Civitas: The Smart City Middleware, from Sensors to Big Data

Software development for smart cities bring into light new concerns, such as how to deal with scalability, heterogeneity (sensors, actuators, high performance computing devices, etc.), geolocation information or privacy issues, among some. Traditional approaches to distributed systems fail to address these challenges, because they were mainly devoted to enterprise IT environments. This paper proposes a middleware framework, called Civitas, specially devoted to support the task of service development for the Smart City paradigm. This paper also analyzes the main drawbacks of traditional approaches to middleware service development and how these are overcome in the middleware framework proposed here.


On the exploitation of a high-throughput SHA-256 FPGA design for HMAC

High-throughput and area-efficient designs of hash functions and corresponding mechanisms for Message Authentication Codes (MACs) are in high demand due to new security protocols that have arisen and call for security services in every transmitted data packet. For instance, IPv6 incorporates the IPSec protocol for secure data transmission. However, the IPSec's performance bottleneck is the HMAC mechanism which is responsible for authenticating the transmitted data. HMAC's performance bottleneck in its turn is the underlying hash function. In this article a high-throughput and small-size SHA-256 hash function FPGA design and the corresponding HMAC FPGA design is presented. Advanced optimization techniques have been deployed leading to a SHA-256 hashing core which performs more than 30% better, compared to the next better design. This improvement is achieved both in terms of throughput as well as in terms of throughput/area cost factor. It is the first reported SHA-256 hashing core that exceeds 11Gbps (after place and route in Xilinx Virtex 6 board).


Service Migration Patterns -- Decision Support and Best Practices for the Migration of Existing Service-Based Applications to Cloud Environments

In many ways cloud computing is an extension of the service-oriented computing (SOC) approach to create resilient and elastic hosting environments and applications. Service-oriented Architectures (SOA), thus, share many architectural properties with cloud environments and cloud applications, such as the distribution of application functionality among multiple application components (services) and their loosely coupled integration to form a distributed application. Existing service-based applications are, therefore, ideal candidates to be moved to cloud environments in order to benefit from the cloud properties, such as elasticity or pay-per-use pricing models. In order for such an application migration and the overall restructuring of an IT application landscape to be successful, decisions have to be made regarding (i) the portion of the application stack to be migrated and (ii) the process to follow during the migration in order to guarantee an acceptable service level to application users. In this paper, we present best practices how we addressed these challenges in form of service migration patterns as well as a methodology how these patterns should be applied during the migration of a service-based application or multiples thereof. Also, we present an implementation of the approach, which has been used to migrate a web-application stack from Amazon Web Services to the T-Systems cloud offering Dynamic Services for Infrastructure (DSI).


Comparison of optical see-through head-mounted displays for surgical interventions with object-anchored 2D-display

Optical see-through head-mounted displays (OST-HMD) feature an unhindered and instantaneous view of the surgery site and can enable a mixed reality experience for surgeons during procedures. In this paper, we present a systematic approach to identify the criteria for evaluation of OST-HMD technologies for specific clinical scenarios, which benefit from using an object-anchored 2D-display visualizing medical information. Criteria for evaluating the performance of OST-HMDs for visualization of medical information and its usage are identified and proposed. These include text readability, contrast perception, task load, frame rate, and system lag. We choose to compare three commercially available OST-HMDs, which are representatives of currently available head-mounted display technologies. A multi-user study and an offline experiment are conducted to evaluate their performance. Statistical analysis demonstrates that Microsoft HoloLens performs best among the three tested OST-HMDs, in terms of contrast perception, task load, and frame rate, while ODG R-7 offers similar text readability. The integration of indoor localization and fiducial tracking on the HoloLens provides significantly less system lag in a relatively motionless scenario. With ever more OST-HMDs appearing on the market, the proposed criteria could be used in the evaluation of their suitability for mixed reality surgical intervention. Currently, Microsoft HoloLens may be more suitable than ODG R-7 and Epson Moverio BT-200 for clinical usability in terms of the evaluated criteria. To the best of our knowledge, this is the first paper that presents a methodology and conducts experiments to evaluate and compare OST-HMDs for their use as object-anchored 2D-display during interventions.


Extracting and Retargeting Color Mappings from Bitmap Images of Visualizations

Visualization designers regularly use color to encode quantitative or categorical data. However, visualizations “in the wild” often violate perceptual color design principles and may only be available as bitmap images. In this work, we contribute a method to semi-automatically extract color encodings from a bitmap visualization image. Given an image and a legend location, we classify the legend as describing either a discrete or continuous color encoding, identify the colors used, and extract legend text using OCR methods. We then combine this information to recover the specific color mapping. Users can also correct interpretation errors using an annotation interface. We evaluate our techniques using a corpus of images extracted from scientific papers and demonstrate accurate automatic inference of color mappings across a variety of chart types. In addition, we present two applications of our method: automatic recoloring to improve perceptual effectiveness, and interactive overlays to enable improved reading of static visualizations.


A BIOMECHANICAL ANALYSIS OF FRONT VERSUS BACK SQUAT: INJURY IMPLICATIONS

The aim of this study was to examine the differences in trunk and lower limb kinematics between the front and back squat. 2D kinematic data was collected as participants (n = 12) completed three repetitions of both front and back squat exercises at 50 % of their back squat one repetition maximum. Stance width was standardised at 107(±10) % of biacromial breadth. The Wilcoxon signed ranks test was used to examine significant differences in dependent variables between both techniques. Results showed that the back squat exhibited a significantly greater trunk lean than the front squat throughout (p < 0.05) with no differences occurring in knee joint kinematics. The results of this study in conjunction with other squat related literature (Russell et al., 1989) suggest that the back squat gives rise to an increased risk of lower back injury.


A content-driven reputation system for the wikipedia

We present a content-driven reputation system for Wikipedia authors. In our system, authors gain reputation when the edits they perform to Wikipedia articles are preserved by subsequent authors, and they lose reputation when their edits are rolled back or undone in short order. Thus, author reputation is computed solely on the basis of content evolution; user-to-user comments or ratings are not used. The author reputation we compute could be used to flag new contributions from low-reputation authors, or it could be used to allow only authors with high reputation to contribute to controversialor critical pages. A reputation system for the Wikipedia could also provide an incentive for high-quality contributions. We have implemented the proposed system, and we have used it to analyze the entire Italian and French Wikipedias, consisting of a total of 691, 551 pages and 5, 587, 523 revisions. Our results show that our notion of reputation has good predictive value: changes performed by low-reputation authors have a significantly larger than average probability of having poor quality, as judged by human observers, and of being later undone, as measured by our algorithms.


Energy Management Fuzzy Logic Supervisory for Electric Vehicle Power Supplies System

This paper introduces an energy management strategy based on fuzzy logic supervisory for road electric vehicle, combining a fuel cell power source and two energy storage devices, i.e., batteries and ultracapacitors. The control strategy is designed to achieve the high-efficiency operation region of the individual power source and to regulate current and voltage at peak and average power demand, without compromising the performance and efficiency of the overall system. A multiple-input power electronic converter makes the interface among generator, energy storage devices, and the voltage dc-link bus. Classical regulators implement the control loops of each input of the converter. The supervisory system coordinates the power flows among the power sources and the load. The paper is mainly focused on the fuzzy logic supervisory for energy management of a specific power electronic converter control algorithm. Nevertheless, the proposed system can be easily adapted to other converters arrangements or to distributed generation applications. Simulation and experimental results on a 3-kW prototype prove that the fuzzy logic is a suitable energy management control strategy.


Byzantine Agreement , Made Trivial Silvio Micali

We present a very simple, cryptographic, Byzantine-agreement protocol that, with n = 3t + 1 players, t of which are malicious, halts in expected 9 rounds.


A wireless electrocardiogram detection for personal health monitoring

The current paper presents low-power analog integrated circuits (ICs) for wireless electrocardiogram (ECG) detection in personal health monitoring. Considering the power-efficient communication in the body sensor network (BSN), the required low-power analog ICs are developed for a healthcare system through miniaturization and system integration. The proposed system comprises the design and implementation with three subsystems, namely, (1) the ECG acquisition node, (2) the protocol for standard IEEE 802.15.4 ZigBee system, and (3) the radio frequency (RF) transmitter circuits. A preamplifier, a low-pass filter, and a successive-approximation analog-to-digital converter (SA-ADC) are integrated to detect an ECG signal. For high integration, the ZigBee protocol is adopted for wireless communication. To transmit the ECG signal through wireless communication, a quadrature voltage-controlled oscillator and a 2.4 GHz low-IF transmitter with a power amplifier and up-conversion mixer are also developed. In the receiver, a 2.4 GHz fully integrated CMOS radio-frequency front-end with a low-noise amplifier, and a quadrature mixer is proposed. The low-power wireless bio-signal acquisition SoC (WBSA-SoC) has been implemented in TSMC 0.18-μm standard CMOS process. The measurement results on the human body reveal that the ECG signals can be acquired effectively by the proposed SoC.


