title: 56-Gb/s Optical Transmission Performance of an InP HBT PAM4 Driver Compensating for Nonlinearity of Extinction Curve of EAM

abstract: This paper presents a four-level pulse amplitude modulation (PAM4) driver IC that can compensate for the nonlinearity of the extinction curve of an electroabsorption modulator (EAM). The driver was designed and fabricated by using our InP HBT technology ( $f_{t}\,= \,{\text{290 GHz}}$ , $f_{{\rm{max}}}\,= \,{\text{320 GHz}}$ ). To obtain a high extinction ratio, when an EAM is driven in the nonlinear region with PAM4 signals, eye patterns of PAM4 optical waveforms are unequally spaced. This driver can adjust the modulation voltage swing for each intensity level to achieve clear eye openings in the optical domain. The results show clear eye openings of the optical output waveform with compensation for the nonlinearity. To evaluate the effect of the compensation quantitatively, the bit error rate (BER) was measured. Minimum receiver sensitivities of –9 and –8 dBm were observed for the back-to-back (B to B) configuration and transmission over 20-km-long single-mode fiber with the compensation at a BER of $2\,\times \,10^{- 4}$ , while error-free operation was not observed at the BER without our proposed compensation technique. Furthermore, our PAM4 driver compensates for nonlinearity of various extinction curves of an EAM. For the B to B configuration, a minimum receiver sensitivity below –8 dBm was observed at the BER for each extinction curve.


Message Queues in Industrial IoT


title: There is more to context than location

abstract: Abstract Context is a key issue in interaction between human and computer, describing the surrounding facts that add meaning. In mobile computing location is usually used to approximate context and to implement context-aware applications. We propose that ultra-mobile computing, characterized by devices that are operational and operated while on the move (e.g. PDAs, mobile phones, wearable computers), can significantly benefit from a wider notion of context. To structure the field we introduce a working model for context, discuss mechanisms to acquire context beyond location, and application of context-awareness in ultra-mobile computing. We investigate the utility of sensors for context-awareness and present two prototypical implementations — a light-sensitive display and an orientation-aware PDA interface. The concept is then extended to a model for sensor fusion to enable more sophisticated context recognition. Based on an implementation of the model an experiment is described and the feasibility of the approach is demonstrated. Further, we explore fusion of sensors for acquisition of information on more sophisticated contexts.


title: GREP: Guaranteeing Reliability with Enhanced Protection in NFV

abstract: Network Function Virtualization (NFV) is a promising technique to greatly improve the effectiveness and flexibility of network management through a process called Service Function Chain (SFC) mapping, which can efficiently provision network services over a virtualized and shared middlebox platform. However, such an evolution towards software-defined middlebox introduces new challenges to network services which require high reliability. Sufficient redundancy can protect the network services when physical failures occur, but in doing so, the efficiency of physical resources may be greatly decreased. This paper presents GREP, a novel online algorithm that can minimize the physical resources consumption while guaranteeing the required high reliability with a polynomial time complexity. Simulation results show that our proposed algorithm can significantly improve the request acceptance ratio and reduce resource consumption.


title: Towards a standard for pointing device evaluation , perspectives on 27 years of Fitts ’ law research in HCI

abstract: This paper makes seven recommendations to HCI researchers wishing to construct Fitts' law models for either movement time prediction, or for the comparison of conditions in an experiment. These seven recommendations support (and in some cases supplement) the methods described in the recent ISO 9241-9 standard on the evaluation of pointing devices. In addition to improving the robustness of Fitts' law models, these recommendations (if widely employed) will improve the comparability and consistency of forthcoming publications. Arguments to support these recommendations are presented, as are concise reviews of 24 published Fitts' law models of the mouse, and 9 studies that used the new ISO standard.


title: Reverse engineering of embedded software using syntactic pattern recognition

abstract: When a secure component executes sensitive operations, the information carried by the power consumption can be used to recover secret information Many different techniques have been developped to recover this secret, but only few of them focus on the recovering of the executed code itself Indeed, the code knowledge acquired through this step of Simple Power Analysis (SPA) can help to identify implementation weaknesses and to improve further kinds of attacks In this paper we present a new approach improving the SPA based on a pattern recognition methodology, that can be used to automatically identify the processed instructions that leak through power consumption We firstly process a geometrical classification with chosen instructions to enable the automatic identification of any sequence of instructions Such an analysis is used to reverse general purpose code executions of a recent secure component.


Understanding Structural and Semantic Heterogeneity in the Context of Database Schema Integration


title: Can fish suffer?: perspectives on sentience, pain, fear and stress

abstract: In contrast to other major forms of livestock agriculture, there is a paucity of scientific information on the welfare of fish raised under intensive aquacultural conditions. This reflects an adherence to the belief that these animals have not evolved the salient biological characteristics that are hypothesised to permit sentience. In this review, we evaluate the scientific evidence for the existence of sentience in fish, and in particular, their ability to experience pain, fear and psychological stress. Teleost fish are considered to have marked differences in some aspects of brain structure and organization as compared to tetrapods, yet they simultaneously demonstrate functional similarities and a level of cognitive development suggestive of sentience. Anatomical, pharmacological and behavioural data suggest that affective states of pain, fear and stress are likely to be experienced by fish in similar ways as in tetrapods. This implies that fish have the capacity to suffer, and that welfare consideration for farmed fish should take these states into account. We suggest that the concept of animal welfare can be applied legitimately to fish. It is therefore appropriate to recognize and study the welfare of farmed fish.


title: Fast and accurate prediction of sentence specificity

abstract: Recent studies have demonstrated that specificity is an important characterization of texts potentially beneficial for a range of applications such as multi-document news summarization and analysis of science journalism. The feasibility of automatically predicting sentence specificity from a rich set of features has also been confirmed in prior work. In this paper we present a practical system for predicting sentence specificity which exploits only features that require minimum processing and is trained in a semi-supervised manner. Our system outperforms the state-of-the-art method for predicting sentence specificity and does not require part of speech tagging or syntactic parsing as the prior methods did. With the tool that we developed — SPECITELLER — we study the role of specificity in sentence simplification. We show that specificity is a useful indicator for finding sentences that need to be simplified and a useful objective for simplification, descriptive of the differences between original and simplified sentences.


title: From Pretty Good To Great: Enhancing PGP using Bitcoin and the Blockchain

abstract: PGP is built upon a Distributed Web of Trust in which a user’s trustworthiness is established by others who can vouch through a digital signature for that user’s identity. Preventing its wholesale adoption are a number of inherent weaknesses to include (but not limited to) the following: 1) Trust Relationships are built on a subjective honor system, 2) Only first degree relationships can be fully trusted, 3) Levels of trust are difficult to quantify with actual values, and 4) Issues with the Web of Trust itself (Certification and Endorsement). Although the security that PGP provides is proven to be reliable, it has largely failed to garner large scale adoption. In this paper, we propose several novel contributions to address the aforementioned issues with PGP and associated Web of Trust. To address the subjectivity of the Web of Trust, we provide a new certificate format based on Bitcoin which allows a user to verify a PGP certificate using Bitcoin identity-verification transactions - forming first degree trust relationships that are tied to actual values (i.e., number of Bitcoins transferred during transaction). Secondly, we present the design of a novel Distributed PGP key server that leverages the Bitcoin transaction blockchain to store and retrieve our certificates.


title: An algorithm for detecting brain tumors in MRI images

abstract: In this paper, a computer-based method for defining tumor region in the brain using MRI images is presented. A classification of brain into healthy brain or a brain having a tumor is first done which is then followed by further classification into begnin or malignant tumor. The algorithm incorporates steps for preprocessing, image segmentation, feature extraction and image classification using neural network techniques. Finally the tumor area is specified by region of interest technique as confirmation step. A user friendly Matlab GUI program has been constructed to test the proposed algorithm.


title: A Note on the Fundamental Limits of Coded Caching

abstract: The fundamental limit of coded caching is investigated for the case with $N=3$ files and $K=3$ users. An improved outer bound is obtained through the computational approach developed by the author in an earlier work. This result is part of the online collection of "Solutions of Computed Information Theoretic Limits (SCITL)".


title: Single-Image SVBRDF Capture with a Rendering-Aware Deep Network

abstract: A deep learning method to capture spatially varying materials from a single picture. Our deep network uses procedural materials as training data, and image re-renderings as a measure of quality.


Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series


title: Performance evaluation of fast TCP and TCP Westwood+ for multimedia streaming in wireless environment

abstract: Although TCP is one of the key protocols of the Internet infrastructure, it is not optimized for either wireless environment or multimedia streaming applications. It fails to meet the service requirement of streaming applications because of its strict adherence to congestion control. Besides, non-congestion packet loss fools TCP to slow down its sending rate over wireless links. Fast TCP and TCP Westwood+ are two well known TCP variants which are supposed to resolve these problems. This paper analyzes the performance of TCP Westwood+ and Fast TCP for various issues in wireless network for multimedia streaming. Simulation results show that, Fast TCP shows better goodput, average throughput, and fairness index but TCP Westwood+ shows less jitter and better performance in presence of heavy reverse flows.


title: Situated Analytics

abstract: We consider the question of how Augmented Reality can be used for applications that require interactive analytical reasoning embedded in the physical environment. We present novel interaction and visualization techniques for supporting such reasoning, which we call Situated Analytics. Our approach provides situated and abstract information representation with real-time analytical interaction. Three methods of interaction, filtering, finding and ranking are explored with a diminished reality presentation method are employed to highlight analytical results. We conducted a user study to evaluate user performance with three shopping analytics tasks, comparing the Situated Analytics prototype to manual analysis. The results showed that users preferred the Situated Analytics prototype, compared to manual method, and were performed more quickly and accurately.


