title: A Survey of Computational Offloading in Mobile Cloud Computing

abstract: With the rapid development of mobile cloud computing (MCC), the computational offloading in MCC arises. This paper focuses on the computational offloading topic in MCC. It analyzes the existing research, discusses computational offloading decisions, gives a simple comparison of computational offloading framework, and reviews the data offloading in the big data environment. Finally, the issues and challenges on the computational offloading in MCC are examined.


title: Chinese Shadow Puppetry with an Interactive Interface Using the Kinect Sensor

abstract: This paper addresses the problem of using body gestures to control the Chinese shadow puppets with the Microsoft Kinect sensor. By analyzing the motion of the actors in the Chinese famous drama, Wusong Fights the Tiger, we propose a general framework for controlling two shadow puppets, a human model and an animal model. A performer can conduct simple actions such as turning the head, stretching the arms or kicking the legs. However, it is more difficult for a normal performer to simulate more complicated movements, for example, back flips and splits. Therefore we define some special postures to represent these difficult movements. Besides, in order to be compatible with the Chinese drama style, we use water color to paint the background scenery and the foreground characters. We show some preliminary results which demonstrate the effectiveness of this work.


title: Shifting the balance: antibiotic effects on host–microbiota mutualism

abstract: Antibiotics have been used effectively as a means to treat bacterial infections in humans and animals for over half a century. However, through their use, lasting alterations are being made to a mutualistic relationship that has taken millennia to evolve: the relationship between the host and its microbiota. Host-microbiota interactions are dynamic; therefore, changes in the microbiota as a consequence of antibiotic treatment can result in the dysregulation of host immune homeostasis and an increased susceptibility to disease. A better understanding of both the changes in the microbiota as a result of antibiotic treatment and the consequential changes in host immune homeostasis is imperative, so that these effects can be mitigated.


title: Certificateless Provable Data Possession Scheme for Cloud-Based Smart Grid Data Management Systems

abstract: The smart grid is considered to be the next-generation power system because of its reliability, efficiency, and cost-effectiveness. In recent years, the smart grid technology has attracted a lot of attention from both academia and industry. Advances in smart grid technologies are enabling more data to be collected and analyzed in real-time for many kinds of smart grid applications. As the amount of data increases, the traditional smart grid data management system cannot provide sufficient storage and processing capacities. To address these challenges, cloud computing is being introduced into the power system and the cloud-based smart grid data management system has been proposed to better support smart grid applications. In this cloud-based system, the data are stored and analyzed by the remote cloud server according to the requirements of smart grid applications. However, the loss of physical control over the smart grid data makes it a significant challenge in ensuring the integrity of the data. Many provable data possession schemes have been proposed in the past few years. However, most of them suffer from serious security weaknesses or poor performance. We propose an efficient certificateless provable data possession (CL-PDP) scheme for cloud-based smart grid applications. Security analysis shows that the proposed scheme is provably secure in a robust security model and can satisfy several security requirements. Performance analysis demonstrates that the proposed scheme results in lower computation costs as compared to two recently proposed CL-PDP schemes.


title: Enterprise Risk Management: Review, Critique, and Research Directions

abstract: Many regulators, rating agencies, executives, and academics have responded to recent corporate scandals and business failures by advocating and embracing a new approach to risk management: enterprise risk management (ERM). ERM proposes the integrated management of all the risks an organization faces, which inherently requires alignment of risk management with corporate governance and strategy. Academic research on ERM is still in its infancy with articles largely in accounting and finance journals, but rarely in management journals. We argue that ERM offers an important new domain of research for management scholars. A critical review of ERM research allows us to identify limitations and gaps that management scholars are best equipped to address. The paper not only identifies how management scholars can contribute to ERM research, but also points out why ERM research (and practice) needs management research for its development.


title: Classification of Spreadsheet Errors

abstract: This paper describes a framework for a systematic classification of spreadsheet errors. This classification or taxonomy of errors is aimed at facilitating analysis and comprehension of the different types of spreadsheet errors. The taxonomy is an outcome of an investigation of the widespread problem of spreadsheet errors and an analysis of specific types of these errors. This paper contains a description of the various elements and categories of the classification and is supported by appropriate examples.