16 Personalization in E-Commerce Applications

This chapter is about personalization and adaptation in electronic commerce (e-commerce) applications. In the first part, we briefly introduce the challenges posed by e-commerce and we discuss how personalization strategies can help companies to face such challenges. Then, we describe the aspects of personalization, taken as a general technique for the customization of services to the user, which have been successfully employed in e-commerce Web sites. To conclude, we present some emerging trends and and we discuss future perspectives.


Motivation and Personality : A Neuropsychological Perspective

Personality is strongly influenced by motivation systems that organise responses to rewards and punishments and that drive approach and avoidance behavior. Neuropsychological research has identified: (a) two avoidance systems, one related to pure active avoidance and escape, and one to passive avoidance and behavioral inhibition produced by goal-conflict; and (b) two approach systems, one related to the actions of reward seeking and one to experience and behavior related to pleasure on receiving reward. These systems mediate fluid moment-by-moment reactions to changing stimuli, with relatively stable person-specific sensitivities to these stimuli manifested in personality traits. We review what is known about these motivational traits, integrating the theory-driven approach based on animal learning paradigms with the empirical tradition of the Big Five personality model. People differ from one another, and this fact is obvious to everyone. It is common to talk about people’s personalities using lexical terms to describe their characteristic ways of thinking, feeling and behaving (e.g., ‘bold’, ‘lazy’, ‘intelligent’), and we use these descriptors to infer people’s intentions and likely future behavior. Personality psychologists have long analyzed the ratings of large numbers of trait descriptive adjectives to produce the most widely used taxonomy of personality: the Big Five, which includes the dimensions of Extraversion, Neuroticism, Conscientiousness, Agreeableness, and Openness to Experience ⁄ Intellect (John, Naumann, & Soto, 2008). These Big Five traits also emerge from existing personality questionnaires that were not designed specifically to measure them (e.g., Markon, Krueger, & Watson, 2005), suggesting it is a good candidate for a consensual model in personality psychology. What the Big Five model does not immediately offer, however, is an explanation for the causal sources of personality traits. Why do people think, feel, and act in the ways that they do? People react to situations, of course; but different people react differently to the same situation, suggesting that they have different behavioral propensities. In order to answer this why question, we must discover what drives people’s actions and reactions. Inferring motivation from observed personality has been something of a dark art in psychology. However, one promising approach to this question is based on the biology of motivational control systems, studied by psychologists for over a century in non-human animals, and for somewhat less time in humans. This approach operates on the premise that stable individual differences in behavior (personality traits) must be due to relatively stable individual differences in the operation of brain systems that produce (state) behavior from moment-tomoment. From this perspective, each of our many traits reflects the operations of a set of brain systems that has evolved to respond to a different class of functional requirements (Denissen & Penke, 2008; McNaughton, 1989; Nettle, 2006; Pickering & Gray, 1999). Social and Personality Psychology Compass 7/3 (2013): 158–175, 10.1111/spc3.12016 a 2013 Blackwell Publishing Ltd In what follows, we focus on those motivational processes and personality traits most closely aligned with biological research on reactions to reward and punishment and associated approach and avoidance behavior. This focus is warranted both by the importance of these phenomena for motivation and by the existence of extensive research on them. Our aim is to offer an introduction for researchers wishing to explore the role of motivation in personality from the perspective of these underlying psychobiological systems. Only after a description of what is known about the operation of these systems do we branch out to consider the personality traits associated with them. Our major assumption is that most fundamental personality traits have a motivational core; and we aim to show that the descriptive personality research tradition, which produced the Big Five, can be integrated with the experimental research tradition that has focused on the sensitivities of basic motivation systems. In this review, we focus on systems related to approach and avoidance primarily at the level of explanation that Gray (1975) labeled ‘the conceptual nervous system’, which is based on analysis of behavior as well as neurobiology and attempts to describe important psychological processes without specifying their exact instantiation in the nervous system – this approach has afforded a detailed analysis of reactions to classes of motivationally significant stimuli and can be used to derive predictions concerning the functions of the real nervous system (e.g., in fMRI studies). Rather than going into extensive detail regarding the biological basis of the systems, we focus primarily on their functions, discussing biological evidence only when it is necessary for addressing some functional question. Approach-Avoidance Theories of Motivation and Their Relation to Personality The most important classes of motivational stimuli can be grouped into ‘rewards’ and ‘punishments’. Animals can be seen as cybernetic systems with attractors and repulsors (positive and negative goals) that have evolved to promote survival and reproduction (Carver & Scheier, 1998; DeYoung, 2010d). Without a tendency to approach beneficial stimuli (e.g., food, drink, and sexual mates) and to avoid aversive stimuli (e.g., predators and poisons) a species would not survive. ‘Reward’ and ‘punishment’ may seem straightforward concepts, but they hide some nonobvious complexities. For the classical behaviorist, rewards increase the frequency of the behavior leading to them, whereas punishments decrease the frequency of behavior leading to them. That is, a ‘reward’ is something a person will work to obtain; and a ‘punishment’ is something a person will work to avoid. But the behaviorist definition of ‘reward’ also includes a different class of stimuli, namely the termination or omission of expected punishment. The effect on behavior and emotion of the ‘hope’ of achieving a reward is similar to that of anticipated ‘relief’ through avoiding a punishment. Similarly, although a ‘punishment’ can be described as something people will work to avoid or escape from (or which they will attack defensively), the omission of an expected reward is experienced as punishing; an effect known as frustrative nonreward. Thus, ‘fear’ has important similarities with ‘frustration’. (For further discussion of this literature, see Corr & McNaughton, 2012.) These complexities can be understood straightforwardly from the cybernetic perspective, in which rewards are any stimuli that indicate progress toward or attainment of a goal, whereas punishments are any stimuli that disrupt progress toward a goal. However, in any experimental situation, it is necessary to confirm that the subject perceives stimuli as actually rewarding and punishing, as there are likely to be significant individual differences in how people react to the same stimuli (for further discussion of this point, see Corr, forthcoming). Motivation and Personality 159 a 2013 Blackwell Publishing Ltd Social and Personality Psychology Compass 7/3 (2013): 158–175, 10.1111/spc3.12016 Current approach-avoidance theories trace their origins to early researchers who posited that two motivation ⁄emotion processes underlie behavior (e.g., Konorski, 1967; Mowrer, 1960; Schneirla, 1959), one related to reward (approach behavior and positive emotions), and the other to punishment (avoidance behavior and negative emotions). Neuroscience measures, including pharmacological manipulation, assessment of neural activity, and neuroanatomical studies, have been used to investigate the neuropsychological systems that underlie reactions to these classes of stimuli, providing confirmation of the hypothesis that distinct systems underlie rewardand punishment-related motivation (Gray & McNaughton, 2000). This animal-based work migrated into personality psychology in the 1970s via Jeffrey A. Gray (e.g., 1970, 1972a,b, 1975, 1977), whose Reinforcement Sensitivity theory (RST) argued that the major traits of personality reflect long-term stabilities in systems that mediate reactions to different classes of reinforcing stimuli, generating emotion and shaping (‘motivating’) approach and avoidance behavior. The leap from understanding motivational systems to understanding personality traits requires the postulate that relatively stable individual differences exist in the operations of these brain-behavioral systems. A personality trait can be defined as a probabilistic constant in equations that predict the frequency and intensity with which individuals exhibit various motivational states, as well as the behavioral, emotional, and cognitive states that accompany these motivational states (DeYoung, 2010c; Fleeson, 2001; Fleeson & Gallagher, 2009). Note that this assumes exposure of the population to a normal range of situations. If situations are limited to prevent exposure to some trait-relevant class of situations, then individual differences in that trait may not be manifest. A neuropsychological approach to personality aims to understand both the biological systems that are responsible for the states associated with any given trait and the parameters of those systems that cause them to differ across individuals. The systems themselves will be present in every intact human brain, but the values of their parameters will vary from person to person. Thus, for example, all people have brain systems that respond to punishing stimuli, but in different individuals these systems respond differently to a given stimulus. It is the typical level of response of such a system in any given individual, averaged across different situations, that is associated with that individual’s score on the personality trait in question. This is not to imply that an individual wil


A New Method for Yielding a Database of Location Fingerprints in WLAN

Location fingerprinting in wireless LAN (WLAN) positioning has received much attention recently. One of the key issues of this technique is generating the database of ‘fingerprints’. The conventional method does not utilise the spatial correlation of measurements sampled at adjacent reference points (RPs), and the ‘training’ process is not an easy task. A new method based on kriging is presented in this paper. An experiment shows that the new method can not only achieve more accurate estimation, but can also greatly reduce the workload and save training time. This can make the fingerprinting technique more flexible and easier to implement. Binghao Li, Yufei Wang, Andrew Dempster, Chris Rizos are with the School of Surveying and Spatial Information System, University of New South Wales, Sydney, Australia Hyung Keun Lee is with the School of Avionics and Telecommunication, Hankuk Aviation University, Kyunggi-do, Korea


Hybrid algorithm for indoor positioning using wireless LAN

Locating an indoor mobile station based on a wireless communication infrastructure, has practical applications. The most widely employed methods today use an RF propagation loss (PL) model or location fingerprinting (LF). The PL method is known to perform poorly compared to LF. But LF requires an extensive training dataset and cannot adapt well to configuration changes or receiver breakdown. In this paper, we develop a hybrid method that combines the strength of these two methods. It first formulates the RF PL in a nonlinear, censored regression model and adjusts the regression function to the observed signal strength in the fingerprint dataset. In the absence of a training dataset, the hybrid method coincides with the PL method, and, as the spatial granularity of the training dataset increases, the result of the algorithm approaches the result of the LF method. It balances the flexibility and accuracy of the two traditional methods, makes intelligent use of missing values, produces error bounds, and can be made dynamic. We evaluate the performance of the algorithm by applying it to a real site and observe satisfactory positioning accuracy.


