title: RATM: Recurrent Attentive Tracking Model

abstract: We present an attention-based modular neural framework for computer vision. The framework uses a soft attention mechanism allowing models to be trained with gradient descent. It consists of three modules: a recurrent attention module controlling where to look in an image or video frame, a feature-extraction module providing a representation of what is seen, and an objective module formalizing why the model learns its attentive behavior. The attention module allows the model to focus computation on task-related information in the input. We apply the framework to several object tracking tasks and explore various design choices. We experiment with three data sets, bouncing ball, moving digits and the real-world KTH data set. The proposed Recurrent Attentive Tracking Model performs well on all three tasks and can generalize to related but previously unseen sequences from a challenging tracking data set.


Spring 6-10-2017 ANTECEDENTS OF EMPLOYEES ' INFORMATION SECURITY AWARENESS-REVIEW , SYNTHESIS , AND DIRECTIONS FOR FUTURE RESEARCH


title: Web Usability and User Trust on E-commerce Websites in Pakistan

abstract: Web usability is an integral part of e-commerce. Users are less prone to the websites which are difficult to navigate and slow in response time. E-commerce business is growing aggressively on daily basis, but lack of user trust can impede this growth. Success of online business is largely dependent on getting user’s trust. There are different techniques and models to measure web usability and user trust level, but they are not covering all aspects of web usability. So we proposed a new enhanced SUPR-Q model with six (6) parameters, such as Usability, Effectiveness, Efficiency, Learnability, Satisfaction and Security. We performed an experiment with one hundred twenty (120) participants to measure the web usability and user’s trust on two famous e-commerce websites of Pakistan (daraz.pk & homeshopping.pk). We divided our participants into two equal groups, such as Random and Regular group on the basis of their previous shopping exposure. Our results shows that usability score of Regular group who did shopping most frequently were better than the Random group which was less exposed with shopping experience. Regular group was more satisfied from both websites with the score of 46.8% on daraz.pk and 44.8% on homshopping.pk as compared to Random group. Both groups showed higher usability score on daraz.pk which was 45.2% in case of Regular group and 40% in case of Random group due to the higher effectiveness and efficiency of web interface. The overall results showed that trust on e-commerce website plays vital role in user’s satisfaction and purchasing.


title: A supervised active learning framework for recommender systems based on decision trees

abstract: A key challenge in recommender systems is how to profile new users. A well-known solution for this problem is to ask new users to rate a few items to reveal their preferences and to use active learning to find optimally informative items. Compared to the application of active learning in classification (regression), active learning in recommender systems presents several differences: although there are no ratings for new users, there is an abundance of available ratings--collectively--from past (existing) users. In this paper, we propose an innovative approach for active learning in recommender systems, which aims at taking advantage of this additional information. The main idea is to consider existing users as (hypothetical) new users and solve an active learning problem for each of them. In the end, we aggregate all solved problems in order to learn how to solve the active learning problem for a real new user. As the ratings of existing users (i.e., labels) are known and are used for active learning purposes, the proposed framework is in fact a supervised active learning framework. Based on this framework, we investigate two different types of models: the first model is based on information about average item ratings and the second on matrix factorization. We present experimental results on the Netflix dataset, which show that the proposed approach significantly outperforms state-of-the-art baselines.


title: Wideband electromagnetic energy harvesting from ambient vibrations

abstract: Different bandwidth widening schemes of electromagnetic energy harvesters have been reported in this work. The devices are fabricated on FR4 substrate using laser micromachining techniques. The linear device operate in a narrow band around the resonance; in order to tune resonant frequency of the device electrically, two different types of complex load topologies are adopted. Using capacitive load, the resonant frequency is tuned in the low frequency direction whereas using inductive load, the resonant frequency is tuned in the high frequency direction. An overall tuning range of ∼2.4 Hz is obtained at 0.3g though the output power dropped significantly over the tuning range. In order to improve the off-resonance performance, nonlinear oscillation based systems are adopted. A specially designed spring arm with fixed-guided configuration produced single well nonlinear monostable configuration. With increasing input acceleration, wider bandwidth is obtained with such a system as large displacement, stretchin...


title: Prototype of a fabric actuator with multiple thin artificial muscles for wearable assistive devices

abstract: A prototype fabric actuator for use in wearable movement-assisting devices is introduced. Such actuators must be lightweight and produce movements with as many degrees of freedom as natural human motion. This paper presents the structure of a fabric actuator comprising laugh-woven thin artificial muscles and binary control patterns that change the postures of the actuator in three dimensions. Six basic movements (forward bending, backward bending, left bending, right bending, left twisting, and right twisting) are tested with six different digital control patterns generated heuristically, proving that a woven swath of thin McKibben muscles is a viable technology for use in wearable devices.


Robot Modeling and Control [Book Review]


title: Novel Leakage Detection by Ensemble CNN-SVM and Graph-Based Localization in Water Distribution Systems

abstract: In many water distribution systems, a significant amount of water is lost because of leakage during transit from the water treatment plant to consumers. As a result, water leakage detection and localization have been a consistent focus of research. Typically, diagnosis or detection systems based on sensor signals incur significant computational and time costs, whereas the system performance depends on the features selected as input to the classifier. In this paper, to solve this problem, we propose a novel, fast, and accurate water leakage detection system with an adaptive design that fuses a one-dimensional convolutional neural network and a support vector machine. We also propose a graph-based localization algorithm to determine the leakage location. An actual water pipeline network is represented by a graph network and it is assumed that leakage events occur at virtual points on the graph. The leakage location at which costs are minimized is estimated by comparing the actual measured signals with the virtually generated signals. The performance was validated on a wireless sensor network based test bed, deployed on an actual WDS. Our proposed methods achieved 99.3% leakage detection accuracy and a localization error of less than 3 m.


title: CrowdScape: interactively visualizing user behavior and output

abstract: Crowdsourcing has become a powerful paradigm for accomplishing work quickly and at scale, but involves significant challenges in quality control. Researchers have developed algorithmic quality control approaches based on either worker outputs (such as gold standards or worker agreement) or worker behavior (such as task fingerprinting), but each approach has serious limitations, especially for complex or creative work. Human evaluation addresses these limitations but does not scale well with increasing numbers of workers. We present CrowdScape, a system that supports the human evaluation of complex crowd work through interactive visualization and mixed initiative machine learning. The system combines information about worker behavior with worker outputs, helping users to better understand and harness the crowd. We describe the system and discuss its utility through grounded case studies. We explore other contexts where CrowdScape's visualizations might be useful, such as in user studies.


title: Predicting customer churn in banking industry using neural networks

abstract: The aim of this article is to present a case study of usage of one of the data mining methods, neural network, in knowledge discovery from databases in the banking industry. Data mining is automated process of analysing, organization or grouping a large set of data from different perspectives and summarizing it into useful information using special algorithms. Data mining can help to resolve banking problems by finding some regularity, causality and correlation to business information which are not visible at first sight because they are hidden in large amounts of data. In this paper, we used one of the data mining methods, neural network, within the software package Alyuda NeuroInteligence to predict customer churn in bank. The focus on customer churn is to determinate the customers who are at risk of leaving and analysing whether those customers are worth retaining. Neural network is statistical learning model inspired by biological neural and it is used to estimate or approximate functions that can depend on a large number of inputs which are generally unknown. Although the method itself is complicated, there are tools that enable the use of neural networks without much prior knowledge of how they operate. The results show that clients who use more bank services (products) are more loyal, so bank should focus on those clients who use less than three products, and offer them products according to their needs. Similar results are obtained for different network topologies.


title: Sentiment analysis on Twitter posts: An analysis of positive or negative opinion on GoJek

abstract: Online transportation, such as GoJek, is preferred by many users especially in areas where public transport is difficult to access or when there is a traffic jam. Twitter is a popular social networking site in Indonesia that can generate information from users' tweets. In this study, we proposed a system that detect public sentiments based on Twitter post about online transportation services especially GoJek. The system will collect tweets, analyze the tweets sentiments using SVM, and group them into positive and negative sentiment.


title: Exploiting memory device wearout dynamics to improve NAND flash memory system performance

abstract: This paper advocates a device-aware design strategy to improve various NAND flash memory system performance metrics. It is well known that NAND flash memory program/erase (PE) cycling gradually degrades memory device raw storage reliability, and sufficiently strong error correction codes (ECC) must be used to ensure the PE cycling endurance. Hence, memory manufacturers must fabricate enough number of redundant memory cells geared to the worst-case device reliability at the end of memory lifetime. Given the memory device wear-out dynamics, the existing worst-case oriented ECC redundancy is largely under-utilized over the entire memory lifetime, which can be adaptively traded for improving certain NAND flash memory system performance metrics. This paper explores such device-aware adaptive system design space from two perspectives, including (1) how to improve memory program speed, and (2) how to improve memory defect tolerance and hence enable aggressive fabrication technology scaling. To enable quantitative evaluation, we for the first time develop a NAND flash memory device model to capture the effects of PE cycling from the system level. We carry out simulations using the DiskSim-based SSD simulator and a variety of traces, and the results demonstrate up to 32% SSD average response time reduction. We further demonstrate that the potential on achieving very good defect tolerance, and finally show that these two design approaches can be readily combined together to noticeably improve SSD average response time even in the presence of high memory defect rates.


