title: Using supervised machine learning algorithms to detect suspicious URLs in online social networks

abstract: The increasing volume of malicious content in social networks requires automated methods to detect and eliminate such content. This paper describes a supervised machine learning classification model that has been built to detect the distribution of malicious content in online social networks (ONSs). Multisource features have been used to detect social network posts that contain malicious Uniform Resource Locators (URLs). These URLs could direct users to websites that contain malicious content, drive-by download attacks, phishing, spam, and scams. For the data collection stage, the Twitter streaming application programming interface (API) was used and VirusTotal was used for labelling the dataset. A random forest classification model was used with a combination of features derived from a range of sources. The random forest model without any tuning and feature selection produced a recall value of 0.89. After further investigation and applying parameter tuning and feature selection methods, however, we were able to improve the classifier performance to 0.92 in recall.


title: The smart thermostat: using occupancy sensors to save energy in homes

abstract: Heating, ventilation and cooling (HVAC) is the largest source of residential energy consumption. In this paper, we demonstrate how to use cheap and simple sensing technology to automatically sense occupancy and sleep patterns in a home, and how to use these patterns to save energy by automatically turning off the home's HVAC system. We call this approach the smart thermostat. We evaluate this approach by deploying sensors in 8 homes and comparing the expected energy usage of our algorithm against existing approaches. We demonstrate that our approach will achieve a 28% energy saving on average, at a cost of approximately $25 in sensors. In comparison, a commercially-available baseline approach that uses similar sensors saves only 6.8% energy on average, and actually increases energy consumption in 4 of the 8 households.


title: Introducing computational thinking in education courses

abstract: As computational thinking becomes a fundamental skill for the 21st century, K-12 teachers should be exposed to computing principles. This paper describes the implementation and evaluation of a computational thinking module in a required course for elementary and secondary education majors. We summarize the results from open-ended and multiple-choice questionnaires given both before and after the module to assess the students' attitudes toward and understanding of computational thinking. The results suggest that given relevant information about computational thinking, education students' attitudes toward computer science becomes more favorable and they will be more likely to integrate computing principles in their future teaching.


title: Authorship classification: a discriminative syntactic tree mining approach

abstract: In the past, there have been dozens of studies on automatic authorship classification, and many of these studies concluded that the writing style is one of the best indicators for original authorship. From among the hundreds of features which were developed, syntactic features were best able to reflect an author's writing style. However, due to the high computational complexity for extracting and computing syntactic features, only simple variations of basic syntactic features such as function words, POS(Part of Speech) tags, and rewrite rules were considered. In this paper, we propose a new feature set of k-embedded-edge subtree patterns that holds more syntactic information than previous feature sets. We also propose a novel approach to directly mining them from a given set of syntactic trees. We show that this approach reduces the computational burden of using complex syntactic structures as the feature set. Comprehensive experiments on real-world datasets demonstrate that our approach is reliable and more accurate than previous studies.


title: Emotional imagery: assessing pleasure and arousal in the brain's reward circuitry.

abstract: Research on emotional perception and learning indicates appetitive cues engage nucleus accumbens (NAc) and medial prefrontal cortex (mPFC), whereas amygdala activity is modulated by the emotional intensity of appetitive and aversive cues. This study sought to determine patterns of functional activation and connectivity among these regions during narrative emotional imagery. Using event-related fMRI, we investigate activation of these structures when participants vividly imagine pleasant, neutral, and unpleasant scenes. Results indicate that pleasant imagery selectively activates NAc and mPFC, whereas amygdala activation was enhanced during both pleasant and unpleasant im- agery. NAc and mPFC activity were each correlated with the rated pleasure of the imagined scenes, while amygdala activity was correlated with rated emotional arousal. Functional connectivity of NAc and mPFC was evident throughout imagery, regardless of hedonic content, while correlated activation of the amygdala with NAc and mPFC was specific to imagining pleasant scenes. These findings pro- vide strong evidence that pleasurable text-driven imagery engages a core appetitive circuit, including NAc, mPFC, and the amygdala. Hum Brain Mapp 31:1446-1457, 2010. V C 2010 Wiley-Liss, Inc.


title: A Survey of Multiobjective Evolutionary Clustering

abstract: Data clustering is a popular unsupervised data mining tool that is used for partitioning a given dataset into homogeneous groups based on some similarity/dissimilarity metric. Traditional clustering algorithms often make prior assumptions about the cluster structure and adopt a corresponding suitable objective function that is optimized either through classical techniques or metaheuristic approaches. These algorithms are known to perform poorly when the cluster assumptions do not hold in the data. Multiobjective clustering, in which multiple objective functions are simultaneously optimized, has emerged as an attractive and robust alternative in such situations. In particular, application of multiobjective evolutionary algorithms for clustering has become popular in the past decade because of their population-based nature. Here, we provide a comprehensive and critical survey of the multitude of multiobjective evolutionary clustering techniques existing in the literature. The techniques are classified according to the encoding strategies adopted, objective functions, evolutionary operators, strategy for maintaining nondominated solutions, and the method of selection of the final solution. The pros and cons of the different approaches are mentioned. Finally, we have discussed some real-life applications of multiobjective clustering in the domains of image segmentation, bioinformatics, web mining, and so forth.


title: Measuring productivity of software development teams

abstract: This paper gives an exhaustive literature review of the techniques and models available to measure the productivity of software development teams. Definition of productivity, measuring individual programmer's productivity, and measuring software development team productivity are discussed. Based on the literature review it was found that software productivity measurement can be done using SLOC (Source Lines of Code), function points, use case points, object points, and feature points. Secondary research findings indicate that the team size, response time, task complexity, team climate and team cohesion have an impact on software development team productivity. List of factors affecting the software development team productivity are studied and reviewed.


title: User Awareness of Security Countermeasures and Its Impact on Information Systems Misuse: A Deterrence Approach

abstract: Intentional insider misuse of information systems resources (i.e., IS misuse) represents a significant threat to organizations. For example, industry statistics suggest that between 50%--75% of security incidents originate from within an organization. Because of the large number of misuse incidents, it has become important to understand how to reduce such behavior. General deterrence theory suggests that certain controls can serve as deterrent mechanisms by increasing the perceived threat of punishment for IS misuse. This paper presents an extended deterrence theory model that combines work from criminology, social psychology, and information systems. The model posits that user awareness of security countermeasures directly influences the perceived certainty and severity of organizational sanctions associated with IS misuse, which leads to reduced IS misuse intention. The model is then tested on 269 computer users from eight different companies. The results suggest that three practices deter IS misuse: user awareness of security policies; security education, training, and awareness (SETA) programs; and computer monitoring. The results also suggest that perceived severity of sanctions is more effective in reducing IS misuse than certainty of sanctions. Further, there is evidence that the impact of sanction perceptions vary based on one's level of morality. Implications for the research and practice of IS security are discussed.


title: Anchor-free backscatter positioning for RFID tags with high accuracy

abstract: RFID technology has been widely adopted in a variety of applications from logistics to access control. Many applications gain benefits from knowing the exact position of an RFID-tagged object. Existing localization algorithms in wireless network, however, can hardly be directly employed due to tag's limited capabilities in terms of energy and memory. For example, the RSS based methods are vulnerable to both distance and tag orientation, while AOA based methods put a strict constraint on the antennas' spacing that reader's directional antennas are too large to meet. In this paper, we propose BackPos, a fine-grained backscatter positioning technique using the COTS RFID products with detected phase. Our study shows that the phase is indeed a stable indicator highly related to tag's position and preserved over frequency or tag orientation, but challenged by its periodicity and tag's diversity. We attempt to infer the distance differences from phases detected by antennas under triangle constraint. Further, hyperbolic positioning using the distance differences is employed to shrink the tag's candidate positions until filtering out the real one. In combination with interrogation zone, we finally relax the triangle constraint and allow arbitrary deployment of antennas by sacrificing the feasible region. We implement a prototype of BackPos with COTS RFID products and evaluate this design in various scenarios. The results show that BackPos achieves the mean accuracy of 12 .8cm with variance of 3.8cm .


title: Bounding Box Embedding for Single Shot Person Instance Segmentation

abstract: We present a bottom-up approach for the task of object instance segmentation using a single-shot model. The proposed model employs a fully convolutional network which is trained to predict class-wise segmentation masks as well as the bounding boxes of the object instances to which each pixel belongs. This allows us to group object pixels into individual instances. Our network architecture is based on the DeepLabv3+ model, and requires only minimal extra computation to achieve pixel-wise instance assignments. We apply our method to the task of person instance segmentation, a common task relevant to many applications. We train our model with COCO data and report competitive results for the person class in the COCO instance segmentation task.


title: Investigating user needs for non-driving-related activities during automated driving

abstract: In this paper, we investigate which non-driving-related activities drivers want to perform while driving highly or fully automated. Beyond the available advanced driving assistance functions, we expect that highly automated driving will soon be available in production vehicles. While many technological aspects have been investigated, it is not yet clear (a) which activities the drivers want to perform once they do not have to steer or monitor their car any more and (b) which of those will be feasible. In contrast to prior (survey-based) research, we investigate the driver's needs for such activities by employing a combination of a web survey, in-situ observations, and an in-situ survey. Also, we have a look at the specific requirements of the European / German market in contrast to prior research conducted mostly for English-speaking countries. The findings indicate that besides traditional activities (talking to passengers, listening to music), daydreaming, writing text messages, eating and drinking, browsing the Internet, and calling are most wanted for highly automated driving. This shows the potential for mobile and ubiquitous multimedia applications in the car.