FUSIONS OF CNN AND SVM CLASSIFIERS FOR RECOGNIZING HANDWRITTEN CHARACTERS


title: Fast fingerprinting based indoor localization by Wi-Fi signals

abstract: Indoor localization is vital for location based services as well as security purposes. Wireless local area network (WLAN) positioning systems based on fingerprints of received signal strength (RSS) measured from access points, can be used for localization. Due to the effectiveness of fingerprinting in indoor positioning, fingerprinting network designers try to lower the cost of using this technique in terms of time and energy. In this paper, a fast fingerprinting method is presented, using indoor path-loss model instead of using reference points with complicated measurement devices. The accuracy of the localization by the proposed model is compared with the experimental RSS data collected via smart-phones and the feasibility of the idea is proved.


title: Deep edge-color invariant features for 2D/3D car fine-grained classification

abstract: Given an untextured 3D car models dataset, are we able to learn a robust make and model classifier which will be applied on real color images? One solution consists in finding a common representation between synthetic edge images and real color images. To address this issue, we introduce novel edge-color invariant features for 2D/3D car fine-grained classification. These features are learned simultaneously on real color and edge images of cars using a deep architecture. Using these accurate features, we propose to learn on edges synthetic images a fine-grained classifier which will be afterwards applied to real color images. The 3D car models dataset allows to automatically generate multi-views synthetic images using non-photorealistic edge rendering. Experimentally, we show efficiency of our learned edge-color invariant features for make and model recognition.


title: Learning to Predict Severity of Software Vulnerability Using Only Vulnerability Description

abstract: Software vulnerabilities pose significant security risks to the host computing system. Faced with continuous disclosure of software vulnerabilities, system administrators must prioritize their efforts, triaging the most critical vulnerabilities to address first. Many vulnerability scoring systems have been proposed, but they all require expert knowledge to determine intricate vulnerability metrics. In this paper, we propose a deep learning approach to predict multi-class severity level of software vulnerability using only vulnerability description. Compared with intricate vulnerability metrics, vulnerability description is the "surface level" information about how a vulnerability works. To exploit vulnerability description for predicting vulnerability severity, discriminative features of vulnerability description have to be defined. This is a challenging task due to the diversity of software vulnerabilities and the richness of vulnerability descriptions. Instead of relying on manual feature engineering, our approach uses word embeddings and a one-layer shallow Convolutional Neural Network (CNN) to automatically capture discriminative word and sentence features of vulnerability descriptions for predicting vulnerability severity. We exploit large amounts of vulnerability data from the Common Vulnerabilities and Exposures (CVE) database to train and test our approach.


title: Automatic classification of sleep stages based on the time-frequency image of EEG signals

abstract: In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals.


title: EyeRing: a finger-worn assistant

abstract: Finger-worn interfaces are a vastly unexplored space for interaction design. It opens a world of possibilities for solving day-to-day problems, for visually impaired people and sighted people. In this work we present EyeRing, a novel design and concept of a finger-worn device. We show how the proposed system may serve for numerous applications for visually impaired people such as recognizing currency notes and navigating, as well as helping sighted people to tour an unknown city or intuitively translate signage. The ring apparatus is autonomous, however it is counter parted by a mobile phone or computation device to which it connects wirelessly, and an earpiece for information retrieval. Finally, we will discuss how finger worn sensors may be extended and applied to other domains.


title: Compliance-Aware Bandits

abstract: Motivated by clinical trials, we study bandits with observable non-compliance. At each step, the learner chooses an arm, after, instead of observing only the reward, it also observes the action that took place. We show that such noncompliance can be helpful or hurtful to the learner in general. Unfortunately, naively incorporating compliance information into bandit algorithms loses guarantees on sublinear regret. We present hybrid algorithms that maintain regret bounds up to a multiplicative factor and can incorporate compliance information. Simulations based on real data from the International Stoke Trial show the practical potential of these algorithms.


Using Deep Neural Networks to Learn Syntactic


title: A Scalable Parallel Algorithm for Self-Organizing Maps with Applications to Sparse Data Mining Problems

abstract: We describe a scalable parallel implementation of the self organizing map (SOM) suitable for data-mining applications involving clustering or segmentation against large data sets such as those encountered in the analysis of customer spending patterns. The parallel algorithm is based on the batch SOM formulation in which the neural weights are updated at the end of each pass over the training data. The underlying serial algorithm is enhanced to take advantage of the sparseness often encountered in these data sets. Analysis of a realistic test problem shows that the batch SOM algorithm captures key features observed using the conventional on-line algorithm, with comparable convergence rates.Performance measurements on an SP2 parallel computer are given for two retail data sets and a publicly available set of census data.These results demonstrate essentially linear speedup for the parallel batch SOM algorithm, using both a memory-contained sparse formulation as well as a separate implementation in which the mining data is accessed directly from a parallel file system. We also present visualizations of the census data to illustrate the value of the clustering information obtained via the parallel SOM method.


title: Food recognition and recipe analysis: integrating visual content, context and external knowledge

abstract: The central role of food in our individual and social life, combined with recent technological advances, has motivated a growing interest in applications that help to better monitor dietary habits as well as the exploration and retrieval of food-related information. We review how visual content, context and external knowledge can be integrated effectively into food-oriented applications, with special focus on recipe analysis and retrieval, food recommendation, and the restaurant context as emerging directions.


Elements of algebra


Automatic Summary Generation for Scientific Data Charts.


title: Independently Controllable Features

abstract: Finding features that disentangle the different causes of variation in real data is a difficult task, that has nonetheless received considerable attention in static domains like natural images. Interactive environments, in which an agent can deliberately take actions, offer an opportunity to tackle this task better, because the agent can experiment with different actions and observe their effects. We introduce the idea that in interactive environments, latent factors that control the variation in observed data can be identified by figuring out what the agent can control. We propose a naive method to find factors that explain or measure the effect of the actions of a learner, and test it in illustrative experiments.


title: Memetic particle swarm optimization

abstract: We propose a new Memetic Particle Swarm Optimization scheme that incorporates local search techniques in the standard Particle Swarm Optimization algorithm, resulting in an efficient and effective optimization method, which is analyzed theoretically. The proposed algorithm is applied to different unconstrained, constrained, minimax and integer programming problems and the obtained results are compared to that of the global and local variants of Particle Swarm Optimization, justifying the superiority of the memetic approach.


title: A 31.3-dBm Bulk CMOS T/R Switch Using Stacked Transistors With Sub-Design-Rule Channel Length in Floated p-Wells

abstract: A 31.3-dBm 900-MHz bulk CMOS T/R switch with transmit (TX) and receive (RX) insertion losses of 0.5 and 1.0 dB and isolation of 29 dB is demonstrated. The switch utilizes a floating-body technique, feed-forward capacitors, and 3-stack 3.3-V MOSFETs with 0.26-mum sub-design-rule (SDR) channel length. Using these, a 28-dBm 2.4-GHz T/R switch with TX and RX insertion losses of 0.8 and 1.2 dB, and isolation of 24 dB is also demonstrated. The power handling capability is limited by an abrupt output power drop before reaching the normal 1-dB compression point. The circuits are implemented in the UMC 130-nm mixed-mode triple-well CMOS process.


title: Automatic real-time detection of endoscopic procedures using temporal features

abstract: Endoscopy is used for inspection of the inner surface of organs such as the colon. During endoscopic inspection of the colon or colonoscopy, a tiny video camera generates a video signal, which is displayed on a monitor for interpretation in real-time by physicians. In practice, these images are not typically captured, which may be attributed by lack of fully automated tools for capturing, analysis of important contents, and quick and easy retrieval of these contents. This paper presents the description and evaluation results of our novel software that uses new metrics based on image color and motion over time to automatically record all images of an individual endoscopic procedure into a single digitized video file. The software automatically discards out-patient video frames between different endoscopic procedures. We validated our software system on 2464h of live video (over 265 million frames) from endoscopy units where colonoscopy and upper endoscopy were performed. Our previous classification method achieved a frame-based sensitivity of 100.00%, but only a specificity of 89.22%. Our new method achieved a frame-based sensitivity and specificity of 99.90% and 99.97%, a significant improvement. Our system is robust for day-to-day use in medical practice.


title: Resolver compatible capacitive rotary position sensor

abstract: This paper describes a capacitive rotary position sensor that is characterized by its high compatibility with commercial resolvers. The main components are two electrode plates. Both parts are composed of simple circular or annular electrodes. The sensor is excited by two sinusoidal voltages, which are same as the excitation voltages of resolvers. These excitation voltages are applied to the stator transmitting electrodes. Then, these voltages arise on rotor electrodes by capacitive couplings. The stator pick-up electrodes detect voltages on the rotor that are amplitude-modulated due to the capacitance changes depending on the rotor position. The specifications of these signals are same as those of resolvers. Therefore, the rotor position can be calculated by the same way as resolvers. Due to the similarities with resolvers (excitation signals, generated signals and the way of calculate positions), this sensor can easily replace a commercial resolver. In this paper, the principle is verified by a prototype sensor, which shows a non-linearity error of +/−4 degrees.