title: Errors of Omission in English-Speaking Children's Production of Plurals and the Past Tense: The Effects of Frequency, Phonology, and Competition

abstract: How do English-speaking children inflect nouns for plurality and verbs for the past tense? We assess theoretical answers to this question by considering errors of omission, which occur when children produce a stem in place of its inflected counterpart (e.g., saying “dress” to refer to 5 dresses). A total of 307 children (aged 3;11–9;9) participated in 3 inflection studies. In Study 1, we show that errors of omission occur until the age of 7 and are more likely with both sibilant regular nouns (e.g., dress) and irregular nouns (e.g., man) than regular nouns (e.g., dog). Sibilant nouns are more likely to be inflected if they are high frequency. In Studies 2 and 3, we show that similar effects apply to the inflection of verbs and that there is an advantage for “regular-like” irregulars whose inflected form, but not stem form, ends in d/t. The results imply that (a) stems and inflected forms compete for production and (b) children generalize both product-oriented and source-oriented schemas when learning about inflectional morphology.


title: Expanding retrieval practice promotes short-term retention, but equally spaced retrieval enhances long-term retention.

abstract: Expanding retrieval practice (T. K. Landauer & R. A. Bjork, 1978) is regarded as a superior technique for promoting long-term retention relative to equally spaced retrieval practice. In Experiments 1 and 2, the authors found that expanding retrieval practice of vocabulary word pairs produced short-term benefits 10 min after learning, conceptually replicating Landauer and Bjork’s results. However, equally spaced retrieval produced superior retention 2 days later. This pattern occurred both with and without feedback after test trials. In Experiment 3, the 1st test occurred immediately or after a brief delay, and repeated tests were expanding or equally spaced. Delaying the first test improved long-term retention, regardless of how the repeated tests were spaced. The important factor for promoting long-term retention is delaying initial retrieval to make it more difficult, as is done in equally spaced retrieval but not in expanding retrieval. Expanding the interval between repeated tests had little effect on long-term retention in 3 experiments.


title: On Service-Orientation for Automotive Software

abstract: Background: During the last decades, the functional power and complexity of automotive E/E architectures grew radically and is going to grow further in the future. For highly and fully automated driving, functions with the highest safety integrity level need to be realized, requiring new development methodologies and a new level of formal rigor. Aim: We investigate to what extent SOA concepts are applicable for safety-critical embedded automotive software systems and whether this concept is appealing to E/E architects. Method: We conducted a survey research by interviewing system architects at our industrial partner, then we applied the grounded theory method in order to derive a theory and a set of requirements for an automotive SOA approach. Additionally, we illustrate the approach using a function needed in a highly automated driving scenario. Results: We present a formal service model and an automotive SOA framework. Both aspects, i.e., architecture structuring and formal service description resulted from the analyzed interview data. Limitation: This approach has not been evaluated extensively, yet. Conclusion: Our first results suggest that SOA concepts are indeed successfully applicable in (continuous) automotive software engineering and are a means to cope with complexity and safety requirements.


title: Pursuing Pleasure or Virtue: The Differential and Overlapping Well-Being Benefits of Hedonic and Eudaimonic Motives

abstract: Hedonia (seeking pleasure and comfort) and eudaimonia (seeking to use and develop the best in oneself) are often seen as opposing pursuits, yet each may contribute to well-being in different ways. We conducted four studies (two correlational, one experience-sampling, and one intervention study) to determine outcomes associated with activities motivated by hedonic and eudaimonic aims. Overall, results indicated that: between persons (at the trait level) and within persons (at the momentary state level), hedonic pursuits related more to positive affect and carefreeness, while eudaimonic pursuits related more to meaning; between persons, eudaimonia related more to elevating experience (awe, inspiration, and sense of connection with a greater whole); within persons, hedonia related more negatively to negative affect; between and within persons, both pursuits related equally to vitality; and both pursuits showed some links with life satisfaction, though hedonia’s links were more frequent. People whose lives were high in both eudaimonia and hedonia had: higher degrees of most well-being variables than people whose lives were low in both pursuits (but did not differ in negative affect or carefreeness); higher positive affect and carefreeness than predominantly eudaimonic individuals; and higher meaning, elevating experience, and vitality than predominantly hedonic individuals. In the intervention study, hedonia produced more well-being benefits at short-term follow-up, while eudaimonia produced more at 3-month follow-up. The findings show that hedonia and eudaimonia occupy both overlapping and distinct niches within a complete picture of well-being, and their combination may be associated with the greatest well-being.


title: A Review of Latest Web Tools and Libraries for State-of-the-art Visualization

abstract: Most of the existing visualization and simulation applications run on the client machine and require an installation process. Browser based interactive visualizer for scientific and medical applications remain an unheard concept despite all advancements in computer and software technology and it remains a fairly difficult process to quickly prototype a visualization on a PC or a smart device. In this paper, we review and employed state-of-the-art web technologies, third-party libraries and frameworks to compare and develop some interactive browser-based, mobile friendly web applications. These latest web technologies have the potential to fulfill the promise of interactive browser based custom visualization applications. We presented and compared some of the latest web based tools available today. We also introduced couple of lightweight and interactive web based visualizer and simulator tools which are under development.


title: Spinal Instability Neoplastic Score (SINS): Reliability Among Spine Fellows and Resident Physicians in Orthopedic Surgery and Neurosurgery

abstract: Study Design ::: Reliability analysis. ::: ::: ::: Objectives ::: The Spinal Instability Neoplastic Score (SINS) was developed for assessing patients with spinal neoplasia. It identifies patients who may benefit from surgical consultation or intervention. It also acts as a prognostic tool for surgical decision making. Reliability of SINS has been established for spine surgeons, radiologists, and radiation oncologists, but not yet among spine surgery trainees. The purpose of our study is to determine the reliability of SINS among spine residents and fellows, and its role as an educational tool. ::: ::: ::: Methods ::: Twenty-three residents and 2 spine fellows independently scored 30 de-identified spine tumor cases on 2 occasions, at least 6 weeks apart. Intraclass correlation coefficient (ICC) measured interobserver and intraobserver agreement for total SINS scores. Fleiss's kappa and Cohen's kappa analysis evaluated interobserver and intraobserver agreement of 6 component subscores (location, pain, bone lesion quality, spinal alignment, vertebral body collapse, and posterolateral involvement of spinal elements). ::: ::: ::: Results ::: Total SINS scores showed near perfect interobserver (0.990) and intraobserver (0.907) agreement. Fleiss's kappa statistics revealed near perfect agreement for location; substantial for pain; moderate for alignment, vertebral body collapse, and posterolateral involvement; and fair for bone quality (0.948, 0.739, 0.427, 0.550, 0.435, and 0.382). Cohen's kappa statistics revealed near perfect agreement for location and pain, substantial for alignment and vertebral body collapse, and moderate for bone quality and posterolateral involvement (0.954, 0.814, 0.610, 0.671, 0.576, and 0.561, respectively). ::: ::: ::: Conclusions ::: The SINS is a reliable and valuable educational tool for spine fellows and residents learning to judge spinal instability.


title: Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Data

abstract: Sentiment analysis is a widely studied NLP task where the goal is to determine opinions, emotions, and evaluations of users towards a product, an entity or a service that they are reviewing. One of the biggest challenges for sentiment analysis is that it is highly language dependent. Word embeddings, sentiment lexicons, and even annotated data are language specific. Further, optimizing models for each language is very time consuming and labor intensive especially for recurrent neural network models. From a resource perspective, it is very challenging to collect data for different languages. In this paper, we look for an answer to the following research question: can a sentiment analysis model trained on a language be reused for sentiment analysis in other languages, Russian, Spanish, Turkish, and Dutch, where the data is more limited? Our goal is to build a single model in the language with the largest dataset available for the task, and reuse it for languages that have limited resources. For this purpose, we train a sentiment analysis model using recurrent neural networks with reviews in English. We then translate reviews in other languages and reuse this model to evaluate the sentiments. Experimental results show that our robust approach of single model trained on English reviews statistically significantly outperforms the baselines in several different languages.


title: Feature extraction methods for character recognition-A survey

abstract: This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. Different feature extraction methods are designed for different representations of the characters, such as solid binary characters, character contours, skeletons (thinned characters) or gray-level subimages of each individual character. The feature extraction methods are discussed in terms of invariance properties, reconstructability and expected distortions and variability of the characters. The problem of choosing the appropriate feature extraction method for a given application is also discussed. When a few promising feature extraction methods have been identified, they need to be evaluated experimentally to find the best method for the given application.


title: Endowing Robots with Longer-term Autonomy by Recovering from External Disturbances in Manipulation through Grounded Anomaly Classification and Recovery Policies

abstract: Robot manipulation is increasingly poised to interact with humans in co-shared workspaces. Despite increasingly robust manipulation and control algorithms, failure modes continue to exist whenever models do not capture the dynamics of the unstructured environment. To obtain longer-term horizons in robot automation, robots must develop introspection and recovery abilities. We contribute a set of recovery policies to deal with anomalies produced by external disturbances as well as anomaly classification through the use of non-parametric statistics with memoized variational inference with scalable adaptation. A recovery critic stands atop of a tightly-integrated, graph-based online motion-generation and introspection system that resolves a wide range of anomalous situations. Policies, skills, and introspection models are learned incrementally and contextually in a task. Two task-level recovery policies: re-enactment and adaptation resolve accidental and persistent anomalies respectively. The introspection system uses non-parametric priors along with Markov jump linear systems and memoized variational inference with scalable adaptation to learn a model from the data. Extensive real-robot experimentation with various strenuous anomalous conditions is induced and resolved at different phases of a task and in different combinations. The system executes around-the-clock introspection and recovery and even elicited self-recovery when misclassifications occurred.