title: Force control on antagonistic Twist-drive Actuator robot

abstract: In recent researches relating to robotic fingers, a novel power transmission mechanism had been designed, and developed as Twist-drive Actuators or Twisted String Actuator systems (TSA). These actuation structures, state of the art, include compact designs, light-weight mechanical structures, inherent compliance, and variable gearing, resulting in the fabrication of anthropomorphic robotic hands. However, those studies mentioned position control of the joint, and focused on the control performance of the pulling forces of a twisted string. This paper, therefore, introduces a novel joint mechanism composed of an Antagonistically-twisted Round-belt Actuator (ARA), which is able to make rotating motion by means of contraction forces induced by twisting small-diameter elastic round-belts. A noteworthy point in this paper is that the ARA robot is extremely well-suited for contact force control, which is exerted on the tip of the single joint robot. First, we demonstrate the existence of hysteresis characteristics of static contraction forces induced by the round-belt twisting, in which three belts are simultaneously twisted at constant low-speed. In addition, there exists a stress relaxation phenomenon when strongly twisting the elastic round-belts. We reveal that such sorts of discontinuous and nonlinear properties do not influence control performance in either the contraction force on the belts or contact force on the tip of the robot. Finally, this paper clearly shows stable and accurate tracking performance of the contact force of the ARA robot. In these experiments, we newly develop a twin/triple-twisted round-belt structure for the agonist side actuator, thus enhancing the contact forces effectively.


Research on EEMD for vibration extreme feature preserving of rotating machinery


title: Neurohex: A Deep Q-learning Hex Agent

abstract: DeepMind's recent spectacular success in using deep convolutional neural nets and machine learning to build superhuman level agents --- e.g. for Atari games via deep Q-learning and for the game of Go via Reinforcement Learning --- raises many questions, including to what extent these methods will succeed in other domains. In this paper we consider DQL for the game of Hex: after supervised initialization, we use selfplay to train NeuroHex, an 11-layer CNN that plays Hex on the 13x13 board. Hex is the classic two-player alternate-turn stone placement game played on a rhombus of hexagonal cells in which the winner is whomever connects their two opposing sides. Despite the large action and state space, our system trains a Q-network capable of strong play with no search. After two weeks of Q-learning, NeuroHex achieves win-rates of 20.4% as first player and 2.1% as second player against a 1-second/move version of MoHex, the current ICGA Olympiad Hex champion. Our data suggests further improvement might be possible with more training time.


title: Assessing patient suitability for short-term cognitive therapy with an interpersonal focus

abstract: In the current study, the development and initial validation of the Suitability for Short-Term Cognitive Therapy (SSCT) interview procedure is reported. The SSCT is an interview and rating procedure designed to evaluate the potential appropriateness of patients for short-term cognitive therapy with an interpersonal focus. It consists of a 1-hour, semistructured interview, focused on eliciting information from the patient relevant to nine selection criteria. The procedures involved in the development of this scale are described in detail, and preliminary evidence suggesting that the selection criteria can be rated reliably is presented. In addition, data indicating that scores on the SSCT scale predict the outcome of short-term cognitive therapy on multiple dependent measures, including both therapist and patient perspectives, are reported. It is concluded that the SSCT is a potentially useful scale for identifying patients who may be suitable, or unsuitable, for the type of short-term cognitive therapy administered in the present study.


title: SAGA: array storage as a DB with support for structural aggregations

abstract: In recent years, many Array DBMSs, including SciDB and RasDaMan have emerged to meet the needs of data management applications where the natural structures are the arrays. These systems, like their relational counterparts, involve an expensive data ingestion phase. The paradigm of using native storage as a DB and providing database-like support (e.g., the NoDB approach) has recently been shown to be an effective approach for dealing with infrequently queried data, where data ingestion costs cannot be justified, though only in context of relational data. Applications that generate massive arrays, such as the scientific simulations, often store the data in one of a small number of array storage formats, like NetCDF or HDF5. Thus, a natural question is, "can database-like functionality be supported over native array storage?". In this paper, we present algorithms, different partitioning strategies, and an analytical model for supporting structural (grid, sliding, hierarchical, and circular) aggregations over native array storage, and describe implementation of this approach in a system we refer to as S tructural AG gregations over A rray storage (SAGA). We show how the relative performance of different partitioning strategies changes with varying amount of computation in the aggregation function and different levels of data skew, and our model is effective in choosing the best partitioning strategy. Performance comparison with SciDB shows that despite working on native array storage, the aggregation costs with our system are lower. Finally, we also show that our structural aggregation implementations achieve high parallel efficiency.


title: Transistor Selection and Design of a VHF DC-DC Power Converter

abstract: This paper explores the design and performance of dc-dc power converters operating in the very high frequency (VHF, 30-300 MHz) range. Methods are presented for assessment and comparison of device losses in VHF operation under soft switching and soft gating conditions. These methods are applied to the development of a 2 W resonant boost converter operating at a switching frequency of 30 MHz. Design of the power stage, resonant gate drive, and control circuitry are treated in detail, and experimental results demonstrating the performance of the converter are presented.


title: A Context-Aware Fog Enabled Scheme for Real-Time Cross-Vertical IoT Applications

abstract: As the Internet of Things (IoT) paradigm is maturing, innovative, and novel services are being envisioned. An upcoming trend is the depiction of services enacted through seamless integration of multiple vertical IoT services, termed as cross-vertical or unified IoT services in this paper. Traditional Cloud-based centralized network architectures cannot cater to real-time responses demanded by such unified IoT applications. Moreover, introducing Fog nodes within the network architecture, though a promising alternative, cannot sustain the burden of a huge number of applications that culminates in massive data handling. In this paper, we envision employing lessons learned from context-aware computing, specifically context sharing among interdependent vertical IoT applications to address this delay requirement of such unified IoT applications by enacting context sharing among Fog nodes for minimizing system delay. The detailed network model and context sharing mechanism have been presented and the service time minimization has been framed as an optimization problem. Algorithms for context sharing and delay tolerant load balancing have been presented and simulation results carried out demonstrate the efficacy of the proposed methodology.


Human-Robot Dialogues for Explaining Activities


title: Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan

abstract: Abstract Kazakhstan is the second largest country to emerge from the collapse of the Soviet Union. Consequent to the abrupt institutional changes surrounding the disintegration of the Soviet Union in the early 1990s, Kazakhstan has reportedly undergone extensive land cover/land use change. Were the institutional changes sufficiently great to affect land surface phenology at spatial resolutions and extents relevant to mesoscale meteorological models? To explore this question, we used the NDVI time series (1985–1988 and 1995–1999) from the Pathfinder Advanced Very High Resolution Radiometer (AVHRR) Land (PAL) dataset, which consists of 10 days maximum NDVI composites at a spatial resolution of 8 km. Daily minimum and maximum temperatures were extracted from the NCEP Reanalysis Project and 10 days composites of accumulated growing degree-days (AGDD) were produced. We selected for intensive study seven agricultural areas ranging from regions with rain-fed spring wheat cultivation in the north to regions of irrigated cotton and rice in the south. We applied three distinct but complementary statistical analyses: (1) nonparametric testing of sample distributions; (2) simple time series analysis to evaluate trends and seasonality; and (3) simple regression models describing NDVI as a quadratic function of AGDD. The irrigated areas displayed different temporal developments of NDVI between 1985–1988 and 1995–1999. As the temperature regime between the two periods was not significantly different, we conclude that observed differences in the temporal development of NDVI resulted from changes in agricultural practices. In the north, the temperature regime was also comparable for both periods. Based on extant socioeconomic studies and our model analyses, we conclude that the changes in the observed land surface phenology in the northern regions are caused by large increases in fallow land dominated by weedy species and by grasslands under reduced grazing pressure. Using multiple lines of evidence allowed us to build a case of whether differences in land surface phenology were mostly the result of anthropogenic influences or interannual climatic fluctuations.


title: Affective Videogames and Modes of Affective Gaming: Assist Me, Challenge Me, Emote Me (ACE)

abstract: In this paper we describe the fundamentals of affective gaming from a physiological point of view, covering some of the origins of the genre, how affective videogames operate and current conceptual and technological capabilities. We ground this overview of the ongoing research by taking an in-depth look at one of our own early biofeedback-based affective games. Based on our analysis of existing videogames and our own experience with affective videogames, we propose a new approach to game design based on several high-level design heuristics: assist me, challenge me and emote me (ACE), a series of gameplay "tweaks" made possible through affective videogames.


title: DESIGNING BUSINESS MODELS FOR PLATFORM AS A SERVICE: TOWARDS A DESIGN THEORY Research-in-Progress

abstract: Platform as a Service (PaaS) solutions are changing the ways that software is produced, distributed, consumed, and priced. Unlike Software as a Service (SaaS) or Infrastructure as a Service (IaaS), PaaS allows for value co-creation by offering complementary components and applications that are developed in emerging ecosystems of third party developers. Despite increasing interest among practitioners and researchers, there has been little work in understanding how PaaS business models should be designed to establish a flourishing ecosystem. We seek to develop a design theory that facilitates the design of PaaS business models, taking into account the specifics of multisided business models. Four meta-requirements describe the purpose and scope of our design theory, and six design principles guide PaaS providers in designing effective business models. By focusing on designing business models, our research goes beyond previous approaches for studying PaaS.


title: The Role of High-Altitude Platforms (HAPs) in the Global Wireless Connectivity

abstract: Since 1990s, the investigations of aerospace communication segment have not only been concerned with satellites, but increasingly with lower altitude repeaters flying in the stratosphere. They are the so-called high-altitude platforms (HAPs) with important advantages with respect to satellites in terms of reduced cost of implementation, deployment, and launch. However, HAPs are characterized by a reduced coverage, as compared with satellites. Nevertheless, in recent literature, HAPs are not regarded as competitors of the satellite technology. On the contrary, the emphasis is on the effective and seamless integration among heterogeneous aerospace segments (GEO, LEO, and HAP) and aerospace segments with terrestrial wireless networks in order to globally extend the broadband wireless connectivity. This paper is focused on the role of HAPs in providing global connectivity in future communication systems and services. Potentialities, enabling technologies, and challenges are presented from the perspective of the integrated terrestrial/HAPs/satellite communications infrastructure.


