as a verb, participant objects as noun phrases, properties of those objects as adjectival modifiers in those noun phrases, spatial relations between those participants as prepositional phrases, and characteristics of the event as prepositional-phrase adjuncts and adverbial modifiers. Extracting the information needed to render these linguistic entities requires an approach to event recognition that recovers object tracks, the trackto-role assignments, and changing body posture.


title: Local Spectral Graph Convolution for Point Set Feature Learning

abstract: Feature learning on point clouds has shown great promise, with the introduction of effective and generalizable deep learning frameworks such as pointnet++. Thus far, however, point features have been abstracted in an independent and isolated manner, ignoring the relative layout of neighboring points as well as their features. In the present article, we propose to overcome this limitation by using spectral graph convolution on a local graph, combined with a novel graph pooling strategy. In our approach, graph convolution is carried out on a nearest neighbor graph constructed from a point's neighborhood, such that features are jointly learned. We replace the standard max pooling step with a recursive clustering and pooling strategy, devised to aggregate information from within clusters of nodes that are close to one another in their spectral coordinates, leading to richer overall feature descriptors. Through extensive experiments on diverse datasets, we show a consistent demonstrable advantage for the tasks of both point set classification and segmentation.


End-to-End Argumentation Mining in Student Essays


title: Superconducting Devices in Wind Farm

abstract: Wind power is very promising in the near future and drawing more and more attentions from the governments and enterprises world wide. The global wind power industry expanded rapidly in the recent several years. In 2009, the world's total generation capacity of wind power was 157.9 million kW, of which ~ 31% was newly installed within the year. From industrial reports, the installed wind power capacity will increase by more than 30% per year in the following decade, especially in China, where GDP and power consumption are boosting quickly. It is estimated that in China, the installed wind power capacity will exceed 150 million kW and supply ~ 15% of the country's needs by the year of 2020. In a common view, wind energy is clean, renewable and abundant. The estimated global resource of wind power is up to 2.74 × 1012 kW, while the exploitable capacity is ~ 2 × 1010 kW. Further more, wind power is free of environmental impacts compared to traditional power resources, such as the hydro, thermal and nuclear power. However, energy density of wind power is low, and the wind energy resources are distributed, i.e., the majority of them is located in the rural areas, the coasts and the offshore sea shelves. At the background of global energy shortage, governments and enterprises are pushing forward the construction of new and large wind farms in these outfields. In the past several years, following the quick developments of wind power plants in the plains and highlands, the United States, Japan and Europe began to install offshore wind power turbines. For example, in April 2010, the first offshore wind plant in Germany was installed in the North Sea. This plant consisted of twelve 5 MW turbines, with annual power generation capacity of 220 million kWh. The brilliant future of wind power emphasizes the motivation on the technical upgrades in the wind farms, including the introduction of various high temperature superconducting (HTS) devices. In the past decade, many research and test operation efforts were paid on the new and high efficiency power applications, such as “directdriven” permanent magnet (PM) generators and HTS generators; magnet, flying-wheel and battery energy storage systems; fault current limiters; solid state transformers and electronic voltage regulators. These devices are designed to solve the problems occurring in the quick boosting up of the wind farms and the strict requirements on connecting them to the main frame of the power grids. Generally, these problems can be described as the optimization of the generator capacity, the size and weight of the wind turbine system, the stability of the output, as well as the tolerance of the system against fluctuations from the driving force, aka the wind, and the load. One of the key approaches to achieve the optimization is the superconducting


A theory of human motivation


title: Design and Implementation of 8-Bit Vedic Multiplier Using CMOS Logic

abstract: A multiplier is a vital element in almost all the processors and contributes significantly to the total power utilization of the system. The novel point is the efficient use of Vedic algorithm (sutras) that reduces the number of computational steps considerably compared with any conventional method. The schematic for this multiplier is designed using Tanner Tool. The design is then verified in T-SPICE using 180 nm CMOS technology model library file. The analysis is made for voltage ranges of 2.5V to 5V, to simulate the design. A CMOS multiplier, with low power consumption and high linearity is proposed. The results prove that the proposed multiplier consumes 75% less power compared to the gate level analysis done earlier. The core area of the proposed multiplier is 720 um2. Paper presents a methodical design methodology for this improved performance digital multiplier based on Vedic mathematics.


title: A modified algorithm for multi-target detection in hyperspectral image

abstract: In the area of hyperspectral image detection, Constrained Energy Minimization (CEM) algorithm has received considerable interest recently. However, it is very sensitive to noise and can only detect one target at one time. In order to solve this problem, Winner-Take-All Constrained Energy Minimization (WTACEM) was proposed. Based on it, we propose a modified multi-target detection approach. We study the sample correlation matrix and find if we exclude the desired target pixel vectors from the correlation matrix, we can achieve better results. The simulation results indicate that the errors caused by the modified WTACEM have decreased obviously and the output is more close to the true abundance fraction. The experiment also shows that when the abundance fraction of target increases, the detection performance improves.


title: Implementation of flash flood monitoring system based on wireless sensor network in Bangladesh

abstract: This research involved in the design & implementation of water monitoring system that provides a solution to remotely monitoring water levels, early warning of events, sending notifications when critical water levels are reached through mobile and accessing all information via a website. It enables people to obtain readings remotely instead of having to travel to each monitoring stations to collect data and gives protection of the safety of monitoring personnel under certain dangerous circumstances. The system consists of the field sensor, Arduino Ethernet-shield, data collection software module, web console module, Mobile based monitoring module and distributed servers. The field sensor acquires the real-time data as water-level information from river stations. The data is then stored to distributed web servers and represented to website so that everyone gets the status of water levels. The proposed monitoring system presents useful characteristics as large network capacity, sensor hardware compatibility, long-range communication, low power consumption, and capable of taking challenge to natural environment threats like weather conditions and various type of water mixed up with others materials.


