title: Supporting data aspects in pig latin

abstract: In this paper we apply the aspect-oriented programming (AOP) paradigm to Pig Latin, a dataflow language for cloud computing, used primarily for the analysis of massive data sets. Missing from Pig Latin is support for cross-cutting data concerns. Data, like code, has cross-cutting concerns such as versioning, privacy, and reliability. AOP techniques can be used to weave metadata around Pig data. The metadata imbues the data with additional semantics that must be observed in the evaluation of Pig Latin programs. In this paper we show how to modify Pig Latin to process data woven together with metadata. The data weaver is a layer that maps a Pig Latin program to an augmented Pig Latin program using Pig Latin templates or patterns. We also show how to model additional levels of advice, i.e., meta-metadata.


title: Systematic Assessment of Clinical Outcomes in Bone-Level and Tissue-Level Endosseous Dental Implants

abstract: PURPOSE ::: The aim of the present systematic review was to address the following question: in patients treated with dental implants placed in pristine bone, what are the clinical and radiographic outcomes of bone-level (BL) implants in comparison to tissue-level (TL) implants after restoration with dental prostheses? ::: ::: ::: MATERIALS AND METHODS ::: Scanning of online literature databases from 1966 to January 2012, supplemented by hand searching, was conducted to identify relevant prospective randomized controlled trials, controlled clinical trials, and cohort studies. Sequential screenings at the title, abstract, and full-text levels were performed independently and in duplicate. A meta-analysis was conducted to compile data from the primary studies included in this systematic review. ::: ::: ::: RESULTS ::: The search strategy revealed a total of 5,998. Screening at the title level resulted in 752 papers, while screening at the abstract level yielded 92 publications. Full-text reading identified nine articles that fulfilled the inclusion criteria of this review. The pooled estimated difference between BL and TL implants in mean marginal bone loss was 0.05 mm (95% confidence interval [CI], -0.03 to 0.13 mm), with no statistically significant difference between the groups at 1 year after placement of the definitive prostheses. The relative risk of implant loss was estimated at 1.00 (95% CI, 0.99 to 1.02) at 1 year and at 1.01 (95% CI, 0.99 to 1.03) at 3 years after restoration, indicating no evidence of an increased risk of implant loss in BL compared to TL implants. ::: ::: ::: CONCLUSIONS ::: No statistically significant differences in bone loss and survival rates were detected between BL and TL dental implants over a short-term observation period (1 to 3 years). Thus, both implant systems fulfill the requirements for the replacement of missing teeth in implant dentistry.


title: Functional cartography of complex metabolic networks

abstract: High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodology that enables us to extract and display information contained in complex networks. Specifically, we demonstrate that one can (i) find functional modules in complex networks, and (ii) classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a ``cartographic representation'' of complex networks. Metabolic networks are among the most challenging biological networks and, arguably, the ones with more potential for immediate applicability. We use our method to analyze the metabolic networks of twelve organisms from three different super-kingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that low-degree metabolites that connect different modules are more conserved than hubs whose links are mostly within a single module.


title: HCFI: Hardware-enforced Control-Flow Integrity

abstract: Control-flow hijacking is the principal method for code-reuse techniques like Return-oriented Programming (ROP) and Jump-oriented Programming (JOP). For defending against such attacks, the community has proposed Control-flow Integrity (CFI), a technique capable of preventing exploitation by verifying that every (indirect) control-flow transfer points to a legitimate address. Enabling CFI in real systems is not straightforward, since in many cases the actual Control-flow Graph (CFG) of a program can be only approximated. Even in the case that there is perfect knowledge of the CFG, ensuring that all return instructions will return to their actual call sites, without employing a shadow stack, is questionable. On the other hand, the community has expressed concerns related to significant overheads stemming from enabling a shadow stack. In this paper, we acknowledge the importance of a shadow stack for supporting and strengthening any CFI policy. In addition, we project that implementing a full-featured CFI-enabled Instruction Set Architecture (ISA) in actual hardware with an in-chip secure memory can be efficiently carried out and the prototype experiences negligible overheads. For supporting our case, we implement by modifying a SPARC SoC and evaluate the prototype on an FPGA board by running all SPECInt benchmarks instrumented with a fine-grained CFI policy. The evaluation shows that HCFI can effectively protect applications from code-reuse attacks, while adding less than 1% runtime overhead.


title: A brief survey on anonymization techniques for privacy preserving publishing of social network data

abstract: Nowadays, partly driven by many Web 2.0 applications, more and more social network data has been made publicly available and analyzed in one way or another. Privacy preserving publishing of social network data becomes a more and more important concern. In this paper, we present a brief yet systematic review of the existing anonymization techniques for privacy preserving publishing of social network data. We identify the new challenges in privacy preserving publishing of social network data comparing to the extensively studied relational case, and examine the possible problem formulation in three important dimensions: privacy, background knowledge, and data utility. We survey the existing anonymization methods for privacy preservation in two categories: clustering-based approaches and graph modification approaches.


title: An Efficient Hybrid Intrusion Detection System based on C5.0 and SVM

abstract: Nowadays, much attention has been paid to intrusion detection system (IDS) which is closely linked to the safe use of network services. Several machine-learning paradigms including neural networks, linear genetic programming (LGP), support vector machines (SVM), Bayesian networks, multivariate adaptive regression splines (MARS) fuzzy inference systems (FISs), etc. have been investigated for the design of IDS. In this paper, we develop a hybrid method of C5.0 and SVM and investigate and evaluate the performance of our proposed method with DARPA dataset. The motivation for using the hybrid approach is to improve the accuracy of the intrusion detection system when compared to using individual SVM and individual SVM.


title: Correlates, Causes, Effects, and Treatment of Test Anxiety:

abstract: Results of 562 studies were integrated by meta-analysis to show the nature, effects, and treatment of academic test anxiety. Effect sizes were computed through the method invented by Glass (Glass, McGaw, & Smith, 1981). Correlations and effect-size groups were tested for consistency and significance with inferential statistics by Hedges and Olkin (1985). Test anxiety (TA) causes poor performance. It relates inversely to students’ self-esteem and directly to their fears of negative evaluation, defensiveness, and other forms of anxiety. Conditions (causes) giving rise to differential TA levels include ability, gender, and school grade level. A variety of treatments are effective in reducing test anxiety. Contrary to prior perceptions, improved test performance and grade point average (GPA) consistently accompany TA reduction.


title: A shape memory alloy-based tendon-driven actuation system for biomimetic artificial fingers, part I: design and evaluation

abstract: In this paper, a new biomimetic tendon-driven actuation system for prosthetic and wearable robotic hand applications is presented. It is based on the combination of compliant tendon cables and one-way shape memory alloy (SMA) wires that form a set of agonist–antagonist artificial muscle pairs for the required flexion/extension or abduction/adduction of the finger joints. The performance of the proposed actuation system is demonstrated using a 4 degree-of-freedom (three active and one passive) artificial finger testbed, also developed based on a biomimetic design approach. A microcontroller-based pulse-width-modulated proportional-derivation (PWM-PD) feedback controller and a minimum jerk trajectory feedforward controller are implemented and tested in an ad hoc fashion to evaluate the performance of the finger system in emulating natural joint motions. Part II describes the dynamic modeling of the above nonlinear system, and the model-based controller design.


title: A Branch and Bound Algorithm for Computing k-Nearest Neighbors

abstract: Computation of the k-nearest neighbors generally requires a large number of expensive distance computations. The method of branch and bound is implemented in the present algorithm to facilitate rapid calculation of the k-nearest neighbors, by eliminating the necesssity of calculating many distances. Experimental results demonstrate the efficiency of the algorithm. Typically, an average of only 61 distance computations were made to find the nearest neighbor of a test sample among 1000 design samples.


title: Causal Discovery from Subsampled Time Series Data by Constraint Optimization

abstract: This paper focuses on causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system. Previous work has shown that such subsampling can lead to significant errors about the system’s causal structure if not properly taken into account. In this paper, we first consider the search for the system timescale causal structures that correspond to a given measurement timescale structure. We provide a constraint satisfaction procedure whose computational performance is several orders of magnitude better than previous approaches. We then consider finite-sample data as input, and propose the first constraint optimization approach for recovering the system timescale causal structure. This algorithm optimally recovers from possible conflicts due to statistical errors. More generally, these advances allow for a robust and non-parametric estimation of system timescale causal structures from subsampled time series data.


title: Research on Integrated Information Platform of Agricultural Supply Chain Management Based on Internet of Things

abstract: In recent years, accidents in food quality & safety frequently occur, and more and more people have begun to think highly of food quality & safety and encourage food producers to be able to trace the origin of ingredients and process of production along the supply chain. With the development of IT, more and more practices have shown that the supply chain of agricultural products should rely on IT. The using of IT directly decides the degree of agricultural informatization and efficiency of agricultural supply chain management. In this paper, on the basis of introducing the meanings and characteristics of supply chain management and agricultural supply chain management, it also analyzes the information flow's attributes throughout the process of agricultural supply and the technological attributes of Internet of Things, finally, the designing method and architecture of integrated information platform of agricultural supply chain management based on internet of things was discussed in detail.