title: Positioning and deciding: key factors for talent development in soccer

abstract: Talent identification and development implicate recognizing youth players who will be successful in the future and guiding them to the top. A major determinant of this success is tactical skills. To identify possible key factors that help in predicting success over time, this study assesses the tactical skills of 105 elite youth soccer players who participated in a talent development program at an earlier stage of their sport career (mean age 17.8 +/- 0.9). These skills were related to their adult performance level, specifically whether they became professionals (n = 52) or amateurs (n = 53). Defenders, midfielders and attackers completed the Tactical Skills Inventory for Sports with scales for declarative and procedural knowledge in either attacking or defensive situations. A logistic regression analysis was performed to identify the tactical skills that contribute to professional performance level in adulthood. Positioning and deciding appeared to be the tactical skill that best predicts adult performance level (P <0.05). This is especially true for midfielders, with the correct classification of elite youth players in the range of 80%. For players scoring high on this skill, the odds ratios indicated a 6.60 times greater chance that a player became a professional than players scoring low (P <0.05).


title: A Vision-Based Fast Chinese Postal Envelope Identification System *

abstract: This paper describes a method of real-time processing of machine-printed Chinese postage envelopes in an automated postal system. We propose a vision-based postal envelope identification method, and a system that can be applied to rapidly moving machine-printed Chinese postage envelopes through the postal process. Our system uses a high-speed camera to capture images of envelopes running on a conveying device, and then automatically recognizes the postal address and postcode on each envelope. A laser sensor is used to trigger the camera to capture the images. Our system supports a vocabulary of 4,590 categories of characters, including 4,516 frequently used Chinese characters defined in the GB2312-80 standard, 62 alphanumeric characters, and 12 punctuation marks and symbols. Supported font styles include the Song, Fang Song, Kai, and Hei fonts, among others, at a printed font size of 7.5 points and above. The results of an experimental trial of the system with 761 envelope images representing 25,060 characters revealed that an envelope with an average of 32.9 characters could be processed and recognized within 81.38 milliseconds. The character recognition rate of postal addresses is 98.72%. Furthermore, our system also provides a method for the real-time storage of envelope images and recognition results into a database, which can be used in subsequent envelope querying, tracking and management. The results of an experimental trial with real live mails in a postal center indicated that our system could achieve a speed of 21,000 envelopes per hour, with the character recognition rate of postal addresses as high as 98.92%.


title: Robotics in Power Systems: Enabling a More Reliable and Safe Grid

abstract: Power systems face numerous challenges to address the growing need for sustainable energy worldwide. Several tasks directly focused on power systems operations can be either partially or completely automated by an unmanned or teleoperated robotic system. Robots can improve efficiency, reduce labor costs, and, most importantly, avoid operational risks, since power plants and transmission lines are hostile environments for humans. Although there has been some preliminary development of robotic applications in several tasks related to power systems, the technology is still undergoing research and development; it is highly dependent on the nature of the tasks to be performed, and a unified framework of applications does not yet exist. The aim of this article is to present the main contributions from the robotics field to the inspection and maintenance of power systems through the analysis of state-of-the-art and current advances, thus providing the reader with a comprehensive and insightful analysis of implemented examples and future trends.


Effectiveness of Junior Faculty Mentoring Relationships in the Colleges of Pharmacy in Metro Manila , Philippines


title: Learning analytics: envisioning a research discipline and a domain of practice

abstract: Learning analytics are rapidly being implemented in different educational settings, often without the guidance of a research base. Vendors incorporate analytics practices, models, and algorithms from datamining, business intelligence, and the emerging "big data" fields. Researchers, in contrast, have built up a substantial base of techniques for analyzing discourse, social networks, sentiments, predictive models, and in semantic content (i.e., "intelligent" curriculum). In spite of the currently limited knowledge exchange and dialogue between researchers, vendors, and practitioners, existing learning analytics implementations indicate significant potential for generating novel insight into learning and vital educational practices. This paper presents an integrated and holistic vision for advancing learning analytics as a research discipline and a domain of practices. Potential areas of collaboration and overlap are presented with the intent of increasing the impact of analytics on teaching, learning, and the education system.


title: Measurement and Analysis of Large-Scale Network File System Workloads

abstract: In this paper we present the analysis of two large-scale network file system workloads. We measured CIFS traffic for two enterprise-class file servers deployed in the NetApp data center for a three month period. One file server was used by marketing, sales, and finance departments and the other by the engineering department. Together these systems represent over 22TB of storage used by over 1500 employees, making this the first ever large-scale study of the CIFS protocol. ::: ::: We analyzed how our network file system workloads compared to those of previous file system trace studies and took an in-depth look at access, usage, and sharing patterns. We found that our workloads were quite different from those previously studied; for example, our analysis found increased read-write file access patterns, decreased read-write ratios, more randomfile access, and longer file lifetimes. In addition, we found a number of interesting properties regarding file sharing, file re-use, and the access patterns of file types and users, showing that modern file system workload has changed in the past 5-10 years. This change in workload characteristics has implications on the future design of network file systems, which we describe in the paper.


