title: A Randomized Trial of Deep-Brain Stimulation for Parkinson's Disease

abstract: BACKGROUND ::: Neurostimulation of the subthalamic nucleus reduces levodopa-related motor complications in advanced Parkinson's disease. We compared this treatment plus medication with medical management. ::: ::: ::: METHODS ::: In this randomized-pairs trial, we enrolled 156 patients with advanced Parkinson's disease and severe motor symptoms. The primary end points were the changes from baseline to six months in the quality of life, as assessed by the Parkinson's Disease Questionnaire (PDQ-39), and the severity of symptoms without medication, according to the Unified Parkinson's Disease Rating Scale, part III (UPDRS-III). ::: ::: ::: RESULTS ::: Pairwise comparisons showed that neurostimulation, as compared with medication alone, caused greater improvements from baseline to six months in the PDQ-39 (50 of 78 pairs, P=0.02) and the UPDRS-III (55 of 78, P<0.001), with mean improvements of 9.5 and 19.6 points, respectively. Neurostimulation resulted in improvements of 24 to 38 percent in the PDQ-39 subscales for mobility, activities of daily living, emotional well-being, stigma, and bodily discomfort. Serious adverse events were more common with neurostimulation than with medication alone (13 percent vs. 4 percent, P<0.04) and included a fatal intracerebral hemorrhage. The overall frequency of adverse events was higher in the medication group (64 percent vs. 50 percent, P=0.08). ::: ::: ::: CONCLUSIONS ::: In this six-month study of patients under 75 years of age with severe motor complications of Parkinson's disease, neurostimulation of the subthalamic nucleus was more effective than medical management alone. (ClinicalTrials.gov number, NCT00196911 [ClinicalTrials.gov].).


title: Natural Language Processing in Serious Games: A state of the art.

abstract: In the last decades, Natural Language Processing (NLP) has obtained a high level of success. Interactions between NLP and Serious Games have started and some of them already include NLP techniques. The objectives of this paper are twofold: on the one hand, providing a simple framework to enable analysis of potential uses of NLP in Serious Games and, on the other hand, applying the NLP framework to existing Serious Games and giving an overview of the use of NLP in pedagogical Serious Games. In this paper we present 11 serious games exploiting NLP techniques. We present them systematically, according to the following structure: first, we highlight possible uses of NLP techniques in Serious Games, second, we describe the type of NLP implemented in the each specific Serious Game and, third, we provide a link to possible purposes of use for the different actors interacting in the Serious Game.


title: Mining GitHub for Novel Change Metrics to Predict Buggy Files in Software Systems

abstract: Code change metrics mined from source control repositories have proven to be the most reliable predictors of bugs in contemporary software engineering research. Yet a definitive modus operandi for obtaining the required data from a particular software configuration management (SCM) repository needs to be put forward. In this paper, we define a modus operandi to extract some popular change metrics from the Eclipse repository on Github, which can be generalized for any open source Github repository. We define few code change metrics that are intuitively significant for predicting bugs. Bug prediction models built with these metrics along with the existing prominent code change metrics prove to be competent and consistent as per our experiments on five different versions of Eclipse JDT project. We explored Naive Bayes Tree algorithm to build a prediction model and have found it to perform better than other commonly used algorithms in this problem domain.


title: Understanding success and failure in customer relationship management

abstract: Abstract Customer Relationship Management (CRM) systems can help organizations manage customer interactions more effectively. Like many new technologies, CRM has been accompanied by vendor hype and stories of implementation failure. Work on critical success factors (CSFs) should encourage more appropriate implementation practice; however many CSF studies conclude with a list of factors but provide little further guidance. In particular, there is a need for stronger theoretical models of the entire CRM innovation process which can be used by managers to understand better the underlying causes of success and failure. This paper adopts a novel approach to this problem by firstly developing a conceptual model of CRM innovation and then converting this model into a dynamic simulation model. Some early simulation results illustrating changes in CRM benefits and organizational support over time are presented together with a discussion of the underlying causes and suggestions for how managers can counteract potential innovation failure.


title: Bioinformatics and Computational Biology Solutions using R and Bioconductor

abstract: Full four-color book. ::: ::: Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. ::: ::: All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. ::: ::: Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.


title: An Empirical Comparison of Machine Learning Models for Time Series Forecasting

abstract: In this work we present a large scale comparison study for the major machine learning models for time series forecasting. Specifically, we apply the models on the monthly M3 time series competition data (around a thousand time series). There have been very few, if any, large scale comparison studies for machine learning models for the regression or the time series forecasting problems, so we hope this study would fill this gap. The models considered are multilayer perceptron, Bayesian neural networks, radial basis functions, generalized regression neural networks (also called kernel regression), K-nearest neighbor regression, CART regression trees, support vector regression, and Gaussian processes. The study reveals significant differences between the different methods. The best two methods turned out to be the multilayer perceptron and the Gaussian process regression. In addition to model comparisons, we have tested different preprocessing methods and have shown that they have different impacts on the performance.


title: What recommenders recommend: an analysis of recommendation biases and possible countermeasures

abstract: Most real-world recommender systems are deployed in a commercial context or designed to represent a value-adding service, e.g., on shopping or Social Web platforms, and typical success indicators for such systems include conversion rates, customer loyalty or sales numbers. In academic research, in contrast, the evaluation and comparison of different recommendation algorithms is mostly based on offline experimental designs and accuracy or rank measures which are used as proxies to assess an algorithm's recommendation quality. In this paper, we show that popular recommendation techniques--despite often being similar when compared with the help of accuracy measures--can be quite different with respect to which items they recommend. We report the results of an in-depth analysis in which we compare several recommendations strategies from different perspectives, including accuracy, catalog coverage and their bias to recommend popular items. Our analyses reveal that some recent techniques that perform well with respect to accuracy measures focus their recommendations on a tiny fraction of the item spectrum or recommend mostly top sellers. We analyze the reasons for some of these biases in terms of algorithmic design and parameterization and show how the characteristics of the recommendations can be altered by hyperparameter tuning. Finally, we propose two novel algorithmic schemes to counter these popularity biases.


title: Building a Japanese-Chinese Dictionary Using Kanji/Hanzi Conversion

abstract: A new bilingual dictionary can be built using two existing bilingual dictionaries, such as Japanese-English and English-Chinese to build Japanese-Chinese dictionary. However, Japanese and Chinese are nearer languages than English, there should be a more direct way of doing this. Since a lot of Japanese words are composed of kanji, which are similar to hanzi in Chinese, we attempt to build a dictionary for kanji words by simple conversion from kanji to hanzi. Our survey shows that around 2/3 of the nouns and verbal nouns in Japanese are kanji words, and more than 1/3 of them can be translated into Chinese directly. The accuracy of conversion is 97%. Besides, we obtain translation candidates for 24% of the Japanese words using English as a pivot language with 77% accuracy. By adding the kanji/hanzi conversion method, we increase the candidates by 9%, to 33%, with better quality candidates.


The Internal Structure of Small Clauses: New Evidence from Inversion


title: Attention and Language Ensemble for Scene Text Recognition with Convolutional Sequence Modeling

abstract: Recent dominant approaches for scene text recognition are mainly based on convolutional neural network (CNN) and recurrent neural network (RNN), where the CNN processes images and the RNN generates character sequences. Different from these methods, we propose an attention-based architecture1 which is completely based on CNNs. The distinctive characteristics of our method include: (1) the method follows encoder-decoder architecture, in which the encoder is a two-dimensional residual CNN and the decoder is a deep one-dimensional CNN. (2) An attention module that captures visual cues, and a language module that models linguistic rules are designed equally in the decoder. Therefore the attention and language can be viewed as an ensemble to boost predictions jointly. (3) Instead of using a single loss from language aspect, multiple losses from attention and language are accumulated for training the networks in an end-to-end way. We conduct experiments on standard datasets for scene text recognition, including Street View Text, IIIT5K and ICDAR datasets. The experimental results show our CNN-based method has achieved state-of-the-art performance on several benchmark datasets, even without the use of RNN.


title: A Unified Framework for Multimodal Domain Adaptation

abstract: Domain adaptation aims to train a model on labeled data from a source domain while minimizing test error on a target domain. Most of existing domain adaptation methods only focus on reducing domain shift of single-modal data. In this paper, we consider a new problem of multimodal domain adaptation and propose a unified framework to solve it. The proposed multimodal domain adaptation neural networks(MDANN) consist of three important modules. (1) A covariant multimodal attention is designed to learn a common feature representation for multiple modalities. (2) A fusion module adaptively fuses attended features of different modalities. (3) Hybrid domain constraints are proposed to comprehensively learn domain-invariant features by constraining single modal features, fused features, and attention scores. Through jointly attending and fusing under an adversarial objective, the most discriminative and domain-adaptive parts of the features are adaptively fused together. Extensive experimental results on two real-world cross-domain applications (emotion recognition and cross-media retrieval) demonstrate the effectiveness of the proposed method.