title: Marketing 4.0: A New Value Added to the Marketing through the Internet of Things

abstract: As the world has changed over the last decades because of the information technologies evolution, so did Marketing. Now, the major solution from the information technology evolution, i.e. Internet is being integrated with Marketing in order to reach a new generation of it, Marketing 4.0. This new generation is required, since customers are not only looking for products to satisfy their basic needs, wants, desires, and anxieties. They also need to satisfy their creativity and values such as defined in Marketing 3.0, but the most important, they also require to be part of the product, i.e. to be able to participate, interact with the products, and as a consequence of the impact from the Information technology era, be able to share their experience and check that the product really satisfies what it promises. Therefore, marketing is not moving more around the product, at the same way that Internet is not moving more around the data. Now, both Marketing and Internet are moving around the customers, powering theirs interaction with the products, considering the values from the users, and offering to them more data. This work presents the key elements from the new Marketing 4.0, its relation and extension from the Marketing 3.0, and finally several technologies from the Internet of Things to enable the interaction of the user with the products and Internet.


title: Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth

abstract: BACKGROUND ::: Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. ::: ::: ::: OBJECTIVE ::: This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. ::: ::: ::: METHODS ::: Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer's disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. ::: ::: ::: RESULTS ::: By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. ::: ::: ::: CONCLUSION ::: The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases.


title: Applying Kernel Methods to Argumentation Mining.

abstract: The area of argumentation theory is an increasingly important area of artificial intelligence and mechanisms that are able to automatically detect the argument structure provide a novel area of research. This paper considers the use of kernel methods for argumentation detection and classification. It shows that a classification accuracy of 65%, can be attained using Natural Language Processing based kernel approaches, which do not require any heuristic choice of features.


title: Prediction Model of Stock Prices Based on Correlative Analysis and Neural Networks

abstract: There is a low forecasting accuracy when we forecast the price date by the traditional BP network only considering a single closing price as the time series vector. But, if we try to add other factors vector to the BP network input vector, we will find there are low training accuracy caused by the a large number of factors .In order to solve the issues raised above ,the author sets up a two-step Forecast approach with the combination between SOFM network and BP network. First, we uses the Gray Correlation Analysis to choose the set of variable which can describe the characteristics of the state of the stock market from a number of technical indicators. Then we can classify the state of stock market by the SOFM network which has the capacity of self-organizing classification. And base on the classification, we use BP network to accurately predict . The results of experiment showed that the predictive accuracy of SOFM-BP model is more improved than that of traditional BP neural network model. And it is feasible and effective to forecast China’s stock market by SOFM-BP model, which has a prospective future


title: Do It for the Viewers!: Audience Engagement Behaviors of Young YouTubers

abstract: Online user-generated video sharing communities, such as YouTube, are becoming more popular than conventional studio-produced content. These communities provide every user with the opportunity to create and promote their own video content---a compelling venue for children and teenagers to share their stories and voices. In this study, we investigate the practices of youth video creators on YouTube. To do so, we conducted a content analysis of the audience engagement practices of 100 youth author channels, comparing them to adult and professional YouTubers when appropriate. We found that most youth authors are aware of and actively engage with their imaginary or real audiences on YouTube. They emulate the conversational and audience engagement practices seen in professional quality YouTube channels, but may not have the video editing or other meta-content skills or experience to do so. Based on our findings, we point to a number of implications for future research and design in this space.


title: Sensing the Worst: Neurophenomenological Perspectives on Neutral Stimuli Misperception in Schizophrenia Spectrum

abstract: While investigating social cognitive impairments in schizophrenia, prominent evidence has been found that patients with schizophrenia show a tendency to misclassify neutral stimuli as negatively valenced. Within this population, patients presenting delusions are more prone to this phenomenon. In a previous study, Schizophrenia spectrum (SzSp) patients rated positive, negative and neutral stimuli that were multimodally presented, while assessed with a checklist exploring anomalous subjective experiences and evaluated for positive and negative symptomatology. In the present work, we aimed to further explore the relationship between neutral stimuli misperception, anomalous experiences and positive/negative symptoms in SzSp patients. To this end, we adopted a dimensional approach by reconstructing from available data: (1) four a priori scales representing essential dimensions of SzSp experiential pathology following Parnas et al. (2005); and (2) five clinically meaningful factors to describe illness severity derived by Toomey et al. (1997). Results showed that although overall patients correctly recognized the target emotions, those who misinterpreted neutral auditory cues as negatively valenced also presented higher scores in Perplexity (PY), Bizarre Delusions (BD) and Disorganization (Di) dimensions. Moreover, a positive association between BD and both PY and Self-Disorder (SD) dimensions emerged, suggesting that psychotic symptoms may be directly linked to patients’ subjectivity. In an attempt to comprehensively capture the multilayered neutral stimuli misperception phenomenon in SzSp, we aimed at bridging phenomenology and neurobiology by connecting the levels of molecular neurochemistry (i.e., altered dopaminergic neurotransmission), system neuroscience (aberrant salience of perceptual details) and psychopathology (the chain involving hyper-reflexivity, self-disorders and the emergence of delusions).


title: Quantifying and Comparing Phylogenetic Evolutionary Rates for Shape and Other High-Dimensional Phenotypic Data

abstract: Many questions in evolutionary biology require the quantification and comparison of rates of phenotypic evolution. Recently, phylogenetic comparative methods have been developed for comparing evolutionary rates on a phylogeny for single, univariate traits (� 2 ), and evolutionary rate matrices (R) for sets of traits treated simultaneously. However, high-dimensional traits like shape remain under-examined with this framework, because methods suited for such data have not been fully developed. In this article, I describe a method to quantify phylogenetic evolutionary rates for high-dimensional multivariate data (� 2 ), found from the equivalency between statistical methods based on covariance matrices and those based on distance matrices (R-mode and Q-mode methods). I then use simulations to evaluate the statistical performance of hypothesis-testing procedures that compare � 2 for two or more groups of species on a phylogeny. Under both isotropic and non-isotropic conditions, and for differing numbers of trait dimensions, the proposed method displays appropriate Type I error and high statistical power for detecting known differences in � 2 among groups. In contrast, the Type I error rate of likelihood tests based on the evolutionary rate matrix (R) increases as the number of trait dimensions (p) increases, and becomes unacceptably large when only a few trait dimensions are considered. Further, likelihood tests based on R cannot be computed when the number of trait dimensions equals or exceeds the number of taxa in the phylogeny (i.e., when p ≥ N). These results demonstrate that tests based on � 2 provide a useful means of comparing evolutionary rates for high-dimensional data that are otherwise not analytically accessible to methods based on the evolutionary rate matrix. This advance thus expands the phylogenetic comparative toolkit for high-dimensional phenotypic traits like shape. Finally, I illustrate the utility of the new approach by evaluating rates of head shape evolution in a lineage of Plethodon salamanders. (Evolutionary rates; geometric morphometrics; macroevolution; morphological evolution; phylogenetic comparative method.).


title: DBpedia -- A Crystallization Point for the Web of Data

abstract: The DBpedia project is a community effort to extract structured information from Wikipedia and to make this information accessible on the Web. The resulting DBpedia knowledge base currently describes over 2.6 million entities. For each of these entities, DBpedia defines a globally unique identifier that can be dereferenced over the Web into a rich RDF description of the entity, including human-readable definitions in 30 languages, relationships to other resources, classifications in four concept hierarchies, various facts as well as data-level links to other Web data sources describing the entity. Over the last year, an increasing number of data publishers have begun to set data-level links to DBpedia resources, making DBpedia a central interlinking hub for the emerging Web of Data. Currently, the Web of interlinked data sources around DBpedia provides approximately 4.7 billion pieces of information and covers domains such as geographic information, people, companies, films, music, genes, drugs, books, and scientific publications. This article describes the extraction of the DBpedia knowledge base, the current status of interlinking DBpedia with other data sources on the Web, and gives an overview of applications that facilitate the Web of Data around DBpedia.


title: A Perspective Analysis of Traffic Accident using Data Mining Techniques

abstract: database. It is commonly used in a marketing, surveillance, fraud detection and scientific discovery. In data mining, machine learning is mainly focused as research which is automatically learnt to recognize complex patterns and make intelligent decisions based on data. Nowadays traffic accidents are the major causes of death and injuries in this world. Roadway patterns are useful in the development of traffic safety control policy. This paper deals with the some of classification models to predict the severity of injury that occurred during traffic accidents. I have compared Naive Bayes Bayesian classifier, AdaBoostM1 Meta classifier, PART Rule classifier, J48 Decision Tree classifier and Random Forest Tree classifier for classifying the type of injury severity of various traffic accidents. The final result shows that the Random Forest outperforms than other four algorithms.


title: Twitter Sentiment Analysis: The Good the Bad and the OMG!

abstract: In this paper, we investigate the utility of linguistic features for detecting the sentiment of Twitter messages. We evaluate the usefulness of existing lexical resources as well as features that capture information about the informal and creative language used in microblogging. We take a supervied approach to the problem, but leverage existing hashtags in the Twitter data for building training data.