WLAN location determination via clustering and probability distributions

We present a WLAN location determination technique, the Joint Clustering technique, that uses: (1) signal strength probability distributions to address the noisy wireless channel, and (2) clustering of locations to reduce the computational cost of searching the radio map. The Joint Clustering technique reduces computational cost by more than an order of magnitude, compared to the current state of the art techniques, allowing non-centralized implementation on mobile clients. Results from 802.11-equipped iPAQ implementations show that the new technique gives user location to within 7 feet with over 90% accuracy.


A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification

Identifying discourse relations that are not overtly marked with discourse connectives remains a challenging problem. The absence of explicit clues indicates a need for the combination of world knowledge and weak contextual clues, which can hardly be learned from a small amount of manually annotated data. In this paper, we address this problem by augmenting the input text with external knowledge and context and by adopting a neural network model that can effectively handle the augmented text. Experiments show that external knowledge did improve the classification accuracy. On the other hand, contextual information provided no significant gain for implicit discourse relations while it worked for explicit ones.


SIGN LANGUAGE RECOGNITION: STATE OF THE ART

Sign language is used by deaf and hard hearing people to exchange information between their own community and with other people. Computer recognition of sign language deals from sign gesture acquisition and continues till text/speech generation. Sign gestures can be classified as static and dynamic. However static gesture recognition is simpler than dynamic gesture recognition but both recognition systems are important to the human community. The sign language recognition steps are described in this survey. The data acquisition, data preprocessing and transformation, feature extraction, classification and results obtained are examined. Some future directions for research in this area also suggested.


Non-Orthogonal Multiple Access Based Integrated Terrestrial-Satellite Networks

In this paper, we investigate the downlink transmission of a non-orthogonal multiple access (NOMA)-based integrated terrestrial-satellite network, in which the NOMA-based terrestrial networks and the satellite cooperatively provide coverage for ground users while reusing the entire bandwidth. For both terrestrial networks and the satellite network, multi-antennas are equipped and beamforming techniques are utilized to serve multiple users simultaneously. A channel quality-based scheme is proposed to select users for the satellite, and we then formulate the terrestrial user pairing as a max–min problem to maximize the minimum channel correlation between users in one NOMA group. Since the terrestrial networks and the satellite network will cause interference to each other, we first investigate the capacity performance of the terrestrial networks and the satellite networks separately, which can be decomposed into the designing of beamforming vectors and the power allocation schemes. Then, a joint iteration algorithm is proposed to maximize the total system capacity, where we introduce the interference temperature limit for the satellite since the satellite can cause interference to all base station users. Finally, numerical results are provided to evaluate the user paring scheme as well as the total system performance, in comparison with some other proposed algorithms and existing algorithms.


Handling class imbalance in customer churn prediction

0957-4174/$ see front matter 2008 Elsevier Ltd. A doi:10.1016/j.eswa.2008.05.027 * Corresponding author. Tel.: +32 9 264 89 80; fax: E-mail address: dirk.vandenpoel@UGent.be (D. Va URL: http://www.crm.UGent.be (D. Van den Poel). Customer churn is often a rare event in service industries, but of great interest and great value. Until recently, however, class imbalance has not received much attention in the context of data mining [Weiss, G. M. (2004). Mining with rarity: A unifying framework. SIGKDD Explorations, 6 (1), 7–19]. In this study, we investigate how we can better handle class imbalance in churn prediction. Using more appropriate evaluation metrics (AUC, lift), we investigated the increase in performance of sampling (both random and advanced under-sampling) and two specific modelling techniques (gradient boosting and weighted random forests) compared to some standard modelling techniques. AUC and lift prove to be good evaluation metrics. AUC does not depend on a threshold, and is therefore a better overall evaluation metric compared to accuracy. Lift is very much related to accuracy, but has the advantage of being well used in marketing practice [Ling, C., & Li, C. (1998). Data mining for direct marketing problems and solutions. In Proceedings of the fourth international conference on knowledge discovery and data mining (KDD-98). New York, NY: AAAI Press]. Results show that under-sampling can lead to improved prediction accuracy, especially when evaluated with AUC. Unlike Ling and Li [Ling, C., & Li, C. (1998). Data mining for direct marketing problems and solutions. In Proceedings of the fourth international conference on knowledge discovery and data mining (KDD98). New York, NY: AAAI Press], we find that there is no need to under-sample so that there are as many churners in your training set as non churners. Results show no increase in predictive performance when using the advanced sampling technique CUBE in this study. This is in line with findings of Japkowicz [Japkowicz, N. (2000). The class imbalance problem: significance and strategies. In Proceedings of the 2000 international conference on artificial intelligence (IC-AI’2000): Special track on inductive learning, Las Vegas, Nevada], who noted that using sophisticated sampling techniques did not give any clear advantage. Weighted random forests, as a cost-sensitive learner, performs significantly better compared to random forests, and is therefore advised. It should, however always be compared to logistic regression. Boosting is a very robust classifier, but never outperforms any other technique. 2008 Elsevier Ltd. All rights reserved.


community2vec: Vector representations of online communities encode semantic relationships

Vector embeddings of words have been shown to encode meaningful semantic relationships that enable solving of complex analogies. This vector embedding concept has been extended successfully to many different domains and in this paper we both create and visualize vector representations of an unstructured collection of online communities based on user participation. Further, we quantitatively and qualitatively show that these representations allow solving of semantically meaningful community analogies and also other more general types of relationships. These results could help improve community recommendation engines and also serve as a tool for sociological studies of community relatedness.


7th order sub-millimeter wave frequency multiplier based on graphene implemented using a microstrip transition between two rectangular waveguides

In this work, a 7th order sub-millimeter wave frequency multiplier for Terahertz applications is presented. The multiplier is based on a microstrip line with a small gap where a graphene film layer is deposited. The multiplication phenomena is reached via the nonlinear behavior of the graphene film using an input signal provided by the WR28 standard waveguide operating in the Ka frequency band (31 to 40 GHz). A prototype of the multiplier has been manufactured and the behavior of the output power in the 220-280 GHz band versus the input power has been experimentally characterized.


An FPGA Platform for Real-Time Simulation of Spiking Neuronal Networks

In the last years, the idea to dynamically interface biological neurons with artificial ones has become more and more urgent. The reason is essentially due to the design of innovative neuroprostheses where biological cell assemblies of the brain can be substituted by artificial ones. For closed-loop experiments with biological neuronal networks interfaced with in silico modeled networks, several technological challenges need to be faced, from the low-level interfacing between the living tissue and the computational model to the implementation of the latter in a suitable form for real-time processing. Field programmable gate arrays (FPGAs) can improve flexibility when simple neuronal models are required, obtaining good accuracy, real-time performance, and the possibility to create a hybrid system without any custom hardware, just programming the hardware to achieve the required functionality. In this paper, this possibility is explored presenting a modular and efficient FPGA design of an in silico spiking neural network exploiting the Izhikevich model. The proposed system, prototypically implemented on a Xilinx Virtex 6 device, is able to simulate a fully connected network counting up to 1,440 neurons, in real-time, at a sampling rate of 10 kHz, which is reasonable for small to medium scale extra-cellular closed-loop experiments.


Induction of mitochondrial-mediated apoptosis by Morinda citrifolia (Noni) in human cervical cancer cells.

Cervical cancer is the second most common cause of cancer in women and has a high mortality rate. Cisplatin, an antitumor agent, is generally used for its treatment. However, the administration of cisplatin is associated with side effects and intrinsic resistance. Morinda citrifolia (Noni), a natural plant product, has been shown to have anti-cancer properties. In this study, we used Noni, cisplatin, and the two in combination to study their cytotoxic and apoptosis-inducing effects in cervical cancer HeLa and SiHa cell lines. We demonstrate here, that Noni/Cisplatin by themselves and their combination were able to induce apoptosis in both these cell lines. Cisplatin showed slightly higher cell killing as compared to Noni and their combination showed additive effects. The observed apoptosis appeared to be mediated particularly through the up-regulation of p53 and pro-apoptotic Bax proteins, as well as down- regulation of the anti-apoptotic Bcl-2, Bcl-XL proteins and survivin. Augmentation in the activity of caspase-9 and -3 was also observed, suggesting the involvement of the intrinsic mitochondrial pathway of apoptosis for both Noni and Cisplatin in HeLa and SiHa cell lines.