title: Rule Based Metadata Extraction Framework from Academic Articles

abstract: Metadata of scientific articles such as title, abstract, keywords or index terms, body text, conclusion, reference and others play a decisive role in collecting, managing and storing academic data in scientific databases, academic journals and digital libraries. An accurate extraction of these kinds of data from scientific papers is crucial to organize and retrieve important scientific information for researchers as well as librarians. Research social network systems and academic digital library systems provide academic data extracting, organizing and retrieving services. Mostly these types of services are not free or open source. They also have some performance problems and extracting limitations in the number of PDF (Portable Document Format) files that you can upload to the extraction systems. In this paper, a completely free and open source Java based high performance metadata extraction framework is proposed. This frameworks extraction speed is 9-10 times faster than existing metadata extraction systems. It is also flexible in that it allows uploading of unlimited number of PDF files. In this approach, titles of papers are extracted using layout features, font and size characteristics of text. Other metadata fields such as abstracts, body text, keywords, conclusions and references are extracted from PDF files using fixed rule sets. Extracted metadata are stored in both Oracle database and XML (Extensible Markup Language) file. This framework can be used to make scientific collections in digital libraries, online journals, online and offline scientific databases, government research agencies and research centers.


title: Human Walking Gait with 11-DOF Humanoid Robot through Robust Neural Fuzzy Networks Tracking Control

abstract: This study describes the human-like walking ability of a humanoid robot, consisted of upper and lower arms, as well as thighs, shanks, and feet, total 11 segments. To imitate the human gait, the profile of each segment of a human body while walking is recorded. These profiles were used to train the humanoid robot to achieve human-like walking ability. In order to build a more realistic robot walking control, perturbations that are typically encountered by humans while walking are considered in the robust design of the walking control. These perturbations include the ground reaction force, the impulse disturbance, and internal biological electromyo noise interference. These perturbations are learned and then cancelled by adaptive neural fuzzy networks and cancellation residue with external disturbance is also attenuated by H∞ tracking again. With nonlinear adaptive H∞ tracking control including neural fuzzy networks, the humanoid robot is able to emulate the human walking gait. The robust tracking control algorithm proposed in this paper achieves human-like walking control ability, enabling humanoid robots to walk like real human beings.


title: Weather Forecasting using Incremental K-means Clustering

abstract: Clustering is a powerful tool which has been used in several forecasting works, such as time series forecasting, real time storm detection, flood forecasting and so on. In this paper, a generic methodology for weather forecasting is proposed by the help of incremental K-means clustering algorithm. Weather forecasting plays an important role in day to day applications.Weather forecasting of this paper is done based on the incremental air pollution database of west Bengal in the years of 2009 and 2010. This paper generally uses typical K-means clustering on the main air pollution database and a list of weather category will be developed based on the maximum mean values of the clusters.Now when the new data are coming, the incremental K-means is used to group those data into those clusters whose weather category has been already defined. Thus it builds up a strategy to predict the weather of the upcoming data of the upcoming days. This forecasting database is totally based on the weather of west Bengal and this forecasting methodology is developed to mitigating the impacts of air pollutions and launch focused modeling computations for prediction and forecasts of weather events. Here accuracy of this approach is also measured.


title: Annotating Works for Music Education: Propositions for a Musical Forms and Structures Ontology and a Musical Performance Ontology

abstract: Web applications and mobile tablets are changing the way musicians practice their instrument. Now, they can access instantaneously thousands of musical scores online and play them while watching their tablet, put on their music stand. However musicians may have difficulties in getting appropriate tips and advice to play the chosen piece correctly. This is why we conceived a collaborative platform to annotate digital scores on tablets in previous work. However, we noticed that the current Music Ontol-ogy (MO) do not allow to tag these annotations appropriately. Thus, we present in this paper a proposition for a Musical Forms and Structures Ontology (MFSO) and a Musical Performance Ontology (MPO) based on music practice. A construction methodology and a model are first detailed. Then, a practical use case is presented. Lastly, inherent theoretical and practical difficulties encountered during the ontology framework's conception are discussed.


title: Collective Intelligence and its Implementation on the Web: algorithms to develop a collective mental map

abstract: Collective intelligence is defined as the ability of a group to solve more problems than its individual members. It is argued that the obstacles created by individual cognitive limits and the difficulty of coordination can be overcome by using a collective mental map (CMM). A CMM is defined as an external memory with shared read/write access, that represents problem states, actions and preferences for actions. It can be formalized as a weighted, directed graph. The creation of a network of pheromone trails by ant colonies points us to some basic mechanisms of CMM development: averaging of individual preferences, amplification of weak links by positive feedback, and integration of specialised subnetworks through division of labor. Similar mechanisms can be used to transform the World-Wide Web into a CMM, by supplementing it with weighted links. Two types of algorithms are explored: 1) the co-occurrence of links in web pages or user selections can be used to compute a matrix of link strengths, thus generalizing the technique of &201C;collaborative filtering&201D;; 2) learning web rules extract information from a user&2018;s sequential path through the web in order to change link strengths and create new links. The resulting weighted web can be used to facilitate problem-solving by suggesting related links to the user, or, more powerfully, by supporting a software agent that discovers relevant documents through spreading activation.