title: Trait Procrastination Among Dental Students in India and Its Influence on Academic Performance

abstract: Trait procrastination is believed to be highly prevalent among college students and detrimental to their educational performance. As the scenario among dental students is virtually unknown, this study was conducted to evaluate the prevalence of trait procrastination among dental students and to analyze its influence on their academic performance. A total of 174 fourth-year dental students from three dental colleges in India voluntarily completed the Lay9s Procrastination Scale-student version (LPS). The mean percentage marks scored in the subsequent final university examinations were used as a measure of academic performance. The descriptive statistics were computed to evaluate the prevalence of significant procrastination (LPS score ≥60). Mann-Whitney U test and multiple linear regressions were used to assess the influence of age and gender on procrastination severity, and the latter was again used to analyze the association between procrastination severity and academic performance. The results indicated that 27 percent (n=47) of the students exhibited a significant extent of trait procrastination; neither age nor gender affected its severity (p


title: Massive MIMO for Next Generation Wireless Systems

abstract: Multi-user MIMO offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multi-user MIMO, as originally envisioned, with roughly equal numbers of service antennas and terminals and frequency-division duplex operation, is not a scalable technology. Massive MIMO (also known as large-scale antenna systems, very large MIMO, hyper MIMO, full-dimension MIMO, and ARGOS) makes a clean break with current practice through the use of a large excess of service antennas over active terminals and time-division duplex operation. Extra antennas help by focusing energy into ever smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include extensive use of inexpensive low-power components, reduced latency, simplification of the MAC layer, and robustness against intentional jamming. The anticipated throughput depends on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. This article presents an overview of the massive MIMO concept and contemporary research on the topic.


title: Gamification and Sustainable Consumption: Overcoming the Limitations of Persuasive Technologies

abstract: The current patterns of production and consumption in the industrialized world are not sustainable. The goods and services we consume cause resource extractions, greenhouse gas emissions and other environmental impacts that are already affecting the conditions of living on Earth. To support the transition toward sustainable consumption patterns, ICT applications that persuade consumers to change their behavior into a “green” direction have been developed in the field of Persuasive Technology (PT). Such persuasive systems, however, have been criticized for two reasons. First, they are often based on the assumption that information (e.g., information on individual energy consumption) causes behavior change, or a change in awareness and attitude that then changes behavior. Second, PT approaches assume that the designer of the system starts from objective criteria for “sustainable” behavior and is able to operationalize them in the context of the application. In this chapter, we are exploring the potential of gamification to overcome the limitations of persuasive systems. Gamification, the process of using game elements in a non-game context, opens up a broader design space for ICT applications created to support sustainable consumption. In particular, a gamification-based approach may give the user more autonomy in selecting goals and relating individual action to social interaction. The idea of gamification may also help designers to view the user’s actions in a broader context and to recognize the relevance of different motivational aspects of social interaction, such as competition and cooperation. Based on this discussion we define basic requirements to be used as guidance in gamification-based motivation design for sustainable consumption.


title: Operator adjustable impedance in bilateral remote manipulation

abstract: Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1989.


title: A 22mW 10-bit 150-MS/s pipelined ADC in 1.2V 65nm CMOS

abstract: This paper presents the low-power implementation of a 10-bit 150-MS/s pipelined analog-to-digital converter (ADC) in a standard 65 nm digital CMOS. The ADC removes the track-and-hold amplifier (THA) to reduce the power consumption. A 1.5 bit/stage architecture is used in the first stage to lower front-end design complexity. Three 2.5-bit stages are followed to reduce the stage number in the pipeline chain. Operational amplifiers (op-amps) sharing technique is used between consecutive stages for further power saving. A high swing continuous-time common mode feedback (CMFB) circuit is adopted in the op-amp design. Simulation results shows the proposed ADC achieves 9.8 ENOB with a 23 MHz input. The power consumption is 22 mW from a 1.2 V supply voltage. The active area is 640 µm × 470 µm.


title: Theory IIIb: Generalization in Deep Networks

abstract: A main puzzle of deep neural networks (DNNs) revolves around the apparent absence of"overfitting", defined in this paper as follows: the expected error does not get worse when increasing the number of neurons or of iterations of gradient descent. This is surprising because of the large capacity demonstrated by DNNs to fit randomly labeled data and the absence of explicit regularization. Recent results by Srebro et al. provide a satisfying solution of the puzzle for linear networks used in binary classification. They prove that minimization of loss functions such as the logistic, the cross-entropy and the exp-loss yields asymptotic,"slow"convergence to the maximum margin solution for linearly separable datasets, independently of the initial conditions. Here we prove a similar result for nonlinear multilayer DNNs near zero minima of the empirical loss. The result holds for exponential-type losses but not for the square loss. In particular, we prove that the weight matrix at each layer of a deep network converges to a minimum norm solution up to a scale factor (in the separable case). Our analysis of the dynamical system corresponding to gradient descent of a multilayer network suggests a simple criterion for ranking the generalization performance of different zero minimizers of the empirical loss.


title: UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities

abstract: Smart city advancements are driving massive transformations of healthcare, the largest global industry. The drivers include increasing demands for ubiquitous, preventive, and personalized healthcare, to be provided to the public at reduced risks and costs. Mobile cloud computing could potentially meet the future healthcare demands by enabling anytime, anywhere capture and analyses of patients’ data. However, network latency, bandwidth, and reliability are among the many challenges hindering the realization of next-generation healthcare. This paper proposes a ubiquitous healthcare framework, UbeHealth, that leverages edge computing, deep learning, big data, high-performance computing (HPC), and the Internet of Things (IoT) to address the aforementioned challenges. The framework enables an enhanced network quality of service using its three main components and four layers. Deep learning, big data, and HPC are used to predict network traffic, which in turn are used by the Cloudlet and network layers to optimize data rates, data caching, and routing decisions. Application protocols of the traffic flows are classified, enabling the network layer to meet applications’ communication requirements better and to detect malicious traffic and anomalous data. Clustering is used to identify the different kinds of data originating from the same application protocols. A proof of concept UbeHealth system has been developed based on the framework. A detailed literature review is used to capture the design requirements for the proposed system. The system is described in detail including the algorithmic implementation of the three components and four layers. Three widely used data sets are used to evaluate the UbeHealth system.


title: A New Benchmark and Progress Toward Improved Weakly Supervised Learning

abstract: Knowledge Matters: Importance of Prior Information for Optimization [7], by Gulcehre et. al., sought to establish the limits of current black-box, deep learning techniques by posing problems which are difficult to learn without engineering knowledge into the model or training procedure. In our work, we completely solve the previous Knowledge Matters problem using a generic model, pose a more difficult and scalable problem, All-Pairs, and advance this new problem by introducing a new learned, spatially-varying histogram model called TypeNet which outperforms conventional models on the problem. We present results on All-Pairs where our model achieves 100% test accuracy while the best ResNet models achieve 79% accuracy. In addition, our model is more than an order of magnitude smaller than Resnet-34. The challenge of solving larger-scale All-Pairs problems with high accuracy is presented to the community for investigation.


title: Project Halo Towards a Digital Aristotle

abstract: Project Halo is a multistaged effort, sponsored by Vulcan Inc, aimed at creating Digital Aristotle, an application that will encompass much of the world's scientific knowledge and be capable of applying sophisticated problem solving to answer novel questions. Vulcan envisions two primary roles for Digital Aristotle: as a tutor to instruct students in the sciences and as an interdisciplinary research assistant to help scientists in their work. As a first step towards this goal, we have just completed a six-month pilot phase designed to assess the state of the art in applied knowledge representation and reasoning (KR&/R). Vulcan selected three teams, each of which was to formally represent 70 pages from the advanced placement (AP) chemistry syllabus and deliver knowledge-based systems capable of answering questions on that syllabus. The evaluation quantified each system's coverage of the syllabus in terms of its ability to answer novel, previously unseen questions and to provide human- readable answer justifications. These justifications will play a critical role in building user trust in the question-answering capabilities of Digital Aristotle. Prior to the final evaluation, a "failure taxonomy' was collaboratively developed in an attempt to standardize failure analysis and to facilitate cross-platform comparisons. Despite differences in approach, all three systems did very well on the challenge, achieving performance comparable to the human median. The analysis also provided key insights into how the approaches might be scaled, while at the same time suggesting how the cost of producing such systems might be reduced. This outcome leaves us highly optimistic that the technical challenges facing this effort in the years to come can be identified and overcome. This article presents the motivation and longterm goals of Project Halo, describes in detail the six-month first phase of the project -- the Halo Pilot -- its KR&R challenge, empirical evaluation, results, and failure analysis. The pilot's outcome is used to define challenges for the next phase of the project and beyond.


title: Drowsy Driving Detection Based on the Driver's Head Movement using Infrared Sensors

abstract: In the driving circumstance, the driver?s condition is directly connected with the car accident. Therefore, there are many researches to decrease the car accidents by keeping the driver?s state in a good condition. While many of these researches are focused on the driver?s face, our approach is focused on the driver?s head movement. By collecting the driver?s head position and extracting the feature points, the proposed system calculates the driver?s physical state: Normal driving or Drowsy driving. The experimental results showed 78% of success rate to find the drowsy driving in the real driving situations.