title: Assessing Benefits of Business Intelligence Systems – A Case Study

abstract: Several arguments can be found in business intelligence literature that the use of business intelligence systems can bring multiple benefits, for example, via faster and easier access to information, savings in information technology (‘IT’) and greater customer satisfaction all the way through to the improved competitiveness of enterprises. Yet, most of these benefits are often very difficult to measure because of their indirect and delayed effects on business success. On top of the difficulties in justifying investments in information technology (‘IT’), particularly business intelligence (‘BI’), business executives generally want to know whether the investment is worth the money and if it can be economically justified. In looking for an answer to this question, various methods of evaluating investments can be employed. We can use the classic return on investment (‘ROI’) calculation, cost-benefit analysis, the net present value (‘NPV’) method, the internal rate of return (‘IRR’) and others. However, it often appears in business practice that the use of these methods alone is inappropriate, insufficient or unfeasible for evaluating an investment in business intelligence systems. Therefore, for this purpose, more appropriate methods are those based mainly on a qualitative approach, such as case studies, empirical analyses, user satisfaction analyses, and others that can be employed independently or can help us complete the whole picture in conjunction with the previously mentioned methods. Since there is no universal approach to the evaluation of an investment in information technology and business intelligence, it is necessary to approach each case in a different way based on the specific circumstances and purpose of the evaluation. This paper presents a case study in which the evaluation of an investment in on-line analytical processing (‘OLAP’) technology in the company Melamin was made through an


title: Android Malware Detection Using Complex-Flows

abstract: This paper proposes a new technique to detect mobile malware based on information flow analysis. Our approach examines the structure of information flows to identify patterns of behavior present in them and which flows are related, those that share partial computation paths. We call such flows Complex-Flows, as their structure, patterns, and relations accurately capture the complex behavior exhibited by both recent malware and benign applications. N-gram analysis is used to identify unique and common behavioral patterns present in Complex-Flows. The N-gram analysis is performed on sequences of API calls that occur along Complex-Flows' control flow paths. We show the precision of our technique by applying it to different data sets totaling 7,798 apps. These data sets consist of both recent and older generation benign and malicious apps to demonstrate the effectiveness of our approach across different generations of apps.


title: Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC

abstract: State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.


title: Second generation pleated pneumatic artificial muscle and its robotic applications

abstract: This paper reports on the second generation of the pleated pneumatic artificial muscle (PPAM) which has been developed to extend the lifespan of its first prototype. This type of artificial muscle was developed to overcome dry friction and material deformation which is present in the widely used McKibben muscle. The essence of the PPAM is its pleated membrane structure which enables the muscle to work at low pressures and at large contractions. There is growing interest in this kind of actuation for robotics applications due to its high power to weight ratio and adaptable compliance, especially for legged locomotion and robot applications in direct contact with a human. This paper describes the design of the second-generation PPAM, for which specifically the membrane layout has been changed. In terms of this new layout the mathematical model, developed for the first prototype, has been reformulated. This paper gives an elaborate discussion on this mathematical model that represents the force generation an...


title: Satellite remote sensing of wetlands

abstract: To conserve and manage wetland resources, it is important to inventoryand monitor wetlands and their adjacent uplands. Satellite remote sensing hasseveral advantages for monitoring wetland resources, especially for largegeographic areas. This review summarizes the literature on satellite remotesensing of wetlands, including what classification techniques were mostsuccessful in identifying wetlands and separating them from other land covertypes. All types of wetlands have been studied with satellite remote sensing.Landsat MSS, Landsat TM, and SPOT are the major satellite systems that have beenused to study wetlands; other systems are NOAA AVHRR, IRS-1B LISS-II and radarsystems, including JERS-1, ERS-1 and RADARSAT. Early work with satellite imageryused visual interpretation for classification. The most commonly used computerclassification method to map wetlands is unsupervised classification orclustering. Maximum likelihood is the most common supervised classificationmethod. Wetland classification is difficult because of spectral confusion withother landcover classes and among different types of wetlands. However,multi-temporal data usually improves the classification of wetlands, as doesancillary data such as soil data, elevation or topography data. Classifiedsatellite imagery and maps derived from aerial photography have been comparedwith the conclusion that they offer different but complimentary information.Change detection studies have taken advantage of the repeat coverage andarchival data available with satellite remote sensing. Detailed wetland maps canbe updated using satellite imagery. Given the spatial resolution of satelliteremote sensing systems, fuzzy classification, subpixel classification, spectralmixture analysis, and mixtures estimation may provide more detailed informationon wetlands. A layered, hybrid or rule-based approach may give better resultsthan more traditional methods. The combination of radar and optical data providethe most promise for improving wetland classification.