Applying Machine Learning to Text Segmentation for Information Retrieval

We propose a self-supervised word segmentation technique for text segmentation in Chinese information retrieval. This method combines the advantages of traditional dictionary based, character based and mutual information based approaches, while overcoming many of their shortcomings. Experiments on TREC data show this method is promising. Our method is completely language independent and unsupervised, which provides a promising avenue for constructing accurate multi-lingual or cross-lingual information retrieval systems that are flexible and adaptive. We find that although the segmentation accuracy of self-supervised segmentation is not as high as some other segmentation methods, it is enough to give good retrieval performance. It is commonly believed that word segmentation accuracy is monotonically related to retrieval performance in Chinese information retrieval. However, for Chinese, we find that the relationship between segmentation and retrieval performance is in fact nonmonotonic; that is, at around 70% word segmentation accuracy an over-segmentation phenomenon begins to occur which leads to a reduction in information retrieval performance. We demonstrate this effect by presenting an empirical investigation of information retrieval on Chinese TREC data, using a wide variety of word segmentation algorithms with word segmentation accuracies ranging from 44% to 95%, including 70% word segmentation accuracy from our self-supervised word-segmentation approach. It appears that the main reason for the drop in retrieval performance is that correct compounds and collocations are preserved by accurate segmenters, while they are broken up by less accurate (but reasonable) segmenters, to a surprising advantage. This suggests that words themselves might be too broad a notion to conveniently capture the general semantic meaning of Chinese text. Our research suggests machine learning techniques can play an important role in building adaptable information retrieval systems and different evaluation standards for word segmentation should be given to different applications.


Kirigami skins make a simple soft actuator crawl

Bioinspired soft machines made of highly deformable materials are enabling a variety of innovative applications, yet their locomotion typically requires several actuators that are independently activated. We harnessed kirigami principles to significantly enhance the crawling capability of a soft actuator. We designed highly stretchable kirigami surfaces in which mechanical instabilities induce a transformation from flat sheets to 3D-textured surfaces akin to the scaled skin of snakes. First, we showed that this transformation was accompanied by a dramatic change in the frictional properties of the surfaces. Then, we demonstrated that, when wrapped around an extending soft actuator, the buckling-induced directional frictional properties of these surfaces enabled the system to efficiently crawl.


Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection

We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that task-space motion of the gripper will result in successful grasps, using only monocular camera images and independently of camera calibration or the current robot pose. This requires the network to observe the spatial relationship between the gripper and objects in the scene, thus learning hand-eye coordination. We then use this network to servo the gripper in real time to achieve successful grasps. To train our network, we collected over 800,000 grasp attempts over the course of two months, using between 6 and 14 robotic manipulators at any given time, with differences in camera placement and hardware. Our experimental evaluation demonstrates that our method achieves effective real-time control, can successfully grasp novel objects, and corrects mistakes by continuous servoing.


A PRINCIPAL AXIS TRANSFORMATION FOR NON-HERMITIAN MATRICES CARL ECKART AND GALE YOUNG The availability of the principal axis transformation for hermitian

each two nonparallel elements of G cross each other. Obviously the conclusions of the theorem do not hold. The following example will show that the condition that no two elements of the collection G shall have a complementary domain in common is also necessary. In the cartesian plane let M be a circle of radius 1 and center at the origin, and iVa circle of radius 1 and center at the point (5, 5). Let d be a collection which contains each continuum which is the sum of M and a horizontal straight line interval of length 10 whose left-hand end point is on the circle M and which contains no point within M. Let G2 be a collection which contains each continuum which is the sum of N and a vertical straight line interval of length 10 whose upper end point is on the circle N and which contains no point within N. Let G = Gi+G2 . No element of G crosses any other element of G, but uncountably many have a complementary domain in common with some other element of the collection. However, it is evident that no countable subcollection of G covers the set of points each of which is common to two continua of the collection G. I t is not known whether or not the condition that each element of G shall separate some complementary domain of every other one can be omitted.


Tooth survival following non-surgical root canal treatment: a systematic review of the literature.

AIMS
To investigate (i) the effect of study characteristics on reported tooth survival after root canal treatment (RCTx) and (ii) the effect of clinical factors on the proportion of root filled teeth surviving after RCTx.

METHODOLOGY
Longitudinal human clinical studies investigating tooth survival after RCTx which were published up to the end of 2007 were identified electronically (MEDLINE and Cochrane database 1966-2007 December, week 4). In addition, four journals (Dental Traumatology, International Endodontic Journal, Journal of Endodontics, Oral Surgery Oral Medicine Oral Pathology Oral Radiology & Endodontics), bibliographies of all relevant articles and review articles were hand searched. Two reviewers (Y-LN, KG) assessed and selected the studies based on specified inclusion criteria and extracted the data onto a pre-designed proforma, independently. The criteria were as follows: (i) clinical study on RCTx; (ii) stratified analysis of primary and secondary RCTx available; (iii) sample size given and larger than 10; (iv) at least 6-month postoperative review; (v) success based on survival of tooth; and (vi) proportion of teeth surviving after treatment given or could be calculated from the raw data. Three strands of evidence or analyses were used to triangulate a consensus view. The reported findings from individual studies, including those excluded for quantitative analysis, were utilized for the intuitive synthesis, which constituted the first strand of evidence. Secondly, the pooled weighted proportion of teeth surviving and thirdly the combined effects of potential prognostic factors were estimated using the fixed and random effects meta-analyses on studies fulfilling all the inclusion criteria.

RESULTS
Of the 31 articles identified, 14 studies published between 1993 and 2007 were included. The majority of studies were retrospective (n = 10) and only four prospective. The pooled percentages of reported tooth survival over 2-3, 4-5 and 8-10 years following RCTx were 86% (95% CI: 75%, 98%), 93% (95% CI: 92%, 94%) and 87% (95% CI: 82%, 92%), respectively. Substantial differences in study characteristics were found to hinder effective direct comparison of findings. Evidence for the effect of prognostic factors on tooth survival was weak. Based on the data available for meta-analyses, four conditions were found to significantly improve tooth survival. In descending order of influence, the conditions increasing observed proportion of survival were as follows: (i) a crown restoration after RCTx; (ii) tooth having both mesial and distal proximal contacts; (iii) tooth not functioning as an abutment for removable or fixed prosthesis; and (iv) tooth type or specifically non-molar teeth. Statistical heterogeneity was substantial in some cases but its source could not be investigated because of insufficient available information.

CONCLUSIONS
The pooled proportion of teeth surviving over 2-10 years following RCTx ranged between 86% and 93%. Four factors (listed above) were identified as significant prognostic factors with concurrence between all three strands of evidence.


100 top-cited scientific papers in limb prosthetics

Research has tremendously contributed to the developments in both practical and fundamental aspects of limb prosthetics. These advancements are reflected in scientific articles, particularly in the most cited papers. This article aimed to identify the 100 top-cited articles in the field of limb prosthetics and to investigate their main characteristics. Articles related to the field of limb prosthetics and published in the Web of Knowledge database of the Institute for Scientific Information (ISI) from the period of 1980 to 2012. The 100 most cited articles in limb prosthetics were selected based on the citation index report. All types of articles except for proceedings and letters were included in the study. The study design and level of evidence were determined using Sackett's initial rules of evidence. The level of evidence was categorized either as a systematic review or meta-analysis, randomized controlled trial, cohort study, case-control study, case series, expert opinion, or design and development. The top cited articles in prosthetics were published from 1980 to 2012 with a citation range of 11 to 90 times since publication. The mean citation rate was 24.43 (SD 16.7) times. Eighty-four percent of the articles were original publications and were most commonly prospective (76%) and case series studies (67%) that used human subjects (96%) providing level 4 evidence. Among the various fields, rehabilitation (47%), orthopedics (29%), and sport sciences (28%) were the most common fields of study. The study established that studies conducted in North America and were written in English had the highest citations. Top cited articles primarily dealt with lower limb prosthetics, specifically, on transtibial and transradial prosthetic limbs. Majority of the articles were experimental studies.


Requirements engineering and the creative process in the video game industry

The software engineering process in video game development is not clearly understood, hindering the development of reliable practices and processes for this field. An investigation of factors leading to success or failure in video game development suggests that many failures can be traced to problems with the transition from preproduction to production. Three examples, drawn from real video games, illustrate specific problems: 1) how to transform documentation from its preproduction form to a form that can be used as a basis for production;, 2) how to identify implied information in preproduction documents; and 3) how to apply domain knowledge without hindering the creative process. We identify 3 levels of implication and show that there is a strong correlation between experience and the ability to identify issues at each level. The accumulated evidence clearly identifies the need to extend traditional requirements engineering techniques to support the creative process in video game development.


An integrated conceptual model for information system security risk management supported by enterprise architecture management

Risk management is today a major steering tool for any organisation wanting to deal with information system (IS) security. However, IS security risk management (ISSRM) remains a difficult process to establish and maintain, mainly in a context of multi-regulations with complex and inter-connected IS. We claim that a connection with enterprise architecture management (EAM) contributes to deal with these issues. A first step towards a better integration of both domains is to define an integrated EAM-ISSRM conceptual model. This paper is about the elaboration and validation of this model. To do so, we improve an existing ISSRM domain model, i.e. a conceptual model depicting the domain of ISSRM, with the concepts of EAM. The validation of the EAM-ISSRM integrated model is then performed with the help of a validation group assessing the utility and usability of the model.