title: Language Proficiency And Academic Success: Relationships Between Proficiency In Two Langauges And Achievement Among Mexican American Students

abstract: Abstract While considerable research has focused on second language development and academic success, the debate continues on how the development of the first language benefits the acquisition of the second. The intent of the present study was to examine the strength of the relation among proficiency in English and Spanish and academic success. Relations among oral language, literacy, and academic achievement were examined. A significant connection was found between proficiency in English and standardized achievement scores, as well as grade point averages. Similarly, the results reveal significant correlations between reading and writing in Spanish and achievement scores, as well as grade point average. The strongest relations were found between Written Language and academic success.


title: Self-Confidence and Sports Performance

abstract: Self-confidence (SC) is one of the most cited factors thought to affect athletic performance. SC is said to playa critical role in athletes 'success; in contrast, lack ofSC seems to be closely associated with athletic failure. Thus, confidence is an importantfactor that distinguishes successful athletes from unsuccessful ones in terms of both their mental states as well as their performances. However, the precise nature of SC was quite unclear until the publication of Feltz's seminal chapter on this issue. The term self-confidence refers to one's belief that he or she can successfully execute a desired behavior (i.e., his or her belief of "T II get the job done"). Feltz argued that the exact relationships of SC and performance in sport have not been scientifically clarified in a satisfactory manner. To promote our understanding of the precise nature of these relationships, she first presented several definitions of SC and briefly discussed them with reference to some related terms, such as perceived ability, self-concept, self-esteem, and performance expectancies. Feltz presented three of the major theoretical approaches available at that time for studying these relationships: Bandura 's self-efficacy theory, Harter's perceived competence model, and Vealey 's concept of sport confidence. She not only described the theoretical approaches but also provided extensive research evidence in sport and reviewed some relevant criticisms. Feltz summarized and compared these three approaches and derived some important conclusions referring to the (at that time) updated scientific status of the relationships between SC and sport performance. This seminal chapter did not, of course, completely resolve the riddle of SC in sport. However, Feltz organized and presented the theoretical knowledge relevant to SC in sport at that time and undoubtedly had a profound influence on many researchers in this area, which is theoretically intriguing and has a substantial practical importance for those actively involved in sport.


title: Physical layer aspects of NG-PON2 standards—Part 1: Optical link design [Invited]

abstract: NG-PON2 is the industry's first multiple wavelength (per direction), standards-based passive optical network system that is compatible with power-split optical distribution networks. The physical media dependent layer recommendation (ITU-T G.989.2) is the result of over three years of collaborative work by members of the FSAN and ITU-T Study Group 15, Question 2 groups. This two-part paper provides the technical insight and rationales behind the recently approved standard. The first part of the paper focuses on optical link design topics, including the optical distribution network characteristics, wavelength plan, Raman fiber nonlinearity related degradation, and interchannel cross-talk tolerance. It also describes the wavelength-tuning capability of optical network units and its impact on the physical media dependent layer specification.


title: Correlating cyber incident information to establish situational awareness in Critical Infrastructures

abstract: Protecting Critical Infrastructures (CIs) against contemporary cyber attacks has become a crucial as well as complex task. Modern attack campaigns, such as Advanced Persistent Threats (APTs), leverage weaknesses in the organization's business processes and exploit vulnerabilities of several systems to hit their target. Although their life-cycle can last for months, these campaigns typically go undetected until they achieve their goal. They usually aim at performing data exfiltration, cause service disruptions and can also undermine the safety of humans. Novel detection techniques and incident handling approaches are therefore required, to effectively protect CI's networks and timely react to this type of threats. Correlating large amounts of data, collected from a multitude of relevant sources, is necessary and sometimes required by national authorities to establish cyber situational awareness, and allow to promptly adopt suitable countermeasures in case of an attack. In this paper we propose three novel methods for security information correlation designed to discover relevant insights and support the establishment of cyber situational awareness.


title: Paraphrase Fragment Extraction from Monolingual Comparable Corpora

abstract: We present a novel paraphrase fragment pair extraction method that uses a monolingual comparable corpus containing different articles about the same topics or events. The procedure consists of document pair extraction, sentence pair extraction, and fragment pair extraction. At each stage, we evaluate the intermediate results manually, and tune the later stages accordingly. With this minimally supervised approach, we achieve 62% of accuracy on the paraphrase fragment pairs we collected and 67% extracted from the MSR corpus. The results look promising, given the minimal supervision of the approach, which can be further scaled up.


title: Deep CNN-Based Blind Image Quality Predictor

abstract: Image recognition based on convolutional neural networks (CNNs) has recently been shown to deliver the state-of-the-art performance in various areas of computer vision and image processing. Nevertheless, applying a deep CNN to no-reference image quality assessment (NR-IQA) remains a challenging task due to critical obstacles, i.e., the lack of a training database. In this paper, we propose a CNN-based NR-IQA framework that can effectively solve this problem. The proposed method—deep image quality assessor (DIQA)—separates the training of NR-IQA into two stages: 1) an objective distortion part and 2) a human visual system-related part. In the first stage, the CNN learns to predict the objective error map, and then the model learns to predict subjective score in the second stage. To complement the inaccuracy of the objective error map prediction on the homogeneous region, we also propose a reliability map. Two simple handcrafted features were additionally employed to further enhance the accuracy. In addition, we propose a way to visualize perceptual error maps to analyze what was learned by the deep CNN model. In the experiments, the DIQA yielded the state-of-the-art accuracy on the various databases.