Temporal Generative Adversarial Nets.


title: Gender differences in mathematics anxiety and the relation to mathematics performance while controlling for test anxiety

abstract: BackgroundMathematics anxiety (MA), a state of discomfort associated with performing mathematical tasks, is thought to affect a notable proportion of the school age population. Some research has indicated that MA negatively affects mathematics performance and that girls may report higher levels of MA than boys. On the other hand some research has indicated that boys’ mathematics performance is more negatively affected by MA than girls’ performance is. The aim of the current study was to measure girls’ and boys’ mathematics performance as well as their levels of MA while controlling for test anxiety (TA) a construct related to MA but which is typically not controlled for in MA studies.MethodsFour-hundred and thirty three British secondary school children in school years 7, 8 and 10 completed customised mental mathematics tests and MA and TA questionnaires.ResultsNo gender differences emerged for mathematics performance but levels of MA and TA were higher for girls than for boys. Girls and boys showed a positive correlation between MA and TA and a negative correlation between MA and mathematics performance. TA was also negatively correlated with mathematics performance, but this relationship was stronger for girls than for boys. When controlling for TA, the negative correlation between MA and performance remained for girls only. Regression analyses revealed that MA was a significant predictor of performance for girls but not for boys.ConclusionsOur study has revealed that secondary school children experience MA. Importantly, we controlled for TA which is typically not controlled for in MA studies. Girls showed higher levels of MA than boys and high levels of MA were related to poorer levels of mathematics performance. As well as potentially having a detrimental effect on ‘online’ mathematics performance, past research has shown that high levels of MA can have negative consequences for later mathematics education. Therefore MA warrants attention in the mathematics classroom, particularly because there is evidence that MA develops during the primary school years. Furthermore, our study showed no gender difference in mathematics performance, despite girls reporting higher levels of MA. These results might suggest that girls may have had the potential to perform better than boys in mathematics however their performance may have been attenuated by their higher levels of MA. Longitudinal research is needed to investigate the development of MA and its effect on mathematics performance.


title: Deep Attentional Structured Representation Learning for Visual Recognition

abstract: Structured representations, such as Bags of Words, VLAD and Fisher Vectors, have proven highly effective to tackle complex visual recognition tasks. As such, they have recently been incorporated into deep architectures. However, while effective, the resulting deep structured representation learning strategies typically aggregate local features from the entire image, ignoring the fact that, in complex recognition tasks, some regions provide much more discriminative information than others. ::: In this paper, we introduce an attentional structured representation learning framework that incorporates an image-specific attention mechanism within the aggregation process. Our framework learns to predict jointly the image class label and an attention map in an end-to-end fashion and without any other supervision than the target label. As evidenced by our experiments, this consistently outperforms attention-less structured representation learning and yields state-of-the-art results on standard scene recognition and fine-grained categorization benchmarks.


title: Burden of Depressive Disorders by Country, Sex, Age, and Year: Findings from the Global Burden of Disease Study 2010

abstract: Background: Depressive disorders were a leading cause of burden in the Global Burden of Disease (GBD) 1990 and 2000 studies. Here, we analyze the burden of depressive disorders in GBD 2010 and present severity proportions, burden by country, region, age, sex, and year, as well as burden of depressive disorders as a risk factor for suicide and ischemic heart disease. Methods and Findings: Burden was calculated for major depressive disorder (MDD) and dysthymia. A systematic review of epidemiological data was conducted. The data were pooled using a Bayesian meta-regression. Disability weights from population survey data quantified the severity of health loss from depressive disorders. These weights were used to calculate years lived with disability (YLDs) and disability adjusted life years (DALYs). Separate DALYs were estimated for suicide and ischemic heart disease attributable to depressive disorders. Depressive disorders were the second leading cause of YLDs in 2010. MDD accounted for 8.2% (5.9%–10.8%) of global YLDs and dysthymia for 1.4% (0.9%–2.0%). Depressive disorders were a leading cause of DALYs even though no mortality was attributed to them as the underlying cause. MDD accounted for 2.5% (1.9%–3.2%) of global DALYs and dysthymia for 0.5% (0.3%–0.6%). There was more regional variation in burden for MDD than for dysthymia; with higher estimates in females, and adults of working age. Whilst burden increased by 37.5% between 1990 and 2010, this was due to population growth and ageing. MDD explained 16 million suicide DALYs and almost 4 million ischemic heart disease DALYs. This attributable burden would increase the overall burden of depressive disorders from 3.0% (2.2%–3.8%) to 3.8% (3.0%–4.7%) of global DALYs. Conclusions: GBD 2010 identified depressive disorders as a leading cause of burden. MDD was also a contributor of burden allocated to suicide and ischemic heart disease. These findings emphasize the importance of including depressive disorders as a public-health priority and implementing cost-effective interventions to reduce its burden. Please see later in the article for the Editors’ Summary.


title: Securing cognitive radio vehicular Ad hoc networks with trusted lightweight cloud computing

abstract: Vehicular Ad hoc NETworks (VANETs) as the basic infrastructure can facilitate applications and services of connected vehicles (CVs). Cognitive radio (CR) technology is an effective supplement and enhancement for VANETs. It can reduce the impact of deficiency of spectrum resource in VANETs. Although CR-VANETs can utilize the unused licensed spectrum effectively, the distributed nature of CR-VANETs may open a door for different attacks, such as spectrum sensing data falsification attack. In this paper, we propose a joint RSU and vehicle-based light-weighted cloud for CR-VANETs. Based on this cloud computing model, we propose a new service named Spectrum Sensing as a Service (SSaaS), which can perform a cooperative spectrum sensing in CR-VANETs with cloud computing assistance to secure the spectrum sensing procedure. As a result, a reliable service can be obtained in CR-VANETs. Simulation results show that the cloud computing in CR-VANETs can effectively reduce latency and improve the security of CR-VANETs.


title: A New Soft Robot Control Method

abstract: Traditional rigid robots, such as those used in manufacturing, have been effective at precise, accurate, rapid motions in well-structured environments for many decades now. However, they operate largely behind cages due to the danger of injury when moving in close proximity to people. A significant and recent shift in robotics involves trading rigid links and rigid actuators for soft, deformable links and compliant actuators. These soft robots generally have lower inertia and avoid many of the problems caused by the high effective inertia resulting from the high gear ratios necessary for rigid robots.


title: Reasoning about Quantities in Natural Language

abstract: Little work from the Natural Language Processing community has targeted the role of quantities in Natural Language Understanding. This paper takes some key steps towards facilitating reasoning about quantities expressed in natural language. We investigate two different tasks of numerical reasoning. First, we consider Quantity Entailment, a new task formulated to understand the role of quantities in general textual inference tasks. Second, we consider the problem of automatically understanding and solving elementary school math word problems. In order to address these quantitative reasoning problems we first develop a computational approach which we show to successfully recognize and normalize textual expressions of quantities. We then use these capabilities to further develop algorithms to assist reasoning in the context of the aforementioned tasks.