In Silico Investigation on Potential Off-target Binding and Repurposable Drugs in the Treatment of Spinal Muscular Atrophy


title: Robot trajectory optimization using approximate inference

abstract: The general stochastic optimal control (SOC) problem in robotics scenarios is often too complex to be solved exactly and in near real time. A classical approximate solution is to first compute an optimal (deterministic) trajectory and then solve a local linear-quadratic-gaussian (LQG) perturbation model to handle the system stochasticity. We present a new algorithm for this approach which improves upon previous algorithms like iLQG. We consider a probabilistic model for which the maximum likelihood (ML) trajectory coincides with the optimal trajectory and which, in the LQG case, reproduces the classical SOC solution. The algorithm then utilizes approximate inference methods (similar to expectation propagation) that efficiently generalize to non-LQG systems. We demonstrate the algorithm on a simulated 39-DoF humanoid robot.


title: Danger Theory Concepts Improving Malware Detection of Intrusion Detection Systems That Uses Exact Graphs

abstract: This paper describes the development of an intrusion detection system (IDS) that incorporates ideas of danger theory which enhances the IDS's performance when compared to not using danger theory. Both systems use Exact Graphs to store the series of system calls in the database. Both the original and enhanced IDSs were first trained using a series of normal system calls. In the enhanced IDS, the system also responds to hardware signals changes which correspond to danger signals. Results of the comparison show that the danger theory enhanced system outperforms the original system.


title: Brain white matter structure andCOMTgene are linked to second-language learning in adults

abstract: Adult human brains retain the capacity to undergo tissue reorganization during second-language learning. Brain-imaging studies show a relationship between neuroanatomical properties and learning for adults exposed to a second language. However, the role of genetic factors in this relationship has not been investigated. The goal of the current study was twofold: (i) to characterize the relationship between brain white matter fiber-tract properties and second-language immersion using diffusion tensor imaging, and (ii) to determine whether polymorphisms in the catechol-O-methyltransferase (COMT) gene affect the relationship. We recruited incoming Chinese students enrolled in the University of Washington and scanned their brains one time. We measured the diffusion properties of the white matter fiber tracts and correlated them with the number of days each student had been in the immersion program at the time of the brain scan. We found that higher numbers of days in the English immersion program correlated with higher fractional anisotropy and lower radial diffusivity in the right superior longitudinal fasciculus. We show that fractional anisotropy declined once the subjects finished the immersion program. The relationship between brain white matter fiber-tract properties and immersion varied in subjects with different COMT genotypes. Subjects with the Methionine (Met)/Valine (Val) and Val/Val genotypes showed higher fractional anisotropy and lower radial diffusivity during immersion, which reversed immediately after immersion ended, whereas those with the Met/Met genotype did not show these relationships. Statistical modeling revealed that subjects’ grades in the language immersion program were best predicted by fractional anisotropy and COMT genotype.


title: Deep Convolutional Decision Jungle for Image Classification

abstract: We propose a novel method called deep convolutional decision jungle (CDJ) and its learning algorithm for image classification. The CDJ maintains the structure of standard convolutional neural networks (CNNs), i.e. multiple layers of multiple response maps fully connected. Each response map-or node-in both the convolutional and fully-connected layers selectively respond to class labels s.t. each data sample travels via a specific soft route of those activated nodes. The proposed method CDJ automatically learns features, whereas decision forests and jungles require pre-defined feature sets. Compared to CNNs, the method embeds the benefits of using data-dependent discriminative functions, which better handles multi-modal/heterogeneous data; further,the method offers more diverse sparse network responses, which in turn can be used for cost-effective learning/classification. The network is learnt by combining conventional softmax and proposed entropy losses in each layer. The entropy loss,as used in decision tree growing, measures the purity of data activation according to the class label distribution. The back-propagation rule for the proposed loss function is derived from stochastic gradient descent (SGD) optimization of CNNs. We show that our proposed method outperforms state-of-the-art methods on three public image classification benchmarks and one face verification dataset. We also demonstrate the use of auxiliary data labels, when available, which helps our method to learn more discriminative routing and representations and leads to improved classification.


title: Detection and Removing Cross Site Scripting Vulnerability in PHP Web Application

abstract: Cross Site Scripting (XSS) vulnerability acts as one of the chief widespread security issues in web applications. By reviewing the literature pertaining to XSS vulnerability, it has been found that many investigations have directed their energy only on XSS vulnerability detection, but not many studies have concentrated on removing XSS vulnerability. This paper embed the removal stage of XSS vulnerability to our previous approach of detection XSS vulnerability, in a way to make the approach fully to detect and remove XSS vulnerability from PHP source code. We conducted two experiments to detect and remove Reflected and Stored XSS vulnerability. The results show that our approach is able to detect and remove XSS vulnerability in PHP source code. More research is required in the field of removing XSS vulnerability from the application source code before deployment.


title: Ontology-based security assessment for software products

abstract: This paper proposes an ontology-based approach to analyzing and assessing the security posture for software products. It provides measurements of trust for a software product based on its security requirements and evidence of assurance, which are retrieved from an ontology built for vulnerability management. Our approach differentiates with the previous work in the following aspects: (1) It is a holistic approach emphasizing that the system assurance cannot be determined or explained by its component assurance alone. Instead, the software system as a whole determines its assurance level. (2) Our approach is based on widely accepted standards such as CVSS, CVE, CWE, CPE, and CAPEC. Our ontology integrated these standards seamlessly thus provides a solid foundation for security assessment. (3) Automated tools have been built to support our approach, delivering the environmental scores for software products.


title: Irregular lattices for complex shape grammar facade parsing

abstract: High-quality urban reconstruction requires more than multi-view reconstruction and local optimization. The structure of facades depends on the general layout, which has to be optimized globally. Shape grammars are an established method to express hierarchical spatial relationships, and are therefore suited as representing constraints for semantic facade interpretation. Usually inference uses numerical approximations, or hard-coded grammar schemes. Existing methods inspired by classical grammar parsing are not applicable on real-world images due to their prohibitively high complexity. This work provides feasible generic facade reconstruction by combining low-level classifiers with mid-level object detectors to infer an irregular lattice. The irregular lattice preserves the logical structure of the facade while reducing the search space to a manageable size. We introduce a novel method for handling symmetry and repetition within the generic grammar. We show competitive results on two datasets, namely the Paris 2010 and the Graz 50. The former includes only Hausmannian, while the latter includes Classicism, Biedermeier, Historicism, Art Nouveau and post-modern architectural styles.


title: Group Invariant Scattering

abstract: This paper constructs translation invariant operators on L2(R^d), which are Lipschitz continuous to the action of diffeomorphisms. A scattering propagator is a path ordered product of non-linear and non-commuting operators, each of which computes the modulus of a wavelet transform. A local integration defines a windowed scattering transform, which is proved to be Lipschitz continuous to the action of diffeomorphisms. As the window size increases, it converges to a wavelet scattering transform which is translation invariant. Scattering coefficients also provide representations of stationary processes. Expected values depend upon high order moments and can discriminate processes having the same power spectrum. Scattering operators are extended on L2 (G), where G is a compact Lie group, and are invariant under the action of G. Combining a scattering on L2(R^d) and on Ld (SO(d)) defines a translation and rotation invariant scattering on L2(R^d).


title: Design of sensor node for air quality crowdsensing

abstract: Information on air-quality in urban environments is typically measured only at limited number of sites, due to cost of measurement of atmospheric concentrations of toxic gases (CO, NO 2 , SO 2 ) within accuracy boundaries defined by regulative bodies. Low spatial resolution of the mentioned environmental parameters hinders their applications in localization of the air-pollution sources, traffic regulation or studies of chronic respiratory diseases related to personal pollution exposure. Thus, we propose complementing the existing air quality monitoring infrastructure by a network of mobile sensors enabling the citizens to participate in measurement (e.g. “crowdsensing”). In this paper, we present the design of such battery-powered, wearable sensor node, housing two electrochemical gas sensors, temperature, relative humidity and atmospheric pressure sensors, with Bluetooth connectivity. Electrical, mechanical and software design are shown. Next, sensor node was characterized by evaluating the sensing accuracy and the autonomy in laboratory conditions. Accuracy within ±1 °C, ±2% RH, ±2 hPa, and ±0.6 ppm CO is shown. Autonomy is estimated at 65 h. Preliminary results of the outdoor functional test are demonstrated.


title: Limiting privacy breaches in privacy preserving data mining

abstract: There has been increasing interest in the problem of building accurate data mining models over aggregate data, while protecting privacy at the level of individual records. One approach for this problem is to randomize the values in individual records, and only disclose the randomized values. The model is then built over the randomized data, after first compensating for the randomization (at the aggregate level). This approach is potentially vulnerable to privacy breaches: based on the distribution of the data, one may be able to learn with high confidence that some of the randomized records satisfy a specified property, even though privacy is preserved on average.In this paper, we present a new formulation of privacy breaches, together with a methodology, "amplification", for limiting them. Unlike earlier approaches, amplification makes it is possible to guarantee limits on privacy breaches without any knowledge of the distribution of the original data. We instantiate this methodology for the problem of mining association rules, and modify the algorithm from [9] to limit privacy breaches without knowledge of the data distribution. Next, we address the problem that the amount of randomization required to avoid privacy breaches (when mining association rules) results in very long transactions. By using pseudorandom generators and carefully choosing seeds such that the desired items from the original transaction are present in the randomized transaction, we can send just the seed instead of the transaction, resulting in a dramatic drop in communication and storage cost. Finally, we define new information measures that take privacy breaches into account when quantifying the amount of privacy preserved by randomization.