title: Electrophysiologic Responses of Human Sural Nerve to Temperature

abstract: The purpose of this study was to assess the conduction, specifically the latency and amplitude of the sensory nerve action potential (SNAP), of the sural nerve as a function of intraneural temperature of the leg. The electrophysiologic responses of the sural nerve were determined at different temperatures in 22 healthy adults. Distal sensory latency and amplitude of the sural SNAP was determined at 1°C intervals over a limb temperature range of 23° to 40°C. Limb temperature was monitored with a thermistor probe placed subcutaneously near the sural nerve. Ice bath soaks were used for cooling and infrared radiation for warming the limbs. An analysis of covariance was performed for the SNAP latencies and amplitudes to determine the effect of gender and leg (right or left) at each temperature level. No effect of gender or leg on neural conduction was detected in individual subjects. A regression analysis was then used on pooled data to determine the effect of temperature on sural SNAP latency and amplitude. An inverse linear change in the latency of sural SNAP was observed over the temperature ranges used. Mean latency increased 0.1 msec per 1°C increase in subcutaneous temperature. A direct relationship between amplitude of the SNAP and temperature was determined. Mean amplitude increased 0.3 μ V per 1°C increase in subcutaneous temperature. The results of this study support previous reports, which state that SNAP latency is indirectly related to the intraneural temperature. Clinical electromyographers must monitor the temperature of the lower leg and foot whenever sensorineural conduction of the lower limbs is performed.


title: Inversion therapy in patients with pure single level lumbar discogenic disease: a pilot randomized trial

abstract: Purpose: Backache and sciatica due to protuberant disc disease is a major cause of lost working days and health expenditure. Surgery is a well-established option in the management flowchart. There ...


title: Guidance: A visual sensing platform for robotic applications

abstract: Visual sensing, such as vision based localization, navigation, tracking, are crucial for intelligent robots, which have shown great advantage in many robotic applications. However, the market is still in lack of a powerful visual sensing platform to deal with most of the visual processing tasks. In this paper we introduce a powerful and efficient platform, Guidance, which is composed of one processor and multiple (up to five) stereo sensing units. Basic visual tasks including visual odometry, obstacle avoidance, depth generation, are given as built-in functions. Additionally, with the aid of a well documented SDK, Guidance is extremely flexible for users to develop other applications, such as autonomous navigation, SLAM, tracking.


title: Autophagy and apoptosis: where do they meet?

abstract: Autophagy and apoptosis are two important cellular processes with complex and intersecting protein networks; as such, they have been the subjects of intense investigation. Recent advances have elucidated the key players and their molecular circuitry. For instance, the discovery of Beclin-1’s interacting partners has resulted in the identification of Bcl-2 as a central regulator of autophagy and apoptosis, which functions by interacting with both Beclin-1 and Bax/Bak respectively. When localized to the endoplasmic reticulum and mitochondria, Bcl-2 inhibits autophagy. Cellular stress causes the displacement of Bcl-2 from Beclin-1 and Bax, thereby triggering autophagy and apoptosis, respectively. The induction of autophagy or apoptosis results in disruption of complexes by BH3-only proteins and through post-translational modification. The mechanisms linking autophagy and apoptosis are not fully defined; however, recent discoveries have revealed that several apoptotic proteins (e.g., PUMA, Noxa, Nix, Bax, XIAP, and Bim) modulate autophagy. Moreover, autophagic proteins that control nucleation and elongation regulate intrinsic apoptosis through calpain- and caspase-mediated cleavage of autophagy-related proteins, which switches the cellular program from autophagy to apoptosis. Similarly, several autophagic proteins are implicated in extrinsic apoptosis. This highlights a dual cellular role for autophagy. On one hand, autophagy degrades damaged mitochondria and caspases, and on the other hand, it provides a membrane-based intracellular platform for caspase processing in the regulation of apoptosis. In this review, we highlight the crucial factors governing the crosstalk between autophagy and apoptosis and describe the mechanisms controlling cell survival and cell death.


title: Linking received packet to the transmitter through physical-fingerprinting of controller area network

abstract: The Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle data communication. Simplicity, robustness, and suitability for real-time systems are the salient features of the CAN bus protocol. However, it lacks the basic security features such as massage authentication, which makes it vulnerable to the spoofing attacks. In a CAN network, linking CAN packet to the sender node is a challenging task. This paper aims to address this issue by developing a framework to link each CAN packet to its source. Physical signal attributes of the received packet consisting of channel and node (or device) which contains specific unique artifacts are considered to achieve this goal. Material and design imperfections in the physical channel and digital device, which are the main contributing factors behind the device-channel specific unique artifacts, are leveraged to link the received electrical signal to the transmitter. Generally, the inimitable patterns of signals from each ECUs exist over the course of time that can manifest the stability of the proposed method. Uniqueness of the channel-device specific attributes are also investigated for time-and frequency-domain. Feature vector is made up of both time and frequency domain physical attributes and then employed to train a neural network-based classifier. Performance of the proposed fingerprinting method is evaluated by using a dataset collected from 16 different channels and four identical ECUs transmitting same message. Experimental results indicate that the proposed method achieves correct detection rates of 95.2% and 98.3% for channel and ECU classification, respectively.