A simple regression method for mapping quantitative trait loci in line crosses using flanking markers

The use of flanking marker methods has proved to be a powerful tool for the mapping of quantitative trait loci (QTL) in the segregating generations derived from crosses between inbred lines. Methods to analyse these data, based on maximum-likelihood, have been developed and provide good estimates of QTL effects in some situations. Maximum-likelihood methods are, however, relatively complex and can be computationally slow. In this paper we develop methods for mapping QTL based on multiple regression which can be applied using any general statistical package. We use the example of mapping in an F2 population and show that these regression methods produce very similar results to those obtained using maximum likelihood. The relative simplicity of the regression methods means that models with more than a single QTL can be explored and we give examples of two linked loci and of two interacting loci. Other models, for example with more than two QTL, with environmental fixed effects, with between family variance or for threshold traits, could be fitted in a similar way. The ease, speed of application and generality of regression methods for flanking marker analyses, and the good estimates they obtain, suggest that they should provide the method of choice for the analysis of QTL mapping data from inbred line crosses.


Comparative analysis of big data transfer protocols in an international high-speed network

Large-scale scientific installations generate voluminous amounts of data (or big data) every day. These data often need to be transferred using high-speed links (typically with 10 Gb/s or more link capacity) to researchers located around the globe for storage and analysis. Efficiently transferring big data across countries or continents requires specialized big data transfer protocols. Several big data transfer protocols have been proposed in the literature, however, a comparative analysis of these protocols over a long distance international network is lacking in the literature. We present a comparative performance and fairness study of three open-source big data transfer protocols, namely, GridFTP, FDT, and UDT, using a 10 Gb/s high-speed link between New Zealand and Sweden. We find that there is limited performance difference between GridFTP and FDT. GridFTP is stable in terms of handling file system and TCP socket buffer. UDT has an implementation issue that limits its performance. FDT has issues with small buffer size limiting its performance, however, this problem is overcome by using multiple flows. Our work indicates that faster file systems and larger TCP socket buffers in both the operating system and application are useful in improving data transfer rates.


Self-presentation and gender on MySpace

Available online 15 August 2008 Within the cultural context of MySpace, this study explores the ways emerging adults experience social networking. Through focus group methodology, the role of virtual peer interaction in the development of personal, social, and gender identities was investigated. Findings suggest that college students utilize MySpace for identity exploration, engaging in social comparison and expressing idealized aspects of the selves they wish to become. The public nature of self and relationship displays introduce feedback mechanisms by which emerging adults can legitimize images as associated with the self. Also, male–female differences in self-presentation parallel, and possibly intensify, gender norms offline. Our study suggests that social networking sites provide valuable opportunities for emerging adults to realize possible selves; however, increased pressure for female sexual objectification and intensified social comparison may also negatively impact identity development. A balanced view, presenting both opportunities and drawbacks, should be encouraged in policies regarding youth participation in social networking sites. Published by Elsevier Inc.


How Envy Influences SNS Intentions to Use

Social networking sites (SNS) have grown to be one of the most prevalent technologies, providing users a variety of benefits. However, SNS also provide user sufficient opportunities to access others’ positive information. This would elicit envy. In the current study, we develop a theoretical framework that elaborates the mechanism through which online envy is generated and influences SNS usage. We specify that online users experience two types of envy and each one could have distinct influences on continuance intention to use of SNS. Our findings provide valuable implications for both academic researchers and IS practitioners.


A Theory of Social Comparison Processes

In this paper we shall present a further development of a previously published theory concerning opinion influence processes in social groups (7). This further development has enabled us to extend the theory to deal with other areas, in addition to opinion formation, in which social comparison is important. Specifically, we shall develop below how the theory applies to the appraisal and evaluation of abilities as well as opinions. Such theories and hypotheses in the area of social psychology are frequently viewed in terms of how “plausible” they seem. “Plausibility” usually means whether or not the theory or hypothesis fits one’s intuition or one’s common sense. In this meaning much of the theory which is to be presented here is not” plausible “. The theory does, however, explain a considerable amount of data and leads to testable derivations. Three experiments, specifically designed to test predictions from this extension of the theory, have now been completed (5, 12, 19). They all provide good corroboration. We will in the following pages develop the theory and present the relevant data.


Facebook use, envy, and depression among college students: Is facebooking depressing?

It is not—unless it triggers feelings of envy. This study uses the framework of social rank theory of depression and conceptualizes Facebook envy as a possible link between Facebook surveillance use and depression among college students. Using a survey of 736 college students, we found that the effect of surveillance use of Facebook on depression is mediated by Facebook envy. However, when Facebook envy is controlled for, Facebook use actually lessens depression. 2014 Elsevier Ltd. All rights reserved.


Learning Actionable Representations from Visual Observations

In this work we explore a new approach for robots to teach themselves about the world simply by observing it. In particular we investigate the effectiveness of learning task-agnostic representations for continuous control tasks. We extend Time-Contrastive Networks (TCN) that learn from visual observations by embedding multiple frames jointly in the embedding space as opposed to a single frame. We show that by doing so, we are now able to encode both position and velocity attributes significantly more accurately. We test the usefulness of this self-supervised approach in a reinforcement learning setting. We show that the representations learned by agents observing themselves take random actions, or other agents perform tasks successfully, can enable the learning of continuous control policies using algorithms like Proximal Policy Optimization (PPO) using only the learned embeddings as input. We also demonstrate significant improvements on the real-world Pouring dataset with a relative error reduction of 39.4% for motion attributes and 11.1% for static attributes compared to the single-frame baseline. Video results are available at https://sites.google.com/view/actionablerepresentations


Survey of Surveys (SoS) - Mapping The Landscape of Survey Papers in Information Visualization

Information visualization as a field is growing rapidly in popularity since the first information visualization conference in 1995. However, as a consequence of its growth, it is increasingly difficult to follow the growing body of literature within the field. Survey papers and literature reviews are valuable tools for managing the great volume of previously published research papers, and the quantity of survey papers in visualization has reached a critical mass. To this end, this survey paper takes a quantum step forward by surveying and classifying literature survey papers in order to help researchers understand the current landscape of Information Visualization. It is, to our knowledge, the first survey of survey papers (SoS) in Information Visualization. This paper classifies survey papers into natural topic clusters which enables readers to find relevant literature and develops the first classification of classifications. The paper also enables researchers to identify both mature and less developed research directions as well as identify future directions. It is a valuable resource for both newcomers and experienced researchers in and outside the field of Information Visualization and Visual Analytics.


MATLAB functions to analyze directional (azimuthal) data - I: Single-sample inference

Data that represent azimuthal directions cannot be analyzed statistically in the same manner as ordinary variables that measure, for example, size or quality. Many statistical techniques have been developed for dealing with directional data, but available software for the unique calculations is scarce. This paper describes a MATLAB script, located on the IAMG server, that calculates descriptive statistics and performs inference on azimuthal parameters. The calculations include tests for specific distributions, and tests on the preferred direction and concentration of vectors about this direction. The inference methods use large-sample approximations, plus resampling methods (bootstrap) for smaller sample sizes. r 2005 Elsevier Ltd. All rights reserved.


Underwater Image Super-Resolution by Descattering and Fusion

Underwater images are degraded due to scatters and absorption, resulting in low contrast and color distortion. In this paper, a novel self-similarity-based method for descattering and super resolution (SR) of underwater images is proposed. The traditional approach of preprocessing the image using a descattering algorithm, followed by application of an SR method, has the limitation that most of the high-frequency information is lost during descattering. Consequently, we propose a novel high turbidity underwater image SR algorithm. We first obtain a high resolution (HR) image of scattered and descattered images by using a self-similarity-based SR algorithm. Next, we apply a convex fusion rule for recovering the final HR image. The super-resolved images have a reasonable noise level after descattering and demonstrate visually more pleasing results than conventional approaches. Furthermore, numerical metrics demonstrate that the proposed algorithm shows a consistent improvement and that edges are significantly enhanced.


DeepXplore: Automated Whitebox Testing of Deep Learning Systems

Deep learning (DL) systems are increasingly deployed in safety- and security-critical domains including self-driving cars and malware detection, where the correctness and predictability of a system's behavior for corner case inputs are of great importance. Existing DL testing depends heavily on manually labeled data and therefore often fails to expose erroneous behaviors for rare inputs.
 We design, implement, and evaluate DeepXplore, the first whitebox framework for systematically testing real-world DL systems. First, we introduce neuron coverage for systematically measuring the parts of a DL system exercised by test inputs. Next, we leverage multiple DL systems with similar functionality as cross-referencing oracles to avoid manual checking. Finally, we demonstrate how finding inputs for DL systems that both trigger many differential behaviors and achieve high neuron coverage can be represented as a joint optimization problem and solved efficiently using gradient-based search techniques.
 DeepXplore efficiently finds thousands of incorrect corner case behaviors (e.g., self-driving cars crashing into guard rails and malware masquerading as benign software) in state-of-the-art DL models with thousands of neurons trained on five popular datasets including ImageNet and Udacity self-driving challenge data. For all tested DL models, on average, DeepXplore generated one test input demonstrating incorrect behavior within one second while running only on a commodity laptop. We further show that the test inputs generated by DeepXplore can also be used to retrain the corresponding DL model to improve the model's accuracy by up to 3%.