title: Simulation of Smart Home Activity Datasets

abstract: A globally ageing population is resulting in an increased prevalence of chronic conditions which affect older adults. Such conditions require long-term care and management to maximize quality of life, placing an increasing strain on healthcare resources. Intelligent environments such as smart homes facilitate long-term monitoring of activities in the home through the use of sensor technology. Access to sensor datasets is necessary for the development of novel activity monitoring and recognition approaches. Access to such datasets is limited due to issues such as sensor cost, availability and deployment time. The use of simulated environments and sensors may address these issues and facilitate the generation of comprehensive datasets. This paper provides a review of existing approaches for the generation of simulated smart home activity datasets, including model-based approaches and interactive approaches which implement virtual sensors, environments and avatars. The paper also provides recommendation for future work in intelligent environment simulation.


title: Facial expression recognition based on Local Binary Patterns: A comprehensive study

abstract: Automatic facial expression analysis is an interesting and challenging problem, and impacts important applications in many areas such as human-computer interaction and data-driven animation. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. In this paper, we empirically evaluate facial representation based on statistical local features, Local Binary Patterns, for person-independent facial expression recognition. Different machine learning methods are systematically examined on several databases. Extensive experiments illustrate that LBP features are effective and efficient for facial expression recognition. We further formulate Boosted-LBP to extract the most discriminant LBP features, and the best recognition performance is obtained by using Support Vector Machine classifiers with Boosted-LBP features. Moreover, we investigate LBP features for low-resolution facial expression recognition, which is a critical problem but seldom addressed in the existing work. We observe in our experiments that LBP features perform stably and robustly over a useful range of low resolutions of face images, and yield promising performance in compressed low-resolution video sequences captured in real-world environments.


title: Deriving User Preferences of Mobile Apps from Their Management Activities

abstract: App marketplaces host millions of mobile apps that are downloaded billions of times. Investigating how people manage mobile apps in their everyday lives creates a unique opportunity to understand the behavior and preferences of mobile device users, infer the quality of apps, and improve user experience. Existing literature provides very limited knowledge about app management activities, due to the lack of app usage data at scale. This article takes the initiative to analyze a very large app management log collected through a leading Android app marketplace. The dataset covers 5 months of detailed downloading, updating, and uninstallation activities, which involve 17 million anonymized users and 1 million apps. We present a surprising finding that the metrics commonly used to rank apps in app stores do not truly reflect the users’ real attitudes. We then identify behavioral patterns from the app management activities that more accurately indicate user preferences of an app even when no explicit rating is available. A systematic statistical analysis is designed to evaluate machine learning models that are trained to predict user preferences using these behavioral patterns, which features an inverse probability weighting method to correct the selection biases in the training process.


title: Dielectric properties of human normal, malignant and cirrhotic liver tissue: in vivo and ex vivo measurements from 0.5 to 20 GHz using a precision open-ended coaxial probe.

abstract: Hepatic malignancies have historically been treated with surgical resection. Due to the shortcomings of this technique, there is interest in other, less invasive, treatment modalities, such as microwave hepatic ablation. Crucial to the development of this technique is the accurate knowledge of the dielectric properties of human liver tissue at microwave frequencies. To this end, we characterized the dielectric properties of in vivo and ex vivo normal, malignant and cirrhotic human liver tissues from 0.5 to 20 GHz. Analysis of our data at 915 MHz and 2.45 GHz indicates that the dielectric properties of ex vivo malignant liver tissue are 19 to 30% higher than normal tissue. The differences in the dielectric properties of in vivo malignant and normal liver tissue are not statistically significant (with the exception of effective conductivity at 915 MHz, where malignant tissue properties are 16% higher than normal). Also, the dielectric properties of in vivo normal liver tissue at 915 MHz and 2.45 GHz are 16 to 43% higher than ex vivo. No statistically significant differences were found between the dielectric properties of in vivo and ex vivo malignant tissue (with the exception of effective conductivity at 915 MHz, where malignant tissue properties are 28% higher than normal). We report the one-pole Cole-Cole parameters for ex vivo normal, malignant and cirrhotic liver tissue in this frequency range. We observe that wideband dielectric properties of in vivo liver tissue are different from the wideband dielectric properties of ex vivo liver tissue, and that the in vivo data cannot be represented in terms of a Cole-Cole model. Further work is needed to uncover the mechanisms responsible for the observed wideband trends in the in vivo liver data.