title: Tight results for clustering and summarizing data streams

abstract: In this paper we investigate algorithms and lower bounds for summarization problems over a single pass data stream. In particular we focus on histogram construction and K-center clustering. We provide a simple framework that improves upon all previous algorithms on these problems in either the space bound, the approximation factor or the running time. The framework uses a notion of "streamstrapping" where summaries created for the initial prefixes of the data are used to develop better approximation algorithms. We also prove the first non-trivial lower bounds for these problems. We show that the stricter requirement that if an algorithm accurately approximates the error of every bucket or every cluster produced by it, then these upper bounds are almost the best possible. This property of accurate estimation is true of all known upper bounds on these problems.


title: Characterizing and detecting performance bugs for smartphone applications

abstract: Smartphone applications’ performance has a vital impact on user experience. However, many smartphone applications suffer from bugs that cause significant performance degradation, thereby losing their competitive edge. Unfortunately, people have little understanding of these performance bugs. They also lack effective techniques to fight with such bugs. To bridge this gap, we conducted a study of 70 real-world performance bugs collected from eight large-scale and popular Android applications. We studied the characteristics (e.g., bug types and how they manifested) of these bugs and identified their common patterns. These findings can support follow-up research on performance bug avoidance, testing, debugging and analysis for smartphone applications. To demonstrate the usefulness of our findings, we implemented a static code analyzer, PerfChecker, to detect our identified performance bug patterns. We experimentally evaluated PerfChecker by applying it to 29 popular Android applications, which comprise 1.1 million lines of Java code. PerfChecker successfully detected 126 matching instances of our performance bug patterns. Among them, 68 were quickly confirmed by developers as previously-unknown issues that affect application performance, and 20 were fixed soon afterwards by following our optimization suggestions.


title: SPV: secure path vector routing for securing BGP

abstract: As our economy and critical infrastructure increasingly relies on the Internet, the insecurity of the underlying border gateway routing protocol (BGP) stands out as the Achilles heel. Recent misconfigurations and attacks have demonstrated the brittleness of BGP. Securing BGP has become a priority.In this paper, we focus on a viable deployment path to secure BGP. We analyze security requirements, and consider tradeoffs of mechanisms that achieve the requirements. In particular, we study how to secure BGP update messages against attacks. We design an efficient cryptographic mechanism that relies only on symmetric cryptographic primitives to guard an ASPATH from alteration, and propose the Secure Path Vector (SPV) protocol. In contrast to the previously proposed S-BGP protocol, SPV is around 22 times faster. With the current effort to secure BGP, we anticipate that SPV will contribute several alternative mechanisms to secure BGP, especially for the case of incremental deployments.


Offline Handwritten Character Recognition Using Neural Network


title: CityBeat: real-time social media visualization of hyper-local city data

abstract: With the increasing volume of location-annotated content from various social media platforms like Twitter, Instagram and Foursquare, we now have real-time access to people's daily documentation of local activities, interests and attention. In this demo paper, we present CityBeat, a real-time visualization of hyper-local social media content for cities. The main objective of CityBeat is to provide users -- with a specific focus on journalists -- with information about the city's ongoings, and alert them to unusual activities. The system collects a stream of geo-tagged photos as input, uses time series analysis and classification techniques to detect hyper-local events, and compute trends and statistics. The demo includes a visualization of this information that is designed to be installed on a large-screen in a newsroom, as an ambient display.


title: 3D geological modeling using sketches and annotations from geologic maps

abstract: Constructing 3D geological models is a fundamental task in oil/gas exploration and production. A critical stage in the existing 3D geological modeling workflow is moving from a geological interpretation (usually 2D) to a 3D geological model. The construction of 3D geological models can be a cumbersome task mainly because of the models' complexity, and inconsistencies between the interpretation and modeling tasks. To narrow the gap between interpretation and modeling tasks, we propose a sketched based approach. Our main goal is to mimic how domain experts interpret geological structures and allow the creation of models directly from the interpretation task, therefore avoiding the drawbacks of a separate modeling stage. Our sketch-based modeler is based on standard annotations of 2D geological maps and on geologists' interpretation sketches. Specific geological rules and constraints are applied and evaluated during the sketch-based modeling process to guarantee the construction of a valid 3D geologic model.


NeuroGym: A Scientific Executive Review Introduction


title: Automated document classification for news article in Bahasa Indonesia based on term frequency inverse document frequency (TF-IDF) approach

abstract: The exponential growth of the data may lead us to the information explosion era, an era where most of the data cannot be managed easily. Text mining study is believed to prevent the world from entering that era. One of the text mining studies that may prevent the explosion era is text classification. It is a way to classify articles into several predefined categories. In this research, the classifier implements TF-IDF algorithm. TF-IDF is an algorithm that counts the word weight by considering frequency of the word (TF) and in how many files the word can be found (IDF). Since the IDF could see the in how many files a term can be found, it can control the weight of each word. When a word can be found in so many files, it will be considered as an unimportant word. TF-IDF has been proven to create a classifier that could classify news articles in Bahasa Indonesia in a high accuracy; 98.3%.


title: Formalizing place in geographic information systems

abstract: The past four decades have witnessed a rapid and accelerating growth in the use of computers to handle geographic information. As machines, computers require that inputs be formalized, following well-defined rules and using shared definitions of terms. This requirement has created a fundamental tension with the informal world of human discourse, and nowhere is this more apparent than over the vague concept of place. The chapter explores this tension from various perspectives: current methods of geographic representation in digital form, inherent ambiguities, the case of the gazetteer, the role of volunteered geographic information, and place as an expression of context. Examples are used to illustrate the basic principles.