title: Crosswords to Computers: A Critical Review of Popular Approaches to Cognitive Enhancement

abstract: Cognitive enhancement strategies have gained recent popularity and have the potential to benefit clinical and non-clinical populations. As technology advances and the number of cognitively healthy adults seeking methods of improving or preserving cognitive functioning grows, the role of electronic (e.g., computer and video game based) cognitive training becomes more relevant and warrants greater scientific scrutiny. This paper serves as a critical review of empirical evaluations of publically available electronic cognitive training programs. Many studies have found that electronic training approaches result in significant improvements in trained cognitive tasks. Fewer studies have demonstrated improvements in untrained tasks within the trained cognitive domain, non-trained cognitive domains, or on measures of everyday function. Successful cognitive training programs will elicit effects that generalize to untrained, practical tasks for extended periods of time. Unfortunately, many studies of electronic cognitive training programs are hindered by methodological limitations such as lack of an adequate control group, long-term follow-up and ecologically valid outcome measures. Despite these limitations, evidence suggests that computerized cognitive training has the potential to positively impact one’s sense of social connectivity and self-efficacy.


title: Bootstrapped two-electrode biosignal amplifier

abstract: Portable biomedical instrumentation has become an important part of diagnostic and treatment instrumentation. Low-voltage and low-power tendencies prevail. A two-electrode biopotential amplifier, designed for low-supply voltage (2.7-5.5 V), is presented. This biomedical amplifier design has high differential and sufficiently low common mode input impedances achieved by means of positive feedback, implemented with an original interface stage. The presented circuit makes use of passive components of popular values and tolerances. The amplifier is intended for use in various two-electrode applications, such as Holter monitors, external defibrillators, ECG monitors and other heart beat sensing biomedical devices.


title: A.: Exploiting a Web of Semantic Data for Interpreting Tables

abstract: Much of the world’s knowledge is contained in structured documents like spreadsheets, database relations and tables in documents found on the Web and in print. The information in these tables might be much more valuable if it could be appropriately exported or encoded in RDF, making it easier to share, understand and integrate with other information. This is especially true if it could be linked into the growing linked data cloud. We describe techniques to automatically infer a (partial) semantic model for information in tables using both table headings, if available, and the values stored in table cells and to export the data the table represents as linked data. The techniques have been prototyped for a subset of linked data that covers the core of Wikipedia.


title: The Combinatorics of Flat Folds: a Survey

abstract: We survey results on the foldability of flat origami models. The main topics are the question of when a given crease pattern can fold flat, the combinatorics of mountain and valley creases, and counting how many ways a given crease pattern can be folded. In particular, we explore generalizations of Maekawa's and Kawasaki's Theorems, develop a necessary and sufficient condition for a given assignment of mountains and valleys to fold up in a special case of single vertex folds, and describe recursive formulas to enumerate the number of ways that single vertex in a crease pattern can be folded.


title: Robula+: an algorithm for generating robust XPath locators for web testing

abstract: Automated test scripts are used with success in many web development projects, so as to automatically verify key functionalities of the web application under test, reveal possible regressions and run a large number of tests in short time. However, the adoption of automated web testing brings advantages but also novel problems, among which the test code fragility problem. During the evolution of the web application, existing test code may easily break and testers have to correct it. In the context of automated DOM-based web testing, one of the major costs for evolving the test code is the manual effort necessary to repair broken web page element locators - lines of source code identifying the web elements e.g. form fields and buttons to interact with.


title: A QoS-enhanced intelligent stochastic real-time packet scheduler for multimedia IP traffic

abstract: A re-configurable, QoS-enhanced intelligent stochastic real-time optimal fair packet scheduler, QUEST, for IP routers is proposed and investigated. The objective is to maximize the system QoS subject to the constraint that the processor utilization is kept at 100%. All past work on router schedulers for multimedia traffic were of earlier generation, in that they focused on maximizing utilization whereas being QoS-aware but without explicitly maximizing the QoS. Keeping utilization fixed at nearly 100%, QoS is dynamically maximized, thus moving to the next generation. QUEST’s other unique advantages are three-fold. First, it solves the challenging problem of starvation for low priority processes; second, it solves the major bottleneck of Earliest Deadline First scheduler’s failure at heavy traffic loads. Finally, QUEST offers the benefit of arbitrarily pre-programming the process utilization ratio. Three classes of multimedia IP traffic, namely, VoIP, IPTV and HTTP have been considered. Two most important QoS metrics, namely, packet loss rate (PLR) and mean waiting time, are addressed. All claims are supported by discrete event and Monte Carlo simulations. The proposed scheduler outperforms benchmark schedulers and offers 37% improvement in packet loss rate and 23% improvement in mean waiting time over the best competing current scheduler Accuracy-aware EDF. The proposed scheduler was validated in a test-bed platform of a NetFPGA® router and results were observed with Paessler® PRTG network monitor.


MONEY LAUNDERING ANALYSIS BASED ON TIME VARIANT BEHAVIORAL TRANSACTION PATTERNS USING DATA


title: Evaluation of HDR tone mapping methods using essential perceptual attributes

abstract: The problem of reproducing high dynamic range images on media with restricted dynamic range has gained a lot of interest in the computer graphics community. There exist various approaches to this issue, which span several research areas including computer graphics, image processing, color vision, physiological aspects, etc. These approaches assume a thorough knowledge of both the objective and subjective attributes of an image. However, no comprehensive overview and analysis of such attributes has been published so far. In this contribution, we present an overview about the effects of basic image attributes in high dynamic range tone mapping. Furthermore, we propose a scheme of relationships between these attributes, leading to the definition of an overall image quality measure. We present results of subjective psychophysical experiments that we have performed to prove the proposed relationship scheme. Moreover, we also present an evaluation of existing tone mapping methods (operators) with regard to these attributes. Finally, the execution of with reference and without a real reference perceptual experiments gave us the opportunity to relate the obtained subjective results. Our effort is not just useful to get into the tone mapping field or when implementing a tone mapping method, but it also sets the stage for well-founded quality comparisons between tone mapping methods. By providing good definitions of the different attributes, user-driven or fully automatic comparisons are made possible.


title: Weather tracking system using MQTT and SQLite

abstract: Every person is enthusiastic in accessing the information about certain place or object remotely and to make the things around them smart using new emerging technologies. This paper mainly focus an implementation of a framework of publishing data from ESP8266 to SQLite data server hosted on Raspberry pi module using MQTT Protocol. Here the sensor is assembled to ESP8266 microcontroller to read the values. A python application is developed to sense the values from sensor and to publish the data to raspberry pi. Now, ESP8266 acts as MQTT client and sensor data is sent to the MQTT broker running on Raspberry pi module. Additionally SQLite data server is also hosted on the Raspberry pi module and the data from the ESP8266 is stored in JSON format. This data from server is formatted using HTML file and the formatted data can be accessed remotely.


title: Cancer Metastasis: Building a Framework

abstract: Metastasis occurs when genetically unstable cancer cells adapt to a tissue microenvironment that is distant from the primary tumor. This process involves both the selection of traits that are advantageous to cancer cells and the concomitant recruitment of traits in the tumor stroma that accommodate invasion by metastatic cells. Recent conceptual and technological advances promote our understanding of the origins and nature of cancer metastasis.


title: Sparse Stereo Disparity Map Densification Using Hierarchical Image Segmentation

abstract: We describe a novel method for propagating disparity values using hierarchical segmentation by waterfall and robust regression models. High confidence disparity values obtained by state of the art stereo matching algorithms are interpolated using a coarse to fine approach. We start from a coarse segmentation of the image and try to fit each region’s disparities using robust regression models. If the fit is not satisfying, the process is repeated on a finer region’s segmentation. Erroneous values in the initial sparse disparity maps are generally excluded, as we use robust regressions algorithms and left-right consistency checks. Final disparity maps are therefore not only denser but can also be more accurate. The proposed method is general and independent from the sparse disparity map generation: it can therefore be used as a post-processing step for any stereo-matching algorithm.


title: Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors

abstract: According to nihseniorhealth.gov (a website for older adults), falling represents a great threat as people get older, and providing mechanisms to detect and prevent falls is critical to improve people's lives. Over 1.6 million U.S. adults are treated for fall-related injuries in emergency rooms every year suffering fractures, loss of independence, and even death. It is clear then, that this problem must be addressed in a prompt manner, and the use of pervasive computing plays a key role to achieve this. Fall detection (FD) and fall prevention (FP) are research areas that have been active for over a decade, and they both strive for improving people's lives through the use of pervasive computing. This paper surveys the state of the art in FD and FP systems, including qualitative comparisons among various studies. It aims to serve as a point of reference for future research on the mentioned systems. A general description of FD and FP systems is provided, including the different types of sensors used in both approaches. Challenges and current solutions are presented and described in great detail. A 3-level taxonomy associated with the risk factors of a fall is proposed. Finally, cutting edge FD and FP systems are thoroughly reviewed and qualitatively compared, in terms of design issues and other parameters.