title: Banknote Counterfeit Detection through Background Texture Printing Analysis

abstract: This paper is focused on the detection of counterfeit photocopy banknotes. The main difficulty is to work on a real industrial scenario without any constraint about the acquisition device and with a single image. The main contributions of this paper are twofold: first the adaptation and performance evaluation of existing approaches to classify the genuine and photocopy banknotes using background texture printing analysis, which have not been applied into this context before. Second, a new dataset of Euro banknotes images acquired with several cameras under different luminance conditions to evaluate these methods. Experiments on the proposed algorithms show that mixing SIFT features and sparse coding dictionaries achieves quasi perfect classification using a linear SVM with the created dataset. Approaches using dictionaries to cover all possible texture variations have demonstrated to be robust and outperform the state-of-the-art methods using the proposed benchmark.


title: Realistic 3D Face Modeling by Fusing Multiple 2D Images

abstract: In this paper, we propose a fully automatic and efficient algorithm for realistic 3D face reconstruction by fusing multiple 2D face images. Firstly, an efficient multi-view 2D face alignment algorithm is utilized to localize the facial points of the face images; and then the intrinsic shape and texture models are inferred by the proposed Syncretized Shape Model (SSM) and Syncretized Texture Model (STM), respectively. Compared with other related works, our proposed algorithm has the following characteristics: 1) the inferred shape and texture are more realistic owing to the constraints and co-enhancement among the multiple images; 2) it is fully automatic, without any user interaction; and 3) the shape and pose parameter estimation is efficient via EM approach and unit quaternion based pose representation, and is also robust as a result of the dynamic correspondence approach. The experimental results show the effectiveness of our proposed algorithm for 3D face reconstruction.


Improving Usability Throug Visualization -- Predictive Web Usage Visualizations can . . .


title: Effects of mindfulness training on different components of impulsivity in borderline personality disorder: results from a pilot randomized study

abstract: Impulsivity is considered a core characteristic of borderline personality disorder (BPD). Previous research on the effects of mindfulness training (MT) has shown that it might modify impulsivity-related aspects of BPD. Therefore, the aim of this study was to investigate the impact of MT on various facets of impulsivity in BPD patients. Subjects with BPD diagnosis (n = 64) were randomly assigned to 10 weeks of MT (n = 32) or interpersonal effectiveness skills training (IE; n = 32). All participants were assessed pre- and post-intervention with a self-reported measure of impulsivity and five behavioral neuropsychological tasks to evaluate response inhibition, tolerance for delay rewards, and time perception. An interaction effect of time × group was only observed for some of the behavioral paradigms used. Participants in the MT group improved their ability to delay gratification and showed changes in time perception, consistent with a decrease in impulsivity. No differences were observed between treatments in terms of trait impulsivity and response inhibition. Mindfulness training might improve some aspects of impulsivity but not others. Further study is warranted to better determine the effects of mindfulness training on the components of impulsivity. ClinicalTrials.gov Identifier: NCT02397031.


title: Image Anomaly Detection with Generative Adversarial Networks

abstract: Many anomaly detection methods exist that perform well on low-dimensional problems however there is a notable lack of effective methods for high-dimensional spaces, such as images. Inspired by recent successes in deep learning we propose a novel approach to anomaly detection using generative adversarial networks. Given a sample under consideration, our method is based on searching for a good representation of that sample in the latent space of the generator; if such a representation is not found, the sample is deemed anomalous. We achieve state-of-the-art performance on standard image benchmark datasets and visual inspection of the most anomalous samples reveals that our method does indeed return anomalies.


The Study and Implementation of Shazam ’ s Audio Fingerprinting Algorithm for Advertisement Identification


What Action Causes This? Towards Naive Physical Action-Effect Prediction


A mixed-method empirical study of Function-as-a-Service software development in industrial practice


Outlier detection in multivariate time series: Exploiting reconstructions from random projections


Neural Approaches Towards Text Summarization


title: Bilinear Attention Networks

abstract: Attention networks in multimodal learning provide an efficient way to utilize given visual information selectively. However, the computational cost to learn attention distributions for every pair of multimodal input channels is prohibitively expensive. To solve this problem, co-attention builds two separate attention distributions for each modality neglecting the interaction between multimodal inputs. In this paper, we propose bilinear attention networks (BAN) that find bilinear attention distributions to utilize given vision-language information seamlessly. BAN considers bilinear interactions among two groups of input channels, while low-rank bilinear pooling extracts the joint representations for each pair of channels. Furthermore, we propose a variant of multimodal residual networks to exploit eight-attention maps of the BAN efficiently. We quantitatively and qualitatively evaluate our model on visual question answering (VQA 2.0) and Flickr30k Entities datasets, showing that BAN significantly outperforms previous methods and achieves new state-of-the-arts on both datasets.


title: Sleep deprivation-induced reduction in cortical functional response to serial subtraction.

abstract: Thirteen normal volunteers were studied with fMRI during arithmetic performance after a normal night of sleep and following sleep deprivation (SD). Aims included determining whether the prefrontal cortex (PFC) and the parietal lobe arithmetic areas are vulnerable to the effects of SD. After a normal night of sleep, activation localized to the bilateral PFC, parietal lobes and premotor areas. Following SD, activity in these regions decreased markedly, especially in the PFC. Performance also dropped. Data from the serial subtraction task are consistent with Horne's PFC vulnerability hypothesis but, based on this and other studies, we suggest the localized, functional effects of SD in the brain may vary, in part, with the specific cognitive task.


title: The role of service level agreements in relational management of information technology outsourcing: an empirical study

abstract: This study extends the view that formal contracts and relational governance function as complements rather than as substitutes. We investigate how specific characteristics of service level agreements (SLAs) impact relational governance in information technology outsourcing relationships. Eleven contractual elements (categorized into three SLA characteristics: foundation, change, and governance characteristics) are hypothesized to act as complements of three relational governance attributes: relational norms, harmonious conflict resolution, and mutual dependence. Data for the study were collected through a survey of South Korean IT executives. Results of the study support the fundamental proposition of complementarity between formal contracts and relational governance, and indicate that well-structured SLAs have significant positive influence on the various aspects of relational governance in IT outsourcing relationships. However, the study also reveals that change characteristics of SLAs may act as a substitute for relational governance as these characteristics were found to dampen the level of trust and commitment through moderation effects. Overall, the findings support the proposition that well-developed SLAs not only provide a way to measure the service provider's performance, but also enable effective management of outsourcing engagements through the development of partnership-style relationships with high levels of trust and commitment.


title: FPGA-Based Advanced Real Traffic Light Controller System Design

abstract: Traffic light controller establishes a set of rules and instructions that drivers, pilots, train engineers, and ship captains rely on to avoid collisions and other hazards. Traffic control systems include signs, lights and other devices that communicate specific directions, warnings, or requirements. Traffic light controller (TLC) has been implemented using microcontroller FPGA, and ASIC design. FPGA has many advantages over microcontroller, some of these advantages are; the speed, number of input/output ports and performance which are all very important in TLC design, at the same time ASIC design is more expensive than FPGA. Most of the TLCs implemented on FPGA are simple ones that have been implemented as examples of FSM. This paper concerned with an FPGA design implementation of a low cost 24-hour advanced traffic light controller system that was built as a term project of a VLSI design subject using VHDL. The implemented traffic light is one of the real and complex traffic lights in Kingdom of Bahrain, for four roads and motorway with sensors and camera. The system has been successfully tested and implemented in hardware using Xilinx Spartan 3 FPGA. The system has many advantages over the exciting TLC.


title: Phonetic aspects of Chinese Shaanxi Xi'an dialect

abstract: Purpose of this study is to create articulation features for visual speech synthesis. Phonetic alphabet of Shaanxi Xi'an dialect has been created based on the Chinese Pinyin Scheme, which is the official Romanization system for Mandarin. It shares the common phonemes with Mandarin and supplements some special phonemes and Tone-Contour patterns. Firstly, this paper illustrates the corresponding relationship of phonemes comparing this dialect with Mandarin. Then a series of SAMPA code for Shaanxi Xi'an dialect has been developed. A detailed description of the Tone-Contour patterns of Shaanxi Xi'an dialect is given and contrasted with that of Mandarin. The correspondent regularities on consonants, vowels in the whole syllable pronunciation are also expressed between Mandarin and Shaanxi Xi'an dialect.


title: Benchmarking multivariate solvers of SciPy on the noiseless testbed

abstract: In this article we benchmark eight multivariate local solvers as well as the global Differential Evolution algorithm from the Python SciPy library on the BBOB noiseless testbed. We experiment with different parameter settings and termination conditions of the solvers. More focus is given to the L-BFGS-B and Nelder-Mead algorithms. For the first we investigate the effect of the maximum number of variable metric corrections used for the Hessian approximation and show that larger values than the default are of advantage. For the second we investigate the effect of adaptation of parameters, which is proved crucial for the performance of the method with increasing dimensionality.


title: Adolescent social media use and mental health from adolescent and parent perspectives

abstract: Abstract This study investigated adolescent and parent reports of adolescent social media use and its relation to adolescent psychosocial adjustment. The sample consisted of 226 participants (113 parent-adolescent dyads) from throughout the United States, with adolescents (55 males, 51 females, 7 unreported) ranging from ages 14 to 17. Parent and adolescent reports of the number of adolescents’ social media accounts were moderately correlated with parent-reported DSM-5 symptoms of inattention, hyperactivity/impulsivity, ODD, anxiety, and depressive symptoms, as well as adolescent-reported fear of missing out (FoMO) and loneliness. Lastly, anxiety and depressive symptoms were highest among adolescents with a relatively high number of parent-reported social media accounts and relatively high FoMO. The implications of these findings and need for related longitudinal studies are discussed.