title: Asthma in Children in Relation to Pre-term Birth and Fetal Growth Restriction

abstract: To assess the impact of parental asthma on risk of pre-term birth (PTB) and intrauterine growth restriction, and their subsequent association with childhood asthma. Three sequential cross-sectional surveys were conducted in 1993 (3,746), 1998 (1,964) and 2006 (1,074) in the same 15 schools among 5–11 year old children in Merseyside using the same respiratory health questionnaire completed by parents (sample size in brackets). Between 1993 and 2006, prevalence of PTB varied between 12.4 and 15.2 %, and of small for gestational age (SGA or growth restricted) babies between 2.1 and 4.6 %, and maternal asthma prevalence between 8.1 and 13.4 %. For the combined surveys mothers with asthma were more likely to have a PTB than non-asthmatic mothers (OR 1.39, 95 % CI 1.10–1.95, p < 0.001), and in the 2006 survey were more likely to have an SGA baby. 40.9 % of PTBs of asthmatic mothers developed doctor diagnosed asthma compared to 34.3 % for term babies (adjusted OR 1.65, 1.34–2.04, p < 0.001). The corresponding estimates for the symptom triad of cough, wheeze and breathlessness were 19.4 and 17.6 % (adjusted OR 1.78, 0.79–3.98). Conversely SGA babies were less likely to develop doctor diagnosed asthma (adjusted OR 0.49, 0.27–0.90, p < 0.021), or the symptom triad of cough, wheeze and breathlessness (adjusted OR 0.22, 0.05–0.97, p < 0.043), whether or not the mother was asthmatic. Maternal asthma is an independent risk factor for PTB which predisposes to childhood asthma. Intrauterine growth restriction was protective against childhood asthma.


title: Design and Implementation of Self-Adaptive PD Controller Based on Fuzzy Logic Algorithm for Omni-Directional Fast Robots in Presence of Model Uncertainties

abstract: In this paper, a self-adaptive PD (SAPD) is employed for motion control of omni-directional robots. The method contains a PD controller that can be tuned online using a fuzzy logic system (FLS). Fast and accurate positioning is one of significant challenges in robot platforms. In addition, some uncertainties have adverse effects on traditional control system's performance during the robot's motion. Slow responses, low accuracy and instability are the most important drawbacks of widespread controllers in presence of uncertain dynamics. Since the fuzzy algorithm can deal with uncertainties and nonlinearities, the proposed method can tackle the mentioned problems. The controller is designed based on an uncertain model and implemented on a four wheeled omni-directional fast robot. The novelty of this article is proposing an enhanced version of well-known gain scheduling PD controller to improve positioning performance of the robot in different circumstances. Experimental results show that the method can provide a desirable performance in the presence of uncertainties.


title: Ballpark Learning: Estimating Labels from Rough Group Comparisons

abstract: We are interested in estimating individual labels given only coarse, aggregated signal over the data points. In our setting, we receive sets ("bags") of unlabeled instances with constraints on label proportions. We relax the unrealistic assumption of known label proportions, made in previous work; instead, we assume only to have upper and lower bounds, and constraints on bag differences. We motivate the problem, propose an intuitive formulation and algorithm, and apply our methods to real-world scenarios. Across several domains, we show how using only proportion constraints and no labeled examples, we can achieve surprisingly high accuracy. In particular, we demonstrate how to predict income level using rough stereotypes and how to perform sentiment analysis using very little information. We also apply our method to guide exploratory analysis, recovering geographical differences in twitter dialect.


title: Practical Full Resolution Learned Lossless Image Compression

abstract: We propose the first practical learned lossless image compression system, L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and JPEG 2000. At the core of our method is a fully parallelizable hierarchical probabilistic model for adaptive entropy coding which is optimized end-to-end for the compression task. In contrast to recent autoregressive discrete probabilistic models such as PixelCNN, our method i) models the image distribution jointly with learned auxiliary representations instead of exclusively modeling the image distribution in RGB space, and ii) only requires three forward-passes to predict all pixel probabilities instead of one for each pixel. As a result, L3C obtains over two orders of magnitude speedups when sampling compared to the fastest PixelCNN variant (Multiscale-PixelCNN). Furthermore, we find that learning the auxiliary representation is crucial and outperforms predefined auxiliary representations such as an RGB pyramid significantly.


title: Designing a case study protocol for application in IS research

abstract: A review of the literature has shown that there is a growing call for the use of the case research method in IS research. However, it has been noted that there are few guidelines on how to conduct case research in the field of IS, particularly with respect to the development of Case Study Protocols. This article therefore endeavours to overcome this shortcoming by presenting a set of guidelines that may be used by in the development of such protocols. In essence, a Case Study Protocol (CSP) is a set of comprehensive guidelines that is an integral part of the case research design and contains the procedures for conducting the research, the research instrument itself, and the guidelines for data analysis. By developing a CSP, researchers are forced to consider all issues relevant to their research and this in turn contributes to more rigorous (case) research that has greater internal and external validity.