title: On the non-equivalence of observables in phase-space reconstructions from recorded time series

abstract: In practical problems of phase-space reconstruction, it is usually the case that the reconstruction is much easier using a particular recorded scalar variable. This seems to contradict the general belief that all variables of a dynamical system are equivalent in phase-space reconstruction problems. This paper will argue that, in many cases, the choice of a particular scalar time series from which to reconstruct the original dynamics could be critical. It is argued that different dynamical variables do not provide the same level of information (observability) of the underlying dynamics and, as a consequence, the quality of a global reconstruction critically depends on the recorded variable. Examples in which the choice of observables is critical are discussed and the level of information contained in a given variable is quantified in the case where the original system is known. A clear example of such a situation arises in the R ¨ ossler system for which the performance of a global vector field reconstruction technique is investigated using time series of variables x, y or z, taken one at a time.


title: Feeling bad on Facebook: depression disclosures by college students on a social networking site. Depression and Anxiety 28(6):447–455

abstract: Background: Depression is common and frequently undiagnosed among college students. Social networking sites are popular among college students and can include displayed depression references. The purpose of this study was to evaluate college students’ Facebook disclosures that met DSM criteria for a depression symptom or a major depressive episode (MDE). Methods: We selected public Facebook profiles from sophomore and junior undergraduates and evaluated personally written text: ‘‘status updates.’’ We applied DSM criteria to 1-year status updates from each profile to determine prevalence of displayed depression symptoms and MDE criteria. Negative binomial regression analysis was used to model the association between depression disclosures and demographics or Facebook use characteristics. Results: Two hundred profiles were evaluated, and profile owners were 43.5% female with a mean age of 20 years. Overall, 25% of profiles displayed depressive symptoms and 2.5% met criteria for MDE. Profile owners were more likely to reference depression, if they averaged at least one online response from their friends to a status update disclosing depressive symptoms (exp(B) 52.1, Po.001), or if they used Facebook more frequently (Po.001). Conclusion: College students commonly display symptoms consistent with depression on Facebook. Our findings suggest that those who receive online reinforcement from their friends are more likely to discuss their depressive symptoms publicly on Facebook. Given the frequency of depression symptom displays on public profiles, social networking sites could be an innovative avenue for combating stigma surrounding mental health conditions or for identifying students at risk for depression. Depression and Anxiety 28:447–455, 2011. r 2011 Wiley-Liss, Inc.


title: A Conceptual Framework for Supporting a Rapid Design of Web Applications for Data Analysis of Electrical Quality Assurance Data for the LHC

abstract: The Large Hadron Collider (LHC) is one of the most complex machines ever build. It is composed of many components which constitute a large system. The tunnel and the accelerator is just one of a very critical fraction of the whole LHC infrastructure. Hardware comissioning as one of the critical processes before running the LHC is implemented during the Long Shutdown (LS) states of the macine, where Electrical Quality Assurance (ELQA) is one of its key components. Here a huge data is collected when implementing various ELQA electrical tests. In this paper we present a conceptual framework for supporting a rapid design of web applications for ELQA data analysis. We show a framework's main components, their possible integration with other systems and machine learning algorithms and a simple use case of prototyping an application for Electrical Quality Assurance of the LHC.


title: Compassion: An evolutionary analysis and empirical review

abstract: What is compassion? And how did it evolve? In this review, we integrate 3 evolutionary arguments that converge on the hypothesis that compassion evolved as a distinct affective experience whose primary function is to facilitate cooperation and protection of the weak and those who suffer. Our empirical review reveals compassion to have distinct appraisal processes attuned to undeserved suffering; distinct signaling behavior related to caregiving patterns of touch, posture, and vocalization; and a phenomenological experience and physiological response that orients the individual to social approach. This response profile of compassion differs from those of distress, sadness, and love, suggesting that compassion is indeed a distinct emotion. We conclude by considering how compassion shapes moral judgment and action, how it varies across different cultures, and how it may engage specific patterns of neural activation, as well as emerging directions of research.


title: A Real Time Audio Fingerprinting System for Advertisement Tracking and Reporting in FM Radio

abstract: In this paper we present a system designed to detect, identify and track commercial segments transmitted by a radio station. The program is entirely written in Visual C++ and uses state of the art audio fingerprinting technologies to achieve a great performance, being able to operate several times faster than real time while keeping a moderate computational load.


title: Smart filament emission controller for supersonic gas chromatography-mass spectrometry using real time digital signal processor

abstract: In electron ionization source, electrons are produced through thermionic emission by heating a wire filament, accelerating the electrons by high voltage and ionizing the analyzed molecules. In such a system, one important parameter is the filament emission current that determines the ionization rate, therefore, one needs to regulate this current. On the one hand, fast responses control is needed to keep the emission current constant, but on the other hand, we need to protect the filament from damage that occurs by large filaments current transients and over heating. To control our filament current and emission current, we developed a digital circuit based on a DSP controller that has several modes of operation. We used a smart algorithm that has a fast response to a small signal and a slow response to a large signal. In addition, we have several protective measures that prevent the current from reaching unsafe values.


title: Video segmentation by tracing discontinuities in a trajectory embedding

abstract: Our goal is to segment a video sequence into moving objects and the world scene. In recent work, spectral embedding of point trajectories based on 2D motion cues accumulated from their lifespans, has shown to outperform factorization and per frame segmentation methods for video segmentation. The scale and kinematic nature of the moving objects and the background scene determine how close or far apart trajectories are placed in the spectral embedding. Such density variations may confuse clustering algorithms, causing over-fragmentation of object interiors. Therefore, instead of clustering in the spectral embedding, we propose detecting discontinuities of embedding density between spatially neighboring trajectories. Detected discontinuities are strong indicators of object boundaries and thus valuable for video segmentation. We propose a novel embedding discretization process that recovers from over-fragmentations by merging clusters according to discontinuity evidence along inter-cluster boundaries. For segmenting articulated objects, we combine motion grouping cues with a center-surround saliency operation, resulting in “context-aware”, spatially coherent, saliency maps. Figure-ground segmentation obtained from saliency thresholding, provides object connectedness constraints that alter motion based trajectory affinities, by keeping articulated parts together and separating disconnected in time objects. Finally, we introduce Gabriel graphs as effective per frame superpixel maps for converting trajectory clustering to dense image segmentation. Gabriel edges bridge large contour gaps via geometric reasoning without over-segmenting coherent image regions. We present experimental results of our method that outperform the state-of-the-art in challenging motion segmentation datasets.


title: Demystifying 'selfie': a rampant social media activity

abstract: ‘Selfie’, the Oxford Dictionary’s word of the year in 2013, has been gaining popularity as a global phenomenon and its usage is growing with technological advancements in front-facing cameras and photo-editing software. Earlier studies hold a lopsided view of either criticising selfies as ‘vain’ and ‘narcissist’ or appreciate them as ‘feel good’ for ‘positive identity formation’. The current study intends to take a fresh look at the act and explores reasons and motivators of young college students in India, as they take selfies and traces the usage pattern and its likely relationship with the motivators of selfie-taking. Qualitative data were gathered through a focused group discussion conducted among graduate students with an average of 23.5 years who volunteered to participate in the discussion. Results show that male and female students have varying reasons for taking selfies and it is often an act of fun and assertion of one’s right to ‘self-depiction’. Selfies have a life-cycle which ends after they receive reviews on the social media platform. Results from the study can aid policy-makers in India and theoretically add to the nascent field of literature in the area.


title: SETL: A programmable semantic extract-transform-load framework for semantic data warehouses

abstract: In order to create better decisions for business analytics, organizations increasingly use external structured, semi-structured, and unstructured data in addition to the (mostly structured) internal data. Current Extract-Transform-Load (ETL) tools are not suitable for this “open world scenario” because they do not consider semantic issues in the integration processing. Current ETL tools neither support processing semantic data nor create a semantic Data Warehouse (DW), a repository of semantically integrated data. This paper describes our programmable Semantic ETL (SETL) framework. SETL builds on Semantic Web (SW) standards and tools and supports developers by offering a number of powerful modules, classes, and methods for (dimensional and semantic) DW constructs and tasks. Thus it supports semantic data sources in addition to traditional data sources, semantic integration, and creating or publishing a semantic (multidimensional) DW in terms of a knowledge base. A comprehensive experimental evaluation comparing SETL to a solution made with traditional tools (requiring much more hand-coding) on a concrete use case, shows that SETL provides better programmer productivity, knowledge base quality, and performance.