Analysis and improvement of HITS algorithm for detecting Web communities


title: The Relationship Between Self-Compassion and Well-Being: A Meta-Analysis

abstract: Background ::: Self-compassion describes a positive and caring attitude of a person toward her- or himself in the face of failures and individual shortcomings. As a result of this caring attitude, individuals high in self-compassion are assumed to experience higher individual well-being. The present meta-analysis examines the relationship between self-compassion and different forms of well-being. ::: ::: Method ::: The authors combined k = 79 samples, with an overall sample size of N = 16,416, and analyzed the central tendencies of effect sizes (Pearson correlation coefficients) with a random-effect model. ::: ::: Results ::: We found an overall magnitude of the relationship between self-compassion and well-being of r = .47. The relationship was stronger for cognitive and psychological well-being compared to affective well-being. Sample characteristics and self-esteem were tested as potential moderators. In addition, a subsample of studies indicated a causal effect of self-compassion on well-being. ::: ::: Conclusions ::: The results clearly highlight the importance of self-compassion for individuals’ well-being. Future research should further investigate the relationship between self-compassion and the different forms of well-being, and focus on the examination of possible additional moderators.


title: Combining UML, ASTD and B for the formal specification of an access control filter

abstract: Combination of formal and semi-formal methods is more and more required to produce specifications that can be, on the one hand, understood and thus validated by both designers and users and, on the other hand, precise enough to be verified by formal methods. This motivates our aim to use these complementary paradigms in order to deal with security aspects of information systems. This paper presents a methodology to specify access control policies starting with a set of graphical diagrams: UML for the functional model, SecureUML for static access control and ASTD for dynamic access control. These diagrams are then translated into a set of B machines. Finally, we present the formal specification of an access control filter that coordinates the different kinds of access control rules and the specification of functional operations. The goal of such B specifications is to rigorously check the access control policy of an information system taking advantage of tools from the B method.


title: Written register of nasality in preschool children O registro escrito da nasalidade em crianças de educação infantil

abstract: PURPOSE: To verify how children in the last year of preschool register nasality in their writing, observing these registers regarding syllabic positions of onset and coda, as well as the phonological classes involved in substitution cases. METHODS: The material used came from a writing activity conducted with 19 children enrolled in the last level of preschool. It was carried out a guessing game, in which children should write the names of 24 fruits. Fifteen of those names showed nasality context. The main parameter for analysis was the syllabic position of nasal graphemes in Portuguese. RESULTS: Most children wrote down the nasality, with more occurrence in syllable onset position. In this position there was a higher number of conventional registers, while in coda position the non-conventional registers prevailed. Substitutions occurred mainly among graphemes that corresponded to the sonorants class. CONCLUSION: The relevance of syllabic constituents is high for the characterization of the points of lowest and highest difficulty in the acquisition of nasality in writing. It is also relevant how children retrieve phonetic-phonological similarities among nasal graphemes that they substitute and those that are substituted.


title: Bending angle prediction and control of soft pneumatic actuators with embedded flex sensors – A data-driven approach

abstract: In this paper, a purely data-driven modelling approach is presented for predicting and controlling the free bending angle response of a typical soft pneumatic actuator (SPA), embedded with a resistive flex sensor. An experimental setup was constructed to test the SPA at different input pressure values and orientations, while recording the resulting feedback from the embedded flex sensor and on-board pressure sensor. A calibrated high speed camera captures image frames during the actuation, which are then analysed using an image processing program to calculate the actual bending angle and synchronise it with the recorded sensory feedback. Empirical models were derived based on the generated experimental data using two common data-driven modelling techniques; regression analysis and artificial neural networks. Both techniques were validated using a new dataset at untrained operating conditions to evaluate their prediction accuracy. Furthermore, the derived empirical model was used as part of a closed-loop PID controller to estimate and control the bending angle of the tested SPA based on the real-time sensory feedback generated. The tuned PID controller allowed the bending SPA to accurately follow stepped and sinusoidal reference signals, even in the presence of pressure leaks in the pneumatic supply. This work demonstrates how purely data-driven models can be effectively used in controlling the bending of SPAs under different operating conditions, avoiding the need for complex analytical modelling and material characterisation. Ultimately, the aim is to create more controllable soft grippers based on such SPAs with embedded sensing capabilities, to be used in applications requiring both a ‘soft touch’ as well as a more controllable object manipulation.


title: MonoPerfCap: Human Performance Capture From Monocular Video

abstract: We present the first marker-less approach for temporally coherent 3D performance capture of a human with general clothing from monocular video. Our approach reconstructs articulated human skeleton motion as well as medium-scale non-rigid surface deformations in general scenes. Human performance capture is a challenging problem due to the large range of articulation, potentially fast motion, and considerable non-rigid deformations, even from multi-view data. Reconstruction from monocular video alone is drastically more challenging, since strong occlusions and the inherent depth ambiguity lead to a highly ill-posed reconstruction problem. We tackle these challenges by a novel approach that employs sparse 2D and 3D human pose detections from a convolutional neural network using a batch-based pose estimation strategy. Joint recovery of per-batch motion allows to resolve the ambiguities of the monocular reconstruction problem based on a low dimensional trajectory subspace. In addition, we propose refinement of the surface geometry based on fully automatically extracted silhouettes to enable medium-scale non-rigid alignment. We demonstrate state-of-the-art performance capture results that enable exciting applications such as video editing and free viewpoint video, previously infeasible from monocular video. Our qualitative and quantitative evaluation demonstrates that our approach significantly outperforms previous monocular methods in terms of accuracy, robustness and scene complexity that can be handled.


title: Lipoedema: presentation and management

abstract: Lipoedema is a distinct clinical condition characterized by bilateral, symmetrical enlargement of the buttocks and lower limbs owing to excess deposition of subcutaneous fat. It is found almost exclusively in women. The common features associated with this condition are ‘column- shaped’ legs with sparing of the feet, bruising, sensitivity to pressure, and orthostatic oedema. The progression to lipo-lymphoedema or morbid obesity is possible. Conservative measures used in the management of lymphoedema can prevent progression/limit the orthostatic oedema. Surgical procedures may also play a part in the management of lipoedema.


title: Educational Data Mining and Learning Analytics: Applications to Constructionist Research

abstract: Constructionism can be a powerful framework for teaching complex content to novices. At the core of constructionism is the suggestion that by enabling learners to build creative artifacts that require complex content to function, those learners will have opportunities to learn this content in contextualized, personally meaningful ways. In this paper, we investigate the relevance of a set of approaches broadly called “educational data mining” or “learning analytics” (henceforth, EDM) to help provide a basis for quantitative research on constructionist learning which does not abandon the richness seen as essential by many researchers in that paradigm. We suggest that EDM may have the potential to support research that is meaningful and useful both to researchers working actively in the constructionist tradition but also to wider communities. Finally, we explore potential collaborations between researchers in the EDM and constructionist traditions; such collaborations have the potential to enhance the ability of constructionist researchers to make rich inferences about learning and learners, while providing EDM researchers with many interesting new research questions and challenges.


title: Dual-band reconfigurable antenna with a very wide tunability range

abstract: A new technique for designing dual-band reconfigurable slot antennas is presented. Dual-frequency operation is achieved by loading a slot antenna with two lumped variable capacitors (varactors) placed in proper locations along the slot. Loading the slot antenna with lumped capacitors shifts down the resonant frequencies of the first and second resonances of the antenna. However, these frequency shifts depend not only on the values of the capacitors, but also on their locations along the slot antenna. Here, it is shown that by choosing the locations of the varactors appropriately, it is possible to obtain a dual-band antenna whose first and second resonant frequencies can be controlled individually. In other words, the frequency of either the first or the second band can be fixed, while the other one is electronically tuned. Using such a design, an electronically tunable dual-band antenna is designed and fabricated using two identical varactors having a capacitance range of 0.5-2.25 pF. The antenna is shown to have a frequency ratio (f/sub R/=f/sub 2//f/sub 1/) ranging from 1.3 to 2.67. An important feature of this antenna is its consistent radiation pattern, polarization, and polarization purity at both bands and across its entire tunable frequency range.


title: A quantum particle swarm optimization

abstract: The particle swarm optimization algorithm is a new methodology in evolutionary computation. It has been found to be extremely effective is solving a wide range of engineering problems, however, it is of low efficiency in dealing with the discrete problems. In this paper, a new discrete particle swarm optimization algorithm based on quantum individual is proposed. It is simpler and more powerful than the algorithms available. The simulation experiments and its application in the CDMA also prove its high efficiency.


title: Conceptual modeling: Definition, purpose and benefits

abstract: Over the last decade there has been a growing interest in ‘conceptual modeling’ for simulation. This is signified by a greater intensity of research and volume of papers on the topic. What is becoming apparent, however, is that when it comes to conceptual modeling there are quite different views and opinions. These differences may be beneficial for creating a debate that takes the field forward, but they can also lead to confusion. The purpose of this panel is for leading researchers to identify and discuss their views on conceptual modeling. In particular we will debate the definition, purpose and benefits of conceptual modeling for the field of simulation. Through the discussion we hope to highlight common ground and key areas of difference.


title: The user as a sensor: navigating users with visual impairments in indoor spaces using tactile landmarks

abstract: Indoor navigation systems for users who are visually impaired typically rely upon expensive physical augmentation of the environment or expensive sensing equipment; consequently few systems have been implemented. We present an indoor navigation system called Navatar that allows for localization and navigation by exploiting the physical characteristics of indoor environments, taking advantage of the unique sensing abilities of users with visual impairments, and minimalistic sensing achievable with low cost accelerometers available in smartphones. Particle filters are used to estimate the user's location based on the accelerometer data as well as the user confirming the presence of anticipated tactile landmarks along the provided path. Navatar has a high possibility of large-scale deployment, as it only requires an annotated virtual representation of an indoor environment. A user study with six blind users determines the accuracy of the approach, collects qualitative experiences and identifies areas for improvement.