title: An empirical framework for defect prediction using machine learning techniques with Android software

abstract: Display Omitted Use of appropriate and large number data sets for comparing 18 ML techniques for defect prediction using object-oriented metrics.Effective performance of the predicted models assessed using appropriate performance measures.Reliability of the results evaluated using statistical test and post-hoc analysis.Validating the predicted models using inter-release validation on various releases of seven application packages of Android software. ContextSoftware defect prediction is important for identification of defect-prone parts of a software. Defect prediction models can be developed using software metrics in combination with defect data for predicting defective classes. Various studies have been conducted to find the relationship between software metrics and defect proneness, but there are few studies that statistically determine the effectiveness of the results. ObjectiveThe main objectives of the study are (i) comparison of the machine-learning techniques using data sets obtained from popular open source software (ii) use of appropriate performance measures for measuring the performance of defect prediction models (iii) use of statistical tests for effective comparison of machine-learning techniques and (iv) validation of models over different releases of data sets. MethodIn this study we use object-oriented metrics for predicting defective classes using 18 machine-learning techniques. The proposed framework has been applied to seven application packages of well known, widely used Android operating system viz. Contact, MMS, Bluetooth, Email, Calendar, Gallery2 and Telephony. The results are validated using 10-fold and inter-release validation methods. The reliability and significance of the results are evaluated using statistical test and post-hoc analysis. ResultsThe results show that the area under the curve measure for Nave Bayes, LogitBoost and Multilayer Perceptron is above 0.7 in most of the cases. The results also depict that the difference between the ML techniques is statistically significant. However, it is also proved that the Support Vector Machines based techniques such as Support Vector Machines and voted perceptron do not possess the predictive capability for predicting defects. ConclusionThe results confirm the predictive capability of various ML techniques for developing defect prediction models. The results also confirm the superiority of one ML technique over the other ML techniques. Thus, the software engineers can use the results obtained from this study in the early phases of the software development for identifying defect-prone classes of given software.


title: Comparing support vector machines with gaussian kernels to radial basis function classi ers

abstract: The support vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights, and threshold that minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by X-means clustering, and the weights are computed using error backpropagation. We consider three machines, namely, a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the United States postal service database of handwritten digits, the SV machine achieves the highest recognition accuracy, followed by the hybrid system. The SV approach is thus not only theoretically well-founded but also superior in a practical application.


title: Poor man's lemmatisation for automatic error classification

abstract: This publication has emanated from research supported by QTLEAP project – ECs FP7 (FP7/2007- ::: 2013) under grant agreement number 610516: ::: “QTLEAP: Quality Translation by Deep Language Engineering Approaches” and by a research ::: grant from Science Foundation Ireland (SFI) under ::: Grant Number SFI/12/RC/2289. We are grateful to ::: the reviewers for their valuable feedbac


title: The hyper-cube framework for ant colony optimization

abstract: Ant colony optimization is a metaheuristic approach belonging to the class of model-based search algorithms. In this paper, we propose a new framework for implementing ant colony optimization algorithms called the hyper-cube framework for ant colony optimization. In contrast to the usual way of implementing ant colony optimization algorithms, this framework limits the pheromone values to the interval [0,1]. This is obtained by introducing changes in the pheromone value update rule. These changes can in general be applied to any pheromone value update rule used in ant colony optimization. We discuss the benefits coming with this new framework. The benefits are twofold. On the theoretical side, the new framework allows us to prove that in the ant system, the ancestor of all ant colony optimization algorithms, the average quality of the solutions produced increases in expectation over time when applied to unconstrained problems. On the practical side, the new framework automatically handles the scaling of the objective function values. We experimentally show that this leads on average to a more robust behavior of ant colony optimization algorithms.


title: Towards monocrystalline silicon thin films grown on glass by liquid phase crystallization

abstract: Abstract Liquid phase crystallization of silicon is a promising technology to grow crystalline silicon thin films on glass. It has already been demonstrated that open circuit voltages of up to 656 mV and efficiencies of up to 11.8% can be achieved by this technique. Nevertheless further improvements are required to become competitive with wafer based silicon solar cells. A possibility to improve the quality is to enlarge the grain size and to control the crystallographic orientation of the resulting layers. While a preferential {100} surface orientation can be triggered utilizing suitable crystallization parameters, the in-plane orientation still remains random. Also the grain size stays within the same range. By introducing a local monocrystalline seed at the beginning of the crystallization process, we are able to control both the surface and the in-plane orientation of the silicon films. In addition the grain size is significantly increased in scanning direction and only limited by the substrate size of 5 cm. This high morphological quality is accompanied by an improved electrical quality confirmed by photoluminescence imaging and Hall measurements. This is a big step towards the final goal to directly grow monocrystalline silicon thin films on glass substrates.


title: Automated choreography synthesis using a Gaussian process leveraging consumer-generated dance motions

abstract: We propose a novel method of automatically generating dance choreography using machine learning. In a typical approach to automatic choreography, a dance is constructed by concatenating segments of existing dances which are maximally correlated to the target audio features with connectivity constraints. However, researchers using this approach are unable to produce dances with much variety, since the set of examples used in these experiments (usually motion-capture of existing choreographies) is limited and costly to produce. To solve this issue, we propose a probabilistic model which maps beat structures to dance movements using a Gaussian process, trained with a large amount of consumer-generated dance motion obtained from the web. The main contribution of our work is the combination of two approaches: the previously mentioned correlation based approach which seeks for relationships between music and dance, and a machine learning approach which is based on human motion modeling. Inspection of the generated dances proves that our method can generate choreographies with different characters by switching the training dataset, and highlights opportunities in training with further dance motions on the web to generate more expressive dance choreography.