RECENT PROGRESS IN MULTIPROCESSOR THREAD SCHEDULING


title: Accurate Floating-Point Summation Part II: Sign, $K$-Fold Faithful and Rounding to Nearest

abstract: In Part II of this paper we first refine the analysis of error-free vector transformations presented in Part I. Based on that we present an algorithm for calculating the rounded-to-nearest result of $s:=\sum p_i$ for a given vector of floating-point numbers $p_i$, as well as algorithms for directed rounding. A special algorithm for computing the sign of $s$ is given, also working for huge dimensions. Assume a floating-point working precision with relative rounding error unit eps . We define and investigate a $K$-fold faithful rounding of a real number $r$. Basically the result is stored in a vector $\mathtt{Res}_{\nu}$ of $K$ nonoverlapping floating-point numbers such that $\sum\mathtt{Res}_{\nu}$ approximates $r$ with relative accuracy $\mathtt{eps}^K$, and replacing $\mathtt{Res}_K$ by its floating-point neighbors in $\sum\mathtt{Res}_{\nu}$ forms a lower and upper bound for $r$. For a given vector of floating-point numbers with exact sum $s$, we present an algorithm for calculating a $K$-fold faithful rounding of $s$ using solely the working precision. Furthermore, an algorithm for calculating a faithfully rounded result of the sum of a vector of huge dimension is presented. Our algorithms are fast in terms of measured computing time because they allow good instruction-level parallelism, they neither require special operations such as access to mantissa or exponent, they contain no branch in the inner loop, nor do they require some extra precision. The only operations used are standard floating-point addition, subtraction, and multiplication in one working precision, for example, double precision. Certain constants used in the algorithms are proved to be optimal.


Scale Invariant Feature Transform: A Graphical Parameter Analysis.


title: Generic anomalous vertices detection utilizing a link prediction algorithm

abstract: In the past decade, network structures have penetrated nearly every aspect of our lives.The detection of anomalous vertices in these networks can uncover important insights, such as exposing intruders in a computer network and finding fake reviews online. In this study, we present a novel unsupervised two-layered meta-classifier that can be employed to detect irregular vertices in complex networks solely by using features extracted from the network topology. Our method is based on the hypothesis that a vertex that has many improbable links has a higher likelihood of being anomalous. We evaluated our method on 10 networks, using fully simulated semi-simulated, and real world datasets. In all of the scenarios, our method was able to identify anomalous vertices with lower false positive rates and higher AUCs compared to other methods evaluated. Moreover, we demonstrated that our method can detect anomalies even in incomplete data.


title: A Comprehensive Framework Towards Information Sharing Between Government Agencies

abstract: Recently, there has been increased interest in sharing digitized information between government agencies, with the goals of improving security, reducing costs, and offering better quality service to users of government services. The bulk of previous work in interagency information sharing has focused largely on the sharing of structured information among heterogeneous data sources, whereas government agencies need to share data with varying degrees of structure ranging from free text documents to relational data. In this work, we explore the different technologies available to share information. Specifically, our framework discusses the optional data storage mechanisms required to support a Service Oriented Architecture (SOA). We compare XML document, free text search engine, and relational database technologies and analyze the pros and cons of each approach. We explore these options along the dimensions of information definition, information storage, the access to this information, and finally the maintenance of shared information.


title: Biometrics Systems Under Spoofing Attack: An evaluation methodology and lessons learned

abstract: Biometrics already form a significant component of current and emerging identification technologies. Biometrics systems aim to determine or verify the identity of an individual from their behavioral and/or biological characteristics. Despite significant progress, some biometric systems fail to meet the multitude of stringent security and robustness requirements to support their deployment in some practical scenarios. Among current concerns are vulnerabilities to spoofing?persons who masquerade as others to gain illegitimate accesses to protected data, services, or facilities. While the study of spoofing, or rather antispoofing, has attracted growing interest in recent years, the problem is far from being solved and will require far greater attention in the coming years. This tutorial article presents an introduction to spoofing and antispoofing research. It describes the vulnerabilities, presents an evaluation methodology for the assessment of spoofing and countermeasures, and outlines research priorities for the future.


title: MTPA Trajectory Tracking Control for Interior PMSM Based on Adaptive Parameter Identification

abstract: For an interior permanent magnet synchronous motor (IPMSM) control system, the actual maximum torque per ampere (MTPA) trajectory may deviate from the ideal one with parameter variances, and thus the system may not obtain the maximum torque output. In view of this problem, a novel parameter identification method based on the model reference adaptive system (MRAS) is proposed in this paper. In this method, the deviations of MTPA trajectories with parameter variances are analyzed and evaluated firstly, and then the q-axis inductance and the rotor flux linkage are identified on-line in a full rank MRAS model according to Popov Hyper Stability Theorem. Finally, the feasibility and validity of the designed method are proved through simulation results.


title: Generation Y, web design, and eye tracking

abstract: Generation Y (age 18-31) is a very large and economically powerful generation, containing eighty-two million people and spending $200 billion annually. It is not surprising that companies are interested in gaining the patronage of this group, particularly via the web. Surprisingly, very little research into making web pages appealing to this important demographic has been done. This paper addresses this need through two separate studies. The first, an online survey, provides evidence that our proposed score for predicting the visual appeal of web pages reflects the self report measure of what pages Generation Y likes. To refine these findings, an eye tracking study is conducted using the pages that were most and least liked in Study I. Participants' eye movement is tracked while browsing these pages, providing evidence of what attracts their attention. The results of these two studies suggest that Generation Y may prefer pages that include a main large image, images of celebrities, little text, and a search feature. This research has important implications.


title: An Autocomplete Input Box for Semantic Annotation on the Web

abstract: A large share of websites today allow users to contribute and manage user-generated content. This content is often in textual form and involves names, terms, and keywords that can be ambiguous and difficult to interpret for other users. Semantic annotation can be used to tackle such issues, but this technique has been adopted by only a few websites. This may be attributed to a lack of a standard web input component that allows users to simply and efficiently annotate text. In this paper, we introduce an autocomplete-enabled annotation box that supports users in associating their text with DBpedia resources as they type. This web component can replace existing input fields and does not require particular user skills. Furthermore, it can be used by semantic web developers as a user interface for advanced semantic search and data processing back-ends. Finally, we validate the approach with a preliminary user study.


title: Thermal Conductivity of Monolayer Molybdenum Disulfide Obtained from Temperature-Dependent Raman

abstract: Atomically thin molybdenum disulfide (MoS2 )o ffers potential for advanced devices and an alternative to graphene due to its unique electronic and optical properties. The temperature-dependent Raman spectra of exfoliated, monolayerMoS2intherangeof100� 320Karereportedandanalyzed.Thelinear temperature coefficients of the in-plane E2g 1 and the out-of-plane A1g modes for both suspended and substrate-supported monolayer MoS2 are measured. These data,whencombinedwiththe first-ordercoefficientsfromlaserpower-dependent studies, enable the thermal conductivity to be extracted. The resulting thermal conductivity κ=(34.5(4)W/mKatroomtemperatureagreeswellwiththe first- principles lattice dynamics simulations. However, this value is significantly lower than that of graphene. The results from this work provide important input for the design of MoS2-based devices where thermal management is critical.


title: DC-DC Boost Converter with Constant Output Voltage for Grid Connected Photovoltaic Application System

abstract: The main purpose of this paper is to introduce an approach to design a DC-DC boost converter with constant output voltage for grid connected photovoltaic application system. The boost converter is designed to step up a fluctuating solar panel voltage to a higher constant DC voltage. It uses voltage feedback to keep the output voltage constant. To do so, a microcontroller is used as the heart of the control system which it tracks and provides pulse-width-modulation signal to control power electronic device in boost converter. The boost converter will be able to direct couple with grid-tied inverter for grid connected photovoltaic system. Simulations were performed to describe the proposed design. Experimental works were carried out with the designed boost converter which has a power rating of 100 W and 24 V output voltage operated in continuous conduction mode at 20 kHz switching frequency. The test results show that the proposed design exhibits a good performance.


RELATIONAL NEURAL EXPECTATION MAXIMIZATION


DRAFT : Towards O ( 1 ) Memory


title: Shape and the Stereo Correspondence Problem

abstract: We examine the implications of shape on the process of finding dense correspondence and half-occlusions for a stereo pair of images. The desired property of the disparity map is that it should be a piecewise continuous function which is consistent with the images and which has the minimum number of discontinuities. To zeroth order, piecewise continuity becomes piecewise constancy. Using this approximation, we first discuss an approach for dealing with such a fronto-parallel shapeless world, and the problems involved therein. We then introduce horizontal and vertical slant to create a first order approximation to piecewise continuity. In particular, we emphasize the following geometric fact: a horizontally slanted surface (i.e., having depth variation in the direction of the separation of the two cameras) will appear horizontally stretched in one image as compared to the other image. Thus, while corresponding two images, N pixels on a scanline in one image may correspond to a different number of pixels M in the other image. This leads to three important modifications to existing stereo algorithms: (a) due to unequal sampling, existing intensity matching metrics must be modified, (b) unequal numbers of pixels in the two images must be allowed to correspond to each other, and (c) the uniqueness constraint, which is often used for detecting occlusions, must be changed to an interval uniqueness constraint. We also discuss the asymmetry between vertical and horizontal slant, and the central role of non-horizontal edges in the context of vertical slant. Using experiments, we discuss cases where existing algorithms fail, and how the incorporation of these new constraints provides correct results.


title: Staffing a software project: A constraint satisfaction and optimization-based approach

abstract: Software development is a people intensive activity. The abilities possessed by developers are strongly related to process productivity and final product quality. Thus, one of the most important decisions to be made by a software project manager is how to properly staff the project. However, staffing software projects is not a simple task. There are many alternatives to ponder, several developer-to-activity combinations to evaluate, and the manager may have to choose a team from a larger set of available developers, according to the project and organizational needs. Therefore, to perform the staffing activity with ad hoc procedures can be very difficult and can lead the manager to choose a team that is not the best for a given situation. This work presents an optimization-based approach to support staffing a software project. The staffing problem is modeled and solved as a constraint satisfaction problem. Our approach takes into account the characteristics of the project activities, the available human resources, and constraints established by the software development organization. According to these needs, the project manager selects a utility function to be maximized or minimized by the optimizer. We propose several utility functions, each addressing values that can be sought by the development organization. A decision support tool was implemented and used in an experimental study executed to evaluate the relevance of the proposed approach.


title: Cloning credit cards: a combined pre-play and downgrade attack on EMV contactless

abstract: Recent roll-outs of contactless payment infrastructures-particularly in Austria and Germany - have raised concerns about the security of contactless payment cards and Near Field Communication (NFC). There are well-known attack scenarios like relay attacks and skimming of credit card numbers. However, banks and credit card schemes often mitigate these attacks. They explain that attacks are impractical (e.g. in a relay attack an attacker needs to have RF access to a victim's card while performing a payment transaction) or even impossible (e.g. skimmed data does not contain the dynamic authorization codes that are normally required to perform a payment transaction). This paper introduces an attack scenario on EMV contactless payment cards that permits an attacker to create functional clones of a card that contain the necessary credit card data as well as pre-played authorization codes. The card clones can then be used to perform a limited number of EMV Mag-Stripe transactions at any EMV contactless payment terminal.