An iBeacon-based Indoor Positioning Systems for Hospitals


title: Less-forgetting Learning in Deep Neural Networks

abstract: A catastrophic forgetting problem makes deep neural networks forget the previously learned information, when learning data collected in new environments, such as by different sensors or in different light conditions. This paper presents a new method for alleviating the catastrophic forgetting problem. Unlike previous research, our method does not use any information from the source domain. Surprisingly, our method is very effective to forget less of the information in the source domain, and we show the effectiveness of our method using several experiments. Furthermore, we observed that the forgetting problem occurs between mini-batches when performing general training processes using stochastic gradient descent methods, and this problem is one of the factors that degrades generalization performance of the network. We also try to solve this problem using the proposed method. Finally, we show our less-forgetting learning method is also helpful to improve the performance of deep neural networks in terms of recognition rates.


title: How to Improve Your Search Engine Ranking: Myths and Reality

abstract: Search engines have greatly influenced the way people access information on the Internet, as such engines provide the preferred entry point to billions of pages on the Web. Therefore, highly ranked Web pages generally have higher visibility to people and pushing the ranking higher has become the top priority for Web masters. As a matter of fact, Search Engine Optimization (SEO) has became a sizeable business that attempts to improve their clients’ ranking. Still, the lack of ways to validate SEO’s methods has created numerous myths and fallacies associated with ranking algorithms. In this article, we focus on two ranking algorithms, Google’s and Bing’s, and design, implement, and evaluate a ranking system to systematically validate assumptions others have made about these popular ranking algorithms. We demonstrate that linear learning models, coupled with a recursive partitioning ranking scheme, are capable of predicting ranking results with high accuracy. As an example, we manage to correctly predict 7 out of the top 10 pages for 78p of evaluated keywords. Moreover, for content-only ranking, our system can correctly predict 9 or more pages out of the top 10 ones for 77p of search terms. We show how our ranking system can be used to reveal the relative importance of ranking features in a search engine’s ranking function, provide guidelines for SEOs and Web masters to optimize their Web pages, validate or disprove new ranking features, and evaluate search engine ranking results for possible ranking bias.


title: Secure and Usable User-in-a-Context Continuous Authentication in Smartphones Leveraging Non-Assisted Sensors

abstract: Smartphones are equipped with a set of sensors that describe the environment (e.g., GPS, noise, etc.) and their current status and usage (e.g., battery consumption, accelerometer readings, etc.). Several works have already addressed how to leverage such data for user-in-a-context continuous authentication, i.e., determining if the porting user is the authorized one and resides in his regular physical environment. This can be useful for an early reaction against robbery or impersonation. However, most previous works depend on assisted sensors, i.e., they rely upon immutable elements (e.g., cell towers, satellites, magnetism), thus being ineffective in their absence. Moreover, they focus on accuracy aspects, neglecting usability ones. For this purpose, in this paper, we explore the use of four non-assisted sensors, namely battery, transmitted data, ambient light and noise. Our approach leverages data stream mining techniques and offers a tunable security-usability trade-off. We assess the accuracy, immediacy, usability and readiness of the proposal. Results on 50 users over 24 months show that battery readings alone achieve 97.05% of accuracy and 81.35% for audio, light and battery all together. Moreover, when usability is at stake, robbery is detected in 100 s for the case of battery and in 250 s when audio, light and battery are applied. Remarkably, these figures are obtained with moderate training and storage needs, thus making the approach suitable for current devices.


title: Advanced shape context for plant species identification using leaf image retrieval

abstract: This paper presents a novel method for leaf species identification combining local and shape-based features. Our approach extends the shape context model in two ways. First of all, two different sets of points are distinguished when computing the shape contexts: the voting set, i.e. the points used to describe the coarse arrangement of the shape and the computing set containing the points where the shape contexts are computed. This representation is enriched by introducing local features computed in the neighborhood of the computing points. Experiments show the effectiveness of our approach.


title: Email Network Important Nodes Mining Using Core Number and PageRank

abstract: Mining important persons is significant to computer network and security, especially researches on email network centralization nowadays. Traditional PageRank algorithm is sensitive to the network disturbance because it distributes PR values evenly. This paper proposes a method which decomposes email network into different layers based on the core number, eliminates the interferential nodes from outer layers to decrease impact of interferential nodes and complexity of following procedure. Besides, it improves PageRank algorithm so as to partially solve the bias problem on nodes' weighting, rank the nodes quantitatively to find the important nodes. The experiments indicate that it improves the accuracy and reduces the computational complexity in mining important nodes from email network.


title: A complete proof of correctness of the Knuth-Bendix completion algorithm☆

abstract: Abstract We give in this paper a complete description of the Knuth-Bendix completion algorithm. We prove its correctness in full, isolating carefully the essential abstract notions, so that the proof may be extended to other versions and extensions of the basic algorithm. We show that it defines a semidecision algorithm for the validity problem in the equational theories for which it applies, yielding a decision procedure whenever the algorithm terminates.


title: More Risk-Sensitive Markov Decision Processes

abstract: We investigate the problem of minimizing a certainty equivalent of the total or discounted cost over a finite and an infinite horizon that is generated by a Markov decision process MDP. In contrast to a risk-neutral decision maker this optimization criterion takes the variability of the cost into account. It contains as a special case the classical risk-sensitive optimization criterion with an exponential utility. We show that this optimization problem can be solved by an ordinary MDP with extended state space and give conditions under which an optimal policy exists. In the case of an infinite time horizon we show that the minimal discounted cost can be obtained by value iteration and can be characterized as the unique solution of a fixed-point equation using a “sandwich” argument. Interestingly, it turns out that in the case of a power utility, the problem simplifies and is of similar complexity than the exponential utility case, however has not been treated in the literature so far. We also establish the validity and convergence of the policy improvement method. A simple numerical example, namely, the classical repeated casino game, is considered to illustrate the influence of the certainty equivalent and its parameters. Finally, the average cost problem is also investigated. Surprisingly, it turns out that under suitable recurrence conditions on the MDP for convex power utility, the minimal average cost does not depend on the parameter of the utility function and is equal to the risk-neutral average cost. This is in contrast to the classical risk-sensitive criterion with exponential utility.


title: Providing Adaptive Courses in Learning Management Systems with Respect to Learning Styles *

abstract: Learning management systems (LMS) are commonly used in e-learning but provide little, or in most cases, no adaptivity. However, courses which adapt to the individual needs of students make learning easier for them and lead to a positive effect in learning. In this paper, we introduce a concept for providing adaptivity based on learning styles in LMS. In order to show the effectiveness of our approach, Moodle was extended by an add-on and an experiment with 437 students was performed. From the analysis of the students’ performance and behaviour in the course, we found out that students who learned from a course that matches their learning styles spent significantly less time in the course and achieved in average the same marks than students who got a course that either mismatched with their learning styles or included all available learning objects. Therefore, providing adaptive courses in LMS according to the proposed concept can be seen as effective in supporting students in learning.


title: A Programming Language and System for Heterogeneous Cloud of Things

abstract: Cloud of Things (CoT) is a computing model that combines the widely popular cloud computing with Internet of Things (IoT). One of the major problems with CoT is the latency of accessing distant cloud resources from the devices, where the data is captured. To address this problem, paradigms such as fog computing and Cloudlets have been proposed to interpose another layer of computing between the clouds and devices. Such a three-layered cloud-fog-device computing architecture is touted as the most suitable approach for deploying many next generation ubiquitous computing applications. Programming applications to run on such a platform is quite challenging because disconnections between the different layers are bound to happen in a large-scale CoT system, where the devices can be mobile. This paper presents a programming language and system for a three-layered CoT system. We illustrate how our language and system addresses some of the key challenges in the three-layered CoT. A proof-of-concept prototype compiler and runtime have been implemented and several example applications are developed using it.