title: Development of cognitive control and executive functions from 4 to 13 years: Evidence from manipulations of memory, inhibition, and task switching

abstract: Predictions concerning development, interrelations, and possible independence of working memory, inhibition, and cognitive flexibility were tested in 325 participants (roughly 30 per age from 4 to 13 years and young adults; 50% female). All were tested on the same computerized battery, designed to manipulate memory and inhibition independently and together, in steady state (single-task blocks) and during task-switching, and to be appropriate over the lifespan and for neuroimaging (fMRI). This is one of the first studies, in children or adults, to explore: (a) how memory requirements interact with spatial compatibility and (b) spatial incompatibility effects both with stimulus-specific rules (Simon task) and with higher-level, conceptual rules. Even the youngest children could hold information in mind, inhibit a dominant response, and combine those as long as the inhibition required was steady-state and the rules remained constant. Cognitive flexibility (switching between rules), even with memory demands minimized, showed a longer developmental progression, with 13-year-olds still not at adult levels. Effects elicited only in Mixed blocks with adults were found in young children even in single-task blocks; while young children could exercise inhibition in steady state it exacted a cost not seen in adults, who (unlike young children) seemed to re-set their default response when inhibition of the same tendency was required throughout a block. The costs associated with manipulations of inhibition were greater in young children while the costs associated with increasing memory demands were greater in adults. Effects seen only in RT in adults were seen primarily in accuracy in young children. Adults slowed down on difficult trials to preserve accuracy; but the youngest children were impulsive; their RT remained more constant but at an accuracy cost on difficult trials. Contrary to our predictions of independence between memory and inhibition, when matched for difficulty RT correlations between these were as high as 0.8, although accuracy correlations were less than half that. Spatial incompatibility effects and global and local switch costs were evident in children and adults, differing only in size. Other effects (e.g., asymmetric switch costs and the interaction of switching rules and switching response-sites) differed fundamentally over age.


title: ObliviSync: Practical Oblivious File Backup and Synchronization

abstract: Oblivious RAM (ORAM) protocols are powerful techniques that hide a client's data as well as access patterns from untrusted service providers. We present an oblivious cloud storage system, ObliviSync, that specifically targets one of the most widely-used personal cloud storage paradigms: synchronization and backup services, popular examples of which are Dropbox, iCloud Drive, and Google Drive. This setting provides a unique opportunity because the above privacy properties can be achieved with a simpler form of ORAM called write-only ORAM, which allows for dramatically increased efficiency compared to related work. Our solution is asymptotically optimal and practically efficient, with a small constant overhead of approximately 4x compared with non-private file storage, depending only on the total data size and parameters chosen according to the usage rate, and not on the number or size of individual files. Our construction also offers protection against timing-channel attacks, which has not been previously considered in ORAM protocols. We built and evaluated a full implementation of ObliviSync that supports multiple simultaneous read-only clients and a single concurrent read/write client whose edits automatically and seamlessly propagate to the readers. We show that our system functions under high work loads, with realistic file size distributions, and with small additional latency (as compared to a baseline encrypted file system) when paired with Dropbox as the synchronization service.


title: Secure routing protocol design for UAV Ad hoc NETworks

abstract: UAV Ad hoc NETworks (UAANETs) can be defined as a new form of ad hoc networks in which nodes are Unmanned Aerial Vehicles (UAVs) and Ground Control Station (GCS). Compared to the usual Mobile Ad hoc NETwork (MANET), this new network paradigm has some unique features and brings specific challenges such as node mobility degree, network connectivity patterns, delay-sensitive applications and network security. Indeed, from routing point of view, none of the several UAANET routing protocols proposed in the literature have been designed with security in mind. This lack of consideration can make the certification of UAANETs difficult to obtain. In this paper, we present our vision of such a challenge and the research that we are conducting. The aim is to propose an original secure routing protocol for UAANETs using a Model Driven Development (MDD) approach which will ease the certification of final UAV products. The first preliminary results concerning our secure-routing protocol design will be presented. This paper describes our ongoing research which will provide secure communications for UAV ad hoc networks at the end of the SUANET (Secure Uav Ad-hoc NETwork) project.


title: Autonomously Acquiring Instance-Based Object Models from Experience

abstract: A key aim of current research is to create robots that can reliably manipulate objects. However, in many applications, general-purpose object detection or manipulation is not required: the robot would be useful if it could recognize, localize, and manipulate the relatively small set of specific objects most important in that application, but do so with very high reliability. Instance-based approaches can achieve this high reliability but to work well, they require large amounts of data about the objects that are being manipulated. The first contribution of this paper is a system that automates this data collection using a robot. When the robot encounters a novel object, it collects data that enables it to detect the object, estimate its pose, and grasp it. However for some objects, information needed to infer a successful grasp is not visible to the robot’s sensors; for example, a heavy object might need to be grasped in the middle or else it will twist out of the robot’s gripper. The second contribution of this paper is an approach that allows a robot to identify the best grasp point by attempting to pick up the object and tracking its successes and failures. Because the number of grasp points is very large, we formalize grasping as an N-armed bandit problem and define a new algorithm for best arm identification in budgeted bandits that enables the robot to quickly find an arm corresponding to a good grasp without pulling all the arms. We demonstrate that a stock Baxter robot with no additional sensing can autonomously acquire models for a wide variety of objects and use the models to detect, classify, and manipulate the objects. Additionally, we show that our adaptation step significantly improves accuracy over a non-adaptive system, enabling a robot to improve its pick success rate from 55 to 75% on a collection of 30 household objects. Our instance-based approach exploits the robot’s ability to collect its own training data, enabling experience with the object to directly improve the robot’s performance during future interactions.


Who Invented the Reverse Mode of Differentiation ?


title: Offline and online time in Sequential Decision-Making Problems

abstract: A connection has recently been drawn between Dynamic Optimization Problems (DOPs) and Reinforcement Learning Problems (RLPs) where they can be seen as subsets of a broader class of Sequential Decision-Making Problems (SDMPs). SDMPs require new decisions on an ongoing basis. Typically the underlying environment changes between decisions. The SDMP view is useful as it allows the unified space to be explored. Solutions can be designed for characteristics of problem instances using algorithms from either community. Little has been done on comparing algorithm performance across these communities, particularly under real-world resource constraints.


title: Coordinating for Flexibility in e-Business Supply Chains

abstract: The widespread use of information technology (IT) to create electronic linkages among supply chain partners with the objective of reducing transaction costs may have unintended adverse effects on supply chain flexibility. Increasing business dynamics, changing customer preferences, and disruptive technological shifts pose the need for two kinds of flexibility that interenterprise information systems must address--the ability of interenterprise linkages to support changes in offering characteristics (offering flexibility) and the ability to alter linkages to partner with different supply chain players (partnering flexibility). This study explores how enterprises in supply chains may forge supply chain linkages that enable both types of flexibility jointly, and allow them to deal with ubiquitous change. Drawing on March and Simon's coordination theory, we propose two design principles: (1) advance structuring of interorganizational processes and information exchange that allows partnering organizations to be loosely coupled, and (2) IT-supported dynamic adjustment that allows enterprises to quickly sense change and adapt their supply chain linkages. This study reports on a survey of 41 supply chain relationships in the IT industry. For design principle, our empirical investigation of factors shows (1) that modular design of interconnected processes and structured data connectivity are associated with higher supply chain flexibility, and (2) that deep coordination-related knowledge is critical for supply chain flexibility. Also, sharing a broad range of information with partners is detrimental to supply chain flexibility, and organizations should instead focus on improving the quality of information shared. For industry managers, the study provides clear insights for information infrastructure design. To manage their interdependencies, enterprises need to encapsulate their interconnected processes in modular chunks, and support these with IT platforms for information exchange in structured formats. Enterprises also need to nurture their execution capabilities by putting in place the information systems to process information exchanged with partners, augmenting their understanding of factors such as how partner actions need to trigger adaptive responses. For researchers, the study initiates a new stream of theorizing that focuses on the role of the information infrastructure in managing the tension between competing goals of offering flexibility and partnering flexibility.


title: Solid State Transformer based on cascade multilevel converter for distribution network

abstract: Solid State Transformer (SST), a power electronics based transformer, tends to replace conventional bulk size transformer for distribution of electric power. In this paper a SST has been designed in which cascading has been done for interfacing high voltage power using low power switching devices. Pulse Width Modulation (PWM) has been used for switching technique. The configuration has been simulated in MATLAB Simulink software and its performance at different stage has been shown. Results shows that voltage and current stress is equal to each bridge.