title: Ageing rate of paper insulation used in power transformers Part 1: Oil/paper system with low oxygen concentration

abstract: A substantial quantity of testing has been performed on the degradation of paper insulation over the decades. The aim being to better educate the electrical industry on how to best operate expensive assets, such as transformers. The longevity of paper insulation is frequently tested using accelerated ageing experiments, where the effects of temperature and the chemical environment on paper lifespan can be studied. Such research has resulted in paper life expectancy curves being published by the IEEE and the IEC. The investigations tend to use sealed vessels. However, the disadvantage of using this method is that the water content of the paper changes during the ageing process which then changes the ageing rate. In these ageing experiments the water and oxygen content was controlled using a special test rig to compare the ageing rate to previous work and to determine the ageing effect of paper by combining temperature, water content of paper and oxygen content of the oil. We found that the rate of paper ageing can be more accurately determined by controlling the water and oxygen during the experiment which then produced noticeable changes in predicting life expectancy.


title: An Optimization Methodology for Neural Network Weights and Architectures

abstract: This paper introduces a methodology for neural network global optimization. The aim is the simultaneous optimization of multilayer perceptron (MLP) network weights and architectures, in order to generate topologies with few connections and high classification performance for any data sets. The approach combines the advantages of simulated annealing, tabu search and the backpropagation training algorithm in order to generate an automatic process for producing networks with high classification performance and low complexity. Experimental results obtained with four classification problems and one prediction problem has shown to be better than those obtained by the most commonly used optimization techniques


title: S2D-ProM: A Strategy Oriented Process Model for Secure Software Development

abstract: Building secure software is about taking security into account during all phases of software development. This practice is missing in, widely used, traditional developments due to domain immaturity, newness of the field and process complexity. Software development includes two views, a product view and a process view. Product view defines what the product is, whereas process view describes how the product is developed. Here we are concerned with the process view. Modelling the process allows simulate and analyze a software development process, which can help developers better understand, manage and optimize the software development process. In this paper we present our approach S2D-ProM, for Secure Software Development Process Model, which is a strategy oriented process model. This latter, capture steps and strategies that are required for the development of secure software and provide a two level guidance. The first level guidance is strategic helping developers choosing one among several strategies. The second level guidance is tactical helping developers achieving their selection for producing secure software. The proposed process model is easily extensible and allows building customized processes adapted to context, developer's finalities and product state. This flexibility allows the environment evolving through time to support new securing strategies.


title: Using expression profiling data to identify human microRNA targets

abstract: We demonstrate that paired expression profiles of microRNAs (miRNAs) and mRNAs can be used to identify functional miRNA-target relationships with high precision. We used a Bayesian data analysis algorithm, GenMiR++, to identify a network of 1,597 high-confidence target predictions for 104 human miRNAs, which was supported by RNA expression data across 88 tissues and cell types, sequence complementarity and comparative genomics data. We experimentally verified our predictions by investigating the result of let-7b downregulation in retinoblastoma using quantitative reverse transcriptase (RT)-PCR and microarray profiling: some of our verified let-7b targets include CDC25A and BCL7A. Compared to sequence-based predictions, our high-scoring GenMiR++ predictions had much more consistent Gene Ontology annotations and were more accurate predictors of which mRNA levels respond to changes in let-7b levels.


abstract: Network management is part of traffic engineering and security. The current solutions - Deep Packet Inspection (DPI) and statistical classification, rely on the availability of a training set. In case of these there is a cumbersome need to regularly update the signatures. Further their visibility is limited to classes the classifier has been trained for. Unsupervised algorithms have been envisioned as a an alternative to automatically identify classes of traffic. To address these issues Self Learning Network Traffic Classification is proposed. It uses unsupervised algorithms along with an adaptive seeding approach to automatically lets classes of traffic to emerge, making them identified and labelled. Unlike traditional classifiers, there is no need of a-priori knowledge of signatures nor a training set to extract the signatures. Instead, Self Learning Network Traffic Classification automatically groups flows into pure (or homogeneous) clusters using simple statistical features. This label assignment (which is still based on some manual intervention) ensures that class labels can be easily discovered. Furthermore, Self Learning Network Traffic Classification uses an iterative seeding approach which will boost its ability to cope with new protocols and applications. Unlike state-of-art classifiers, the biggest advantage of Self Learning Network Traffic Classification is its ability to discover new protocols and applications in an almost automated fashion.


title: Stable walking of a quadruped robot on uneven terrains with friction constraints

abstract: One of advantages of legged robots rather than wheeled robots is their mobilability on uneven terrains. To achieve reliable motion and improve performance characteristics of these robots in such environments, COG path planning is one of the main issues which must be taken into account. In this paper, a new stability criterion is introduced to ensure the robot stability on uneven terrains. To avoid feet slippage, the friction cone is approximated by a pyramid and is applied into optimization problem leading to a linear form. The validity of the proposed algorithm is performed by simulation analysis of a quadruped robot.


title: Affective Interactions Using Virtual Reality: The Link between Presence and Emotions

abstract: Many studies showed the ability of movies and imagery techniques to elicit emotions. Nevertheless, it is less clear how to manipulate the content of interactive media to induce specific emotional responses. In particular, this is true for the emerging medium virtual reality (VR), whose main feature is the ability to induce a feeling of "presence" in the computer-generated world experienced by the user. The main goal of this study was to analyze the possible use of VR as an affective medium. Within this general goal, the study also analyzed the relationship between presence and emotions. The results confirmed the efficacy of VR as affective medium: the interaction with "anxious" and "relaxing" virtual environments produced anxiety and relaxation. The data also showed a circular interaction between presence and emotions: on one side, the feeling of presence was greater in the "emotional" environments; on the other side, the emotional state was influenced by the level of presence. The significance of these r...


title: The ‘Maltreatment and Abuse Chronology of Exposure’ (MACE) Scale for the Retrospective Assessment of Abuse and Neglect During Development

abstract: There is increasing interest in childhood maltreatment as a potent stimulus that may alter trajectories of brain development, induce epigenetic modifications and enhance risk for medical and psychiatric disorders. Although a number of useful scales exist for retrospective assessment of abuse and neglect they have significant limitations. Moreover, they fail to provide detailed information on timing of exposure, which is critical for delineation of sensitive periods. The Maltreatment and Abuse Chronology of Exposure (MACE) scale was developed in a sample of 1051 participants using item response theory to gauge severity of exposure to ten types of maltreatment (emotional neglect, non-verbal emotional abuse, parental physical maltreatment, parental verbal abuse, peer emotional abuse, peer physical bullying, physical neglect, sexual abuse, witnessing interparental violence and witnessing violence to siblings) during each year of childhood. Items included in the subscales had acceptable psychometric properties based on infit and outfit mean square statistics, and each subscale passed Andersen’s Likelihood ratio test. The MACE provides an overall severity score and multiplicity score (number of types of maltreatment experienced) with excellent test-retest reliability. Each type of maltreatment showed good reliability as did severity of exposure across each year of childhood. MACE Severity correlated 0.738 with Childhood Trauma Questionnaire (CTQ) score and MACE Multiplicity correlated 0.698 with the Adverse Childhood Experiences scale (ACE). However, MACE accounted for 2.00- and 2.07-fold more of the variance, on average, in psychiatric symptom ratings than CTQ or ACE, respectively, based on variance decomposition. Different types of maltreatment had distinct and often unique developmental patterns. The 52-item MACE, a simpler Maltreatment Abuse and Exposure Scale (MAES) that only assesses overall exposure and the original test instrument (MACE-X) with several additional items plus spreadsheets and R code for scoring are provided to facilitate use and to spur further development.


title: Discovering facts with boolean tensor tucker decomposition

abstract: Open Information Extraction (Open IE) has gained increasing research interest in recent years. The first step in Open IE is to extract raw subject--predicate--object triples from the data. These raw triples are rarely usable per se, and need additional post-processing. To that end, we proposed the use of Boolean Tucker tensor decomposition to simultaneously find the entity and relation synonyms and the facts connecting them from the raw triples. Our method represents the synonym sets and facts using (sparse) binary matrices and tensor that can be efficiently stored and manipulated. We consider the presentation of the problem as a Boolean tensor decomposition as one of this paper's main contributions. To study the validity of this approach, we use a recent algorithm for scalable Boolean Tucker decomposition. We validate the results with empirical evaluation on a new semi-synthetic data set, generated to faithfully reproduce real-world data features, as well as with real-world data from existing Open IE extractor. We show that our method obtains high precision while the low recall can easily be remedied by considering the original data together with the decomposition.


title: Understanding Dropout

abstract: Dropout is a relatively new algorithm for training neural networks which relies on stochastically "dropping out" neurons during training in order to avoid the co-adaptation of feature detectors. We introduce a general formalism for studying dropout on either units or connections, with arbitrary probability values, and use it to analyze the averaging and regularizing properties of dropout in both linear and non-linear networks. For deep neural networks, the averaging properties of dropout are characterized by three recursive equations, including the approximation of expectations by normalized weighted geometric means. We provide estimates and bounds for these approximations and corroborate the results with simulations. Among other results, we also show how dropout performs stochastic gradient descent on a regularized error function.