title: Cell Energy Balanced Uneven Clustering Hierarchy Scheme for Wireless Sensor Networks

abstract: Employing clustering techniques in routing protocols can improve the scalability of wireless sensor networks.To solve the "hotspot" problem that is caused by the cluster heads close to the sink,which are burdened with heavy relay traffic via multi-hop communication and tend to die earlier and that leads to the network being partitioned,a novel cell energy balanced uneven clustering hierarchy scheme(CEB-UC) is presented.In CEBUC,the entire network has been divided into some cells reasonably such that the number of clusters in each cell is different,and that the number of nodes in each cluster is different.The clusters closer to the sink node have fewer nodes than those farther away from the sink node.Thus these cluster heads can preserve some energy for inter-cluster data relaying.Compared with some classical clustering protocols,such as HEED,LEACH,PEGASIS,EAR-ACM,simulation results show that CEB-UC outperforms significantly in optimizing the cluster heads' energy consumption,balancing the nodes' energy consumption and enlarging the diameter of the entire network.When different data aggregation strategies are employed by the sensor nodes,the cluster heads'energy consumption is reduced by 17%-41%,and the network lifetime is prolonged by 24%-53%.


title: Learning Equivalence Classes of Bayesian Networks Structures

abstract: Approaches to learning Bayesian networks from data typically combine a scoring function with a heuristic search procedure. Given a Bayesian network structure, many of the scoring functions derived in the literature return a score for the entire equivalence class to which the structure belongs. When using such a scoring function, it is appropriate for the heuristic search algorithm to search over equivalence classes of Bayesian networks as opposed to individual structures. We present the general formulation of a search space for which the states of the search correspond to equivalence classes of structures. Using this space, any one of a number of heuristic search algorithms can easily be applied. We compare greedy search performance in the proposed search space to greedy search performance in a search space for which the states correspond to individual Bayesian network structures.


title: Effort increases sensitivity to reward and loss magnitude in the human brain

abstract: It is ecologically adaptive that the amount of effort invested to achieve a reward increases the relevance of the resulting outcome. Here, we investigated the effect of effort on activity in reward and loss processing brain areas by using functional magnetic resonance imaging. In total, 28 subjects were endowed with monetary rewards of randomly varying magnitude after performing arithmetic calculations that were either difficult (high effort), easy (low effort) or already solved (no effort). Subsequently, a forced donation took place, where a varying part of the endowment was transferred to a charity organization, causing a loss for the subject. Results show that reward magnitude positively modulates activity in reward-processing brain areas (subgenual anterior cingulate cortex and nucleus accumbens) only in the high effort condition. Furthermore, anterior insular activity was positively modulated by loss magnitude only after high effort. The results strongly suggest an increasing relevance of outcomes with increasing previous effort.


title: Tangible Interaction in Mixed Reality Systems

abstract: In this chapter, we discuss the design of tangible interaction techniques for mixed reality environments. We begin by recalling some conceptual models of tangible interaction. Then, we propose an engineering-oriented software/hardware co-design process, based on our experience in developing tangible user interfaces. We present three different tangible user interfaces for real-world applications and analyze the feedback from the user studies that we conducted. In summary, we conclude that since tangible user interfaces are part of the real world and provide a seamless interaction with virtual words, they are well adapted to mix together reality and virtuality. Hence, tangible interaction optimizes users’ virtual tasks, especially in manipulating and controlling 3D digital data in 3D space.


title: Edge Detection Techniques - An Overview

abstract: In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. This information is very useful for applications in 3D reconstruction, motion, recognition, image enhancement and restoration, image registration, image compression, and so on. Usually, edge detection requires smoothing and differentiation of the image. Differentiation is an ill-conditioned problem and smoothing results in a loss of information. It is difficult to design a general edge detection algorithm which performs well in many contexts and captures the requirements of subsequent processing stages. Consequently, over the history of digital image processing a variety of edge detectors have been devised which differ in their mathematical and algorithmic properties. This paper is an account of the current state of our understanding of edge detection. We propose an overview of research in edge detection: edge definition, properties of detectors, the methodology of edge detection, the mutual influence between edges and detectors, and existing edge detectors and their implementation.


title: What Makes for Effective Detection Proposals?

abstract: Current top performing object detectors employ detection proposals to guide the search for objects, thereby avoiding exhaustive sliding window search across images. Despite the popularity and widespread use of detection proposals, it is unclear which trade-offs are made when using them during object detection. We provide an in-depth analysis of twelve proposal methods along with four baselines regarding proposal repeatability, ground truth annotation recall on PASCAL, ImageNet, and MS COCO, and their impact on DPM, R-CNN, and Fast R-CNN detection performance. Our analysis shows that for object detection improving proposal localisation accuracy is as important as improving recall. We introduce a novel metric, the average recall (AR), which rewards both high recall and good localisation and correlates surprisingly well with detection performance. Our findings show common strengths and weaknesses of existing methods, and provide insights and metrics for selecting and tuning proposal methods.


title: SymCrash: selective recording for reproducing crashes

abstract: Software often crashes despite tremendous effort on software quality assurance. Once developers receive a crash report, they need to reproduce the crash in order to understand the problem and locate the fault. However, limited information from crash reports often makes crash reproduction difficult. Many "capture-and-replay" techniques have been proposed to automatically capture program execution data from the failing code, and help developers replay the crash scenarios based on the captured data. However, such techniques often suffer from heavy overhead and introduce privacy concerns. Recently, methods such as BugRedux were proposed to generate test input that leads to crash through symbolic execution. However, such methods have inherent limitations because they rely on conventional symbolic execution techniques. In this paper, we propose a dynamic symbolic execution method called SymCon, which addresses the limitation of conventional symbolic execution by selecting functions that are hard to be resolved by a constraint solver and using their concrete runtime values to replace the symbols. We then propose SymCrash, a selective recording approach that only instruments and monitors the hard-to-solve functions. SymCrash can generate test input for crashes through SymCon. We have applied our approach to successfully reproduce 13 failures of 6 real-world programs. Our results confirm that the proposed approach is suitable for reproducing crashes, in terms of effectiveness, overhead, and privacy. It also outperforms the related methods.


title: Video inpainting of occluding and occluded objects

abstract: We present a basic technique to fill-in missing parts of a video sequence taken from a static camera. Two important cases are considered. The first case is concerned with the removal of non-stationary objects that occlude stationary background. We use a priority based spatio-temporal synthesis scheme for inpainting the stationary background. The second and more difficult case involves filling-in moving objects when they are partially occluded. For this, we propose a priority scheme to first inpaint the occluded moving objects and then fill-in the remaining area with stationary background using the method proposed for the first case. We use as input an optical-flow based mask, which tells if an undamaged pixel is moving or is stationary. The moving object is inpainted by copying patches from undamaged frames, and this copying is independent of the background of the moving object in either frame. This work has applications in a variety of different areas, including video special effects and restoration and enhancement of damaged videos. The examples shown in the paper illustrate these ideas.


title: Computational Poetry Workshop: Making Sense of Work in Progress

abstract: Creativity cannot exist in a vacuum; it develops through feedback, learning, reflection and social interaction with others. However, this perspective has been relatively under-investigated in computational creativity research, which typically examines systems that operate individually. We develop a thought experiment showing how structured dialogues can help develop the creative aspects of computer poetry. Centrally in this approach, we ask questions of a poem, inviting it to tell us in what way it may be considered a “creative making.”


title: Five-step FFT algorithm with reduced computational complexity

abstract: We propose a fast Fourier transform algorithm, which removes two steps of twiddle factor multiplications from the conventional five-step FFT algorithm. The proposed FFT algorithm not only reduces the computational complexity of the five-step FFT algorithm by O(n) operations, but also reduces its memory requirement.


title: Antenna-in-package design and module integration for millimeter-wave communication and 5G

abstract: Co-design and integration of RFIC, package, and antennas are critical to enable 5G wireless communications and is particularly challenging at mmWave frequencies. This paper reviews two different aspects of mmWave antenna module packaging and integration for base station and user equipment applications, respectively. We first present the challenges, implementation, and characterization of a 28-GHz phased-array module with 64 dual polarized antennas for 5G base station applications. Next, we describe a compact, low-power, 60-GHz switched-beam transceiver module suitable for handset integration incorporating 4 antennas that supports both normal and end-fire directions for a wide link spatial coverage.


title: Adversarial Attack on Graph Structured Data

abstract: Deep learning on graph structures has shown exciting results in various applications. However, few attentions have been paid to the robustness of such models, in contrast to numerous research work for image or text adversarial attack and defense. In this paper, we focus on the adversarial attacks that fool the model by modifying the combinatorial structure of data. We first propose a reinforcement learning based attack method that learns the generalizable attack policy, while only requiring prediction labels from the target classifier. Also, variants of genetic algorithms and gradient methods are presented in the scenario where prediction confidence or gradients are available. We use both synthetic and real-world data to show that, a family of Graph Neural Network models are vulnerable to these attacks, in both graph-level and node-level classification tasks. We also show such attacks can be used to diagnose the learned classifiers.


title: Algorithms for Hyper-Parameter Optimization

abstract: Several recent advances to the state of the art in image classification benchmarks have come from better configurations of existing techniques rather than novel approaches to feature learning. Traditionally, hyper-parameter optimization has been the job of humans because they can be very efficient in regimes where only a few trials are possible. Presently, computer clusters and GPU processors make it possible to run more trials and we show that algorithmic approaches can find better results. We present hyper-parameter optimization results on tasks of training neural networks and deep belief networks (DBNs). We optimize hyper-parameters using random search and two new greedy sequential methods based on the expected improvement criterion. Random search has been shown to be sufficiently efficient for learning neural networks for several datasets, but we show it is unreliable for training DBNs. The sequential algorithms are applied to the most difficult DBN learning problems from [1] and find significantly better results than the best previously reported. This work contributes novel techniques for making response surface models P(y|x) in which many elements of hyper-parameter assignment (x) are known to be irrelevant given particular values of other elements.