title: The case against physical punishment

abstract: We review the literature on parental physical punishment of children, laying out foundations of a case against physical punishment as a form of discipline. We consider the research on physical punishment finding that physical punishment is associated with a number of undesirable outcomes for children and adolescents. We pay special attention to questions of: parent effects versus child effects; whether parental use of physical punishment is moderated by family, neighborhood, or cultural context, and whether physical punishment can be considered to be part of a continuum of family violence. We close with recommendations for positive parenting policies and practices.


title: Spammers Detection from Product Reviews: A Hybrid Model

abstract: Driven by profits, spam reviews for product promotion or suppression become increasingly rampant in online shopping platforms. This paper focuses on detecting hidden spam users based on product reviews. In the literature, there have been tremendous studies suggesting diversified methods for spammer detection, but whether these methods can be combined effectively for higher performance remains unclear. Along this line, a hybrid PU-learning-based Spammer Detection (hPSD) model is proposed in this paper. On one hand, hPSD can detect multi-type spammers by injecting or recognizing only a small portion of positive samples, which meets particularly real-world application scenarios. More importantly, hPSD can leverage both user features and user relations to build a spammer classifier via a semi-supervised hybrid learning framework. Experimental results on movie data sets with shilling injection show that hPSD outperforms several state-of-the-art baseline methods. In particular, hPSD shows great potential in detecting hidden spammers as well as their underlying employers from a real-life Amazon data set. These demonstrate the effectiveness and practical value of hPSD for real-life applications.


title: A New Switching Strategy for Pulse Width Modulation (PWM) Power Converters

abstract: This paper presents a new switching strategy for pulse width modulation (PWM) power converters. Since the proposed strategy uses independent on/off switching action of the upper or lower arm according to the polarity of the current, the dead time is not needed except instant of current polarity change. Therefore, it is not necessary to compensate the dead time effect and the possibility of arm short is strongly eliminated. The current control of PWM power converters can easily adopt the proposed switching strategy by using the polarity information of the reference current instead of the real current, thus eliminating the problems that commonly arise from real current detection. In order to confirm the usefulness of the proposed switching strategy, experimental tests were done using a single-phase inverter with passive loads, a three-phase inverter for induction motor drives, a three-phase ac/dc PWM converter, a three-phase active power filter, and a class-D amplifier, the results of which are presented in this paper


title: Stance Classification in Out-of-Domain Rumours: A Case Study around Mental Health Disorders

abstract: Social media being a prolific source of rumours, stance classification of individual posts towards rumours has gained attention in the past few years. Classification of stance in individual posts can then be useful to determine the veracity of a rumour. Research in this direction has looked at rumours in different domains, such as politics, natural disasters or terrorist attacks. However, work has been limited to in-domain experiments, i.e. training and testing data belong to the same domain. This presents the caveat that when one wants to deal with rumours in domains that are more obscure, training data tends to be scarce. This is the case of mental health disorders, which we explore here. Having annotated collections of tweets around rumours emerged in the context of breaking news, we study the performance stability when switching to the new domain of mental health disorders. Our study confirms that performance drops when we apply our trained model on a new domain, emphasising the differences in rumours across domains. We overcome this issue by using a little portion of the target domain data for training, which leads to a substantial boost in performance. We also release the new dataset with mental health rumours annotated for stance.


title: Model Predictive Control of Unknown Nonlinear Dynamical Systems Based on Recurrent Neural Networks

abstract: In this paper, we present a neurodynamic approach to model predictive control (MPC) of unknown nonlinear dynamical systems based on two recurrent neural networks (RNNs). The echo state network (ESN) and simplified dual network (SDN) are adopted for system identification and dynamic optimization, respectively. First, the unknown nonlinear system is identified based on the ESN with input-output training and testing samples. Then, the resulting nonconvex optimization problem associated with nonlinear MPC is decomposed via Taylor expansion. To estimate the higher order unknown term resulted from the decomposition, an online supervised learning algorithm is developed. Next, the SDN is applied for solving the relaxed convex optimization problem to compute the optimal control actions over the predicted horizon. Simulation results are provided to demonstrate the effectiveness and characteristics of the proposed approach. The proposed RNN-based approach has many desirable properties such as global convergence and low complexity. It is shown that the RNN-based nonlinear MPC scheme is effective and potentially suitable for real-time MPC implementation in many applications.


title: Spiking Deep Residual Network

abstract: Recently, spiking neural network (SNN) has received significant attentions for its biological plausibility. SNN theoretically has at least the same computational power as traditional artificial neural networks (ANNs), and it has the potential to achieve revolutionary energy-efficiency. However, at current stage, it is still a big challenge to train a very deep SNN. In this paper, we propose an efficient approach to build a spiking version of deep residual network (ResNet), which represents the state-of-the-art convolutional neural networks (CNNs). We employ the idea of converting a trained ResNet to a network of spiking neurons named Spiking ResNet. To address the conversion problem, we propose a shortcut normalisation mechanism to appropriately scale continuous-valued activations to match firing rates in SNN, and a layer-wise error compensation approach to reduce the error caused by discretisation. Experimental results on MNIST, CIFAR-10, and CIFAR-100 demonstrate that the proposed Spiking ResNet yields the state-of-the-art performance of SNNs.


title: 10-bit Driver IC Using 3-bit DAC Embedded Operational Amplifier for Spatial Optical Modulators (SOMs)

abstract: A 10-bit driver IC for laser projection full high-definition TV (HDTV) applications using a spatial optical modulator (SOM) has been developed. For high-speed data transfer between the timing controller and driver IC, the driver IC adopts a mini-LVDS interface that operates up to 400 Mbps. To reduce the chip area, a digital-to-analog converter (DAC) structure including a 7-bit resistor-string DAC and a unity-gain buffer, which has a 3-bit linear DAC, is proposed. The area of the proposed DAC is 40% smaller than that of the typical 8-bit resistor-string DAC. The driver IC, which has 546 channels, is fabricated using a 0.35-mum CMOS process, and its area is 21 700 mum times 3 000 mum. The measured INL and DNL of the output voltages are less than 0.13 LSB, and the settling time is 1.93 mus with 40 pF capacitive loads. The output voltage deviation of the driver IC is achieved as 1.3 mV by compensating the nonuniformity among output channels.


title: Hierarchical correctness proofs for distributed algorithms

abstract: This thesis introduces a new model for distributed computation in asynchronous networks, the input-output automaton. This simple, powerful model captures in a novel way the game-theoretical interaction between a system and its environment, and allows fundamental properties of distributed computation such as fair computation to be naturally expressed. Furthermore, this model can be used to construct modular, hierarchical correctness proofs of distributed algorithms. This thesis defines the input-output automaton model, and presents an interesting example of how this model can be used to construct such proofs.


title: A Parallelization of Dijkstra's Shortest Path Algorithm

abstract: The single source shortest path (SSSP) problem lacks parallel solutions which are fast and simultaneously work-efficient. We propose simple criteria which divide Dijkstra's sequential SSSP algorithm into a number of phases, such that the operations within a phase can be done in parallel. We give a PRAM algorithm based on these criteria and analyze its performance on random digraphs with random edge weights uniformly distributed in [0,1]. We use the G (n, d/n) model: the graph consists of n nodes and each edge is chosen with probability d/n. Our PRAM algorithm needs O(n 1/3 log n) log n) time and O (n log n+dn) work with high probability (whp). We also give extensions to external memory computation. Simulations show the applicability of our approach even on non-random graphs.


title: Development of Wireless Brain Computer Interface With Embedded Multitask Scheduling and its Application on Real-Time Driver's Drowsiness Detection and Warning

abstract: Biomedical signal monitoring systems have been rapidly advanced with electronic and information technologies in recent years. However, most of the existing physiological signal monitoring systems can only record the signals without the capability of automatic analysis. In this paper, we proposed a novel brain-computer interface (BCI) system that can acquire and analyze electroencephalogram (EEG) signals in real-time to monitor human physiological as well as cognitive states, and, in turn, provide warning signals to the users when needed. The BCI system consists of a four-channel biosignal acquisition/amplification module, a wireless transmission module, a dual-core signal processing unit, and a host system for display and storage. The embedded dual-core processing system with multitask scheduling capability was proposed to acquire and process the input EEG signals in real time. In addition, the wireless transmission module, which eliminates the inconvenience of wiring, can be switched between radio frequency (RF) and Bluetooth according to the transmission distance. Finally, the real-time EEG-based drowsiness monitoring and warning algorithms were implemented and integrated into the system to close the loop of the BCI system. The practical online testing demonstrates the feasibility of using the proposed system with the ability of real-time processing, automatic analysis, and online warning feedback in real-world operation and living environments.


title: ClusType: Effective Entity Recognition and Typing by Relation Phrase-Based Clustering

abstract: Entity recognition is an important but challenging research problem. In reality, many text collections are from specific, dynamic, or emerging domains, which poses significant new challenges for entity recognition with increase in name ambiguity and context sparsity, requiring entity detection without domain restriction. In this paper, we investigate entity recognition (ER) with distant-supervision and propose a novel relation phrase-based ER framework, called ClusType, that runs data-driven phrase mining to generate entity mention candidates and relation phrases, and enforces the principle that relation phrases should be softly clustered when propagating type information between their argument entities. Then we predict the type of each entity mention based on the type signatures of its co-occurring relation phrases and the type indicators of its surface name, as computed over the corpus. Specifically, we formulate a joint optimization problem for two tasks, type propagation with relation phrases and multi-view relation phrase clustering. Our experiments on multiple genres---news, Yelp reviews and tweets---demonstrate the effectiveness and robustness of ClusType, with an average of 37% improvement in F1 score over the best compared method.