title: MOSFET Switching Loss Model and Optimal Design of a Current Source Driver Considering the Current Diversion Problem

abstract: A new analytical switching loss model for power MOSFETs driven by the current source driver (CSD) is presented in this paper. The gate current diversion problem, which commonly exists in existing CSDs, is analyzed mathematically. In addition, a new accurate switching loss model considering every switching interval piecewisely is proposed. Based on the proposed loss model, the optimal design of the CSD inductor is achieved to minimize the total power loss for the buck converter. The experimental result verifies the proposed switching loss model and optimal design. The measured loss matches the calculated loss very well; the error between the calculated loss and measured one is less than 10% from 5 A load to 30 A load with 12 V input and 1.3 V output. As compared with the previous study, the efficiency with the optimal CSD inductor is improved from 86.1% to 87.6% at 12 V input and 1.3 V/20 A output and from 82.4% to 84.0% at 12 V input and 1.3 V/30 A output at 1 MHz switching frequency. As compared with the commercial driver-MOSFETs from Renesas and International Rectifier, the buck converter with the optimal CSD still shows better performance.


title: Collusion Resistant Multi-Matrix Masking for Privacy-Preserving Data Collection

abstract: An integral part of any social or medical research is the availability of reliable data. For the integrity of participants'responses, a secure environment for collecting sensitive data is required. This paper introduces a novel privacy-preserving data collection method: collusion resistant multi-matrix masking (CRM3). The CRM3 method requires multiple masking service providers (MSP), each generating its own random masking matrices. The key step is that each participant's data is randomly decomposed into the sum of component vectors, and each component vector is sent to the MSPs for masking in a different order. The CRM3 method publicly releases two sets of masked data: one being right multiplied by random invertible matricesand the other being left multiplied by random orthogonalmatrices. Both MSPs and the released data may be hosted on cloud platforms. Our data collection and release procedure is designed so that MSPs and the data collector are not able to derive the original participants' data hence providing strong privacy protection. However, statistical inference on parameters of interest will produce exactly the same results from the masked data as from the original data, under commonly used statistical methods such as general linear model, contingency table analysis, logistic regression, and Cox proportional hazard regression.


title: Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism

abstract: Firefly algorithm (FA) is a new swarm intelligence optimization algorithm, which has shown an effective performance on many optimization problems. However, it may suffer from premature convergence when solving complex optimization problems. In this paper, we propose a new FA variant, called NSRaFA, which employs a random attraction model and three neighborhood search strategies to obtain a trade-off between exploration and exploitation abilities. Moreover, a dynamic parameter adjustment mechanism is used to automatically adjust the control parameters. Experiments are conducted on a set of well-known benchmark functions. Results show that our approach achieves much better solutions than the standard FA and five other recently proposed FA variants.


title: A switched-capacitor DAC with analog mismatch correction

abstract: This paper describes a background calibration method for enhancing the accuracy and linearity of a switched-capacitor digital-to-analog converter. It can be used alone or in combination with mismatch shaping to achieve very high accuracy and linearity combined with high speed.


title: Segmentation of Legal Documents

abstract: An overwhelming number of legal documents is available in digital form. However, most of the texts are usually only provided in a semi-structured form, i.e. the documents are structured only implicitly using text formatting and alignment. In this form the documents are perfectly understandable by a human, but not by a machine. This is an obstacle towards advanced intelligent legal information retrieval and knowledge systems. The reason for this lack of structured knowledge is that the conversion of texts in conventional form into a structured, machine-readable form, a process called segmentation, is frequently done manually and is therefore very expensive. We introduce a trainable system based on state-of-the-art Information Extraction techniques for the automatic segmentation of legal documents. Our system makes special use of the implicitly given structure in the source digital file as well as of the explicit knowledge about the target structure. Our evaluation on the French IPR Law demonstrates that the system is able to learn an effective segmenter given only a few manually processed training documents. In some cases, even only one seen example is sufficient in order to correctly process the remaining documents.


title: Structured low-rank approximation and its applications

abstract: Fitting data by a bounded complexity linear model is equivalent to low-rank approximation of a matrix constructed from the data. The data matrix being Hankel structured is equivalent to the existence of a linear time-invariant system that fits the data and the rank constraint is related to a bound on the model complexity. In the special case of fitting by a static model, the data matrix and its low-rank approximation are unstructured. We outline applications in system theory (approximate realization, model reduction, output error, and errors-in-variables identification), signal processing (harmonic retrieval, sum-of-damped exponentials, and finite impulse response modeling), and computer algebra (approximate common divisor). Algorithms based on heuristics and local optimization methods are presented. Generalizations of the low-rank approximation problem result from different approximation criteria (e.g., weighted norm) and constraints on the data matrix (e.g., nonnegativity). Related problems are rank minimization and structured pseudospectra.


title: The Berlekamp-Massey Algorithm and the Euclidean Algorithm: a Closer Link

abstract: The two primary decoding algorithms for Reed-Solomon codes are the Berlekamp-Massey algorithm and the Sugiyama et al. adaptation of the Euclidean algorithm, both designed to solve a key equation. In this article an alternative version of the key equation and a new way to use the Euclidean algorithm to solve it are presented, which yield the Berlekamp-Massey algorithm. This results in a new, simpler, and compacter presentation of the Berlekamp-Massey algorithm.


title: Design of concave capacitance sensor for void fraction measurement in gas-liquid flow

abstract: Void fraction is an important parameter to be measured in gas-liquid flow. Several designs of two-electrodes capacitance sensor had been analyzed and optimized for void fraction measurement. The conventional two-electrode concave capacitance sensor showed high sensitivity but it was also the most affected by air-water distribution. Thus far, only two-electrodes concave design had been discussed. In this paper, the concave design is divided into two-, four- and six-electrode using finite element method. The sensitivity of the sensor, effect of air-water distribution and linearity of response are analyzed and compared. The numerical results showed that four-electrode concave sensor is more suitable than the conventional two-electrode for void fraction measurement in gas-liquid flow.


title: Increased theta and alpha EEG activity during nondirective meditation.

abstract: OBJECTIVES ::: In recent years, there has been significant uptake of meditation and related relaxation techniques, as a means of alleviating stress and maintaining good health. Despite its popularity, little is known about the neural mechanisms by which meditation works, and there is a need for more rigorous investigations of the underlying neurobiology. Several electroencephalogram (EEG) studies have reported changes in spectral band frequencies during meditation inspired by techniques that focus on concentration, and in comparison much less has been reported on mindfulness and nondirective techniques that are proving to be just as popular. ::: ::: ::: DESIGN ::: The present study examined EEG changes during nondirective meditation. The investigational paradigm involved 20 minutes of acem meditation, where the subjects were asked to close their eyes and adopt their normal meditation technique, as well as a separate 20-minute quiet rest condition where the subjects were asked to close their eyes and sit quietly in a state of rest. Both conditions were completed in the same experimental session with a 15-minute break in between. ::: ::: ::: RESULTS ::: Significantly increased theta power was found for the meditation condition when averaged across all brain regions. On closer examination, it was found that theta was significantly greater in the frontal and temporal-central regions as compared to the posterior region. There was also a significant increase in alpha power in the meditation condition compared to the rest condition, when averaged across all brain regions, and it was found that alpha was significantly greater in the posterior region as compared to the frontal region. ::: ::: ::: CONCLUSIONS ::: These findings from this study suggest that nondirective meditation techniques alter theta and alpha EEG patterns significantly more than regular relaxation, in a manner that is perhaps similar to methods based on mindfulness or concentration.


title: Attack models and scenarios for networked control systems

abstract: Cyber-secure networked control is modeled, analyzed, and experimentally illustrated in this paper. An attack space defined by the adversary's system knowledge, disclosure, and disruption resources is introduced. Adversaries constrained by these resources are modeled for a networked control system architecture. It is shown that attack scenarios corresponding to replay, zero dynamics, and bias injection attacks can be analyzed using this framework. An experimental setup based on a quadruple-tank process controlled over a wireless network is used to illustrate the attack scenarios, their consequences, and potential counter-measures.


title: BlendForces: a dynamic framework for facial animation

abstract: In this paper we present a new paradigm for the generation and retargeting of facial animation. Like a vast majority of the approaches that have adressed these topics, our formalism is built on blendshapes. However, where prior works have generally encoded facial geometry using a low dimensional basis of these blendshapes, we propose to encode facial dynamics by looking at blendshapes as a basis of forces rather than a basis of shapes. We develop this idea into a dynamic model that naturally combines the blendshapes paradigm with physics-based techniques for the simulation of deforming meshes. Because it escapes the linear span of the shape basis through time-integration and physics-inspired simulation, this approach has a wider expressive range than previous blendshape-based methods. Its inherent physically-based formulation also enables the simulation of more advanced physical interactions, such as collision responses on lip contacts.


title: Cell projection of meshes with non-planar faces

abstract: We review the cell projection method of volume rendering, discussing back-to-front cell sorting, and approximations involved in hardware color computation and interpolation. We describe how the method accommodates cells with non-planar faces using view dependent subdivision into tetrahedra.


title: Named Entity Disambiguation for Noisy Text

abstract: We address the task of Named Entity Disambiguation (NED) for noisy text. We present WikilinksNED, a large-scale NED dataset of text fragments from the web, which is significantly noisier and more challenging than existing news-based datasets. To capture the limited and noisy local context surrounding each mention, we design a neural model and train it with a novel method for sampling informative negative examples. We also describe a new way of initializing word and entity embeddings that significantly improves performance. Our model significantly outperforms existing state-of-the-art methods on WikilinksNED while achieving comparable performance on a smaller newswire dataset.