title: A predictive online path planning and optimization approach for cooperative mobile service robot navigation in industrial applications

abstract: In this paper we address the problem of online trajectory optimization and cooperative collision avoidance when multiple mobile service robots are operating in close proximity to each other. Using cooperative trajectory optimization to obtain smooth transitions in multi-agent path crossing scenarios applies to the demand for more flexibility and efficiency in industrial autonomous guided vehicle (AGV) systems. We introduce a general approach for online trajectory optimization in dynamic environments. It involves an elastic-band based method for time-dependent obstacle avoidance combined with a model predictive trajectory planner that takes into account the robot's kinematic and kinodynamic constraints. We augment that planning approach to be able to cope with shared trajectories of other agents and perform an potential field based cooperative trajectory optimization. Performance and practical feasibility of the proposed approach are demonstrated in simulation as well as in real world experiments carried out on a representative set of path crossing scenarios with two industrial mobile service robots.


title: Clustering RDF Databases Using Tunable-LSH

abstract: The Resource Description Framework (RDF) is a W3C standard for representing graph-structured data, and SPARQL is the standard query language for RDF. Recent advances in Information Extraction, Linked Data Management and the Semantic Web have led to a rapid increase in both the volume and the variety of RDF data that are publicly available. As businesses start to capitalize on RDF data, RDF data management systems are being exposed to workloads that are far more diverse and dynamic than what they were designed to handle. Consequently, there is a growing need for developing workload-adaptive and self-tuning RDF data management systems. To realize this vision, we introduce a fast and efficient method for dynamically clustering records in an RDF data management system. Specifically, we assume nothing about the workload upfront, but as SPARQL queries are executed, we keep track of records that are co-accessed by the queries in the workload and physically cluster them. To decide dynamically (hence, in constant-time) where a record needs to be placed in the storage system, we develop a new locality-sensitive hashing (LSH) scheme, Tunable-LSH. Using Tunable-LSH, records that are co-accessed across similar sets of queries can be hashed to the same or nearby physical pages in the storage system. What sets Tunable-LSH apart from existing LSH schemes is that it can auto-tune to achieve the aforementioned clustering objective with high accuracy even when the workloads change. Experimental evaluation of Tunable-LSH in our prototype RDF data management system, chameleon-db, as well as in a standalone hashtable shows significant end-to-end improvements over existing solutions.


title: Tactile sensing arrays for humanoid robots

abstract: Development of robots capable of operating in unstructured environments or intended to substitute for man in hazardous or inaccessible environments, demands the implementation of sophisticated sensory capabilities, far beyond those available today. In this regard, the development of tactile sensors is one of the key technical challenges in advanced robotics and minimal access surgery. In this work we present arrays of 'taxels' (tactile elements) which will be placed on the distal phalange of the humanoid robot in our lab. We present two different designs and implementations. In the first one, microelectrode arrays(MEAs) of 32 elements, with 1 mm center to center distance, have been designed. The taxel is implemented by epoxy-adhering the sensing material (piezoelectric polymer film of PVDF-TrFE) on a microelectrode. Each taxel is intended to be used as an extended gate of an FET (external to the chip); the taxel collects the charge/voltage generated, as consequence of the applied stress, on the deposited piezoelectric polymer film (i.e. the extended gate itself). The second design and implementation integrates both the taxels array and the FET devices, on the same silicon die.


title: Performance evaluation and tuning for MapReduce computing in Hadoop distributed file system

abstract: This paper proposes a method to facilitate the identification process for a set of configuration parameters to achieve the optimal performance with respect to a benchmark program in HDFS in an automated manner. Performance optimization of Hadoop processes is a tedious yet challenging problem due to the complexity of the systems organization with an extensive list of configuration parameters to be considered. An Automated Benchmarking Configuration Method (ABCM) is developed in this work to facilitate the identification process for the set of configuration parameters that minimizes the execution time of a benchmark, namely TestDFSIO Write and Read in particular. A two-phased configuration parameters selection process with a simple sampling technique is proposed in order to mediate the exponential computation time otherwise. By using the proposed technique, we have automatically found the sets of top five selected optimal configuration parameters that reduced the average execution time by 32% compared to the execution time with the default set of Hadoop configuration parameters.


title: Neural Correlates of Dispositional Mindfulness During Affect Labeling

abstract: Objective:Mindfulness is a process whereby one is aware and receptive to present moment experiences. Although mindfulness-enhancing interventions reduce pathological mental and physical health symptoms across a wide variety of conditions and diseases, the mechanisms underlying these effects remain u


title: Synthesis and in vitro antibacterial activity of 7-(3-alkoxyimino-5-amino/methylaminopiperidin-1-yl)fluoroquinolone derivatives.

abstract: We report herein the design and synthesis of novel 7-(3-alkoxyimino-5-amino/methylaminopiperidin-1-yl)fluoroquinolone derivatives based on the structures of new fluoroquinolones IMB and DZH. The antibacterial activity of these newly synthesized compounds was also evaluated and compared with gemifloxacin, ciprofloxacin, and levofloxacin. Results revealed that all of the target compounds 10-27 have good potency in inhibiting the growth of Staphylococcus aureus including MSSA (MIC: 0.125-8 μg/mL), Staphylococcus epidermidis including MRSE (MIC: 0.25-16 μg/mL), Streptococcus pneumoniae (MIC: 0.125-4 μg/mL), and Escherichia coli (MIC: 0.25-0.5 μg/mL). In particular, some compounds showed useful activity against several fluoroquinolone-resistant strains, and the most active compound 15 was found to be 16-128, 2-32, and 4-8-fold more potent than the three reference drugs against fluoroquinolone-resistant MSSA, MRSA, and MRSE.


title: A novel similarity measure for time series data with applications to gait and activity recognition

abstract: In this abstract, we propose a novel approach to modeling time-series for the purpose of comparing segments of data in order to classify activities based on accelerometer sensor data. Our approach consists of producing an ensemble of simple classifiers that can be built and can classify new data efficiently. We present empirical results from an implementation of our algorithm running on a mobile phone, demonstrating the efficiency and performance of our technique on real-world data. Our algorithm is able to identify individuals based on their gait, and can be used in a semi-supervised setting to label large data sets using a small number of labeled examples. Our method can also be used in an unsupervised setting to visualize time-series data, for example, to identify the number of different activities that occur in an unlabeled data set.


title: Analysis and Prediction of Real Network Traffic

abstract: Short period prediction is a relevant task for many network applications. Tuning the parameters of the prediction model is very crucial to achieve accurate prediction. This work focuses on the design, the empirical evaluation and the analysis of the behavior of training-based models for predicting the throughput of a single link i.e. the incoming input rate in Megabit per second. In this work, a neurofuzzy model ( _ SNF), the AutoRegressive Moving Average (ARMA) model and the Integrated AutoRegressive Moving Average (ARIMA) model are used for predicting. Via experimentation on real network traffic of different links, we study the effect of some parameters on the prediction performance in terms of error. These parameters are the amount of data needed to identify the model (i.e. training set), the number of last observations of the throughput (i.e. lag) needed as inputs for the model, the data granularity, variance and packet size distribution. We also investigate the use of the number of packets or sampled data as inputs for the prediction model. Experimental results show that training-based models, identified with small training set and using only one lag, can provide accurate prediction.We show that counts of packets and especially large packets can be used to efficiently predict the throughput.


title: Evaluation of FDG-PET in pretreatment staging in locally advanced rectal cancer: A prospective study.

abstract: 3563 Background: The utility of adding 18F-Fluorodeoxyglucose-Positron Emission Tomography (PET) to the pre-treatment staging of patients with locally advanced rectal cancer has not been well evaluated. Methods: Following completion of standard work-up, including physical examination, endorectal ultrasound, CT of the abdomen and pelvis, and chest CT or chest x-ray, 150 patients with primary, resectable, biopsy-proven rectal adenocarcinoma requiring neoadjuvant chemoradiation were enrolled in a prospective, single-stage, phase II study evaluating the role of PET. All patients underwent a dedicated whole-body PET prior to initiation of neoadjuvant chemoradiation. The primary endpoint of this analysis was to determine the accuracy of PET in detecting extrapelvic metastatic disease in primary rectal cancer patients considered operable on the basis of currently-standard work-up. Results: Among the 117 eligible patients found to be stage II or III by standard staging techniques, none (0%) were found by PET to h...


title: Neural network controller for microturbine power plants

abstract: Microturbines (MTs) as alternative energy sources demonstrate high potential to meet user's needs for distributed generation application. Efficient model and controller are essential to understand and investigate the MT operation characteristics. This paper introduces a controller based on artificial neural network (ANN) for stand alone MT power plants. In addition, the paper presents a comparison between the performance of the MT when using traditional PI and ANN controllers. MT has three control loops, namely; the power, the temperature, and the speed control loops. In addition, in this paper the MT is connected to a synchronous generator (SG) which includes a voltage control loop. An ANN with four inputs and four outputs is used to replace the four PI control loops in the MT and the SG models. Three test measures have been used to compare the performance of the MT with PI and ANN controllers, absolute mean error (AME), root mean square error (RMSE), and standard deviation error (SDE). Test results of MT with the ANN controller indicate high performance compared to the conventional PI controller.