Establishing the computer-patient working alliance in automated health behavior change interventions.

Current user interfaces for automated patient and consumer health care systems can be improved by leveraging the results of several decades of research into effective patient-provider communication skills. A research project is presented in which several such "relational" skills - including empathy, social dialogue, nonverbal immediacy behaviors, and other behaviors to build and maintain good working relationships over multiple interactions - are explicitly designed into a computer interface within the context of a longitudinal health behavior change intervention for physical activity adoption. Results of a comparison among 33 subjects interacting near-daily with the relational system and 27 interacting near-daily with an identical system with the relational behaviors ablated, each for 30 days indicate, that the use of relational behaviors by the system significantly increases working alliance and desire to continue working with the system. Comparison of the above groups to another group of 31 subjects interacting with a control system near-daily for 30 days also indicated a significant increase in proactive viewing of health information.


Automated Assessment of Surgical Skills Using Frequency Analysis

We present an automated framework for visual assessment of the expertise level of surgeons using the OSATS (Objective Structured Assessment of Technical Skills) criteria. Video analysis techniques for extracting motion quality via frequency coefficients are introduced. The framework is tested on videos of medical students with different expertise levels performing basic surgical tasks in a surgical training lab setting. We demonstrate that transforming the sequential time data into frequency components effectively extracts the useful information differentiating between different skill levels of the surgeons. The results show significant performance improvements using DFT and DCT coefficients over known state-of-the-art techniques.


Functional Near Infrared Spectroscope for Cognition Brain Tasks by Wavelets Analysis and Neural Networks

Brain Computer Interface (BCI) has been recently increased in research. Functional Near Infrared Spectroscope (fNIRs) is one the latest technologies which utilize light in the near-infrared range to determine brain activities. Because near infrared technology allows design of safe, portable, wearable, non-invasive and wireless qualities monitoring systems, fNIRs monitoring of brain hemodynamics can be value in helping to understand brain tasks. In this paper, we present results of fNIRs signal analysis indicating that there exist distinct patterns of hemodynamic responses which recognize brain tasks toward developing a BCI. We applied two different mathematics tools separately, Wavelets analysis for preprocessing as signal filters and feature extractions and Neural networks for cognition brain tasks as a classification module. We also discuss and compare with other methods while our proposals perform better with an average accuracy of 99.9% for classification. Keywords—functional near infrared spectroscope (fNIRs), braincomputer interface (BCI), wavelets, neural networks, brain activity, neuroimaging.


Treacher Collins syndrome: clinical implications for the paediatrician—a new mutation in a severely affected newborn and comparison with three further patients with the same mutation, and review of the literature

Treacher Collins syndrome (TCS) is the most common and well-known mandibulofacial dysostosis caused by mutations in at least three genes involved in pre-rRNA transcription, the TCOF1, POLR1D and POLR1C genes. We present a severely affected male individual with TCS with a heterozygous de novo frameshift mutation within the TCOF1 gene (c.790_791delAG,p.Ser264GlnfsX7) and compare the clinical findings with three previously unpublished, milder affected individuals from two families with the same mutation. We elucidate typical clinical features of TCS and its clinical implications for the paediatrician and mandibulofacial surgeon, especially in severely affected individuals and give a short review of the literature. Conclusion:The clinical data of these three families illustrate that the phenotype associated with this specific mutation has a wide intra- and interfamilial variability, which confirms that variable expressivity in carriers of TCOF1 mutations is not a simple consequence of the mutation but might be modified by the combination of genetic, environmental and stochastic factors. Being such a highly complex disease treatment of individuals with TCS should be tailored to the specific needs of each individual, preferably by a multidisciplinary team consisting of paediatricians, craniofacial surgeons and geneticists.


Quality-of-service in cloud computing: modeling techniques and their applications

Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management.


Smart vehicle accident detection and alarming system using a smartphone

Vehicle accident is the paramount thread for the people's life which causes a serious wound or even dead. The automotive companies have made lots of progress in alleviating this thread, but still the probability of detrimental effect due to an accident is not reduced. Infringement of spFieed is one of the elementary reasons for a vehicle accident. Therewithal, external pressure and change of tilt angle with road surface blameworthy for this mishap. As soon as the emergency service could divulge about an accident, the more the effect would be mitigated. For this purpose, we developed an Android based application that detects an accidental situation and sends emergency alert message to the nearest police station and health care center. This application is integrated with an external pressure sensor to extract the outward force of the vehicle body. It measures speed and change of tilt angle with GPS and accelerometer sensors respectively on Android phone. By checking conditions, this application also capable of reducing the rate of false alarm.


Robust Automatic Measurement of 3D Scanned Models for the Human Body Fat Estimation

In this paper, we present an automatic tool for estimating geometrical parameters from 3-D human scans independent on pose and robustly against the topological noise. It is based on an automatic segmentation of body parts exploiting curve skeleton processing and ad hoc heuristics able to remove problems due to different acquisition poses and body types. The software is able to locate body trunk and limbs, detect their directions, and compute parameters like volumes, areas, girths, and lengths. Experimental results demonstrate that measurements provided by our system on 3-D body scans of normal and overweight subjects acquired in different poses are highly correlated with the body fat estimates obtained on the same subjects with dual-energy X-rays absorptiometry (DXA) scanning. In particular, maximal lengths and girths, not requiring precise localization of anatomical landmarks, demonstrate a good correlation (up to 96%) with the body fat and trunk fat. Regression models based on our automatic measurements can be used to predict body fat values reasonably well.


Asymmetrical impact of trustworthiness attributes on trust, perceived value and purchase intention: a conceptual framework for cross-cultural study on consumer perception of online auction

Lack of trust has been frequently cited to be one of the key factors that discourage customers from participating in e-commerce, while cultural differences affect the formation of trust. As such, this cross-cultural study aims at establishing a conceptual framework to investigate the primary antecedents of online trust and the mechanism that converts consumer trust into perceived value and purchase intention in online auction. Two elements of our model bear mention here. First, the authors divide the consumer trust into two facets, seller and intermediary trust, and intend to assess their reciprocal relationships. Second, the authors intend to examine the asymmetrical impact of the negative and positive trustworthiness attributes on trust, and the interrelationship among trust, perceived value and purchase intention across cultural groups.


Combining Classifiers for Chinese Word Segmentation

In this paper we report results of a supervised machine-learning approach to Chinese word segmentation. First, a maximum entropy tagger is trained on manually annotated data to automatically labels the characters with tags that indicate the position of character within a word. An error-driven transformation-based tagger is then trained to clean up the tagging inconsistencies of the first tagger. The tagged output is then converted into segmented text. The preliminary results show that this approach is competitive compared with other supervised machine-learning segmenters reported in previous studies.


Deep Transfer in Reinforcement Learning by Language Grounding

In this paper, we explore the utilization of natural language to drive transfer for reinforcement learning (RL). Despite the wide-spread application of deep RL techniques, learning generalized policy representations that work across domains remains a challenging problem. We demonstrate that textual descriptions of environments provide a compact intermediate channel to facilitate effective policy transfer. We employ a model-based RL approach consisting of a differentiable planning module, a model-free component and a factorized representation to effectively utilize entity descriptions. Our model outperforms prior work on both transfer and multi-task scenarios in a variety of different environments.


Demystifying automotive safety and security for semiconductor developer

Advances in both semiconductor and automotive industry are today enabling the next generation of vehicles with significant electronics content than ever before. Consumers can now avail vehicle offerings in the form of Electric and Hybrid Electric Vehicles (EV/HEV) that have improved fuel efficiency, provide enhanced driver-passenger comfort and experience through Advance Driver Assistance Systems (ADAS) and car infotainment systems, and more. Increasing electronics, software content, and connectivity drive two consumer concerns — “functional safety” and “security” — to the forefront. In this tutorial, we dissect these concerns from an end application perspective and translate the system level requirements and standards into semiconductor development requirements. We indicate both current and emerging practices, and touch upon areas requiring new or optimal design and electronic design automation (EDA) solutions. While functional safety is the primary focus for deep-dive in this tutorial, we also examine key facets of automotive security which is now emerging as a critical area for further understanding and standardization.


Detecting influenza epidemics using search engine query data

Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year. In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza. One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users.


Freebase: a collaboratively created graph database for structuring human knowledge

Freebase is a practical, scalable tuple database used to structure general human knowledge. The data in Freebase is collaboratively created, structured, and maintained. Freebase currently contains more than 125,000,000 tuples, more than 4000 types, and more than 7000 properties. Public read/write access to Freebase is allowed through an HTTP-based graph-query API using the Metaweb Query Language (MQL) as a data query and manipulation language. MQL provides an easy-to-use object-oriented interface to the tuple data in Freebase and is designed to facilitate the creation of collaborative, Web-based data-oriented applications.