title: Towards a Continuous Knowledge Learning Engine for Chatbots

abstract: Although chatbots have been very popular in recent years, they still have some serious weaknesses which limit the scope of their applications. One major weakness is that they cannot learn new knowledge during the conversation process, i.e., their knowledge is fixed beforehand and cannot be expanded or updated during conversation. In this paper, we propose to build a general knowledge learning engine for chatbots to enable them to continuously and interactively learn new knowledge during conversations. As time goes by, they become more and more knowledgeable and better and better at learning and conversation. We model the task as an open-world knowledge base completion problem and propose a novel technique called lifelong interactive learning and inference (LiLi) to solve it. LiLi works by imitating how humans acquire knowledge and perform inference during an interactive conversation. Our experimental results show LiLi is highly promising.


title: 3D printed capacitive sensors

abstract: Recent advances in the field of 3D printing have utilized embedded electronic interconnects in order to construct advanced electronic devices. This work builds on these advances in order to construct and characterize arbitrarily formed capacitive sensors using fine-pitch copper mesh and embedded copper wires. Three varieties of sensors were fabricated and tested, including a small area wire sensor (320μm width), a large area mesh sensor (2cm2), and a fully embedded demonstration model. In order to test and characterize these sensors in FDM materials, three distinct tests were explored. Specifically, the sensors were able to distinguish between three metallic materials and distinguish salt water from distilled water. These capacitive sensors have many potential sensing applications, such as biomedical sensing, human interface devices, material sensing, electronics characterization, and environmental sensing. As such, this work specifically examines optimum mesh/wire capacitive parameters as well as potential applications such as 3D printed integrated material sensing.


eglect : A multisensory deficit ?


Agenesis of Maxillary Lateral Incisor Treated with Orthodontics and Mini Dental Implants the Combined Technique with Invisible Aligners and Minimally Invasive Implantology of Italian School


title: How Do Third-Grade Students and Their Teachers Construe Each Other?.

abstract: This study aims to identify teachers’ personal constructs about students and students’ personal constructs about teachers and to reveal to what extent they see each other as ideal. The study is descriptive and multiple-case in nature. In this regard, it was conducted with 6 third-grade students with different mean scores and 6 teachers who instructed them in a private school. Teachers’ and students’ views towards each other were identified through a repertory grid technique, which is a semi-structured interviewing method. Teachers’ and students’ constructs towards each other are reciprocal. Students who were represented with successful and positive personal constructs, and thus who were placed closely to the ideal student profile, tended to see their teachers as ideal teachers. Similarly, students who were represented with unsuccessful and negative personal constructs by their teachers, and thus were placed as distant from the ideal student profile, tended to evaluate the teachers who had those representations negatively. Findings of the study were discussed in terms of the expectancy effect model and the transactional model.


title: BIOTEX—Biosensing Textiles for Personalised Healthcare Management

abstract: Textile-based sensors offer an unobtrusive method of continually monitoring physiological parameters during daily activities. Chemical analysis of body fluids, noninvasively, is a novel and exciting area of personalized wearable healthcare systems. BIOTEX was an EU-funded project that aimed to develop textile sensors to measure physiological parameters and the chemical composition of body fluids, with a particular interest in sweat. A wearable sensing system has been developed that integrates a textile-based fluid handling system for sample collection and transport with a number of sensors including sodium, conductivity, and pH sensors. Sensors for sweat rate, ECG, respiration, and blood oxygenation were also developed. For the first time, it has been possible to monitor a number of physiological parameters together with sweat composition in real time. This has been carried out via a network of wearable sensors distributed around the body of a subject user. This has huge implications for the field of sports and human performance and opens a whole new field of research in the clinical setting.