title: TSimNet: An Industrial Time Sensitive Networking Simulation Framework Based on OMNeT++

abstract: The upcoming IEEE Time Sensitive Networking standard will offer a set of new functionality for Ethernet like frame preemption and frame replication that need careful evaluation. The IETF DetNet working group also works on deterministic layer 3 segments and leverages therefore works from the IEEE group. We present here a simulation framework based on OMNeT++ that implements all non time-based features present in TSN in a modular and easily extendable way. The framework gives other researches a stable base to start evaluating existing and future scenarios. An evaluation of frame preemption as well as per-stream policing based on the framework is presented. It was shown, that frame preemption does not always bring latency advantages and highly depends on configuration of the system. The paper concludes with an overview of the computational cost for the use of this framework.


title: Part-based Graph Convolutional Network for Action Recognition

abstract: Human actions comprise of joint motion of articulated body parts or `gestures'. Human skeleton is intuitively represented as a sparse graph with joints as nodes and natural connections between them as edges. Graph convolutional networks have been used to recognize actions from skeletal videos. We introduce a part-based graph convolutional network (PB-GCN) for this task, inspired by Deformable Part-based Models (DPMs). We divide the skeleton graph into four subgraphs with joints shared across them and learn a recognition model using a part-based graph convolutional network. We show that such a model improves performance of recognition, compared to a model using entire skeleton graph. Instead of using 3D joint coordinates as node features, we show that using relative coordinates and temporal displacements boosts performance. Our model achieves state-of-the-art performance on two challenging benchmark datasets NTURGB+D and HDM05, for skeletal action recognition.


title: Consumer trust in Internet shopping in Ireland: towards the development of a more effective trust measurement instrument

abstract: Trust is a complex phenomenon that pervades human relations. It is essential for the success of business-to-consumer electronic commerce, where many of the tools that can be used in its absence (contracts, advance payments, insurance, etc.) may not be available. However, research as to how consumer trust can be built in an online environment is limited and varies considerably in terms of the dimensions of the problem that are examined. Consequently, much of our understanding of the antecedents of trust in online shopping context remains fragmented. This study uses a previously validated measurement instrument to investigate, in an Irish context, the existence and importance of specific perceptions and factors that are thought to predict the generation of consumer trust in Internet shopping. The research results provide evidence that Irish consumers' perception of vendor trustworthiness is the result of specific factors that it is possible for vendors to manage. A modified model that addresses the key dimensions of consumer trust in Internet shopping in Ireland is proposed.


title: Machine Learning with World Knowledge: The Position and Survey

abstract: Machine learning has become pervasive in multiple domains, impacting a wide variety of applications, such as knowledge discovery and data mining, natural language processing, information retrieval, computer vision, social and health informatics, ubiquitous computing, etc. Two essential problems of machine learning are how to generate features and how to acquire labels for machines to learn. Particularly, labeling large amount of data for each domain-specific problem can be very time consuming and costly. It has become a key obstacle in making learning protocols realistic in applications. In this paper, we will discuss how to use the existing general-purpose world knowledge to enhance machine learning processes, by enriching the features or reducing the labeling work. We start from the comparison of world knowledge with domain-specific knowledge, and then introduce three key problems in using world knowledge in learning processes, i.e., explicit and implicit feature representation, inference for knowledge linking and disambiguation, and learning with direct or indirect supervision. Finally we discuss the future directions of this research topic.


title: Exploiting the web of data in model-based recommender systems

abstract: The availability of a huge amount of interconnected data in the so called Web of Data (WoD) paves the way to a new generation of applications able to exploit the information encoded in it. In this paper we present a model-based recommender system leveraging the datasets publicly available in the Linked Open Data (LOD) cloud as DBpedia and LinkedMDB. The proposed approach adapts support vector machine (SVM) to deal with RDF triples. We tested our system and showed its effectiveness by a comparison with different recommender systems techniques -- both content-based and collaborative filtering ones.


title: Understanding Someone Else's Code: An Analysis of Experience", The

abstract: It is the intention of the Journal of Systems and Software to publish, from time to time, articles cut from a different mold. This is one in that series. The object of the CONTROVERSY CORNER articles is both to present information and to stimulate thought. Topics chosen for this coverage are not just traditional formal discussions of research work; they also contain ideas at the fringes of the field's «conventional wisdom.» This series will succeed only to the extent that it stimulates not just thought, but action. If you have a strong reaction to the article that follows, either positive or negative, write to Robert L. Glass, Editor, Journal of Systems and Software, Computing Trends, P.O. Box 213, State College, PA 16804. We will publish the best of the responses as CONTROVERSY REVISITED


title: Graph-based distributed cooperative navigation for a general multi-robot measurement model

abstract: Cooperative navigation (CN) enables a group of cooperative robots to reduce their individual navigation errors. For a general multi-robot (MR) measurement model that involves both inertial navigation data and other onboard sensor readings, taken at different time instances, the various sources of information become correlated. Thus, this correlation should be solved for in the process of information fusion to obtain consistent state estimation. The common approach for obtaining the correlation terms is to maintain an augmented covariance matrix. This method would work for relative pose measurements, but is impractical for a general MR measurement model, because the identities of the robots involved in generating the measurements, as well as the measurement time instances, are unknown a priori. In the current work, a new consistent information fusion method for a general MR measurement model is developed. The proposed approach relies on graph theory. It enables explicit on-demand calculation of the required correlation terms. The graph is locally maintained by every robot in the group, representing all of the MR measurement updates. The developed method calculates the correlation terms in the most general scenarios of MR measurements while properly handling the involved process and measurement noise. A theoretical example and a statistical study are provided, demonstrating the performance of the method for vision-aided navigation based on a three-view measurement model. The method is compared, in a simulated environment, with a fixed-lag centralized smoothing approach. The method is also validated in an experiment that involved real imagery and navigation data. Computational complexity estimates show that the newly developed method is computationally efficient.


title: Getting access to what goes on in people's heads?: reflections on the think-aloud technique

abstract: One of the basic usability testing techniques the HCI community draws on, and which stands out as unique, is thinking aloud. We introduce the many names, uses and modifications of the classical think aloud technique, and ask the rhetorical question: What do researchers think they get when they ask people to think aloud? We answer it by discussing the classical work of Ericsson and Simon(1984), in particular their distinction between vocalisation, verbalisation and retrospective reports and the relation to short term memory. Reintroducing the psychological perspective and the focus on higher order cognitive processes, we argue that access to subjective experience is possible in terms of introspection and describe a technique that invites the user to become a participant in the analysis of his or her own cognitive processes. We suggest that use of think aloud has as a prerequisite explicit descriptions of design, test procedure and framework for analysis. We point out, however, that if the aim is to get access to human thinking, HCI research may benefit from experimental research.


title: Fusion of GPS and structure-from-motion using constrained bundle adjustments

abstract: Two problems occur when bundle adjustment (BA) is applied on long image sequences: the large calculation time and the drift (or error accumulation). In recent work, the calculation time is reduced by local BAs applied in an incremental scheme. The drift may be reduced by fusion of GPS and Structure-from-Motion. An existing fusion method is BA minimizing a weighted sum of image and GPS errors. This paper introduces two constrained BAs for fusion, which enforce an upper bound for the reprojection error. These BAs are alternatives to the existing fusion BA, which does not guarantee a small reprojection error and requires a weight as input. Then the three fusion BAs are integrated in an incremental Structure-from-Motion method based on local BA. Lastly, we will compare the fusion results on a long monocular image sequence and a low cost GPS.


title: Learning to fail: experiencing public failure online through crowdfunding

abstract: Online crowdfunding platforms like Kickstarter are gaining attention among novice creatives as an effective platform for funding their ventures and engaging in creative work with others. However, a focus on financial success of crowdfunding has obscured the fact that over 58% of crowdfunding projects fail to achieve their funding goals. This population of failed creatives however, gives us an audience to study public creative failure in an online environment. We draw inspiration from work in organizational behavior on failure, and work in Human Computer Interaction (HCI) on online behavior, to study online public failure. Using a mixed-methods approach with data scraped from Kickstarter and interview data with failed crowdfunding project creators, we answer the following question: What do project creators on crowdfunding platforms learn and change through the process of failing? We find that creators who relaunch their projects succeed 43% of the time, and that most individuals find failure to be a positive experience. We conclude the paper with a series of design implications for future creative platforms where public failure is part of the creative process.


title: Directly Modulated Semiconductor Lasers

abstract: This paper presents a review and discussion of the directly modulated semiconductor lasers and their applications to optical communications and microwave photonics. A detailed and comprehensive demonstration of directly modulated semiconductor lasers from development history to specific techniques on measurement, analysis, and packaging is provided for the first time to the best of our knowledge. A few typical applications based on directly modulated lasers are also illustrated, such as optical fiber communications, free-space optical communications and microwave photonics. Future directions of research are also highlighted.


title: Analyzing Factors, Construction of Dataset, Estimating Importance of Factor, and Generation of Association Rules for Indian Road Accident

abstract: Convenience and safety of road is very important and crucial in society. Statistics of accidental deaths has shown a growing drift in India. Accidents on road network with vehicle collision can lead to injuries or even deaths and causes economy loss. Reasons for accident includes over speeding, drunken driver and defects in vehicle as well as bad road condition. Places near residential areas, pedestrian crossing, around schools, college and other educational institutions are the major zone of accidents. Mega cities traffic in India is increasing day by day due to increase in the population. In this paper we have figure out the factor influencing the accidents and identified which factor is more accident prone with Info Gain Attribute Evaluator function using WEKA tool. We have also performed association classification using Apriori algorithm and identified best rule for accident dataset.