title: Dynamic User Clustering and Power Allocation for Uplink and Downlink Non-Orthogonal Multiple Access (NOMA) Systems

abstract: In this paper, first we briefly describe the differences in the working principles of uplink and downlink NOMA transmissions. Then, for both uplink and downlink NOMA, we formulate a sum-throughput maximization problem in a cell such that the user clustering (i.e., grouping users into a single cluster or multiple clusters) and power allocations in NOMA cluster(s) can be optimized under transmission power constraints, minimum rate requirements of the users, and SIC constraints. Due to the combinatorial nature of the formulated mixed integer non-linear programming (MINLP) problem, we solve the problem in two steps, i.e., by first grouping users into clusters and then optimizing their respective power allocations. In particular, we propose a low-complexity sub-optimal user grouping scheme. The proposed scheme exploits the channel gain differences among users in a NOMA cluster and group them into a single cluster or multiple clusters in order to enhance the sum-throughput of the system. For a given set of NOMA clusters, we then derive the optimal power allocation policy that maximizes the sum throughput per NOMA cluster and in turn maximizes the overall system throughput. Using KKT optimality conditions, closed-form solutions for optimal power allocations are derived for any cluster size, considering both uplink and downlink NOMA systems. Numerical results compare the performance of NOMA over orthogonal multiple access (OMA) and illustrate the significance of NOMA in various network scenarios.


title: Unexpectedly High Frequency of Genital Involvement in Women with Clinical and Histological Features of Oral Lichen Planus

abstract: The main aims of this cross-sectional study were: (i) to assess the frequency of genital (vulval) lichen planus (VLP) and vulval lichen sclerosus (VLS) in women affected with oral lichen planus (OLP), regardless of the genital symptoms reported; and (ii) to verify whether any demographic, clinical, or histological features of OLP are associated with a higher risk of vulvo-vaginal involvement. Fifty-five women, presenting OLP, consecutively underwent gynaecological examination and, if they demonstrated positive clinical signs of VLP, underwent biopsy. After a drop-out of 14 subjects, 31/41 (75.6%) were found to have signs of genital involvement, of which 13/31 (44.0%) were asymptomatic. Following genital biopsy, 27/31 (87.1%) had histologically confirmed VLP or VLS. Following both univariate and multivariate statistical analyses, no significant association was found


title: A compact low-profile smartwatch antenna for wireless body local network application

abstract: A compact low-profile smartwatch antenna for wireless body area network (WBAN) application is introduced in this paper. Dual-band operating characteristic centered at 2.4GHz and 5.8GHz bands for ISM application could been realized by adopting feeding stub and coupling stub. Moreover, impedance matching and radiation efficiency are compared when the smartwatch antenna is placed in free space and mounted on wrist tissue model. The radiation efficiency is better than 35.0% in lower band and better than 38.5% in upper band when the smartwatch antenna is mounted on wrist tissue model with the distance of 6mm.


title: Mining Efficient Taxi Operation Strategies From Large Scale Geo-Location Data

abstract: Taxi drivers always look for strategies to locate passengers quickly and therefore increase their profit margin. In reality, the passenger seeking strategies are mostly empirical and substantially vary among taxi drivers. From the history taxi data, the top performing taxi drivers can earn 25% more than the ones with mediocre seeking strategy in the same period of time. A better strategy not only helps taxi drivers earn more with less effort, but also reduce fuel consumption and carbon emissions. It is interesting to examine the influential factors in passenger seeking strategies and find algorithms to guide taxi drivers to passenger hotspots with the right timing. With the abundant availability of history taxicab traces, the existing methods of doing taxi business have been radically changed. This paper focuses on the problem of mining efficient operation strategies from a large-scale history taxi traces collected over one year. Our approach presents generic insights into the dynamics of taxicab services with the objective of maximizing the profit margins for the concerned parties. We propose important metrics, such as trip frequency, hot spots, and taxi mileage, and provide valuable insights toward more efficient operation strategies. We analyze these metrics using techniques, such as Newton’s polynomial interpolation and Gamma distribution, to understand their dynamics. Our strategies use the real taxicab traces from the city of Changsha (P.R.China), may predict the taxi rides at different times by 90.68% per day, and increase the taxi drivers income levels up to 19.38% by controlling appropriate mileage per trip and following the route across more urban hot spots.


title: Verification for Machine Learning, Autonomy, and Neural Networks Survey

abstract: This survey presents an overview of verification techniques for autonomous systems, with a focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents thereof. Autonomy in CPS is enabling by recent advances in artificial intelligence (AI) and machine learning (ML) through approaches such as deep neural networks (DNNs), embedded in so-called learning enabled components (LECs) that accomplish tasks from classification to control. Recently, the formal methods and formal verification community has developed methods to characterize behaviors in these LECs with eventual goals of formally verifying specifications for LECs, and this article presents a survey of many of these recent approaches.


title: Point-to-point connectivity between neuromorphic chips using address-events

abstract: This paper discusses connectivity between neuromorphic chips, which use the timing of fixed-height fixed-width pulses to encode information. Address-events (log/sub 2/(N)-bit packets that uniquely identify one of N neurons) are used to transmit these pulses in real time on a random-access time-multiplexed communication channel. Activity is assumed to consist of neuronal ensembles-spikes clustered in space and in time. This paper quantifies tradeoffs faced in allocating bandwidth, granting access, and queuing, as well as throughput requirements, and concludes that an arbitered channel design is the best choice. The arbitered channel is implemented with a formal design methodology for asynchronous digital VLSI CMOS systems, after introducing the reader to this top-down synthesis technique. Following the evolution of three generations of designs, it is shown how the overhead of arbitrating, and encoding and decoding, can be reduced in area (from N to /spl radic/N) by organizing neurons into rows and columns, and reduced in time (from log/sub 2/(N) to 2) by exploiting locality in the arbiter tree and in the row-column architecture, and clustered activity. Throughput is boosted by pipelining and by reading spikes in parallel. Simple techniques that reduce crosstalk in these mixed analog-digital systems are described.


title: Scanpath comparison revisited

abstract: The scanpath comparison framework based on string editing is revisited. The previous method of clustering based on k-means "preevaluation" is replaced by the mean shift algorithm followed by elliptical modeling via Principal Components Analysis. Ellipse intersection determines cluster overlap, with fast nearest-neighbor search provided by the kd-tree. Subsequent construction of Y - matrices and parsing diagrams is fully automated, obviating prior interactive steps. Empirical validation is performed via analysis of eye movements collected during a variant of the Trail Making Test, where participants were asked to visually connect alphanumeric targets (letters and numbers). The observed repetitive position similarity index matches previously published results, providing ongoing support for the scanpath theory (at least in this situation). Task dependence of eye movements may be indicated by the global position index, which differs considerably from past results based on free viewing.


title: Logic design with unipolar memristors

abstract: Memristors are novel devices that could naturally be designed as memory elements. Recently, several methods of designing memristors for logic operations have been proposed. These methods, mostly, make use of bipolar memristors. In this paper, we propose a method for performing logic with unipolar memristors based on OR and NOT logic gates. An integration of the basic building blocks into more complicated logic functions is described and demonstrated. Our results indicate that any logic function could be performed using an external controller. Thus, adding the capability of performing logic computations to numerous types of unipolar memristive materials in addition to their memory capability.


title: Combining Raspberry Pi and Arduino to form a low-cost, real-time autonomous vehicle platform

abstract: This paper presents a novel, low-cost autonomous vehicle platform based on the combination of a Raspberry Pi mini-computer, an Arduino micro-controller, and a Zumo track-driven robot chassis. The Arduino allows the control law to be executed at hard real-time intervals while the Raspberry Pi provides additional computing power, a web interface, and wireless data-streaming for control tuning and debugging. The efficacy of the platform in controls education is assessed after using the platform for demonstration purposes in a first course on feedback control. The entire system including the Raspberry Pi and Arduino Uno costs roughly $215.


title: The intelligent fault diagnosis of wind turbine gearbox based on artificial neural network

abstract: The vibration test system for the gearbox of wind turbine , the wavelet denoising method , the artificial neural networkpsilas essential principles and its features, BP network structures model in the gearbox fault diagnosis are discussed. Tested vibration signals are disposed by the method of wavelet denoising and than as the inputs of BP neural network. By using classical BP neural network, four kinds of typical patterns of gearbox faults have been studied and diagnosed ,and satisfied results have been acquired. The research results indicate that BP neural network have the excellent abilities of parallel distributed processing, self-study, self-adaptation, self-organization, associative memory , and simultaneously its highly non-linear pattern recognition technology is an efficient and feasible tool to solve complicated state identification problems in the gearbox fault diagnosis.


title: Access lens: a gesture-based screen reader for real-world documents

abstract: Gesture-based touch screen user interfaces, when designed to be accessible to blind users, can be an effective mode of interaction for those users. However, current accessible touch screen interaction techniques suffer from one serious limitation: they are only usable on devices that have been explicitly designed to support them. Access Lens is a new interaction method that uses computer vision-based gesture tracking to enable blind people to use accessible gestures on paper documents and other physical objects, such as product packages, device screens, and home appliances. This paper describes the development of Access Lens hardware and software, the iterative design of Access Lens in collaboration with blind computer users, and opportunities for future development.