title: Programming with BIG Data in R: Scaling Analytics from One to Thousands of Nodes

abstract: Abstract We present a tutorial overview showing how one can achieve scalable performance with R. We do so by utilizing several package extensions, including those from the pbdR project. These packages consist of high performance, high-level interfaces to and extensions of MPI, PBLAS, ScaLAPACK, I/O libraries, profiling libraries, and more. While these libraries shine brightest on large distributed platforms, they also work rather well on small clusters and often, surprisingly, even on a laptop with only two cores. Our tutorial begins with recommendations on how to get more performance out of your R code before considering parallel implementations. Because R is a high-level language, a function can have a deep hierarchy of operations. For big data, this can easily lead to inefficiency. Profiling is an important tool to understand the performance of an R code for both serial and parallel improvements. The pbdR packages provide a highly scalable capability for the development of novel distributed data analysis algorithms. This level of scalability is unmatched in other analysis software. Interactive speeds (seconds) are achieved for complex analysis algorithms on data 100 GB and more. This is possible because the interfaces add little overhead to the scalable libraries and their extensions. Furthermore, this is often achieved with little or no change to serial R codes. Our overview includes codes of varying complexity, illustrating reading data in parallel, the process of changing a serial code to a distributed parallel code, and how to engage distributed matrix computation from within R.


title: Effects on cognitive performance of modulating the postprandial blood glucose profile at breakfast

abstract: BACKGROUND/OBJECTIVES ::: Considering the importance of glucose as a brain substrate, the postprandial rate of glucose delivery to the blood could be expected to affect cognitive functions. The purpose was to evaluate to what extent the rate of glucose absorption affected measures of cognitive performance in the postprandial period. In addition, cognitive performance was evaluated in relation to individual glucoregulation. ::: ::: ::: SUBJECTS/METHODS ::: A white wheat bread (WWB) enriched with guar gum (G-WWB) with the capacity to produce a low but sustained blood glucose net increment was developed. The G-WWB was evaluated in the postprandial period after breakfast with respect to effects on cognitive function (working memory and selective attention (SA)) in 40 healthy adults (49-71 years, body mass index 20-29 kg/m(2)), using a high glycaemic index WWB for comparison in a randomised crossover design. ::: ::: ::: RESULTS ::: The G-WWB improved outcome in the cognitive tests (SA test) in the later postprandial period (75-225 min) in comparison with the WWB (P<0.01). Subjects with better glucoregulation performed superior in cognitive tests compared with subjects with worse glucoregulation (P<0.05). ::: ::: ::: CONCLUSIONS ::: Beneficial effects on cognitive performance were observed with the G-WWB in the late postprandial period. The positive effect is suggested to emanate from improved insulin sensitivity, possibly in a combination with an enhanced neural energy supply. The results highlight the importance of carbohydrate foods that induces a low but sustained blood glucose profile in enhancing postprandial cognitive functions.


title: Neurobiology of early life stress and visceral pain: translational relevance from animal models to patient care.

abstract: Background ::: ::: Epidemiological studies show that females are twice as likely to receive a diagnosis of irritable bowel syndrome (IBS) than their male counterparts. Despite evidence pointing to a role for sex hormones in the onset or exacerbation of IBS symptoms, the mechanism by which ovarian hormones may predispose women to develop IBS remains largely undefined. On the other hand, there is a growing body of research showing a correlation between reports of early life stress (ELS) and the diagnosis of IBS. Current treatments available for IBS patients target symptom relief including abdominal pain and alterations in bowel habits, but are not directed to the etiology of the disease. ::: ::: ::: ::: Purpose ::: ::: To better understand the mechanisms by which sex hormones and ELS contribute to IBS, animal models have been developed to mirror complex human experiences allowing for longitudinal studies that investigate the lifelong consequences of ELS. These preclinical models have been successful in recapitulating ELS-induced visceral pain. Moreover, in female rats the influence of cycling hormones on visceral hypersensitivity resembles that seen in women with IBS. Such studies suggest that rodent models of ELS may serve as pivotal tools in determining (i) the etiology of IBS, (ii) novel future treatments for IBS, and (iii) improving individualized patient care. The current review aims to shed light on the progress and the challenges observed by clinicians within the field of gastroenterology and the preclinical science aimed at addressing those challenges in an effort to understand and more efficiently treat functional gastrointestinal disorders (FGIDs) in both children and adults.


title: Impact of Organizational Structure on Knowledge Management in the Jordanian Insurance Companies: From the Perspective of the Supervisory Leadership

abstract: The aim of this study was to investigate the impact of organizational structure on knowledge management and applications (Knowledge acquisition, knowledge storage, dissemination of knowledge, the use of knowledge) In the Jordanian insurance companies considering the organizational structure of a tool help manage knowledge, investment and benefit from its added value. The study sample consisted (162) individual occupants of leadership and supervisory functions in (24) insurance companies operating in Jordan, where the questionnaire was developed to gauge the views of the occupants of these jobs consisting of (27) paragraphs. After a process of data analysis and hypothesis testing using SPSS package, the study found benefit the possibility of relying on the organizational structure in strengthening the application of knowledge management. They study recommends that design of appropriate organizational structures, which is pushing for more production and application of knowledge as a prerequisite for the survival and success.


title: A model-free adaptive control of welding pool dynamics during arc welding

abstract: Arc welding process is characterized as nonlinear, time varying, and uncertain. So it is very difficult to design an effective control scheme by conventional modeling and control methods. Quality control of arc welding process is the key component in robotic welding system. This paper addresses model-free adaptive control with functional reinforce of Al alloy weld pool dynamics during pulsed gas tungsten arc welding (GTAW). This control method only needs the observed input output data and also has functional reinforce to improve the performance of the controller. The shape and size parameters for the weld pool are used to describe the weld pool geometry. The welding current is selected as the control variable, and the backside width of weld pool is selected as the controlled variable. To achieve the goal of full penetration and fine weld seam formation, a model-free adaptive controller with functional reinforce is designed for control of the maximum backside width. This controller of weld pool is independent on mathematic model. Numerical simulations confirmed that the developed control system is effective in achieving the desired fusion state.


title: Mobile Big Data Analytics Using Deep Learning and Apache Spark

abstract: The proliferation of mobile devices, such as smartphones and Internet of Things gadgets, has resulted in the recent mobile big data era. Collecting mobile big data is unprofitable unless suitable analytics and learning methods are utilized to extract meaningful information and hidden patterns from data. This article presents an overview and brief tutorial on deep learning in mobile big data analytics and discusses a scalable learning framework over Apache Spark. Specifically, distributed deep learning is executed as an iterative MapReduce computing on many Spark workers. Each Spark worker learns a partial deep model on a partition of the overall mobile, and a master deep model is then built by averaging the parameters of all partial models. This Spark-based framework speeds up the learning of deep models consisting of many hidden layers and millions of parameters. We use a context-aware activity recognition application with a real-world dataset containing millions of samples to validate our framework and assess its speedup effectiveness.


title: A 1.2 V bandgap reference with an additional 29.6 ppm/°C temperature stable output current

abstract: This paper presents a precise 1.175 V voltage reference bandgap circuit with an additional temperature compensated current output for DC-DC switch mode converters. The nominal temperature coefficient (TC) of the bandgap voltage is 13 ppm/°C from −40 °C to 140 °C. With the proposed circuit architecture output voltage deviations as low as ±18 mV (±3 s) from the nominal bandgap voltage are achieved. The reference output current measures 1 μA with a temperature stability of 29.6 ppm/°C. The circuit is designed in a 0.18 μm HV-CMOS process to operate from a 1.8 V supply voltage with a simulated overall current consumption of approximately 50 μA. The proposed bandgap reference circuit occupies a silicon die area of 0.05mm2.


A Hierarchical Probabilistic Model for Novelty Detection in Text


title: Cyberbullying: The hidden side of college students

abstract: The purpose of this study was to investigate how university students perceive their involvement in the cyberbullying phenomenon, and its impact on their well-being. Thus, this study presents a preliminary approach of how college students' perceived involvement in acts of cyberbullying can be measured. Firstly, Exploratory Factor Analysis (N = 349) revealed a unidimensional structure of the four scales included in the Cyberbullying Inventory for College Students. Then, Item Response Theory (N = 170) was used to analyze the unidimensionality of each scale and the interactions between participants and items. RESULTS revealed good item reliability and Cronbach's α for each scale. RESULTS also showed the potential of the instrument and how college students underrated their involvement in acts of cyberbullying. Additionally, aggression types, coping strategies and sources of help to deal with cyberbullying were identified and discussed. Lastly, age, gender and course-related issues were considered in the analysis. Implications for researchers and practitioners are discussed.


title: Internet Addiction in Students: A Cause of Concern

abstract: The Internet was originally designed to facilitate communication and research activities. However, the dramatic increase in the use of the Internet in recent years has led to pathological use (Internet addiction). This study is a preliminary investigation of the extent of Internet addiction in school children 16-18 years old in India. The Davis Online Cognition Scale (DOCS) was used to assess pathological Internet use. On the basis of total scores obtained (N = 100) on the DOCS, two groups were identified—dependents (18) and non-dependents (21), using mean ± h SD as the criterion for selection. The UCLA loneliness scale was also administered to the subjects. Significant behavioral and functional usage differences were revealed between the two groups. Dependents were found to delay other work to spend time online, lose sleep due to late-night logons, and feel life would be boring without the Internet. The hours spent on the Internet by dependents were greater than those of non-dependents. On the lonel...