Foveated Real-Time Ray Tracing for Virtual Reality Headset


title: Age differences in affective decision making as indexed by performance on the Iowa Gambling Task.

abstract: Contemporary perspectives on age differences in risk taking, informed by advances in developmental neuroscience, have emphasized the need to examine the ways in which emotional and cognitive factors interact to influence decision making. In the present study, a diverse sample of 901 individuals between the ages of 10 and 30 were administered a modified version of the Iowa Gambling Task, which is designed to measure affective decision making. Results indicate that approach behaviors (operationalized as the tendency to play increasingly from the advantageous decks over the course of the task) display an inverted U-shape relation to age, peaking in mid- to late adolescence. In contrast, avoidance behaviors (operationalized as the tendency to refrain from playing from the disadvantageous decks) increase linearly with age, with adults avoiding disadvantageous decks at higher rates than both preadolescents and adolescents. The finding that adolescents, compared to adults, are relatively more approach oriented in response to positive feedback and less avoidant in response to negative feedback is consistent with recent studies of brain development, as well as epidemiological data on various types of risky behavior, and may have important practical implications for the prevention of adolescent risk taking.


title: GSM Based Electrical Control System for Smart Home Application

abstract: This paper presents the development of GSM-based control home appliances for smart home system. The main aim of the prototype development is to reduce electricity wastage. GSM module was used for receiving short message service (SMS) from user?셲 mobile phone that automatically enable the controller to take any further action such as to switch ON and OFF the home appliances such as light, air-conditioner etc. The system was integrated with microcontroller and GSM network interface using assembly language. MPLAB software was utilized to accomplish the integration. The system is activated when user sends the SMS to controller at home. Upon receiving the SMS command, the microcontroller unit then automatically controls the electrical home appliances by switching ON or OFF the device according to the user order. In other word, it read message from the mobile phone and response to control the devices according to the received message. The prototype has been successfully developed and it could provide an effective mechanism in utilizing the energy source efficiently.


title: A Demand Side Management Traffic Shaping and Scheduling Algorithm

abstract: There has been a great trend to optimize traffic in data network. That is because many parameters have been in dynamic change, such as the increasing number of users, the capabilities of the network devices and technical limitations of data networks traffic. In fact, data networks are known for their peaky and bursty responses. During network operations "at a peak time", where a network interface is fully utilized, devices can no longer send or receive data. Demand Side Management is acknowledged to help in reducing demand on resources during peak hours, where peak demands are condensed along with the required capacity and energy. This helps in saving resources and it also reduces the need for network extensions. In this paper we aim to implement an algorithm that allows flattening the utilization profile of network resources. The algorithm is based on a Demand Side Management concept. The algorithm helps in enhancing traffic shaping. In addition, scheduling techniques based on various queuing methods were applied in order to obtain the best performance.


DEEP LEARNING AND INTELLIGENT AUDIO MIXING


title: Automatic Metric Validation for Grammatical Error Correction

abstract: Metric validation in Grammatical Error Correction (GEC) is currently done by observing the correlation between human and metric-induced rankings. However, such correlation studies are costly, methodologically troublesome, and suffer from low inter-rater agreement. We propose MAEGE, an automatic methodology for GEC metric validation, that overcomes many of the difficulties with existing practices. Experiments with \maege\ shed a new light on metric quality, showing for example that the standard $M^2$ metric fares poorly on corpus-level ranking. Moreover, we use MAEGE to perform a detailed analysis of metric behavior, showing that correcting some types of errors is consistently penalized by existing metrics.


title: Simplified high-accuracy calculation of eddy-current loss in round-wire windings

abstract: It has recently been shown that the most commonly used methods for calculating high-frequency eddy-current loss in round-wire windings can have substantial error, exceeding 60%. Previous work includes a formula based on a parametric set of finite-element analysis (FEA) simulations that gives proximity-effect loss for a large range of frequencies, using the parameters from a lookup table based on winding geometry. We improve the formula by decreasing the number of parameters in the formula and also, more importantly, by using simple functions to get the parameters from winding geometry such that a large lookup table is not needed. The function we present is exact in the low frequency limit (diameter much smaller than skin depth) and has error less than 4% at higher frequencies. We make our new model complete by examining the field expression needed to get the total proximity-effect loss and by including the skin-effect loss. We also present experimental results confirming the validity of the model and its superiority to standard methods.


Pairwise FastText Classifier for Entity Disambiguation


title: Kinematics and design of AssistOn-SE: A self-adjusting shoulder-elbow exoskeleton

abstract: We present kinematics, workspace analysis and detailed design of AssistOn-SE, a novel powered exoskeleton for robot-assisted rehabilitation that allows for movements of the shoulder girdle as well as shoulder rotations. AssistOn-SE can both actively and passively enable translational movements of the center of glenohumeral joint, thus can adjust its joint axes corresponding to shoulder rotations, providing an ideal match between human joint axes and the exoskeleton axes. Thanks to this feature, AssistOn-SE not only guarantees ergonomy and comfort throughout the therapy, but also extends the usable range of motion for the shoulder joint. Adjustability feature also significantly shortens the setup time required to attach the patient to the exoskeleton. In addition to typical shoulder rotation exercises, AssistOn-SE can deliver glenohumeral mobilization (scapular elevation/depression and protraction/retraction) and scapular stabilization exercises, extending the type of therapies that can be administered using upper-arm exoskeletons.


title: Beyond position bias: examining result attractiveness as a source of presentation bias in clickthrough data

abstract: Leveraging clickthrough data has become a popular approach for evaluating and optimizing information retrieval systems. Although data is plentiful, one must take care when interpreting clicks, since user behavior can be affected by various sources of presentation bias. While the issue of position bias in clickthrough data has been the topic of much study, other presentation bias effects have received comparatively little attention. For instance, since users must decide whether to click on a result based on its summary (e.g., the title, URL and abstract), one might expect clicks to favor "more attractive" results. In this paper, we examine result summary attractiveness as a potential source of presentation bias. This study distinguishes itself from prior work by aiming to detect systematic biases in click behavior due to attractive summaries inflating perceived relevance. Our experiments conducted on the Google web search engine show substantial evidence of presentation bias in clicks towards results with more attractive titles.


Non-identifier-based adaptive control of dynamical systems: a survey


title: A Novel Approach for Adopting Cloud-Based E-learning System

abstract: The advent of cloud computing in recent years has sparked an interest from different organizations, institutions and users to take its advantage. This is a result of the new economic model for the Information Technology (IT) that cloud computing promises. It promises a shift from an organization required to invest heavily for limited IT resources that are internally managed, to a model where the organization can buy or rent resources that are managed by a cloud provider and pay per use. Although the adoption of cloud computing promises various benefits, a successful adoption of cloud computing in an educational institute still requires an understanding of different dynamics and expertise in diverse domains. Currently, there are inadequate guidelines for adopting cloud computing and building trust. Therefore, this paper presents a framework that specifies a number of steps for academic institutes as well as organizations to adopt cloud computing. The framework is designed by taking into account a range of strategic issues and technological factors from a broad cross section of areas of expertise in order to ensure a successful cloud computing adoption.


title: A 20-V four-quadrant CMOS analog multiplier

abstract: A novel technique is presented for performing the analog multiplication in CMOS technology. The circuit handles a wide range of input voltages. The MSO version of Gilbert's six-transistor cell (GSTC) is the basis for this multiplier. A simple source-coupled pair is used to study the MOS GSTC. Then, a technique is introduced for linearizing the source-coupled circuit. This scheme is extended to the MOS GSTC. The voltage ranges are further increased by introducing the folded CMOS GSTC.


title: A conversational agent as museum guide - Design and evaluation of a real-world application

abstract: This paper describes an application of the conversational agent Max in a real-world setting. The agent is employed as guide in a public computer museum, where he engages with visitors in natural face-to-face communication, provides them with information about the museum or the exhibition, and conducts natural small talk conversations. The design of the system is described with a focus on how the conversational behavior is achieved. Logfiles from interactions between Max and museum visitors were analyzed for the kinds of dialogue people are willing to have with Max. Results indicate that Max engages people in interactions where they are likely to use human-like communication strategies, suggesting the attribution of sociality to the agent.