Many online sites, both retailers and content providers, routinely monitor visitor traffic as a useful measure of their overall success. However, simple summaries such as the total number of visits per month provide little insight about individual-level site-visit patterns, especially in a changing environment such as the Internet. This article develops an individual-level model for evolving visiting behavior based on Internet clickstream data. We capture cross-sectional variation in site-visit behavior as well as changes over time as visitors gain experience with the site. In addition, we examine the relationship between visiting frequency and purchasing propensity at an e-commerce site. We find evidence supporting the notion that people who visit a retail site more frequently have a greater propensity to buy. We also show that changes (i.e., evolution) in an individual's visit frequency over time provides further information regarding which customer segments are likely to have higher purchasing conversion rates. Disciplines Business | Business Analytics | E-Commerce | Management Sciences and Quantitative Methods | Marketing | Technology and Innovation This technical report is available at ScholarlyCommons: https://repository.upenn.edu/marketing_papers/275 Capturing Evolving Visit Behavior in Clickstream Data Wendy W. Moe and Peter S. Fader The Wharton School University of Pennsylvania Marketing Department 1400 Steinberg Hall-Dietrich Hall Philadelphia, PA 19104-6371 telephone: (215) 898-7603 fax: (215) 898-2534 e-mail: wendy@marketing.wharton.upenn.edu


Automatic Generation of Personas Using YouTube Social Media Data

We develop and implement an approach for automating generating personas in real time using actual YouTube social media data from a global media corporation. From the organization's YouTube channel, we gather demographic data, customer interactions, and topical interests, leveraging more than 188,000 subscriber profiles and more than 30 million user interactions. We demonstrate that online user data can be used to develop personas in real time using social media analytics. To get diverse aspects of personas, we collect user data from other social media channels as well and match them with the corresponding user data to generate richer personas. Our results provide corporate insights into competitive marketing, topical interests, and preferred product features for the users of the online news medium. Research implications are that reasonably rich personas can be generated in real time, instead of being the result of a laborious and time-consuming manual development process.


Dual Encoding for Abstractive Text Summarization.

Recurrent neural network-based sequence-to-sequence attentional models have proven effective in abstractive text summarization. In this paper, we model abstractive text summarization using a dual encoding model. Different from the previous works only using a single encoder, the proposed method employs a dual encoder including the primary and the secondary encoders. Specifically, the primary encoder conducts coarse encoding in a regular way, while the secondary encoder models the importance of words and generates more fine encoding based on the input raw text and the previously generated output text summarization. The two level encodings are combined and fed into the decoder to generate more diverse summary that can decrease repetition phenomenon for long sequence generation. The experimental results on two challenging datasets (i.e., CNN/DailyMail and DUC 2004) demonstrate that our dual encoding model performs against existing methods.


World of Warcraft and the impact of game culture and play in an undergraduate game design course

During the past two decades, digital games have become an increasingly popular source of study for academics, educational researchers and instructional designers. Much has been written about the potential of games for teaching and learning, both in the design of educational/serious games and the implementation of off-the-shelf games for learning. Yet relatively little research has been conducted about how game culture and the enmeshed practice of play may impact classroom dynamics. The purpose of this study is to present a case study about how the use of World of Warcraft (WoW) as a teaching tool and medium of play impacted class dynamics in an undergraduate university-level course for game design. Specifically, this study will address how WoW’s game culture and the practice of play impacted (a) student-to-student dynamics and (b) class dynamics. The goal of this study is to explore some of the dynamics of play as a component of learning. 2010 Elsevier Ltd. All rights reserved.


(MP)2T: Multiple People Multiple Parts Tracker

We present a method for multi-target tracking that exploits the persistence in detection of object parts. While the implicit representation and detection of body parts have recently been leveraged for improved human detection, ours is the first method that attempts to temporally constrain the location of human body parts with the express purpose of improving pedestrian tracking. We pose the problem of simultaneous tracking of multiple targets and their parts in a network flow optimization framework and show that parts of this network need to be optimized separately and iteratively, due to inter-dependencies of node and edge costs. Given potential detections of humans and their parts separately, an initial set of pedestrian tracklets is first obtained, followed by explicit tracking of human parts as constrained by initial human tracking. A merging step is then performed whereby we attempt to include part-only detections for which the entire human is not observable. This step employs a selective appearance model, which allows us to skip occluded parts in description of positive training samples. The result is high confidence, robust trajectories of pedestrians as well as their parts, which essentially constrain each other’s locations and associations, thus improving human tracking and parts detection. We test our algorithm on multiple real datasets and show that the proposed algorithm is an improvement over the state-of-the-art.


The LLVM Compiler Framework and Infrastructure Tutorial

The LLVM Compiler Infrastructure (http://llvm.cs. uiuc.edu) is a robust system that is well suited for a wide variety of research and development work. This brief paper introduces the LLVM system and provides pointers to more extensive documentation, complementing the tutorial presented at LCPC. 1 Brief Overview and Goals The LLVM Compiler Infrastructure [2] is a language and target-independent compiler system, designed for both static and dynamic compilation. LLVM (Low Level Virtual Machine) can be used to build both traditional optimizing compilers for high performance programming languages as well as compiler-based tools such as Just-In-Time (JIT) translators, profilers, binary translators, memory sandboxing tools, static analysis tools, and others. A major goal of the LLVM development community is to provide a fast, robust, and well-documented core framework to support high-quality compilers and tools. A second major goal is to develop a broad community of contributors to ensure that the software continues to grow and be relevant, and to maintain its robustness and quality. A third goal is to provide a flexible infrastructure for new compiler development and research. 2 Key LLVM Design Features The LLVM system has three main components: the LLVM Virtual Instruction Set, a collection of reusable libraries for analysis, transformation and code generation; and tools built from these libraries. The LLVM framework is characterized by a clean, simple, and modular design, which allows new users to understand the representation, write new analyses, transformations, and tools easily. The LLVM Virtual Instruction Set is the common intermediate representation shared by all of the LLVM subsystems. It is a simple, mid-level, threeaddress code representation that is designed to be both language-independent and target-independent [1]. The instruction set provides explicit control-flow graphs, explicit dataflow information (in SSA form), and a mid-level, language R. Eigenmann et al. (Eds.): LCPC 2004, LNCS 3602, pp. 15–16, 2005. c © Springer-Verlag Berlin Heidelberg 2005 16 C. Lattner and V. Adve independent type system. The type system is rich enough to support sophisticated, language-independent compiler techniques, including pointer analysis, dependence analysis, and interprocedural dataflow analyses, and transformations based on them. Exactly the same instruction set is used both as a persistent, external representation for compiled code and as the internal representation for all mid-level compiler passes. Making this representation persistent enables all LLVM passes to be used at compile-time, link-time, load-time, run-time, and “idle-time” (between program runs). The key design features and the innovative aspects of the code representation are are described in a paper [2], and the complete instruction set is described in an online reference manual [1]. The LLVM source base mostly consists of modular and reusable libraries. These components include analyses and optimizations, native code generators, JIT compiler support, profile-guided feedback, etc. These compiler components reduce the cost and difficulty of building compilers for new platforms, new source languages, and for building new kinds of tools (debuggers, profilers, etc). The LLVM system also includes several complete tools built from these components, including an ANSI-conforming C/C++ compiler (which uses the GNU Compiler Collection (GCC) parsers). The C/C++ compiler applies a large number of module-level (compile-time) and cross-module (link-time) optimizations. Other LLVM tools include programs to manipulate the IR (e.g. convert between ASCII and binary formats, extract functions from a program, etc), a modular optimizer (which can run any LLVM optimization on an program, driven from the command line), automated compiler debugging tools, and others.


Normal vulvovaginal, perineal, and pelvic anatomy with reconstructive considerations.

A thorough insight into the female genital anatomy is crucial for understanding and performing pelvic reconstructive procedures. The intimate relationship between the genitalia and the muscles, ligaments, and fascia that provide support is complex, but critical to restore during surgery for correction of prolapse or aesthetic reasons. The external female genitalia include the mons pubis, labia majora and minora, clitoris, vestibule with glands, perineal body, and the muscles and fascia surrounding these structures. Through the perineal membrane and the perineal body, these superficial vulvar structures are structurally related to the deep pelvic muscle levator ani with its fascia. The levator ani forms the pelvic floor with the coccygeus muscle and provides vital support to all the pelvic organs and stability to the perineum. The internal female genital organs include the vagina, cervix, uterus, tubes, and ovaries with their visceral fascia. The visceral fascia also called the endopelvic fascia, surrounds the pelvic organs and connects them to the pelvic walls. It is continuous with the paraurethral and paravaginal fascia, which is attached to the perineal membrane. Thus, the internal and external genitalia are closely related to the muscles and fascia, and work as one functioning unit.