title: An Empirical Study on Compositionality in Compound Nouns

abstract: A multiword is compositional if its meaning can be expressed in terms of the meaning of its constituents. In this paper, we collect and analyse the compositionality judgments for a range of compound nouns using Mechanical Turk. Unlike existing compositionality datasets, our dataset has judgments on the contribution of constituent words as well as judgments for the phrase as a whole. We use this dataset to study the relation between the judgments at constituent level to that for the whole phrase. We then evaluate two different types of distributional models for compositionality detection – constituent based models and composition function based models. Both the models show competitive performance though the composition function based models perform slightly better. In both types, additive models perform better than their multiplicative counterparts.


title: Control delay in Reinforcement Learning for real-time dynamic systems: A memoryless approach

abstract: Robots controlled by Reinforcement Learning (RL) are still rare. A core challenge to the application of RL to robotic systems is to learn despite the existence of control delay - the delay between measuring a system's state and acting upon it. Control delay is always present in real systems. In this work, we present two novel temporal difference (TD) learning algorithms for problems with control delay. These algorithms improve learning performance by taking the control delay into account. We test our algorithms in a gridworld, where the delay is an integer multiple of the time step, as well as in the simulation of a robotic system, where the delay can have any value. In both tests, our proposed algorithms outperform classical TD learning algorithms, while maintaining low computational complexity.


title: Isovists as a means to predict spatial experience and behavior

abstract: Two experiments are presented studying interrelations between spatial properties of environments and both experience and spatial behavior. In order to systematically study such interrelations, a generic description of space is required that provides comparability between arbitrarily shaped environments and captures behaviorally relevant properties of space. In this study the suitability of isovist derived measurands for this purpose was explored. Isovist-based descriptions of 16 virtual indoor scenes were correlated with behavioral data from two experimental tasks. For both tasks, an active navigation task and a rating of experiential qualities, strong correlations between subjects' behavior and measurands derived from isovist analysis were found. The general outcomes suggest that isovist measurands are indeed a promising means to predict the experience of space and spatial behavior for the chosen experimental tasks.


Games and Economic Behavior


title: Unsupervised learning of invariant representations with low sample complexity : the magic of sensory cortex or a new framework for machine learning ? by

abstract: Representations that are invariant to translation, scale and other transformations, can considerably reduce the sample complexity of learning, allowing recognition of new object classes from very few examples|a hallmark of human recognition. Empirical estimates of one-dimensional projections of the distribution induced by a group of ane transformations are proven to represent a unique and invariant signature associated with an image. We show how projections yielding invariant signatures for future images can be learned automatically, and updated continuously, during unsupervised visual experience. A module performing ltering and pooling, like simple and complex cells as proposed by Hubel and Wiesel, can compute such estimates. Under this view, a pooling stage estimates a one-dimensional probability distribution. Invariance from observations through a restricted window is equivalent to a sparsity property w.r.t. to a transformation, which yields templates that are a) Gabor for optimal simultaneous invariance to translation and scale or b) very specic for complex, class-dependent transformations such as rotation in depth of faces. Hierarchical architectures consisting of this basic Hubel-Wiesel module inherit its properties of invariance, stability, and discriminability while capturing the compositional organization of the visual world in terms of wholes and parts, and are invariant to complex transformations that may only be locally ane. The theory applies to several existing deep learning convolutional architectures for image and speech recognition. It also suggests that the main computational goal of the ventral stream of visual cortex is to provide a hierarchical representation of new objects/images which is invariant to transformations, stable, and discriminative for recognition|and that this representation may be continuously learned in an unsupervised way during development and natural visual experience.


title: Dermatitis due to orthopaedic implants. A review of the literature and report of three cases.

abstract: The cases of three patients who had dermatosis that was caused by an orthopaedic implant are reported. The main clinical pattern was localized or generalized eczema or urticaria. The diagnostic criteria that have been proposed by various authors are reviewed. Removal of the implant did not always result in rapid disappearance of the dermatosis, presumably because a few particles of metal remained in the area of the implant.


title: Supervised and Unsupervised Linear Learning Techniques for Visual Place Recognition in Changing Environments

abstract: This paper investigates the application of linear learning techniques to the place recognition problem. We present two learning methods, a supervised change prediction technique based on linear regression and an unsupervised change removal technique based on principal component analysis, and investigate how the performance of each is affected by the choice of training data. We show that the change prediction technique presented here succeeds only if it is provided with appropriate and adequate training data, which can be challenging for a mobile robotic system operating in an uncontrolled environment. In contrast, change removal can improve place recognition performance even when trained with as few as 100 samples. This paper shows that change removal can be combined with a number of different image descriptors and can improve performance across a range of different appearance conditions.


title: How good is a span of terms?: exploiting proximity to improve web retrieval

abstract: Ranking search results is a fundamental problem in information retrieval. In this paper we explore whether the use of proximity and phrase information can improve web retrieval accuracy. We build on existing research by incorporating novel ranking features based on flexible proximity terms with recent state-of-the-art machine learning ranking models. We introduce a method of determining the goodness of a set of proximity terms that takes advantage of the structured nature of web documents, document metadata, and phrasal information from search engine user query logs. We perform experiments on a large real-world Web data collection and show that using the goodness score of flexible proximity terms can improve ranking accuracy over state-of-the-art ranking methods by as much as 13%. We also show that we can improve accuracy on the hardest queries by as much as 9% relative to state-of-the-art approaches.


title: Will media influence learning? Reframing the debate

abstract: This article addresses the position taken by Clark (1983) that media do not influence learning under any conditions. The article reframes the questions raised by Clark to explore the conditions under which media will influence learning. Specifically, it posits the need to consider the capabilities of media, and the methods that employ them, as they interact with the cognitive and social processes by which knowledge is constructed. This approach is examined within the context of two major media-based projects, one which uses computers and the other, video. The article discusses the implications of this approach for media theory, research and practice.


title: Using Quantum Computers for Quantum Simulation

abstract: Numerical simulation of quantum systems is crucial to further our understanding of natural phenomena. Many systems of key interest and importance, in areas such as superconducting materials and quantum chemistry, are thought to be described by models which we cannot solve with sufficient accuracy, neither analytically nor numerically with classical computers. Using a quantum computer to simulate such quantum systems has been viewed as a key application of quantum computation from the very beginning of the field in the 1980s. Moreover, useful results beyond the reach of classical computation are expected to be accessible with fewer than a hundred qubits, making quantum simulation potentially one of the earliest practical applications of quantum computers. In this paper we survey the theoretical and experimental development of quantum simulation using quantum computers, from the first ideas to the intense research efforts currently underway.


title: Open-World Planning for Story Generation

abstract: The ability to generate narrative is of importance to computer systems that wish to use story effectively for entertainment, training, or education. One way to generate narrative is to use planning. However, story planners are limited by the fact that they can only operate on the story world provided, which impacts the ability of the planner to find a solution story plan and the quality and structure of the story plan if one is found. We present a planning algorithm for story generation that can nondeterministically make decisions about the description of the initial story world state in a least-commitment fashion.


title: The Model of near infrared sensor output voltage as a function of glucose concentration in solution

abstract: The number of people with diabetes increases every year in the whole world, including Indonesia. Diabetes is a major cause of stroke, heart attack, kidney failure, lower limb amputation and one of the leading causes of death. In order to manage their blood glucose level, diabetics have to test their blood glucose level as often as possible according to certain medical guidance, diet, exercise and consume medicine regularly. Unfortunately, the current blood glucose testing is inconvenient and uncomfortable, even cause pain for diabetic or patient; therefore, a noninvasive blood glucose measurement is highly desirable. Although several research works have already been done in this area, a successful noninvasive method is still in search. In order to contribute in this research area, we study the effect of glucose concentration in solutions with different concentration to the output voltage of a near infrared sensor as a preliminary research to obtain a successful noninvasive blood glucose meter. In this paper, we reported the model of near infrared sensor output voltage as a function of glucose concentration. The main components of the near infrared sensor are a 1450 nm light emitting diode (LED) as light source, and a photodiode that is sensitive to that wavelength as the sensing device. The distance between LED and photodiode is 15mm. The solutions have 50 mg/dl, 100 mg/dl, 200 mg/dl, 300 mg/dl, and 400 mg/dl glucose concentrations. An acrylic cylinder with 40 mm diameter was filled with 5 ml glucose solution for each concentration. The results show that higher glucose concentrations produce lower sensor output voltages. The linear trend line shows good fit with those data. The value of correlation coefficient is −0.99, which indicates strong relationship between the sensor output voltages and glucose concentrations.


title: Performance animation from low-dimensional control signals

abstract: This paper introduces an approach to performance animation that employs video cameras and a small set of retro-reflective markers to create a low-cost, easy-to-use system that might someday be practical for home use. The low-dimensional control signals from the user's performance are supplemented by a database of pre-recorded human motion. At run time, the system automatically learns a series of local models from a set of motion capture examples that are a close match to the marker locations captured by the cameras. These local models are then used to reconstruct the motion of the user as a full-body animation. We demonstrate the power of this approach with real-time control of six different behaviors using two video cameras and a small set of retro-reflective markers. We compare the resulting animation to animation from commercial motion capture equipment with a full set of markers.


title: A improved network security situation awareness model

abstract: In order to reflect the situation of network security assessment performance fully and accurately, a new network security situation awareness model based on information fusion was proposed. Network security situation is the result of fusion three aspects evaluation. In terms of attack, to improve the accuracy of evaluation, a situation assessment method of DDoS attack based on the information of data packet was proposed. In terms of vulnerability, a improved Common Vulnerability Scoring System (CVSS) was raised and maked the assessment more comprehensive. In terms of node weights, the method of calculating the combined weights and optimizing the result by Sequence Quadratic Program (SQP) algorithm which reduced the uncertainty of fusion was raised. To verify the validity and necessity of the method, a testing platform was built and used to test through evaluating 2000 DAPRA data sets. Experiments show that the method can improve the accuracy of evaluation results.