title: O-Mopsi: Mobile Orienteering Game for Sightseeing, Exercising, and Education

abstract: Location-based games have been around already since 2000 but only recently when PokemonGo came to markets it became clear that they can reach wide popularity. In this article, we perform a literature-based analytical study of what kind of issues location-based game design faces, and how they can be solved. We study how to use and verify the location, the role of the games as exergames, use in education, and study technical and safety issues. As a case study, we present O-Mopsi game that combines physical activity with problem solving. It includes three challenges: (1) navigating to the next target, (2) deciding the order of targets, (3) physical movement. All of them are unavoidable and relevant. For guiding the players, we use three types of multimedia: images (targets and maps), sound (user guidance), and GPS (for positioning). We discuss motivational aspects, analysis of the playing, and content creation. The quality of experiences is reported based on playing in SciFest Science festivals during 2011--2016.


title: A pilot study of cyber security and privacy related behavior and personality traits

abstract: Recent research has begun to focus on the factors that cause people to respond to phishing attacks as well as affect user behavior on social networks. This study examines the correlation between the Big Five personality traits and email phishing response. Another aspect examined is how these factors relate to users' tendency to share information and protect their privacy on Facebook (which is one of the most popular social networking sites). This research shows that when using a prize phishing email, neuroticism is the factor most correlated to responding to this email, in addition to a gender-based difference in the response. This study also found that people who score high on the openness factor tend to both post more information on Facebook as well as have less strict privacy settings, which may cause them to be susceptible to privacy attacks. In addition, this work detected no correlation between the participants estimate of being vulnerable to phishing attacks and actually being phished, which suggests susceptibility to phishing is not due to lack of awareness of the phishing risks and that real-time response to phishing is hard to predict in advance by online users. The goal of this study is to better understand the traits that contribute to online vulnerability, for the purpose of developing customized user interfaces and secure awareness education, designed to increase users' privacy and security in the future.


title: The DBCLS BioHackathon: standardization and interoperability for bioinformatics web services and workflows. The DBCLS BioHackathon Consortium*

abstract: Web services have become a key technology for bioinformatics, since life science databases are globally decentralized and the exponential increase in the amount of available data demands for efficient systems without the need to transfer entire databases for every step of an analysis. However, various incompatibilities among database resources and analysis services make it difficult to connect and integrate these into interoperable workflows. To resolve this situation, we invited domain specialists from web service providers, client software developers, Open Bio* projects, the BioMoby project and researchers of emerging areas where a standard exchange data format is not well established, for an intensive collaboration entitled the BioHackathon 2008. The meeting was hosted by the Database Center for Life Science (DBCLS) and Computational Biology Research Center (CBRC) and was held in Tokyo from February 11th to 15th, 2008. In this report we highlight the work accomplished and the common issues arisen from this event, including the standardization of data exchange formats and services in the emerging fields of glycoinformatics, biological interaction networks, text mining, and phyloinformatics. In addition, common shared object development based on BioSQL, as well as technical challenges in large data management, asynchronous services, and security are discussed. Consequently, we improved interoperability of web services in several fields, however, further cooperation among major database centers and continued collaborative efforts between service providers and software developers are still necessary for an effective advance in bioinformatics web service technologies.


title: WiFi-Nano: reclaiming WiFi efficiency through 800 ns slots

abstract: The increase in WiFi physical layer transmission speeds from 1~Mbps to 1 Gbps has reduced transmission times for a 1500 byte packet from 12 ms to 12 us. However, WiFi MAC overheads such as channel access and acks have not seen similar reductions and cumulatively contribute about 150 us on average per packet. Thus, the efficiency of WiFi has deteriorated from over 80% at 1 Mbps to under 10% at 1 Gbps. In this paper, we propose WiFi-Nano, a system that uses 800 ns slots} to significantly improve WiFi efficiency. Reducing slot time from 9 us to 800 ns makes backoffs efficient, but clear channel assessment can no longer be completed in one slot since preamble detection can now take multiple slots. Instead of waiting for multiple slots for detecting preambles, nodes speculatively transmit preambles as their backoff counters expire, while continuing to detect premables using self-interference cancellation. Upon detection of preambles from other transmitters, nodes simply abort their own preamble transmissions, thereby allowing the earliest transmitter to succeed. Further, receivers speculatively transmit their ack preambles at the end of packet reception, thereby reducing ack overhead. We validate the effectiveness of WiFi-Nano through implementation on an FPGA-based software defined radio platform, and through extensive simulations, demonstrate efficiency gains of up to 100%.


title: Class-wise supervised hashing with label embedding and active bits

abstract: Learning to hash has become a crucial technique for big data analytics. Among existing methods, supervised learning approaches play an important role as they can produce compact codes and enable semantic search. However, the size of an instance-pairwise similarity matrix used in most supervised hashing methods is quadratic to the size of labeled training data, which is very expensive in terms of space, especially for a large-scale learning problem. This limitation hinders the full utilization of labeled instances for learning a more precise hashing model. To overcome this limitation, we propose a class-wise supervised hashing method that trains a model based on a class-pairwise similarity matrix, whose size is much smaller than the instance-pairwise similarity matrix in many applications. In addition, besides a set of hash functions, our proposed method learns a set of classwise code-prototypes with active bits for different classes. These class-wise code-prototypes can help to learn more precise compact codes for semantic information retrieval. Experimental results verify the superior effectiveness of our proposed method over other baseline hashing methods.


title: Random Phase Textures: Theory and Synthesis

abstract: This paper explores the mathematical and algorithmic properties of two sample-based texture models: random phase noise (RPN) and asymptotic discrete spot noise (ADSN). These models permit to synthesize random phase textures. They arguably derive from linearized versions of two early Julesz texture discrimination theories. The ensuing mathematical analysis shows that, contrarily to some statements in the literature, RPN and ADSN are different stochastic processes. Nevertheless, numerous experiments also suggest that the textures obtained by these algorithms from identical samples are perceptually similar. The relevance of this study is enhanced by three technical contributions providing solutions to obstacles that prevented the use of RPN or ADSN to emulate textures. First, RPN and ADSN algorithms are extended to color images. Second, a preprocessing is proposed to avoid artifacts due to the nonperiodicity of real-world texture samples. Finally, the method is extended to synthesize textures with arbitrary size from a given sample.


title: CIEL: a universal execution engine for distributed data-flow computing

abstract: This paper introduces CIEL, a universal execution engine for distributed data-flow programs. Like previous execution engines, CIEL masks the complexity of distributed programming. Unlike those systems, a CIEL job can make data-dependent control-flow decisions, which enables it to compute iterative and recursive algorithms. ::: ::: We have also developed Skywriting, a Turing-complete scripting language that runs directly on CIEL. The execution engine provides transparent fault tolerance and distribution to Skywriting scripts and high-performance code written in other programming languages. We have deployed CIEL on a cloud computing platform, and demonstrate that it achieves scalable performance for both iterative and non-iterative algorithms.


title: Dedicated stripline set-up for the characterization of the shielding effectiveness of board-level shields

abstract: In this paper, a novel and versatile stripline set-up dedicated to the characterization of the high-frequency shielding effectiveness of board-level shields is presented. The stripline setup is based upon the stripline set-up described in SAE ARP6248 for the characterization of the high-frequency shielding effectiveness of “on-board” gaskets. In order to allow to easily switch between different board-level shields under test, as well as possible on-board sources, an opening is made in the “bottomplate” of the stripline set-up. The dimensions of this opening are chosen equal to those used in the TEM-cell method for the characterization of integrated circuits. The validity of the whole method is studied based on simulations and measurements up to 14 GHz.


title: Grammatical Inference in Software Engineering: an Overview of the State of the Art

abstract: Grammatical inference – used successfully in a variety of fields such as pattern recognition, computational biology and natural language processing – is the process of automatically inferring a grammar by examining the sentences of an unknown language. Software engineering can also benefit from grammatical inference. Unlike the aforementioned fields, which use grammars as a convenient tool to model naturally occuring patterns, software engineering treats grammars as first-class objects typically created and maintained for a specific purpose by human designers. We introduce the theory of grammatical inference and review the state of the art as it relates to software engineering.


title: SPDF: A schedulable parametric data-flow MoC

abstract: Dataflow programming models are suitable to express multi-core streaming applications. The design of high-quality embedded systems in that context requires static analysis to ensure the liveness and bounded memory of the application. However, many streaming applications have a dynamic behavior. The previously proposed dataflow models for dynamic applications do not provide any static guarantees or only in exchange of significant restrictions in expressive power or automation. To overcome these restrictions, we propose the schedulable parametric dataflow (SPDF) model. We present static analyses and a quasi-static scheduling algorithm. We demonstrate our approach using a video decoder case study.


title: AdaLearner: An adaptive distributed mobile learning system for neural networks

abstract: Neural networks hold a critical domain in machine learning algorithms because of their self-adaptiveness and state-of-the-art performance. Before the testing (inference) phases in practical use, sophisticated training (learning) phases are required, calling for efficient training methods with higher accuracy and shorter converging time. Many existing studies focus on the training optimization on high-performance servers or computing clusters, e.g. GPU clusters. However, training neural networks on resource-constrained devices, e.g. mobile platforms, is an important research topic barely touched. In this paper, we implement AdaLearner-an adaptive distributed mobile learning system for neural networks that trains a single network with heterogenous mobile resources under the same local network in parallel. To exploit the potential of our system, we adapt neural networks training phase to mobile device-wise resources and fiercely decrease the transmission overhead for better system scalability. On three representative neural network structures trained from two image classification datasets, AdaLearner boosts the training phase significantly. For example, on LeNet, 1.75--3.37× speedup is achieved when increasing the worker nodes from 2 to 8, thanks to the achieved high execution parallelism and excellent scalability.