title: Müllerian Anomalies

abstract: The development of the Müllerian system and the female reproductive tract is a complex process involving an integrated series of events with significant potential for abnormal development and anomalies. Structural anomalies of the female reproductive tract may be isolated or occur in association with other congenital anomalies, including renal or bladder anomalies and anorectal malformations. Although rare in occurrence, it is important to be familiar with these conditions for appropriate diagnosis, management, and possible referral. The diagnosis, management, and surgical treatments of female reproductive tract anomalies in girls and young women have advanced with improvements in diagnostic imaging techniques, surgical and nonsurgical techniques and innovative instrumentation and developments.


title: Caring And Well-being : A Lifeworld Approach

abstract: Introduction: The Need for Humanised Care Part 1: Humanising Healthcare: A Lifeworld Approach 1. A Value Framework for the Humanisation of Care 2. A Lifeworld Approach: Revisiting a Humanising Philosophy that Provides an Experiential Context for Considering Health and Illness 3. Lifeworld-led Healthcare is More than Patient-led Care 4. Caring for a Partner with Alzheimer's: an Illustration of Research-based Knowledge for Lifeworld-led Care Part 2: Well-being and Suffering: the Focus of Care 5. An Existential Theory of Well-being: 'Dwelling-Mobility' 6. Kinds of Well-being: Eighteen Directions for Caring 7. Kinds of Suffering: Caring for Vulnerability 8. An Illustration of Well-being as Dwelling-Mobility: Older Peoples' Experiences of Living in Rural Areas Part 3: Developing the Capacity to Care 9. The Creativity of 'Unspecialisation': Contemplative Knowledge and Practical Wisdom 10. Complex Knowledge to Underpin Caring: Embodied Relational Understanding 11. Embodied Interpretation: One Way of Re-presenting Research Findings that may Serve to Sensitise the Empathic Imagination 12. Embodying Nursing Openheartedness: An Illustration of a Core Capacity for Caring 13. Conclusion: Caring for Well-being


title: Distributed Secondary Control for Islanded Microgrids—A Novel Approach

abstract: This paper presents a novel approach to conceive the secondary control in droop-controlled microgrids (MGs). The conventional approach is based on restoring the frequency and amplitude deviations produced by the local droop controllers by using an MG central controller (MGCC). A distributed networked control system is used in order to implement a distributed secondary control (DSC), thus avoiding its implementation in MGCC. The proposed approach is not only able to restore frequency and voltage of the MG but also ensures reactive power sharing. The distributed secondary control does not rely on a central control, so that the failure of a single unit will not produce the fail down of the whole system. Experimental results are presented to show the feasibility of the DSC. The time latency and data drop-out limits of the communication systems are studied as well.


title: Inflammatory granuloma caused by injectable soft tissue filler (Artecoll)

abstract: Artecoll (Artes Medical Inc., San Diego, CA, USA) has recently been developed as a permanent synthetic cosmetic filler. We experienced an inflammatory granuloma resulting from a previous injection of Artecoll at the upper lip, which was regarded as a rare side effect of this filler. A 50-year-old female patient complained of swelling, dull pain, and heat in the right upper nasolabial fold area, which had started one week before her visit to Kyungpook National University Hospital. The patient received topical steroid therapy at a local clinic, which was not effective. At the injection site, a hard nodule was palpated and erythema was observed with mild tenderness. Antibiotic treatment and subsequent incision and drainage did not result in complete cure of the facial swelling, and the facial swelling and pain persisted. Computed tomography showed a lesion approximately 1-cm in size without clear boundaries and relatively increased nodular thickening. Finally, a subdermal lesion was removed via an intraoral vestibular approach. The lesion was diagnosed as inflammatory granuloma by a permanent biopsy. The patient had healed at two months after the filler injection. Although the soft tissue filler is widely used for cosmetic purposes, there is potential for complication, such as the inflammatory granuloma should be considered before treatment.


title: What Enterprise Architecture and Enterprise Systems Usage Can and Can not Tell about Each Other

abstract: There is an increased awareness of the roles that enterprise architecture (EA) and enterprise systems (ES) play in today's organizations. EA and ES usage maturity models are used to assess how well companies are capable of deploying these two concepts while striving to achieve strategic corporate goals. The existence of various architecture and ES usage models raises questions about how they both refer to each other, e.g. if a higher level of architecture maturity implies a higher ES usage level. This paper compares these two types of models by using literature survey results and case-study experiences. We conclude that (i) EA and ES usage maturity models agree on a number of critical success factors and (ii) in a company with a mature architecture function, one is likely to observe, at the early stages of ES initiatives, certain practices associated with a higher level of ES usage maturity.


title: Lambda-Policy Iteration: A Review and a New Implementation

abstract: In this paper we discuss $\l$-policy iteration, a method for exact and approximate dynamic programming. It is intermediate between the classical value iteration (VI) and policy iteration (PI) methods, and it is closely related to optimistic (also known as modified) PI, whereby each policy evaluation is done approximately, using a finite number of VI. We review the theory of the method and associated questions of bias and exploration arising in simulation-based cost function approximation. We then discuss various implementations, which offer advantages over well-established PI methods that use LSPE($\l$), LSTD($\l$), or TD($\l$) for policy evaluation with cost function approximation. One of these implementations is based on a new simulation scheme, called geometric sampling, which uses multiple short trajectories rather than a single infinitely long trajectory.


title: Solving geometric optimization problems using graphics hardware

abstract: We show how to use graphics hardware for tackling optimization problems arising in the field of computationalgeometry. We exemplarily discuss three problems, where combinatorial algorithms are inefficient or hard to implement.Given a set S of n point in the plane, the first two problems are to determine the smallest homotheticscaling of a star shaped polygon P enclosing S and to find the largest empty homothetic scaling of P completelycontained inside an arbitrary polygonal region. Pixel-exact solutions for both problems are computed in real-time.The third problem is a facility location problem and more difficult to solve. Given the Voronoi diagram VoD(S) ofthe n points, we try to position another point p in the plane, such that the resulting Voronoi region of p has maximalarea. As far as we know there exists no traditional solution for this problem for which we present pixel-exactsolutions. ::: ::: ::: ::: Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Geometric algorithms,languages, and systems I.3.3 [Computer Graphics]: Display algorithms


title: Algorithms and applications for answering ranked queries using ranked views

abstract: Ranked queries return the top objects of a database according to a preference function. We present and evaluate (experimentally and theoretically) a core algorithm that answers ranked queries in an efficient pipelined manner using materialized ranked views. We use and extend the core algorithm in the described PREFER and MERGE systems. PREFER precomputes a set of materialized views that provide guaranteed query performance. We present an algorithm that selects a near optimal set of views under space constraints. We also describe multiple optimizations and implementation aspects of the downloadable version of PREFER. Then we discuss MERGE, which operates at a metabroker and answers ranked queries by retrieving a minimal number of objects from sources that offer ranked queries. A speculative version of the pipelining algorithm is described.


title: Automated support for classifying software failure reports

abstract: This paper proposes automated support for classifying reported software failures in order to facilitate prioritizing them and diagnosing their causes. A classification strategy is presented that involves the use of supervised and unsupervised pattern classification and multivariate visualization. These techniques are applied to profiles of failed executions in order to group together failures with the same or similar causes. The resulting classification is then used to assess the frequency and severity of failures caused by particular defects and to help diagnose those defects. The results of applying the proposed classification strategy to failures of three large subject programs are reported. These results indicate that the strategy can be effective.


title: Bilingualism provides a neural reserve for aging populations

abstract: Abstract It has been postulated that bilingualism may act as a cognitive reserve and recent behavioral evidence shows that bilinguals are diagnosed with dementia about 4–5 years later compared to monolinguals. In the present study, we investigated the neural basis of these putative protective effects in a group of aging bilinguals as compared to a matched monolingual control group. For this purpose, participants completed the Erikson Flanker task and their performance was correlated to gray matter (GM) volume in order to investigate if cognitive performance predicts GM volume specifically in areas affected by aging. We performed an ex-Gaussian analysis on the resulting RTs and report that aging bilinguals performed better than aging monolinguals on the Flanker task. Bilingualism was overall associated with increased GM in the ACC. Likewise, aging induced effects upon performance correlated only for monolinguals to decreased gray matter in the DLPFC. Taken together, these neural regions might underlie the benefits of bilingualism and act as a neural reserve that protects against the cognitive decline that occurs during aging.


title: Feature Learning based Deep Supervised Hashing with Pairwise Labels

abstract: Recent years have witnessed wide application of hashing for large-scale image retrieval. However, most existing hashing methods are based on handcrafted features which might not be optimally compatible with the hashing procedure. Recently, deep hashing methods have been proposed to perform simultaneous feature learning and hash-code learning with deep neural networks, which have shown better performance than traditional hashing methods with hand-crafted features. Most of these deep hashing methods are supervised whose supervised information is given with triplet labels. For another common application scenario with pairwise labels, there have not existed methods for simultaneous feature learning and hash-code learning. In this paper, we propose a novel deep hashing method, called deep pairwise-supervised hashing (DPSH), to perform simultaneous feature learning and hashcode learning for applications with pairwise labels. Experiments on real datasets show that our DPSH method can outperform other methods to achieve the state-of-the-art performance in image retrieval applications.