title: Image-based tree modeling

abstract: In this paper, we propose an approach for generating 3D models of natural-looking trees from images that has the additional benefit of requiring little user intervention. While our approach is primarily image-based, we do not model each leaf directly from images due to the large leaf count, small image footprint, and widespread occlusions. Instead, we populate the tree with leaf replicas from segmented source images to reconstruct the overall tree shape. In addition, we use the shape patterns of visible branches to predict those of obscured branches. We demonstrate our approach on a variety of trees.


title: yaSpMV: yet another SpMV framework on GPUs

abstract: SpMV is a key linear algebra algorithm and has been widely used in many important application domains. As a result, numerous attempts have been made to optimize SpMV on GPUs to leverage their massive computational throughput. Although the previous work has shown impressive progress, load imbalance and high memory bandwidth remain the critical performance bottlenecks for SpMV. In this paper, we present our novel solutions to these problems. First, we devise a new SpMV format, called blocked compressed common coordinate (BCCOO), which uses bit flags to store the row indices in a blocked common coordinate (COO) format so as to alleviate the bandwidth problem. We further improve this format by partitioning the matrix into vertical slices to enhance the cache hit rates when accessing the vector to be multiplied. Second, we revisit the segmented scan approach for SpMV to address the load imbalance problem. We propose a highly efficient matrix-based segmented sum/scan for SpMV and further improve it by eliminating global synchronization. Then, we introduce an auto-tuning framework to choose optimization parameters based on the characteristics of input sparse matrices and target hardware platforms. Our experimental results on GTX680 GPUs and GTX480 GPUs show that our proposed framework achieves significant performance improvement over the vendor tuned CUSPARSE V5.0 (up to 229% and 65% on average on GTX680 GPUs, up to 150% and 42% on average on GTX480 GPUs) and some most recently proposed schemes (e.g., up to 195% and 70% on average over clSpMV on GTX680 GPUs, up to 162% and 40% on average over clSpMV on GTX480 GPUs).


title: An incremental algorithm for maintaining the dominator tree of a reducible flowgraph

abstract: We present a new incremental algorithm for the problem of maintaining the dominator tree of a reducible flowgraph as the flowgraph undergoes changes such as the insertion and deletion of edges. Such an algorithm has applications in incremental dataflow analysis and incremental compilation.


title: Topic-Level Random Walk through Probabilistic Model

abstract: In this paper, we study the problem of topic-level random walk, which concerns the random walk at the topic level. Previously, several related works such as topic sensitive page rank have been conducted. However, topics in these methods were predefined, which makes the methods inapplicable to different domains. In this paper, we propose a four-step approach for topic-level random walk. We employ a probabilistic topic model to automatically extract topics from documents. Then we perform the random walk at the topic level. We also propose an approach to model topics of the query and then combine the random walk ranking score with the relevance score based on the modeling results. Experimental results on a real-world data set show that our proposed approach can significantly outperform the baseline methods of using language model and that of using traditional PageRank.


title: Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning

abstract: Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources. It is ideal in the context of MR-only radiotherapy planning as it can jointly regress a synthetic CT (synCT) scan and segment organs-at-risk (OAR) from MRI. We propose a probabilistic multi-task network that estimates: (1) intrinsic uncertainty through a heteroscedastic noise model for spatially-adaptive task loss weighting and (2) parameter uncertainty through approximate Bayesian inference. This allows sampling of multiple segmentations and synCTs that share their network representation. We test our model on prostate cancer scans and show that it produces more accurate and consistent synCTs with a better estimation in the variance of the errors, state of the art results in OAR segmentation and a methodology for quality assurance in radiotherapy treatment planning.


title: Influence of Rectifiers on High-Speed Permanent Magnet Generator Electromagnetic and Temperature Fields in Distributed Power Generation Systems

abstract: A power converter is very necessary in the system of high-speed permanent magnet generators (HSPMG), since the frequency of the output electrical energy is generally hundreds of Hertz or even more than 1000 Hz. However, the converter with switches in fast on–off transitions will have some effects on the generator and, therewith, some current harmonics will be also induced in the device. Taking a 117 kW 60 000 r/min HSPMG as an example, the harmonics in this generator were analyzed. And then, the voltage harmonic content, the current harmonic content, and the current total harmonic distortion could be obtained. In addition, stator core losses and rotor eddy current losses were analyzed comparatively, and the influence of the converter on the generator power factor was further studied. Based on the 3-D coupling field between fluid and temperature, the temperature distributions were obtained when the generator was connected to different loads, and the influence of the rectifier on the generator temperature field could be obtained, which was also compared with the test data.


title: The vehicle routing problem: State of the art classification and review

abstract: Taxonomic review of vehicle routing literature published between 2009 and mid 2015.An adapted version of an existing comprehensive taxonomy is presented.277 VRP articles are classified based on problem characteristics and assumptions.The classification table allows researchers to quickly find relevant literature.Recent trends in VRP literature are discussed. Over the past decades, the Vehicle Routing Problem (VRP) and its variants have grown ever more popular in the academic literature. Yet, the problem characteristics and assumptions vary widely and few literature reviews have made an effort to classify the existing articles accordingly. In this article, we present a taxonomic review of the VRP literature published between 2009 and June 2015. Based on an adapted version of an existing comprehensive taxonomy, we classify 277 articles and analyze the trends in the VRP literature. This classification is the first to categorize the articles to this level of detail.


title: Recursive Decentralized Collaborative Localization for Sparsely Communicating Robots

abstract: This paper provides a new fully-decentralized algorithm for Collaborative Localization based on the extended Kalman filter. The major challenge in decentralized collaborative localization is to track inter-robot dependencies – which is particularly difficult in situations where sustained synchronous communication between robots cannot be guaranteed. Current approaches suffer from the need for particular communication schemes, extensive bookkeeping of measurements, overlyconservative assumptions, or the restriction to specific measurement models. To the best of our knowledge, the algorithm we propose in this paper is the first one that tracks interrobot correlations while fulfilling all of the following relevant conditions: communication is limited to two robots that obtain a relative measurement, the algorithm is recursive in the sense that it does not require storage of measurements and each robot maintains only the latest estimate of its own pose, and it supports generic measurement models. These particularly hard conditions make the approach applicable to a wide range of multi-robot applications. Extensive experiments carried out using real world datasets demonstrate the improved performance of our method compared to several existing approaches.


An efficient cross-lingual model for sentence classification using convolutional neural network


title: Evolution Of A Mars Airplane Concept For The Ares Mars Scout Mission,” 2nd AIAA "Unmanned Unlimited

abstract: ARES (Aerial Regional-scale Environmental Survey of Mars) is a proposed Mars Scout mission using an airplane to provide high-value science measurements in the areas of atmospheric chemistry, surface geology and mineralogy, and crustal magnetism. The use of an airplane for robotic exploration of Mars has been studied for over 25 years. There are, however, significant challenges associated with getting an airplane to Mars and flying through the thin, carbon dioxide Martian atmosphere. The traditional wisdom for aircraft design does not always apply for this type of vehicle and geometric, aerodynamic, and mission constraints result in a limited feasible design space. The ARES airplane design is the result of a concept exploration and evolution involving a number of trade studies, downselects, and design refinements. Industry, university, and NASA partners initially proposed a number of different concepts, drawing heavily on past Mars airplane design experience. Concept downselects were conducted with qualitative evaluation and high- level analyses, focused on the most important parameters for the ARES mission. Following a successful high altitude test flight of the basic configuration, additional design refinement led to the current design. The resulting Mars airplane concept enables the high-value science objectives of the ARES mission to be accomplished while also fulfilling the desire for a simple, low-risk design.


title: Interactive Cloud Experimentation for Biology: An Online Education Case Study

abstract: Interacting with biological systems via experiments is important for academia, industry, and education, but access barriers exist due to training, costs, safety, logistics, and spatial separation. High-throughput equipment combined with web streaming could enable interactive biology experiments online, but no such platform currently exists. We present a cloud experimentation architecture (paralleling cloud computation), which is optimized for a class of domain-specific equipments (biotic processing units - BPU) to share and execute many experiments in parallel remotely and interactively at all time. We implemented an instance of this architecture that enables chemotactic experiments with a slime mold Physarum Polycephelum. A user study in the blended teaching and research setting of a graduate-level biophysics class demonstrated that this platform lowers the access barrier for non-biologists, enables discovery, and facilitates learning analytics. This architecture is flexible for integration with various biological specimens and equipments to facilitate scalable interactive online education, collaborations, research, and citizen science.