title: A steganographic method for digital images with four-pixel differencing and modified LSB substitution

abstract: To improve the embedding capacity and provide an imperceptible visual quality, a novel steganographic method based on four-pixel differencing and modified least significant bit (LSB) substitution is presented. The average difference value of a four-pixel block is exploited to classify the block as a smooth area or an edge area. Secret data are hidden into each pixel by the k-bit modified LSB substitution method, where k is decided by the level which the average difference value falls into. Readjustment will be executed to guarantee the same level that the average difference value belongs to before and after embedding, and to minimize the perceptual distortion. By proving that the readjusting procedure works, a theoretical proof is given to justify our method succeeded in embedding and extracting. Our experimental results have shown that the proposed method not only has an acceptable image quality but also provides a large embedding capacity.


title: Multi-level convolutional channel features for content-based image retrieval

abstract: An effective content-based image retrieval (CBIR) system depends on the discriminative feature which represents an image. In this work, we explore deep convolutional features for a CBIR system. We first show the effectiveness of deep convolutional channel features for a CBIR system. Then we introduce a Multi-Level Pooling method (MLP) to obtain object-aware features from convolutional layers and finally the features extracted from different layers are incorporated to a short representation vector. Through multiple experiments, we show that our approach can achieve state-of-art results on several benchmark retrieval datasets.


title: Customer Lifetime Value Models for Decision Support

abstract: We present and discuss the important business problem of estimating the effect of marketing activities on the Lifetime Value of a customer in the Telecommunications industry. We discuss the components of this problem, in particular customer value and length of service (or tenure) modeling, and present a novel segment-based approach, motivated by the segment-level view marketing analysts usually employ. We describe in detail how we build on this approach to estimate the effects of retention campaigns on Lifetime Value, and also discuss its application in other situations. Our solution has been successfully implemented by the Business Insight (BI) Professional Services.


title: A Mobile Application Offloading Algorithm for Mobile Cloud Computing

abstract: In mobile cloud computing, offloading mobile applications to close remote servers appears as a straightforward solution to overcome mobile terminals processor and battery limitations. Remote execution leverages the high computation capacity of the server to enrich user experience and extend battery autonomy through energy savings. However, application offloading is energy efficient only under various conditions. For that purpose, we propose an original algorithm called MAO(Mobile Application's Offloading) triggered by two conditions: The current CPU load and State of Charge (SoC) of the battery.On the basis of various traffic scenarios mixing interactive and delay tolerant mobile applications, we study through numerical simulations the efficiency of the MAO algorithm and assess its performance in terms of rejected jobs and the amount of energy savings achieved. Rejected jobs are those unable to meet user quality of experience (QoE) and/or energy efficiency requirements. Evaluations on simulated workloads show that both traffic loads and user's radio mobile environment have direct impact on the efficiency of the MAO algorithm.


title: On path planning methods for automotive collision avoidance

abstract: There is a strong trend for increasingly sophisticated Advanced Driver Assistance Systems (ADAS) such as Autonomous Emergency Braking (AEB) systems, Lane Keeping Aid (LKA) systems, and indeed autonomous driving. This trend generates a need for online maneuver generation, for which numerous approaches can be found in the large body of work related to path planning and obstacle avoidance. In order to ease the challenge of choosing a method, this paper reports quantitative and qualitative insights about three different path planning methods: a state lattice planner, predictive constraint-based planning, and spline-based search tree. Each method is described, implemented and compared on two specific traffic situations. The paper will not provide a final answer about which method is best. This depends on several factors such as computational constraints and the formulation of maneuver optimality that is appropriate for a given assistance or safety function. Instead, the conclusions will highlight qualitative merits and drawbacks for each method, in order to provide guidance for choosing a method for a specific application.


title: The TCP outcast problem: Exposing unfairness in data center networks

abstract: In this paper, we observe that bandwidth sharing via TCP in commodity data center networks organized in multi-rooted tree topologies can lead to severe unfairness, which we term as the TCP Outcast problem, under many common traffic patterns. When many flows and a few flows arrive at two ports of a switch destined to one common output port, the small set of flows lose out on their throughput share significantly (almost by an order of magnitude sometimes). The Outcast problem occurs mainly in taildrop queues that commodity switches use. Using careful analysis, we discover that taildrop queues exhibit a phenomenon known as port blackout, where a series of packets from one port are dropped. Port blackout affects the fewer flows more significantly, as they lose more consecutive packets leading to TCP timeouts. In this paper, we show the existence of this TCP Outcast problem using a data center network testbed using real hardware under different scenarios. We then evaluate different solutions such as RED, SFQ, TCP pacing, and a new solution called equal-length routing to mitigate the Outcast problem.


Folksonomies - cooperative classification and communication through shared metadata


title: Plasticity in the Working Memory System: Life Span Changes and Response to Injury

abstract: Working memory acts as a key bridge between perception, long-term memory, and action. The brain regions, connections, and neurotransmitters that underlie working memory undergo dramatic plastic changes during the life span, and in response to injury. Early life reliance on deep gray matter structures fades during adolescence as increasing reliance on prefrontal and parietal cortex accompanies the development of executive aspects of working memory. The rise and fall of working memory capacity and executive functions parallels the development and loss of neurotransmitter function in frontal cortical areas. Of the affected neurotransmitters, dopamine and acetylcholine modulate excitatory-inhibitory circuits that underlie working memory, are important for plasticity in the system, and are affected following preterm birth and adult brain injury. Pharmacological interventions to promote recovery of working memory abilities have had limited success, but hold promise if used in combination with behavioral trainin...


title: Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction

abstract: Labeled sequence transduction is a task of transforming one sequence into another sequence that satisfies desiderata specified by a set of labels. In this paper we propose multi-space variational encoder-decoders, a new model for labeled sequence transduction with semi-supervised learning. The generative model can use neural networks to handle both discrete and continuous latent variables to exploit various features of data. Experiments show that our model provides not only a powerful supervised framework but also can effectively take advantage of the unlabeled data. On the SIGMORPHON morphological inflection benchmark, our model outperforms single-model state-of-art results by a large margin for the majority of languages.


title: High quality 3D reconstruction of indoor environments using RGB-D sensors

abstract: High-quality 3D reconstruction of large-scale indoor scene is the key to combine Simultaneous Localization And Mapping (SLAM) with other applications, such as building inspection and construction monitoring. However, the requirement of global consistency brings challenges to both localization and mapping. In particular, significant localization and mapping error can happen when standard SLAM techniques are used when dealing with the area of featureless walls and roofs. This paper proposed a novel framework aiming to reconstruct a high-quality, globally consistent 3D model for indoor environments using only a RGB-D sensor. We first introduce the sparse and dense feature constraints in the local bundle adjustment. Then, the planar constraints are incorporated in the global bundle adjustment. We fuse the point clouds in a truncated signed distance function volume, from which the high quality mesh can be extracted. Our framework leads to a comprehensive 3D scanning solution for indoor scene, enabling high-quality results and potential applications in building information system. The video of 3D models reconstructed by the method proposed in this paper is available at https://youtu.be/DWMP4YfeNeY.


title: Smart helmet with sensors for accident prevention

abstract: The impact when a motorcyclist involves in a high speed accident without wearing a helmet is very dangerous and can cause fatality. Wearing a helmet can reduce shock from the impact and may save a life. There are many countries enforcing a regulation that requires the motorcycle's rider to wear a helmet when riding on their motorcycle, Malaysia is an example. With this reason, this project is specially developed as to improve the safety of the motorcycle's rider. Motorcyclist will be alarmed when the speed limit is exceeded. A Force Sensing Resistor (FSR) and BLDC Fan are used for detection of the rider's head and detection of motorcycle's speed respectively. A 315 MHz Radio Frequency Module as wireless link which able to communicate between transmitter circuit and receiver circuit. PIC16F84a is a microcontroller to control the entire component in the system. Only when the rider buckled the helmet then only the motorcycle's engine will start. A LED will flash if the motor speed exceeds 100 km/hour.


title: Self-Powered ZigBee Wireless Sensor Nodes for Railway Condition Monitoring

abstract: A track-borne energy transducer is a smart device for harvesting energy of trains or rail transportation systems. In this paper, the authors extend this application through introducing a self-powered ZigBee wireless sensor node. The proposed hardware prototype consists of a ZigBee coordinator at road-side and a series of sensors (Accelerometer, temperature sensor, humidity sensor, and infrared detector) connected to a ZigBee end device at rail-side. The ZigBee end device is powered by the magnetic levitation energy harvester and communicated wirelessly with the ZigBee coordinator. The magnetic levitation oscillator is selected due to its broad-band response characteristics. The results indicate a peak–peak output voltage of 2.3 V under the condition that the vehicle travels over the rail-borne device at the speed of 105 km/h.