title: Gender Prediction in Social Media

abstract: In this paper, we explore the task of gender classification using limited network data with an application to Fotolog. We take a heuristic approach to automating gender inference based on username, followers and network structure. We test our approach on a subset of 100,000 nodes and analyze our results to find that there is a lot of value in these limited information and that there is great promise in further pursuing this approach to classification.


title: Sentiment analysis on Twitter Data-set using Naive Bayes algorithm

abstract: In the last few years, use of social networking sites has been increased tremendously. Nowadays, social networking sites generate a large amount of data. Millions of people conveniently express their views and opinions on a wide array of topics via microblogging websites. In this paper, we will discuss the extraction of sentiment from a famous microblogging website, Twitter where the user posts their views and opinion. We have done sentiment analysis on tweets which help to provide some prediction on business intelligence. We use Hadoop Framework for processing movie data set that is available on the twitter website in the form of reviews, feedback, and comments. Results of sentiment analysis on twitter data will be displayed as different sections presenting positive, negative and neutral sentiments.


title: Meta-learning autoencoders for few-shot prediction

abstract: Compared to humans, machine learning models generally require significantly more training examples and fail to extrapolate from experience to solve previously unseen challenges. To help close this performance gap, we augment single-task neural networks with a meta-recognition model which learns a succinct model code via its autoencoder structure, using just a few informative examples. The model code is then employed by a meta-generative model to construct parameters for the task-specific model. We demonstrate that for previously unseen tasks, without additional training, this Meta-Learning Autoencoder (MeLA) framework can build models that closely match the true underlying models, with loss significantly lower than given by fine-tuned baseline networks, and performance that compares favorably with state-of-the-art meta-learning algorithms. MeLA also adds the ability to identify influential training examples and predict which additional data will be most valuable to acquire to improve model prediction.


title: Oscillometric Blood Pressure Estimation: Past, Present, and Future

abstract: The use of automated blood pressure (BP) monitoring is growing as it does not require much expertise and can be performed by patients several times a day at home. Oscillometry is one of the most common measurement methods used in automated BP monitors. A review of the literature shows that a large variety of oscillometric algorithms have been developed for accurate estimation of BP but these algorithms are scattered in many different publications or patents. Moreover, considering that oscillometric devices dominate the home BP monitoring market, little effort has been made to survey the underlying algorithms that are used to estimate BP. In this review, a comprehensive survey of the existing oscillometric BP estimation algorithms is presented. The survey covers a broad spectrum of algorithms including the conventional maximum amplitude and derivative oscillometry as well as the recently proposed learning algorithms, model-based algorithms, and algorithms that are based on analysis of pulse morphology and pulse transit time. The aim is to classify the diverse underlying algorithms, describe each algorithm briefly, and discuss their advantages and disadvantages. This paper will also review the artifact removal techniques in oscillometry and the current standards for the automated BP monitors.


title: Adaptive Coding and Packet Rates for TCP-Friendly VoIP Flows

abstract: If low-rate VoIP is transmitted over congested links, both the coding and the packet rate need to be adapted to achieve the best conversational quality: On an Ethernet network it is better to jointly change packet rate and speech coding rate; in WLAN networks it is sufficient to change the packet rate. We also study whether TCP-friendly rate control algorithms are suitable for dynamically adapting packet and coding rate of a VoIP flow. Our results show that cannot be used for VoIP applications as they only control the packet rate do not consider packet sizes. Also, they do not transmit small (speech) packets fairly. We present an enhanced version of the TCP friendly rate control (TFRC) protocol that transmits small packets fairly and evaluate it with network simulations.


title: Individual Differences in Visual Working Memory Capacity: Contributions of Attentional Control to Storage

abstract: Visual working memory (VWM) allows us to actively represent a limited amount of visual information. This capacity limitation is known to vary considerably across individuals. Traditionally, this difference has been interpreted as differences in the size of the available mental storage space. Alternatively, several studies have suggested that capacity strongly covaries with individuals' ability to regulate what gets into the VWM store. In this chapter, we report the findings of three experiments that directly examined the nature of the individual differences in a standard VWM capacity estimate. We found that the capacity estimate is heavily influenced by individuals' ability to attentionally regulate the competition induced by excessive number of memory items. This finding provides not only a clear explanation for a robust relationship between VWM capacity and attentional control measures, but also a new perspective to refine our understanding of the variability of VWM capacity.


Experiments on Different Recurrent Neural Networks for English-Hindi Machine Translation


title: Grounding spatial relations for human-robot interaction

abstract: We propose a system for human-robot interaction that learns both models for spatial prepositions and for object recognition. Our system grounds the meaning of an input sentence in terms of visual percepts coming from the robot's sensors in order to send an appropriate command to the PR2 or respond to spatial queries. To perform this grounding, the system recognizes the objects in the scene, determines which spatial relations hold between those objects, and semantically parses the input sentence. The proposed system uses the visual and spatial information in conjunction with the semantic parse to interpret statements that refer to objects (nouns), their spatial relationships (prepositions), and to execute commands (actions). The semantic parse is inherently compositional, allowing the robot to understand complex commands that refer to multiple objects and relations such as: “Move the cup close to the robot to the area in front of the plate and behind the tea box”. Our system correctly parses 94% of the 210 online test sentences, correctly interprets 91% of the correctly parsed sentences, and correctly executes 89% of the correctly interpreted sentences.


title: Code Coverage Measurement for Android Dynamic Analysis Tools

abstract: It is common to inspect an Android application using static or dynamic analysis techniques. Most traditional tools adopt static analysis techniques due to its low cost and high performance properties. However, since an inspected target could be obfuscated, it is also common to work with dynamic analysis techniques so that complete runtime information can be obtained to provide in-depth application behavior. Although there are already a lot of tools based on dynamic analysis techniques, the capability of such a tool is unknown. It is straightforward to understand the capability of a dynamic analysis tool by measuring its code coverage. However, to our knowledge, there is not a universal approach for measuring code coverage for all dynamic analysis tools, especially when a tool is only accessible remotely. In this paper, we propose an approach to measure code coverage for dynamic analysis tools. We design and implement the approach to measure code coverage for both online and off-line dynamic analysis tools. We then pick online tools including ABM, Anubis, Copper Droid, Tracedroid, as well as off-line tools including standard Android emulator, Droid Box, and Droid Scope. Our measurement results show that the average coverage rate for each tool lies between 20% and 60%.We believe that our approach can provide more information for researchers and developers to better understand and improve the capability of dynamic analysis techniques.


title: Event-based text mining for biology and functional genomics

abstract: The assessment of genome function requires a mapping between genome-derived entities and biochemical reactions, and the biomedical literature represents a rich source of information about reactions between biological components. However, the increasingly rapid growth in the volume of literature provides both a challenge and an opportunity for researchers to isolate information about reactions of interest in a timely and efficient manner. In response, recent text mining research in the biology domain has been largely focused on the identification and extraction of ‘events’, i.e. categorised, structured representations of relationships between biochemical entities, from the literature. Functional genomics analyses necessarily encompass events as so defined. Automatic event extraction systems facilitate the development of sophisticated semantic search applications, allowing researchers to formulate structured queries over extracted events, so as to specify the exact types of reactions to be retrieved. This article provides an overview of recent research into event extraction. We cover annotated corpora on which systems are trained, systems that achieve state-of-the-art performance and details of the community shared tasks that have been instrumental in increasing the quality, coverage and scalability of recent systems. Finally, several concrete applications of event extraction are covered, together with emerging directions of research.


title: 3D modelling of leaves from color and ToF data for robotized plant measuring

abstract: Supervision of long-lasting extensive botanic experiments is a promising robotic application that some recent technological advances have made feasible. Plant modelling for this application has strong demands, particularly in what concerns 3D information gathering and speed. This paper shows that Time-of-Flight (ToF) cameras achieve a good compromise between both demands, providing a suitable complement to color vision. A new method is proposed to segment plant images into their composite surface patches by combining hierarchical color segmentation with quadratic surface fitting using ToF depth data. Experimentation shows that the interpolated depth maps derived from the obtained surfaces fit well the original scenes. Moreover, candidate leaves to be approached by a measuring instrument are ranked, and then robot-mounted cameras move closer to them to validate their suitability to being sampled. Some ambiguities arising from leaves overlap or occlusions are cleared up in this way. The work is a proof-of-concept that dense color data combined with sparse depth as provided by a ToF camera yields a good enough 3D approximation for automated plant measuring at the high throughput imposed by the application.


title: Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification

abstract: We propose an end-to-end framework for the dense, pixelwise classification of satellite imagery with convolutional neural networks (CNNs). In our framework, CNNs are directly trained to produce classification maps out of the input images. We first devise a fully convolutional architecture and demonstrate its relevance to the dense classification problem. We then address the issue of imperfect training data through a two-step training approach: CNNs are first initialized by using a large amount of possibly inaccurate reference data, and then refined on a small amount of accurately labeled data. To complete our framework, we design a multiscale neuron module that alleviates the common tradeoff between recognition and precise localization. A series of experiments show that our networks consider a large amount of context to provide fine-grained classification maps.


Pay Attention to the Ending:Strong Neural Baselines for the ROC Story Cloze Task