Automatic Color Calibration for Large Camera Arrays

We present a color calibration pipeline for large camera arrays. We assume static lighting conditions for each camera, such as studio lighting or a stationary array outdoors. We also assume we can place a planar calibration target so it is visible from every camera. Our goal is uniform camera color responses, not absolute color accuracy, so we match the cameras to each other instead of to a color standard. We first iteratively adjust the color channel gains and offsets for each camera to make their responses as similar as possible. This step white balances the cameras, and for studio applications, ensures that the range of intensities in the scene are mapped to the usable output range of the cameras. Residual errors are then calibrated in post-processing. We present results calibrating an array of 100 CMOS image sensors in different physical configurations, including closely or widely spaced cameras with overlapping fields of views, and tightly packed cameras with non-overlapping fields of view. The process is entirely automatic, and the camera configuration runs in less than five minutes on the 100 camera array. CR Categories: I.4.1 [Image Processing and Computer Vision]: Digitization and Image Capture.Camera Calibration;


Learning values across many orders of magnitude

Most learning algorithms are not invariant to the scale of the function that is being approximated. We propose to adaptively normalize the targets used in learning. This is useful in value-based reinforcement learning, where the magnitude of appropriate value approximations can change over time when we update the policy of behavior. Our main motivation is prior work on learning to play Atari games, where the rewards were all clipped to a predetermined range. This clipping facilitates learning across many different games with a single learning algorithm, but a clipped reward function can result in qualitatively different behavior. Using the adaptive normalization we can remove this domain-specific heuristic without diminishing overall performance.


Propagation Models for Trust and Distrust in Social Networks

Semantic Web endeavors have mainly focused on issues pertaining to knowledge representation and ontology design. However, besides understanding information metadata stated by subjects, knowing about their credibility becomes equally crucial. Hence, trust and trust metrics, conceived as computational means to evaluate trust relationships between individuals, come into play. Our major contribution to Semantic Web trust management through this work is twofold. First, we introduce a classification scheme for trust metrics along various axes and discuss advantages and drawbacks of existing approaches for Semantic Web scenarios. Hereby, we devise an advocacy for local group trust metrics, guiding us to the second part which presents Appleseed, our novel proposal for local group trust computation. Compelling in its simplicity, Appleseed borrows many ideas from spreading activation models in psychology and relates their concepts to trust evaluation in an intuitive fashion. Moreover, we provide extensions for the Appleseed nucleus that make our trust metric handle distrust statements.


The Human Element in Autonomous Vehicles

Autonomous vehicle research has been prevalent for well over a decade but only recently has there been a small amount of research conducted on the human interaction that occurs in autonomous vehicles. Although functional software and sensor technology is essential for safe operation, which has been the main focus of autonomous vehicle research, handling all elements of human interaction is also a very salient aspect of their success. This paper will provide an overview of the importance of human vehicle interaction in autonomous vehicles, while considering relevant related factors that are likely to impact adoption. Particular attention will be given to prior research conducted on germane areas relating to control in the automobile, in addition to the different elements that are expected to affect the likelihood of success for these vehicles initially developed for human operation. This paper will also include a discussion of the limited research conducted to consider interactions with humans and the current state of published functioning software and sensor technology that exists.


Data and Code for "Automatic Identification of Narrative Diegesis and Point of View"

The style of narrative news affects how it is interpreted and received by readers. Two key stylistic characteristics of narrative text are point of view and diegesis: respectively, whether the narrative recounts events personally or impersonally, and whether the narrator is involved in the events of the story. Although central to the interpretation and reception of news, and of narratives more generally, there has been no prior work on automatically identifying these two characteristics in text. We develop automatic classifiers for point of view and diegesis, and compare the performance of different feature sets for both. We built a goldstandard corpus where we double-annotated to substantial agreement (κ > 0.59) 270 English novels for point of view and diegesis. As might be expected, personal pronouns comprise the best features for point of view classification, achieving an average F1 of 0.928. For diegesis, the best features were personal pronouns and the occurrences of first person pronouns in the argument of verbs, achieving an average F1 of 0.898. We apply the classifier to nearly 40,000 news texts across five different corpora comprising multiple genres (including newswire, opinion, blog posts, and scientific press releases), and show that the point of view and diegesis correlates largely as expected with the nominal genre of the texts. We release the training data and the classifier for use by the community.


A fast, invariant representation for human action in the visual system.

Humans can effortlessly recognize others' actions in the presence of complex transformations, such as changes in viewpoint. Several studies have located the regions in the brain involved in invariant action recognition; however, the underlying neural computations remain poorly understood. We use magnetoencephalography decoding and a data set of well-controlled, naturalistic videos of five actions (run, walk, jump, eat, drink) performed by different actors at different viewpoints to study the computational steps used to recognize actions across complex transformations. In particular, we ask when the brain discriminates between different actions, and when it does so in a manner that is invariant to changes in 3D viewpoint. We measure the latency difference between invariant and noninvariant action decoding when subjects view full videos as well as form-depleted and motion-depleted stimuli. We were unable to detect a difference in decoding latency or temporal profile between invariant and noninvariant action recognition in full videos. However, when either form or motion information is removed from the stimulus set, we observe a decrease and delay in invariant action decoding. Our results suggest that the brain recognizes actions and builds invariance to complex transformations at the same time and that both form and motion information are crucial for fast, invariant action recognition. NEW & NOTEWORTHY The human brain can quickly recognize actions despite transformations that change their visual appearance. We use neural timing data to uncover the computations underlying this ability. We find that within 200 ms action can be read out of magnetoencephalography data and that this representation is invariant to changes in viewpoint. We find form and motion are needed for this fast action decoding, suggesting that the brain quickly integrates complex spatiotemporal features to form invariant action representations.


Active sampling for entity matching

In entity matching, a fundamental issue while training a classifier to label pairs of entities as either duplicates or non-duplicates is the one of selecting informative training examples. Although active learning presents an attractive solution to this problem, previous approaches minimize the misclassification rate (0-1 loss) of the classifier, which is an unsuitable metric for entity matching due to class imbalance (i.e., many more non-duplicate pairs than duplicate pairs). To address this, a recent paper [1] proposes to maximize recall of the classifier under the constraint that its precision should be greater than a specified threshold. However, the proposed technique requires the labels of all n input pairs in the worst-case.
 Our main result is an active learning algorithm that approximately maximizes recall of the classifier while respecting a precision constraint with provably sub-linear label complexity (under certain distributional assumptions). Our algorithm uses as a black-box any active learning module that minimizes 0-1 loss. We show that label complexity of our algorithm is at most log n times the label complexity of the black-box, and also bound the difference in the recall of classifier learnt by our algorithm and the recall of the optimal classifier satisfying the precision constraint. We provide an empirical evaluation of our algorithm on several real-world matching data sets that demonstrates the effectiveness of our approach.


Device analyzer: largescale mobile data collection

We collected usage information from 12,500 Android devices in the wild over the course of nearly 2 years. Our dataset contains 53 billion data points from 894 models of devices running 687 versions of Android. Processing the collected data presents a number of challenges ranging from scalability to consistency and privacy considerations. We present our system architecture for collection and analysis of this highly-distributed dataset, discuss how our system can reliably collect time-series data in the presence of unreliable timing information, and discuss issues and lessons learned that we believe apply to many other big data collection projects.


Predictors of Computer Use in Community-Dwelling, Ethnically Diverse Older Adults

OBJECTIVE
In this study, we analyzed self-reported computer use, demographic variables, psychosocial variables, and health and well-being variables collected from 460 ethnically diverse, community-dwelling elders to investigate the relationship computer use has with demographics, well-being, and other key psychosocial variables in older adults.

BACKGROUND
Although younger elders with more education, those who employ active coping strategies, or those who are low in anxiety levels are thought to use computers at higher rates than do others, previous research has produced mixed or inconclusive results regarding ethnic, gender, and psychological factors or has concentrated on computer-specific psychological factors only (e.g., computer anxiety). Few such studies have employed large sample sizes or have focused on ethnically diverse populations of community-dwelling elders.

METHOD
With a large number of overlapping predictors, zero-order analysis alone is poorly equipped to identify variables that are independently associated with computer use. Accordingly, both zero-order and stepwise logistic regression analyses were conducted to determine the correlates of two types of computer use: e-mail and general computer use.

RESULTS
Results indicate that younger age, greater level of education, non-Hispanic ethnicity, behaviorally active coping style, general physical health, and role-related emotional health each independently predicted computer usage.

CONCLUSION
Study findings highlight differences in computer usage, especially in regard to Hispanic ethnicity and specific health and well-being factors.

APPLICATION
Potential applications of this research include future intervention studies, individualized computer-based activity programming, or customizable software and user interface design for older adults responsive to a variety of personal characteristics and capabilities.


Searching Solitaire in Real Time

This article presents a new real-time heuristic search method for planning problems with distinct stages. Our multistage nested rollout algorithm allows the user to apply separate heuristics at each stage of the search process and tune the search magnitude for each stage. We propose a searchtree compression that reveals a new state representation for the games of Klondike Solitaire and Thoughtful Solitaire, a version of Klondike Solitaire in which the location of all cards is known. Moreover, we present a Thoughtful Solitaire solver based on these methods that can determine over 80% of Thoughtful Solitaire games in less than 4 seconds. Finally, we demonstrate empirically that no less than 82% and no more than 91.44% of Klondike Solitaire games have winning solutions, leaving less than 10% of games unresolved.