title: Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification

abstract: Diffusion tensor imaging (DTI) is an exciting new MRI modality that can reveal detailed anatomy of the white matter. DTI also allows us to approximate the 3D trajectories of major white matter bundles. By combining the identified tract coordinates with various types of MR parameter maps, such as T2 and diffusion properties, we can perform tract-specific analysis of these parameters. Unfortunately, 3D tract reconstruction is marred by noise, partial volume effects, and complicated axonal structures. Furthermore, changes in diffusion anisotropy under pathological conditions could alter the results of 3D tract reconstruction. In this study, we created a white matter parcellation atlas based on probabilistic maps of 11 major white matter tracts derived from the DTI data from 28 normal subjects. Using these probabilistic maps, automated tract-specific quantification of fractional anisotropy and mean diffusivity were performed. Excellent correlation was found between the automated and the individual tractography-based results. This tool allows efficient initial screening of the status of multiple white matter tracts.


title: BinGo: cross-architecture cross-OS binary search

abstract: Binary code search has received much attention recently due to its impactful applications, e.g., plagiarism detection, malware detection and software vulnerability auditing. However, developing an effective binary code search tool is challenging due to the gigantic syntax and structural differences in binaries resulted from different compilers, architectures and OSs. In this paper, we propose BINGO — a scalable and robust binary search engine supporting various architectures and OSs. The key contribution is a selective inlining technique to capture the complete function semantics by inlining relevant library and user-defined functions. In addition, architecture and OS neutral function filtering is proposed to dramatically reduce the irrelevant target functions. Besides, we introduce length variant partial traces to model binary functions in a program structure agnostic fashion. The experimental results show that BINGO can find semantic similar functions across architecture and OS boundaries, even with the presence of program structure distortion, in a scalable manner. Using BINGO, we also discovered a zero-day vulnerability in Adobe PDF Reader, a COTS binary.


title: Net increase of lactate and glutamate concentration in activated human visual cortex detected with magnetic resonance spectroscopy at 7 tesla

abstract: After the landmark studies reporting changes in the cerebral metabolic rate of glucose (CMR(Glc) ) in excess of those in oxygen (CMR(O2) ) during physiological stimulation, several studies have examined the fate of the extra carbon taken up by the brain, reporting a wide range of changes in brain lactate from 20% to 250%. The present study reports functional magnetic resonance spectroscopy measurements at 7 Tesla using the enhanced sensitivity to study a small cohort (n = 6). Small increases in lactate (19% ± 4%, P < 0.05) and glutamate (4% ± 1%, P < 0.001) were seen within the first 2 min of activation. With the exception of glucose (12% ± 5%, P < 0.001), no other metabolite concentration changes beyond experimental error were significantly observed. Therefore, the present study confirms that lactate and glutamate changes during physiological stimulation are small (i.e. below 20%) and shows that the increased sensitivity allows reproduction of previous results with fewer subjects. In addition, the initial rate of glutamate and lactate concentration increases implies an increase in CMR(O2) that is slightly below that of CMR(Glc) during the first 1-2 min of activation. © 2013 Wiley Periodicals, Inc.


title: Double-Layer Full-Corporate-Feed Hollow-Waveguide Slot Array Antenna in the 60-GHz Band

abstract: In order to achieve a wide bandwidth characteristic of high gain and high efficiency antennas, a double-layer hollow-waveguide slot array is proposed, where a full-corporate-feed waveguide is arranged in the lower layer. This antenna can be built up easily by the process of diffusion bonding of laminated thin metal etching plates, which has high precision and is possibly a low cost technique. The radiating elements and the feed waveguide are designed to suppress the reflection over a wideband. The predicted bandwidth of the reflection less than -14 dB is 8.3% for a 16 × 16-element array antenna. A test antenna is fabricated in the 60-GHz band, and about 80% antenna efficiency with more than 32 dBi is achieved over 4.8 GHz.


title: Dynamic modeling and sliding-mode control of a Ball robot with inverse mouse-ball drive

abstract: This paper presents techniques for dynamic modeling and control of a Ball robot (Ballbot) with inverse mouse-ball drive that is accomplished by simultaneously activating two independent brushless motors. Under this driving scheme, a completely dynamic model of the robot moving in a flat terrain is established constructed based on Lagrangian mechanics. With the model, a sliding-mode control is proposed based on backstepping to accomplish robust balancing and agile path tracking of the robot with exogenous disturbances. Computer simulations are conducted for illustration of the effectiveness of the proposed modeling and control method.


title: Simultaneous food localization and recognition

abstract: The development of automatic nutrition diaries, which would allow to keep track objectively of everything we eat, could enable a whole new world of possibilities for people concerned about their nutrition patterns. With this purpose, in this paper we propose the first method for simultaneous food localization and recognition. Our method is based on two main steps, which consist in, first, produce a food activation map on the input image (i.e. heat map of probabilities) for generating bounding boxes proposals and, second, recognize each of the food types or food-related objects present in each bounding box. We demonstrate that our proposal, compared to the most similar problem nowadays - object localization, is able to obtain high precision and reasonable recall levels with only a few bounding boxes. Furthermore, we show that it is applicable to both conventional and egocentric images.


title: Hierarchical Bayesian inference in the visual cortex

abstract: Traditional views of visual processing suggest that early visual neurons in areas V1 and V2 are static spatiotemporal filters that extract local features from a visual scene. The extracted information is then channeled through a feedforward chain of modules in successively higher visual areas for further analysis. Recent electrophysiological recordings from early visual neurons in awake behaving monkeys reveal that there are many levels of complexity in the information processing of the early visual cortex, as seen in the long-latency responses of its neurons. These new findings suggest that activity in the early visual cortex is tightly coupled and highly interactive with the rest of the visual system. They lead us to propose a new theoretical setting based on the mathematical framework of hierarchical Bayesian inference for reasoning about the visual system. In this framework, the recurrent feedforward/feedback loops in the cortex serve to integrate top-down contextual priors and bottom-up observations so as to implement concurrent probabilistic inference along the visual hierarchy. We suggest that the algorithms of particle filtering and Bayesian-belief propagation might model these interactive cortical computations. We review some recent neurophysiological evidences that support the plausibility of these ideas.


title: A taxonomy based semantic similarity of documents using the cosine measure

abstract: In this paper, we present a new method for calculating semantic similarities between documents. This method is based on cosine similarity calculation between concept vectors of documents obtained from a taxonomy of words that captures IS-A relations. The calculation of semantic similarities between documents is a very time consuming task, since it is necessary first to calculate semantic similarities between each pair of words that appear on different documents. In this paper, we present a new method to calculate semantic similarities between documents which results in faster computational time. Both a taxonomy based semantic similarity and cosine similarity are employed. First, the concept vectors of documents are obtained by extending the terms in the document vectors with their corresponding IS-A concepts. Cosine similarity is then calculated between those concept vectors of documents. Thus, the overall similarity between documents is a combination of cosine similarity and semantic similarity. The proposed semantic similarity is tested in document clustering problem. The experimental results show that our method achieves a good performance.


title: QoS Recommendation in Cloud Services

abstract: As cloud computing becomes increasingly popular, cloud providers compete to offer the same or similar services over the Internet. Quality of service (QoS), which describes how well a service is performed, is an important differentiator among functionally equivalent services. It can help a firm to satisfy and win its customers. As a result, how to assist cloud providers to promote their services and cloud consumers to identify services that meet their QoS requirements becomes an important problem. In this paper, we argue for QoS-based cloud service recommendation, and propose a collaborative filtering approach using the Spearman coefficient to recommend cloud services. The approach is used to predict both QoS ratings and rankings for cloud services. To evaluate the effectiveness of the approach, we conduct extensive simulations. Results show that the approach can achieve more reliable rankings, yet less accurate ratings, than a collaborative filtering approach using the Pearson coefficient.


title: A Direct-Write Printed Antenna on Paper-Based Organic Substrate for Flexible Displays and WLAN Applications

abstract: This paper presents the design, fabrication and measurements of a direct-write printed low-cost and flexible inverted-F antenna on an ultra-low-cost paper-based organic substrate for wireless local area network (WLAN) and flexible display applications. Innovations include the study and utilization of paper as a high-frequency substrate for the first time in the gigahertz (GHz) range, the fabrication technology for the direct-write printing of the antenna as a flexible RF electronic device, and the investigation of antenna flexibility in conjunction with flexible displays. Although paper substrates exhibit relatively high dielectric losses (tanδ ~ 0.065 at 2.45 GHz), the maximum realized gain of the fabricated antenna is measured to be + 1.2 dBi giving a total efficiency ~ 82%. Simulated results of the antenna's return loss and radiation patterns agree well with the measurements, and can lead to a whole new class of flexible low-cost electronic devices of the future.


title: Understanding the Complexity in Electronic Government : Implications from the Digital Divide literature

abstract: E-government has been recognized as a catalyst or tool for government administrative reform. Information technologies have the potential to produce cost savings, improve the quality of services, and make government policies more effective. However, some scholars and practitioners contend e-government has not delivered the promise of more efficient, effective, and democratic public administration. In fact, Heeks (2003) estimates that the failure rate of e-government projects may be as high as 85%. We argue that e-government and digital divide research have been relatively disconnected and important intersections exist between the two. These intersections may be useful to explain some of the failures in e-government projects and policies. Theoretically and practically, e-government and digital divide are intertwined and, therefore, a better understanding can be gained if scholars start analyzing them as complementary social phenomena. This paper reviews current literature in e-government and the digital divide and highlights some important similarities and differences. It also suggests preliminary implications for e-government research and practice.