title: Parent Training Intervention to Manage Externalizing Behaviors in Children With Autism

abstract: Abstract Children with autism spectrum disorder (ASD) are more likely than their typically developing peers to exhibit externalizing behaviors; however, the etiology in children with ASD may be different and related to the core deficits of the disorder. Although parent training interventions have been effective in decreasing externalizing behaviors in typically developing children, the effectiveness in children with ASD has not been established. An in-depth analysis of the child's behavior may provide the foundation upon which to develop an individualized parent training approach. This case study illustrates how a functional assessment interview was used to obtain in-depth information about externalizing behaviors exhibited by a child with ASD who is high functioning and how this information was used to develop an individualized parent training intervention.


title: Row-wise parallel predicate evaluation

abstract: Table scans have become more interesting recently due to greater use of ad-hoc queries and greater availability of multi-core, vector-enabled hardware. Table scan performance is limited by value representation, table layout, and processing techniques. In this paper we propose a new layout and processing technique for efficient one-pass predicate evaluation. Starting with a set of rows with a fixed number of bits per column, we append columns to form a set of banks and then pad each bank to a supported machine word length, typically 16, 32, or 64 bits. We then evaluate partial predicates on the columns of each bank, using a novel evaluation strategy that evaluates column level equality, range tests, IN-list predicates, and conjuncts of these predicates, simultaneously on multiple columns within a bank, and on multiple rows within a machine register. This approach outperforms pure column stores, which must evaluate the partial predicates one column at a time. We evaluate and compare the performance and representation overhead of this new approach and several proposed alternatives.


title: Guiding requirements engineering for software-intensive embedded systems in the automotive industry

abstract: Over the past decade, a dramatic increase of functionality, quantity, size, and complexity of software-intensive embedded systems in the automotive industry can be observed. In particular, the growing complexity drives current requirements engineering practices to the limits. In close cooperation between partners from industry and academia, the recently completed REMsES (Requirements Engineering and Management for software-intensive Embedded Systems) project has developed a guideline to support requirements engineering processes in the automotive industry. The guideline enables the requirements engineers to cope with the challenges that arise due to quantity, size and complexity of software-intensive systems. This article presents the major results of the project, namely, the fundamental principles of the approach, the guideline itself, the tool support, and the major findings obtained during the evaluation of the approach.


title: Push-pull based pseudo DC-link PV microinverter

abstract: In photovoltaic (PV) systems, module level power electronics has focused a lot of attention in the last years. The microinverter, also known as AC module, has shown to be an interesting solution. For this purpose, PV microinverter architectures with pseudo DC-link consisting in a DC-DC converter followed by an unfolding inverter have been analyzed as good alternatives. This paper proposes a new push-pull based pseudo DC-link microinverter. Simulation results are provided to validate the behavior of the proposed microinverter. Comparison with the classical interleaved flyback pseudo DC-link microinverter is given to show the interest of the proposed structure. Results show improvement in terms of conversion efficiency and power quality.


title: A Multi-armed Bandit Approach to Online Spatial Task Assignment

abstract: Spatial crowd sourcing uses workers for performing tasks that require travel to different locations in the physical world. This paper considers the online spatial task assignment problem. In this problem, spatial tasks arrive in an online manner and an appropriate worker must be assigned to each task. However, outcome of an assignment is stochastic since the worker can choose to accept or reject the task. Primary goal of the assignment algorithm is to maximize the number of successful assignments over all tasks. This presents an exploration-exploitation challenge, the algorithm must learn the task acceptance behavior of workers while selecting the best worker based on the previous learning. We address this challenge by defining a framework for online spatial task assignment based on the multi-armed bandit formalization of the problem. Furthermore, we adapt a contextual bandit algorithm to assign a worker based on the spatial features of tasks and workers. The algorithm simultaneously adapts the worker assignment strategy based on the observed task acceptance behavior of workers. Finally, we present an evaluation methodology based on a real world dataset, and evaluate the performance of the proposed algorithm against the baseline algorithms. The results demonstrate that the proposed algorithm performs better in terms of the number of successful assignments.


title: Barriers to use: usability and content accessibility on the Web's most popular sites

abstract: Content accessibility is a key feature in highly usable Web sites, but reports in the popular press typically report that 95% or more of all Web sites are inaccessible to users with disabilities. The present study is a content accessibility compliance audit of 50 of the Web's most popular sites, undertaken to determine if content accessibility can be conceived and reported in continuous, rather than dichotomous, terms. Preliminary results suggest that a meaningful ordinal ranking of content accessibility is not only possible, but also correlates significantly with the results of independent automated usability assessment procedures.


title: Enhancing Collaborative Filtering Music Recommendation by Balancing Exploration and Exploitation

abstract: Collaborative filtering (CF) techniques have shown great success in music recommendation applications. However, traditional collaborative-filtering music recommendation algorithms work in a greedy way, invariably recommending songs with the highest predicted user ratings. Such a purely exploitative strategy may result in suboptimal performance over the long term. Using a novel reinforcement learning approach, we introduce exploration into CF and try to balance between exploration and exploitation. In order to learn users’ musical tastes, we use a Bayesian graphical model that takes account of both CF latent factors and recommendation novelty. Moreover, we designed a Bayesian inference algorithm to efficiently estimate the posterior rating distributions. In music recommendation, this is the first attempt to remedy the greedy nature of CF approaches. Results from both simulation experiments and user study show that our proposed approach significantly improves recommendation performance.