title: Enhanced AODV Protocol for Detection and Prevention of Blackhole Attack in Mobile Ad Hoc Network

abstract: Mobile Ad-hoc Network (MANET) is a kind of wireless network that has the most challenging network infrastructure. It is formed using the mobile nodes without any centralized administration from the security perspective and is a self-configuring fastest emerging wireless technology, each node on the MANET will act like a router which forwards the packets. Dynamic nature of this network makes routing protocols to play a prominent role in setting up efficient route among a pair of nodes. Dynamic Source Routing (DSR) and Ad-hoc On-Demand Distance Vector (ADOV) is a reactive MANET routing protocols. Most of the attacks on MANETs are routing protocol attacks. Attacks on routing protocols, especially internal attacks will cause the damage to MANETs. Sinkhole and black hole attacks are a type of internal attack which is affected by attempting to draw all network traffic to malicious nodes that fake routing update and degrade the performance of the network. The black hole nodes should be detected from the network as early as possible via detection mechanism and should also guarantee the higher detection rate and less cross-over error rate. In this paper, we studied the characteristics of black hole attack and how it will affect the performance of the distance vector routing on demand routing protocol such as (ADOV) protocol, which recognizes the presence of black hole node from packet flow information between nodes and isolates it from the network via applying AODV protocol that one of popular routing protocol. We have evaluated the performance of the system using widely used simulator NS2, results prove the effectiveness of our prevention and detection method.


title: Differential Privacy Models for Location-Based Services

abstract: In this paper, we consider the adaptation of differential privacy to the context of location-based services LBSs, which personalize the information provided to a user based on his current position. Assuming that the LBS provider is queried with a perturbed version of the position of the user instead of his exact one, we rely on differential privacy to quantify the level of indistinguishability i.e., privacy provided by this perturbation with respect to the user's position. In this setting, the adaptation of differential privacy can lead to various models depending on the precise form of indistinguishability required. We discuss the set of properties that hold for these models in terms of privacy, utility and also implementation issues. More precisely, we first introduce and analyze one of these models, the D, e-location privacy, which is directly inspired from the standard differential privacy model. In this context, we describe a general probabilistic model for obfuscation mechanisms for the locations whose output domain is the Euclidean space E2. In this model, we characterize the satisfiability conditions of D, e-location privacy for a particular mechanism and also measure its utility with respect to an arbitrary loss function. Afterwards, we present and analyze symmetric mechanisms in which all locations are perturbed in a unified manner through a noise function, focusing in particular on circular noise functions. We prove that, under certain assumptions, the circular functions are rich enough to provide the same privacy and utility levels as other more complex i.e., non-circular noise functions, while being easier to implement. Finally, we extend our results to a generalized notion for location privacy, called l-privacy capturing both D, e-location privacy and also the notion of e-geo-indistinguishability recently introduced by Andres, Bordenabe, Chatzikokolakis and Palamidessi.


title: Supervised clustering of streaming data for email batch detection

abstract: We address the problem of detecting batches of emails that have been created according to the same template. This problem is motivated by the desire to filter spam more effectively by exploiting collective information about entire batches of jointly generated messages. The application matches the problem setting of supervised clustering, because examples of correct clusterings can be collected. Known decoding procedures for supervised clustering are cubic in the number of instances. When decisions cannot be reconsidered once they have been made --- owing to the streaming nature of the data --- then the decoding problem can be solved in linear time. We devise a sequential decoding procedure and derive the corresponding optimization problem of supervised clustering. We study the impact of collective attributes of email batches on the effectiveness of recognizing spam emails.


title: Total Variation Denoising via the Moreau Envelope

abstract: Total variation denoising is a nonlinear filtering method well suited for the estimation of piecewise-constant signals observed in additive white Gaussian noise. The method is defined by the minimization of a particular non-differentiable convex cost function. This paper describes a generalization of this cost function that can yield more accurate estimation of piecewise constant signals. The new cost function involves a non-convex penalty (regularizer) designed to maintain the convexity of the cost function. The new penalty is based on the Moreau envelope. The proposed total variation denoising method can be implemented using forward-backward splitting.


title: Low Power Magnetic Full-Adder Based on Spin Transfer Torque MRAM

abstract: Power issues have become a major problem of CMOS logic circuits as technology node shrinks below 90 nm. In order to overcome this limitation, emerging logic-in-memory architecture based on nonvolatile memories (NVMs) are being investigated. Spin transfer torque (STT) magnetic random access memory (MRAM) is considered one of the most promising NVMs thanks to its high speed, low power, good endurance, and 3-D back-end integration. This paper presents a novel magnetic full-adder (MFA) design based on perpendicular magnetic anisotropy (PMA) STT-MRAM. It provides advantageous power efficiency and die area compared with conventional CMOS-only full adder (FA). Transient simulations have been performed to validate this design by using an industrial CMOS 40 nm design kit and an accurate STT-MRAM compact model including physical models and experimental measurements.


title: The ‘dark side’ of leadership personality and transformational leadership: An exploratory study

abstract: Abstract There is growing interest in dysfunctional dispositions in the workplace, in particular relating to leaders and managers within organizations. This paper reports an exploratory study investigating the relationship between the ‘dark side’ of leadership personality and transformational leadership. Data were collected from a sample of New Zealand business leaders and senior managers ( n = 80) from scores on the Hogan Development Survey (HDS; Hogan & Hogan, 1997 ) and the Multifactor Leadership Questionnaire (MLQ; Bass & Avolio, 1995 ). Regression analyses revealed the ‘Colorful’ (histrionic) dimension of the HDS to be a positive predictor of transformational leadership, while the ‘Cautious’ (avoidant) and ‘Bold’ (narcissistic) dimensions of the HDS were revealed as negative predictors of transformational leadership. This is one of the first reports to suggest a relationship between histrionic personality and transformational leadership, in a literature preoccupied with the relationship between leadership and narcissism.


title: Google Search Results: Buried If Not Forgotten

abstract: The right to be forgotten or to require that online information be deleted squarely confronts the First Amendment right to free speech. But the underlying problem giving rise to this right is only increasing: harmful information posted online has the real potential to destroy a person’s reputation or livelihood. In addition, the way Internet users get their information – search engines, primarily Google – emphasizes harmful information if it is “popular” under Google’s algorithm. Google’s response to requests for removal is that it cannot control the underlying websites, so removing information from its results is pointless. But in fact, the search results themselves are of crucial importance. And those results are already being altered. If Internet users’ primary access to the vast amount of online information is filtered – and hand-edited – by a search engine, why shouldn’t that editing take into consideration the harmful nature of some information? This Article proposes that Google consider “demoting” references to information in its search results that falls within one of several sensitive categories, and the party requesting removal has unsuccessfully exhausted her remedies with respect to the website publisher of the information. This amounts not to censorship, but to factoring in the nature of the information itself in determining its relevance in response to search requests.


title: Mining an Online Judge System to Support Introductory Computer Programming Teaching

abstract: Computer programming is an activity which requires a set of cognitive processes that naturally develop through practice, writing algorithmic solutions. Students learn a lot from their mistakes, but for this they need feedback on their workouts. Marking students’ work outs is very time consuming, which often limits a teacher’s capacity to offer close guidance individually. The PROBOCA project aims to build a tool, based on the BOCA online judge, suited for the purpose of learning computer programming by practice. In addition to a problem database organized by theme and difficulty, the system provides functionalities to support the teacher in the classroom. One of the main endeavors is to develop a procedure for estimating the degree of difficulty of a certain problem. This “nominal” parameter may then be compared to the difficulty level as perceived by each student. The result is a valuable indicator of those students that are experiencing challenges. This paper presents the preliminary specification of PROBOCA s architecture and functional requirements along with its current state of development.


title: Predicting the popularity of web 2.0 items based on user comments

abstract: In the current Web 2.0 era, the popularity of Web resources fluctuates ephemerally, based on trends and social interest. As a result, content-based relevance signals are insufficient to meet users' constantly evolving information needs in searching for Web 2.0 items. Incorporating future popularity into ranking is one way to counter this. However, predicting popularity as a third party (as in the case of general search engines) is difficult in practice, due to their limited access to item view histories. To enable popularity prediction externally without excessive crawling, we propose an alternative solution by leveraging user comments, which are more accessible than view counts. Due to the sparsity of comments, traditional solutions that are solely based on view histories do not perform well. To deal with this sparsity, we mine comments to recover additional signal, such as social influence. By modeling comments as a time-aware bipartite graph, we propose a regularization-based ranking algorithm that accounts for temporal, social influence and current popularity factors to predict the future popularity of items. Experimental results on three real-world datasets --- crawled from YouTube, Flickr and Last.fm --- show that our method consistently outperforms competitive baselines in several evaluation tasks.