title: A measurement study of inter-vehicular communication using steerable beam directional antenna

abstract: We provide a measurement study of a single vehicle-to-vehicle (V2V) link using 802.11b as the link layer technology. Our goal is to investigate practical usage of steerable beam directional antennas to improve V2V communications. We conduct extensive experiments using commercially available phased-array antennas mounted on cars in two different environments -- suburban roads and highways, with various drive patterns. It is observed that directional beamforming improves the link SNR significantly, that translates to significant range improvements. However, to achieve this performance gain both antenna beams must be steered appropriately in the right direction. We observe that often the best beams indeed point directly to each other (called `LOS beams'), in spite of various sources of reflections that could be present in the environment. We develop and evaluate a simple beam steering approach that uses LOS beams for communication. We present experimental data, demonstrating the performance gains (in terms of SNR and PHY-layer data rates) achieved by this approach. While we have studied a single V2V link, this method can be extended to a multihop V2V network.


title: Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power

abstract: Highlights•We propose an artificial neural network for photovoltaic energy forecasting.•We analyze its sensitivity with respect to the input data sets and error definitions.•Data are taken from experimental activities carried out on a real photovoltaic plant.•The hourly energy prediction covers all the daylight hours of the following day.AbstractIn this paper an artificial neural network for photovoltaic plant energy forecasting is proposed and analyzed in terms of itssensitivity with respect to the input data sets.Furthermore, the accuracy of the method has been studied as a function of the training data sets and error definitions. Theanalysis is based on experimental activities carried out on a real photovoltaic power plant accompanied by clear sky model.In particular, this paper deals with the hourly energy prediction for all the daylight hours of the following day, based on 48 hoursahead weather forecast. This is very important due to the predictive features requested by smart grid application: renewable energysources planning, in particular storage system sizing, and market of energy.⃝c 2015 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rightsreserved.


Air Craft Winglet Design and Performance: Cant Angle Effect


title: Ultra-Low-Noise Seismic Piezoelectric Accelerometer With Integral FET Amplifier

abstract: The design of a seismic piezoelectric (PE) accelerometer with integral electronics (IEPE) having probably the lowest noise floor (for its size and weight) and lowest (near-dc) operating frequencies ever reported to date among this type of sensors is presented. The accelerometer frequency range is from 0.003 to 200 Hz at the ±3 dB level. The noise floor (equivalent input noise acceleration) is about 600, 55, 37, 7 and 3 nG/√(Hz) at frequencies 0.1, 0.5, 1, 10 and 100 Hz, respectively. The sensor incorporates the ultra-low-noise FET-input charge amplifier and the PE transducer operated in the flexural, circular bender bimorph mode. Key factors for low-noise operation are discussed. The contribution of the PE transducer noise and charge amplifier noise is shown at a frequency range from 0.1 to 200 Hz. In addition to ultra-low-noise and near-dc frequency response, the designed accelerometer has other parameters impressive for the seismic IEPE sensors: output impedance ≤ 10 Ω, 100°C maximum operating temperature and 250 G pk shock limit. The sensor's noise floor was measured directly at the National Institute of Standards and Technology (NIST). The experimental results have good correlation with the noise values evaluated based on the measured charge amplifier noise and the theoretical PE transducer thermal noise. The ultra-low-noise floor of the designed sensor allowed using it in the stabilization platform, which carries the world's most frequency stable laser system used for the atomic clock research conducted by NIST.


title: Bandwidth enhancement of substrate integrated waveguide cavity backed slot antenna by offset feeding technique

abstract: In this paper, a novel technique to enhance the bandwidth of substrate integrated waveguide cavity backed slot antenna is demonstrated. The feeding technique to the cavity backed antenna has been modified by introducing offset feeding of microstrip line along with microstrip to grounded coplanar waveguide transition which helps to excite TE 120 mode in the cavity and also to get improvement in impedance matching to the slot antenna simultaneously. The proposed antenna is designed to resonate in X band (8–12 GHz) and shows a resonance at 10.2 GHz with a bandwidth of 4.2% and a gain of 5.6 dBi, 15.6 dB front to back ratio and −30 dB maximum cross polarization level.


title: A 1.2V buck converter with a novel on-chip low-voltage current-sensing scheme

abstract: A low-voltage on-chip current-sensing circuit for buck power converter is proposed. The minimum supply voltage of the proposed current-sensing circuit is 1.2V (where V/sub th/ = 0.85V at room temperature), and the sensing accuracy is higher than 94%. The proposed buck converter is able to operate at a 1.2-V supply with higher than 98% power-conversion efficiency and with a maximum output current of 120mA.


title: An Introduction to Feature Extraction

abstract: This chapter introduces the reader to the various aspects of feature extraction covered in this book. Section 1 reviews definitions and notations and proposes a unified view of the feature extraction problem. Section 2 is an overview of the methods and results presented in the book, emphasizing novel contributions. Section 3 provides the reader with an entry point in the field of feature extraction by showing small revealing examples and describing simple but effective algorithms. Finally, Section 4 introduces a more theoretical formalism and points to directions of research and open problems.


title: Faster Generalized LR Parsing

abstract: Tomita devised a method of generalized LR (GLR) parsing to parse ambiguous grammars efficiently. A GLR parser uses linear-time LR parsing techniques as long as possible, falling back on more expensive general techniques when necessary.


title: Occupational stress and mental health among nurses in a medical intensive care unit of a general hospital in Bandar Abbas in 2013

abstract: BACKGROUND: Many nurses have reported experiencing high levels of occupational stress in their work environment. Stress, as an outcome of stressful workplaces and tasks, affects nursing behavior in hospital wards. The objectives of this research were to determine the prevalence of occupational stress and mental health problems in nurses in the intensive care unit (ICU) at Shahid Mohammadi Hospital in Bandar Abbas in 2013 and to determine the relationship between occupational stress and mental health. ::: ::: METHODS: This cross-sectional study was conducted in 2013 on all of the nurses working in ICU at Shahid Mohammadi Hospital located in Bandar Abbas, Iran. Seventy-two nurses were selected as the population for this study, and all of them were female. Two questionnaires were used in this study, i.e., General Health Questionnaire-28 (GHQ-28) for assessing mental health and an occupational stress test for assessing job stress. Furthermore, the relationship between occupational stress and mental health was examined. One-way analysis of variance (ANOVA), independent samples t-test, and Pearson's product-moment correlation test were used to analyze the data. ::: ::: RESULTS: High and moderate levels of occupational stress were experienced by 83.9% and 10.7% of ICU nurses, respectively. The prevalence of mental disorders, somatic symptoms, anxiety, social dysfunction, and depression were 58.9, 60.7, 62.5, 71.4, and 10.7%, respectively. The findings of the independent samples t-test showed that somatic symptoms had significant relationships with age and working experience (p = 0.01). According to the independent samples t-test, there were no significant differences between somatic symptoms and working different shifts (p > 0.05). ::: ::: CONCLUSIONS: There was a high prevalence of occupational stress among ICU nurses. There was a significant relationship between occupational stress and mental health. Future interventions are needed to codify a comprehensive health program in this field to reduce occupational stress and enhance nurses' levels of mental health.


title: Chronic Intermittent Ethanol Exposure Differentially Alters Microglial Morphology in the Prelimbic Cortex and Nucleus Accumbens core

abstract: Chronic intermittent ethanol (CIE) exposure in rats induces adaptations in the prelimbic cortex (PL) and nucleus accumbens core (NAcore) during withdrawal, thought to contribute to deficits in decision-making and escalation of drinking. Increasing evidence has linked neuroplastic changes associated with the development of alcohol dependence to neuroimmune signaling and activation of microglia. Microglia exhibit a well-defined structure function relationship, with alterations in cytokine release occurring concomitantly with reconfiguration of cellular dimensions. Accordingly, gaining a better understanding of ethanol-induced microglia activation will provide insight into how neuroimmune signaling may contribute to ethanol-induced reshaping of neuronal structure and function. The present study characterized CIE-induced alterations in microglia morphology in the PL and NAcore using confocal microscopy and digital reconstruction. Rats sacrificed at peak withdrawal, 12 hours after discontinuation of 14 days of CIE exposure, exhibited an increased microglia soma size in the PL and NAcore, when compared to Air-exposed control rats. However, CIE-mediated increased soma volume in the PL cortex were accompanied by reduced microglial complexity. While systemic LPS elicited increased soma volume in the PL cortex and NAcore, well beyond levels observed with CIE, LPS- induced increased soma size was associated with increased complexity of microglial processes in the PL cortex. Taken together, our data are consistent with the hypothesis that the microglia activation following CIE is structurally, and likely chemically, distinct from microglia activation associated with induction of the innate neuroimmune system following systemic LPS exposure.


title: From piecemeal to configurational representation of faces

abstract: Unlike older children and adults, children of less than about 10 years of age remember photographs of faces presented upside down almost as well as those shown upright and are easily fooled by simple disguises. The development at age 10 of the ability to encode orientation-specific configurational aspects of a face may reflect completion of certain maturational changes in the right cerebral hemisphere.