Light scattering from human hair fibers


title: Persistence of centrality in random growing trees

abstract: We investigate properties of node centrality in random growing tree models. We focus on a measure of centrality that computes the maximum subtree size of the tree rooted at each node, with the most central node being the tree centroid. For random trees grown according to a preferential attachment model, a uniform attachment model, or a diffusion processes over a regular tree, we prove that a single node persists as the tree centroid after a finite number of steps, with probability 1. Furthermore, this persistence property generalizes to the top K ≥ 1 nodes with respect to the same centrality measure. We also establish necessary and sufficient conditions for the size of an initial seed graph required to ensure persistence of a particular node with probability 1−ϵ, as a function of ϵ: In the case of preferential and uniform attachment models, we derive bounds for the size of an initial hub constructed around the special node. In the case of a diffusion process over a regular tree, we derive bounds for the radius of an initial ball centered around the special node. Our necessary and sufficient conditions match up to constant factors for preferential attachment and diffusion tree models.


title: Security system using biometric technology: Design and implementation of Voice Recognition System (VRS)

abstract: Biometric technology is fast gaining popularity as means of security measures to reduce cases of fraud and theft due to its use of physical characteristics and traits for the identification of individuals. The earliest methods of biometric identification included fingerprint and handwriting while more recent ones include iris/eye scan, face scan, voice print, and hand print. Biometric voice recognition and identification technology focuses on training the system to recognize an individualpsilas unique voice characteristics (i.e., their voice print). The technology lends itself well to a variety of uses and applications, including security access control for cell phones (to eliminate cell phone fraud), ATM manufacturers (to eliminate pin # fraud) and automobile manufacturers (to dramatically reduce theft and carjacking). In this paper, we present an implementation of a security system based on voice identification as the access control key. Verification algorithm is developed using MATLAB (SIMULINK) function blocks which is capable of authenticating a personpsilas identity by his or her voice pattern. A voice match will produce logic dasia1psila while a mismatch, logic dasia0psila. A microcontroller circuit controlling access to a door is built to test the reliability of this voice controlled security system. It is found out that the developed voice recognition software has successfully activated the door opening mechanism using a voice command that ONLY works for the authenticated individual. The system is proven to be able to provide medium-security access control and also has an adjustable security level setting to account for the variations in onepsilas voice each time a voice identification occurs.


title: Multiple receiver wireless power charger for mobile electronic devices in near field

abstract: In recent years, wireless power transfer technology has come to limelight because of its possibility of providing viable solutions to the challenges encountered by electronic device consumers when using traditional methods of charging batteries. This paper presents a multiple receiver wireless power transfer system for charging mobile phone batteries. The circuit consists of a transmitter and two receiver systems. A mathematical analysis of the circuit is carried out and simulated to understand its behavior with variation in coupling coefficient and load. In order to charge mobile phones without an extra communication module over a wide range of coupling coefficients, a variable frequency control algorithm is used. The transmitter circuit uses the algorithm to track the maximum input power of the system. The output voltage of the receiver circuit is controlled to meet the power requirement of the load as coupling coefficient varies by using a switch capacitor modeled as two back to back connected MOSFETs with their drain-to-source capacitance. The proposed converter circuit topology is simulated and the results are presented. The circuit operates over a wide range of coupling coefficient and the power requirement of the mobile phones are met without using an extra communication module. The resonance frequency of the circuit is 5.7MHZ and it is design to deliver 10W (5V, 2A) each to two mobile phones.


title: Quad-Band Microstrip Bandstop Filter Design Using Dual-Mode Open Loop Resonators Having Thin Film Capacitors

abstract: This letter presents a quad-band microstrip bandstop filter based on open loop resonators having thin film chip capacitors. For this purpose, dual-mode properties of open loop resonators are used and four independently controllable resonance frequencies are obtained by two open loop resonators in different electrical length. Even/odd mode analysis of the proposed structure is introduced to clarify the design method. In order to obtain higher degree, two unit cells including two resonators are used. While center frequencies can be independently controlled, the same is not true for the bandwidths and rejection levels. The proposed filter was also fabricated for the experimental verification of the predicted results. Four stopbands at 1.46, 1.86, 2.3 and 2.79 GHz were obtained in a good agreement with the predicted results.


title: Evaluating Pattern Recognition Techniques in Intrusion Detection Systems

abstract: Pattern recognition is the discipline studying the design and operation of systems capable to recognize patterns with specific properties in data sources. Intrusion detection, on the other hand, is in charge of identifying anomalous activities by analyzing a data source, be it the logs of an operating system or in the network traffic. It is easy to find similarities between such research fields, and it is straightforward to think of a way to combine them. As to the descriptions above, we can imagine an Intrusion Detection System (IDS) using techniques proper of the pattern recognition field in order to discover an attack pattern within the network traffic. What we propose in this work is such a system, which exploits the results of research in the field of data mining, in order to discover potential attacks. The paper also presents some experimental results dealing with performance of our system in a real-world operational scenario.


title: Wavelet Feature Based Confusion Character Sets for Gujarati Script

abstract: Indic script recognition is a difficult task due to the large number of symbols that result from concatenation of vowel modifiers to basic consonants and the conjunction of consonants with modifiers etc. Recognition of Gujarati script is a less studied area and no attempt is made so far to constitute confusion sets of Gujarati glyphs. In this paper, we present confusion sets of glyphs in printed Gujarati. Feature vector made up of Daubechies D4 wavelet coefficients were subjected to two different classifiers, giving more than 96% accuracy for a larger set of symbols. Novel application of GR neural-net architecture allows for fast building of a classifier for the large character data set. The combined approach of wavelet feature extraction and GRNN classification has given the highest recognition accuracy reported on this script.


title: States of Curiosity Modulate Hippocampus-Dependent Learning via the Dopaminergic Circuit

abstract: Summary People find it easier to learn about topics that interest them, but little is known about the mechanisms by which intrinsic motivational states affect learning. We used functional magnetic resonance imaging to investigate how curiosity (intrinsic motivation to learn) influences memory. In both immediate and one-day-delayed memory tests, participants showed improved memory for information that they were curious about and for incidental material learned during states of high curiosity. Functional magnetic resonance imaging results revealed that activity in the midbrain and the nucleus accumbens was enhanced during states of high curiosity. Importantly, individual variability in curiosity-driven memory benefits for incidental material was supported by anticipatory activity in the midbrain and hippocampus and by functional connectivity between these regions. These findings suggest a link between the mechanisms supporting extrinsic reward motivation and intrinsic curiosity and highlight the importance of stimulating curiosity to create more effective learning experiences. Video Abstract


title: An Efficient Certificateless Encryption for Secure Data Sharing in Public Clouds

abstract: We propose a mediated certificateless encryption scheme without pairing operations for securely sharing sensitive information in public clouds. Mediated certificateless public key encryption (mCL-PKE) solves the key escrow problem in identity based encryption and certificate revocation problem in public key cryptography. However, existing mCL-PKE schemes are either inefficient because of the use of expensive pairing operations or vulnerable against partial decryption attacks. In order to address the performance and security issues, in this paper, we first propose a mCL-PKE scheme without using pairing operations. We apply our mCL-PKE scheme to construct a practical solution to the problem of sharing sensitive information in public clouds. The cloud is employed as a secure storage as well as a key generation center. In our system, the data owner encrypts the sensitive data using the cloud generated users' public keys based on its access control policies and uploads the encrypted data to the cloud. Upon successful authorization, the cloud partially decrypts the encrypted data for the users. The users subsequently fully decrypt the partially decrypted data using their private keys. The confidentiality of the content and the keys is preserved with respect to the cloud, because the cloud cannot fully decrypt the information. We also propose an extension to the above approach to improve the efficiency of encryption at the data owner. We implement our mCL-PKE scheme and the overall cloud based system, and evaluate its security and performance. Our results show that our schemes are efficient and practical.


title: The role of motivated reasoning in vendor consideration

abstract: This is a conceptual article dealing with the role of motivated reasoning in vendor selection. The authors argue that organizational buyers’ motivated reasoning, along with the task characteristics of perceived importance and familiarity, plays an important role in determining whether an open or a closed consideration will be used for vendor selection. Based on the relationship between motivated reasoning and task characteristics, the authors develop several propositions.


Iterated local search and simulated annealing algorithms for the inventory routing problem