Nutrient uptake of tomato grown under protected cultivation.


title: Virtual Synchronous Generator Control for Damping DC-Side Resonance of VSC-MTDC System

abstract: DC-side oscillation is investigated for a voltage source converter-based multiterminal direct current (VSC-MTDC) system. Confronted with this issue, the virtual synchronous generator (VSG) control strategy is proposed to dampen dc-side oscillations and enhance the inertia of an MTDC network. Furthermore, the working mechanism of VSG technology, to provide the virtual inertia and damping, is discussed comprehensively and thoroughly. The proposed VSG control does not require the precise acquirement of system parameters and can eliminate the wide frequency region instead of a specific frequency point. Moreover, the introduction of VSG control can enhance the inertia, damping, as well as the stability margins of the system. Finally, the performance of the proposed control strategy is validated by the simulations of both time domain and frequency domain.


title: The cognitive neuroscience of creativity

abstract: This article outlines a framework of creativity based on functional neuroanatomy. Recent advances in the field of cognitive neuroscience have identified distinct brain circuits that are involved in specific higher brain functions. To date, these findings have not been applied to research on creativity. It is proposed that there are four basic types of creative insights, each mediated by a distinctive neural circuit. By definition, creative insights occur in consciousness. Given the view that the working memory buffer of the prefrontal cortex holds the content of consciousness, each of the four distinctive neural loops terminates there. When creativity is the result of deliberate control, as opposed to spontaneous generation, the prefrontal cortex also instigates the creative process. Both processing modes, deliberate and spontaneous, can guide neural computation in structures that contribute emotional content and in structures that provide cognitive analysis, yielding the four basic types of creativity. Supportive evidence from psychological, cognitive, and neuroscientific studies is presented and integrated in this article. The new theoretical framework systematizes the interaction between knowledge and creative thinking, and how the nature of this relationship changes as a function of domain and age. Implications for the arts and sciences are briefly discussed.


title: Evaluating Promotional Activities in an Online Two-Sided Market of User-Generated Content

abstract: We measure the value of promotional activities and referrals by content creators to an online platform of user-generated content. To do so, we develop a modeling approach that explains individual-level choices of visiting the platform, creating, and purchasing content as a function of consumer characteristics and marketing activities, allowing for the possibility of interdependence of decisions within and across users. Empirically, we apply our model to Hewlett-Packard's (HP) print-on-demand service of user-created magazines, named MagCloud. We use two distinct data sets to show the applicability of our approach: an aggregate-level data set from Google Analytics, which is a widely available source of data to managers, and an individual-level data set from HP. Our results compare content creator activities, which include referrals and word-of-mouth efforts, with firm-based actions, such as price promotions and public relations. We show that price promotions have strong effects but are limited to the purchase decisions, whereas content creator referrals and public relations efforts have broader effects that impact all consumer decisions at the platform. We provide recommendations as to the level of a firm's investments when “free” promotional activities by content creators exist. These free marketing campaigns are likely to have a substantial presence in most online services of user-generated content.


title: Low-Loss 3-D Multilayer Transmission Lines and Interconnects Fabricated by Additive Manufacturing Technologies

abstract: This paper presents low-loss 3-D transmission lines and vertical interconnects fabricated by aerosol jet printing (AJP) which is an additive manufacturing technology. AJP stacks up multiple layers with minimum feature size as small as 20 $\mu \text{m}$ in the $xy$ -direction and 0.7 $\mu \text{m}$ in the z-direction. It also solves the problem of fabricating vias to realize the vertical transition by 3-D printing. The loss of the stripline is measured to be 0.53 dB/mm at 40 GHz. The vertical transition achieves a broadband bandwidth from 0.1 to 40 GHz. The results of this paper demonstrate the feasibility of utilizing 3-D printing for low-cost multilayer system-on-package RF/millimeter-wave front-ends.


title: Opportunities and risks of software-as-a-service: Findings from a survey of IT executives

abstract: IT providers have heralded software-as-a-service (SaaS) as an excellent complement to on-premises software addressing the shortcomings of previous on-demand software solutions such as application service provision (ASP). However, although some practitioners and academics emphasize the opportunities that SaaS offers companies, others already predict its decline due to the considerable risk involved in its deployment. Ours is the first study to analyze the opportunities and risks associated with adopting SaaS as perceived by IT executives at adopter and non-adopter firms. We first developed a research model grounded in an opportunity-risk framework, which is theoretically embedded in the theory of reasoned action. Subsequently, we analyzed the data collected through a survey of 349 IT executives at German companies. Our findings suggest that in respect to both SaaS adopters and non-adopters, security threats are the dominant factor influencing IT executives' overall risk perceptions. On the other hand, cost advantages are the strongest driver affecting IT executives' perceptions of SaaS opportunities. Furthermore, we find significant differences between adopters' and non-adopters' perceptions of specific SaaS risks and opportunities, such as performance and economic risks as well as quality improvements, and access to specialized resources. Our study provides relevant findings to improve companies' assessment of SaaS offerings. It also offers SaaS providers insights into the factors that should be prioritized or avoided when offering SaaS services to companies at different stages of their technology adoption lifecycle.


title: Quasi-Newton Optimization in Deep Q-Learning for Playing ATARI Games

abstract: Reinforcement Learning (RL) algorithms allow artificial agents to improve their selection of actions so as to increase rewarding experiences in their environments. The learning can become intractably slow as the state space of the environment grows. This has motivated methods to use deep artificial neural networks to learn the state representations. Deep reinforcement learning algorithms require solving a non-convex and nonlinear unconstrained optimization problem. Methods for solving the optimization problems in deep RL are restricted to the class of first-order algorithms, like stochastic gradient descent (SGD). The major drawback of the SGD methods is that they have the undesirable effect of not escaping saddle points. Furthermore, these methods require exhaustive trial and error to fine-tune many learning parameters. Using second derivative information can result in improved convergence properties, but computing the Hessian matrix for large-scale problems is not practical. Quasi-Newton methods, like SGD, require only first-order gradient information, but they can result in superlinear convergence, which makes them attractive alternatives. The limited-memory BFGS approach is one of the most popular quasi-Newton methods that construct positive definite Hessian approximations. In this paper, we introduce an efficient optimization method, based on the limited memory BFGS quasi-Newton method using line search strategy -- as an alternative to SGD methods. Our method bridges the disparity between first order methods and second order methods by continuing to use gradient information to calculate a low-rank Hessian approximations. We provide empirical results on variety of the classic ATARI 2600 games. Our results show a robust convergence with preferred generalization characteristics, as well as fast training time and no need for the experience replaying mechanism.


title: The Safety Rotor—An Electromechanical Rotor Safety System for Drones

abstract: We propose a novel rotor safety mechanism for rotary-wing small unmanned aerial vehicles, such as quadrotors, that avoids problems associated with cages, rotor shrouds, and obstacle avoidance safety systems. The system works by using a passively spinning hoop as a mechanical interference sensor; changes in hoop speed are detected with a reflective IR sensor, which triggers an electrodynamic braking circuit. Experiments demonstrated that the system can rapidly detect incipient collisions and bring the rotors of a retrofitted DJI Phantom 3 to a halt from flight speed within 0.06 s of contact. In testing with finger proxies and surface collisions, the safety rotor provided an effective mechanism for reducing the severity of rotor injuries during impact.


title: Are your votes really counted?: testing the security of real-world electronic voting systems

abstract: Electronic voting systems play a critical role in today's democratic societies, as they are responsible for recording and counting the citizens' votes. Unfortunately, there is an alarming number of reports describing the malfunctioning of these systems, suggesting that their quality is not up to the task. Recently, there has been a focus on the security testing of voting systems to determine if they can be compromised in order to control the results of an election. We have participated in two large-scale projects, sponsored by the Secretaries of State of California and Ohio, whose respective goals were to perform the security testing of the electronic voting systems used in those two states. The testing process identified major flaws in all the systems analyzed, and resulted in substantial changes in the voting procedures of both states. In this paper, we describe the testing methodology that we used in testing two real-world electronic voting systems, the findings of our analysis, and the lessons we learned.


title: Personalization Research in E-Commerce - a State of the Art Review (2000-2008)

abstract: ABSTRACT E-commerce product personalization and website personalization have been a topic of interest to a lot of researchers during the last years. As such, academic research in the area of personalization in e-commerce should rely on a rigorous literature review first in order to identify existing research and acknowledge the state of the art in research. This article provides a vast overview on personalization literature with a special focus on e-commerce (e.g. recommender systems). This literature, respectively our approach, can be used by fellow researchers to identify the basic literature for their own work. It serves as a rigorous way of retrieving literature for other fields in their scientific research. A complete reference list is presented as well as additional figures and tables analyzing the personalization literature in general and according to an emerging classification. The three major categories of personalization research in e-commerce are: "implementation", "theoretical foundations", and "user centric aspects" under which there are several subcategories into which the papers can be classified. The reviewed articles (42 in total out of 15,116) were taken from all the relevant, high ranked (A to C) journals according to the journal ranking list: JOURQUAL2. Keywords: personalization, e-commerce, state of the art, classification, literature review 1. Introduction Personalization plays an important role for service providers in modern e-commerce strategies; it is important to build up a tight customer relationship in order to address customers personally. Speaking about personalization in e-commerce brings up the problems of identifying a customer, gathering information around the customer, and processing data to make a service seem personally adopted for a certain customer (e.g. recommender systems or comparison-shopping systems). One needs to look at two different business software systems when analyzing customer self-service applications in e-commerce (enabling the customer to use services on their own through personalization): (1) ERP systems as back-office systems and (2) Web applications (mainly e-shops) as front-office applications. Personalization is a means of facilitating this ease of use by presenting the customer with a pre-defined service for personal needs [Risch 2007]. It is an interdisciplinary topic with which research papers in the field of marketing and computer science have increasingly been concerned. In order to give researchers a deeper insight into the facets of personalization in e-commerce and to offer a starting-point for this research, the outcome of a rigorous literature investigation on personalization from the field of IS research is presented. The following sections will give an overview on the current literature in the area of e-commerce personalization. The research methodology used for this research is based on a reasonable way for retrieving relevant articles. With the above-mentioned literature in mind, we started our research with an initial set of keywords for our title-based search (section 3). In section 4, we discuss the emerging classification of the collected articles to summarize the results in section 5. Afterwards, we discuss our contribution to the research on personalization (section 6) and point out the limitations (section 7) and future directions of our research (section 8). Founding our research on existing literature makes it a form of secondary research with an explorative and descriptive focus. This article?s structure is derived and inspired from several literature review articles published in the International Journal Production Economics [Ngai et al. 2007], International Journal Management and Enterprise Development [Moon 2007], Communications of the Association for Information Systems [Lee et al. 2003], and the Proceedings of the European Conference on Information Systems [vom Brocke et al. 2009]. 2. …


title: A photovoltaic system model for Matlab/Simulink simulations

abstract: Solar energy maintains life on the earth and it is an infinite source of clean energy. There is an increasing trend for the use of solar cells in industry and domestic appliances because solar energy is expected to play significant role in future smart grids as a distributed renewable source. Optimal and large-scale integration of renewable sources into smart grid is possible by the aid of computer simulations and hence there is a growing demand for computer modeling and simulation of renewable sources. This study presents a generalized photovoltaic (PV) system simulation model for Matlab/Simulink simulation environment. The proposed model is based on a behavioral cell model for modeling solar radiance to electricity conversion and an electrical driver interface for implementing electrical characteristic of power limited systems in power simulations. The model is efficient in computational complexity and it is easy to configure for representing wide-range of PV installations.


SmaRT Visualisation of Legal Rules for Compliance


title: A Survey of E-Commerce Recommender Systems

abstract: Various personal services in business play important roles in the success of current marketing field. The personalized recommendation technique in recommender systems, one of the most important tools of personal service in websites, makes great significance in Internet marketing activities of e-Commerce. Through summarizing and analyzing personalized recommendation research, this paper presents an overview of personalized recommendation technique and proposes future research topics. The research content of this paper mainly includes the following three aspects, (1) the input of recommender systems, such as the acquisition and presentation of customers' interest profile as well as items profiles; (2) the typical methods of various recommendation techniques; and (3) based on current research and application situations, we finally discuss the future research hot topics and give some suggestions for the research on future recommendation technique.


title: Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning

abstract: Finding feasible, collision-free paths for multiagent systems can be challenging, particularly in non-communicating scenarios where each agent's intent (e.g. goal) is unobservable to the others. In particular, finding time efficient paths often requires anticipating interaction with neighboring agents, the process of which can be computationally prohibitive. This work presents a decentralized multiagent collision avoidance algorithm based on a novel application of deep reinforcement learning, which effectively offloads the online computation (for predicting interaction patterns) to an offline learning procedure. Specifically, the proposed approach develops a value network that encodes the estimated time to the goal given an agent's joint configuration (positions and velocities) with its neighbors. Use of the value network not only admits efficient (i.e., real-time implementable) queries for finding a collision-free velocity vector, but also considers the uncertainty in the other agents' motion. Simulation results show more than 26 percent improvement in paths quality (i.e., time to reach the goal) when compared with optimal reciprocal collision avoidance (ORCA), a state-of-the-art collision avoidance strategy.


Improve Chinese Word Embeddings by Exploiting Internal Structure


title: Computation and Computational Thinking

abstract: We recommend using the term Computation in conjunction with a well-defined model of computation whose semantics is clear and which matches the problem being investigated. Computer science already has a number of useful clearly defined models of computation whose behaviors and capabilities are well understood. We should use such models as part of any definition of the term computation. However, for new domains of investigation where there are no appropriate models it may be necessary to invent new formalisms to represent the systems under study.


title: Generating realistic impressions for file-system benchmarking

abstract: The performance of file systems and related software depends on characteristics of the underlying file-system image (i.e., file-system metadata and file contents). Unfortunately, rather than benchmarking with realistic file-system images, most system designers and evaluators rely on ad hoc assumptions and (often inaccurate) rules of thumb. Furthermore, the lack of standardization and reproducibility makes file system benchmarking ineffective. To remedy these problems, we develop Impressions, a framework to generate statistically accurate file-system images with realistic metadata and content. Impressions is flexible, supporting user-specified constraints on various file-system parameters using a number of statistical techniques to generate consistent images. In this paper we present the design, implementation and evaluation of Impressions, and demonstrate its utility using desktop search as a case study. We believe Impressionswill prove to be useful for system developers and users alike.


title: Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks

abstract: We present a novel method for real-time continuous pose recovery of markerless complex articulable objects from a single depth image. Our method consists of the following stages: a randomized decision forest classifier for image segmentation, a robust method for labeled dataset generation, a convolutional network for dense feature extraction, and finally an inverse kinematics stage for stable real-time pose recovery. As one possible application of this pipeline, we show state-of-the-art results for real-time puppeteering of a skinned hand-model.


title: Reconfigurable Electromagnetics Through Metamaterials—A Review

abstract: The introduction of electromagnetic (EM) media with unique field manipulation properties, collectively labelled as metamaterials, has boosted the interest toward the design, the fabrication, and the testing of artificial materials whose features can be engineered according to the applicative requirements and user objectives. In this framework, the long-term promise of reconfigurable metamaterial theory (i.e., the possibility to change the EM response of a 2-D/3-D material arbitrarily and in real time) has given the designers an extremely wide number of new degrees of freedom for the synthesis of innovative adaptive systems. Moreover, successful experimental validations of reconfigurable metamaterials in the entire EM spectrum from microwaves to optical frequencies have further stimulated academic and industrial interests in developing devices with enhanced performances, efficiency, and robustness. Nevertheless, the exploitation of reconfigurable metamaterials in commercial devices is still an open problem with several challenges from both the theoretical and technological viewpoints. This paper is then aimed at reviewing the latest advances on reconfigurable metamaterial engineering from the methodological perspective also providing a comprehensive and balanced survey on latest concepts, current trends, and envisaged future developments on this active field of research.


title: Dynamically reconfigurable system-on-programmable-chip

abstract: Today's high-density FPGAs and intellectual property (IP) components enable the integration of complex systems in one programmable chip. New design strategies and concepts have to be developed in order to utilize the new system-level integration facilities. The approach introduced in this paper describes the implementation of a communication infrastructure that provides a number of on-chip IP-sockets. By using the FPGA feature of partial dynamic reconfiguration, different IP components can be plugged into these sockets at run-time. This leads to a reconfigurable system that can be adapted to varying demands. In this context, we designed a 32-bit RISC processor and an AMBA (Advanced Microcontroller Bus Architecture) on-chip interconnection bus. Finally, we mapped these components on to a reconfigurable system-level FPGA. The resulting hardware sizes and the utilization of the FPGA's resources are presented.


title: Narrow Band Internet of Things

abstract: In this paper, we review the background and state-of-the-art of the narrow-band Internet of Things (NB-IoT). We first introduce NB-IoT general background, development history, and standardization. Then, we present NB-IoT features through the review of current national and international studies on NB-IoT technology, where we focus on basic theories and key technologies, i.e., connection count analysis theory, delay analysis theory, coverage enhancement mechanism, ultra-low power consumption technology, and coupling relationship between signaling and data. Subsequently, we compare several performances of NB-IoT and other wireless and mobile communication technologies in aspects of latency, security, availability, data transmission rate, energy consumption, spectral efficiency, and coverage area. Moreover, we analyze five intelligent applications of NB-IoT, including smart cities, smart buildings, intelligent environment monitoring, intelligent user services, and smart metering. Finally, we summarize security requirements of NB-IoT, which need to be solved urgently. These discussions aim to provide a comprehensive overview of NB-IoT, which can help readers to understand clearly the scientific problems and future research directions of NB-IoT.


Infantile autism: The syndrome and its implications for a neural theory of behavior.


Incremental Segmentation on Private Data without Catastrophic Forgetting


title: Overcoming the challenges in administering biopharmaceuticals: formulation and delivery strategies

abstract: The formulation and delivery of biopharmaceutical drugs, such as monoclonal antibodies and recombinant proteins, poses substantial challenges owing to their large size and susceptibility to degradation. In this Review we highlight recent advances in formulation and delivery strategies — such as the use of microsphere-based controlled-release technologies, protein modification methods that make use of polyethylene glycol and other polymers, and genetic manipulation of biopharmaceutical drugs — and discuss their advantages and limitations. We also highlight current and emerging delivery routes that provide an alternative to injection, including transdermal, oral and pulmonary delivery routes. In addition, the potential of targeted and intracellular protein delivery is discussed.


title: Nonce-Based Cryptography: Retaining Security When Randomness Fails

abstract: We take nonce-based cryptography beyond symmetric encryption, developing it as a broad and practical way to mitigate damage caused by failures in randomness, whether inadvertent bugs or malicious subversion. We focus on definitions and constructions for nonce-based public-key encryption and briefly treat nonce-based signatures. We introduce and construct hedged extractors as a general tool in this domain. Our nonce-based PKE scheme guarantees that if the adversary wants to violate IND-CCA security then it must do both of the following: 1 fully compromise the RNG 2 penetrate the sender system to exfiltrate a seed used by the sender.