title: ERP in Higher Education: A Case Study of SAP and Campus Management

abstract: Enterprise Resource Planning (ERP) software has been applied to industries to support financial accounting, procurement, human resources, customer service management and supply change management. Recently, it is being promoted to new areas, for example, higher education. This case study examines the application of ERP software to the student information management in higher education at a Midwestern university that has replaced its legacy software with an ERP system. This paper also explores critical success factors (CSF) for a successful ERP implementation discussed in the literature. In addition, this paper investigates user acceptance of this new system specifically examining the staff attitudes, concerns expressed and comfort level for the average user.


title: Violent video game effects on aggression, empathy, and prosocial behavior in Eastern and Western countries

abstract: Meta-analytic procedures were used to test the effects of violent video games on aggressive behavior, aggressive cognition, aggressive affect, physiological arousal, empathy/desensitization, and prosocial behavior. Unique features of this meta-analytic review include (a) more restrictive methodological quality inclusion criteria than in past meta-analyses; (b) cross-cultural comparisons; (c) longitudinal studies for all outcomes except physiological arousal; (d) conservative statistical controls; (e) multiple moderator analyses; and (f) sensitivity analyses. Social–cognitive models and cultural differences between Japan and Western countries were used to generate theory-based predictions. Meta-analyses yielded significant effects for all 6 outcome variables. The pattern of results for different outcomes and research designs (experimental, cross-sectional, longitudinal) fit theoretical predictions well. The evidence strongly suggests that exposure to violent video games is a causal risk factor for increased aggressive behavior, aggressive cognition, and aggressive affect and for decreased empathy and prosocial behavior. Moderator analyses revealed significant research design effects, weak evidence of cultural differences in susceptibility and type of measurement effects, and no evidence of sex differences in susceptibility. Results of various sensitivity analyses revealed these effects to be robust, with little evidence of selection (publication) bias.


title: ByLabel: A Boundary Based Semi-Automatic Image Annotation Tool

abstract: This paper presents a novel boundary based semiautomatic tool, ByLabel, for accurate image annotation. Given an image, ByLabel first detects its edge features and computes high quality boundary fragments. Current labeling tools require the human to accurately click on numerous boundary points. ByLabel simplifies this to just selecting among the boundary fragment proposals that ByLabel automatically generates. To evaluate the performance of By-Label, 10 volunteers, with no experiences of annotation, labeled both synthetic and real images. Compared to the commonly used tool LabelMe, ByLabel reduces image-clicks and time by 73% and 56% respectively, while improving the accuracy by 73% (from 1.1 pixel average boundary error to 0.3 pixel). The results show that our ByLabel outperforms the state-of-the-art annotation tool in terms of efficiency, accuracy and user experience. The tool is publicly available: http://webdocs.cs.ualberta.ca/~vis/ bylabel/.


title: Multimodal execution monitoring for anomaly detection during robot manipulation

abstract: Online detection of anomalous execution can be valuable for robot manipulation, enabling robots to operate more safely, determine when a behavior is inappropriate, and otherwise exhibit more common sense. By using multiple complementary sensory modalities, robots could potentially detect a wider variety of anomalies, such as anomalous contact or a loud utterance by a human. However, task variability and the potential for false positives make online anomaly detection challenging, especially for long-duration manipulation behaviors. In this paper, we provide evidence for the value of multimodal execution monitoring and the use of a detection threshold that varies based on the progress of execution. Using a data-driven approach, we train an execution monitor that runs in parallel to a manipulation behavior. Like previous methods for anomaly detection, our method trains a hidden Markov model (HMM) using multimodal observations from non-anomalous executions. In contrast to prior work, our system also uses a detection threshold that changes based on the execution progress. We evaluated our approach with haptic, visual, auditory, and kinematic sensing during a variety of manipulation tasks performed by a PR2 robot. The tasks included pushing doors closed, operating switches, and assisting able-bodied participants with eating yogurt. In our evaluations, our anomaly detection method performed substantially better with multimodal monitoring than single modality monitoring. It also resulted in more desirable ROC curves when compared with other detection threshold methods from the literature, obtaining higher true positive rates for comparable false positive rates.


title: Adaptive Bases for Reinforcement Learning

abstract: We consider the problem of reinforcement learning using function approximation, where the approximating basis can change dynamically while interacting with the environment. A motivation for such an approach is maximizing the value function fitness to the problem faced. Three errors are considered: approximation square error, Bellman residual, and projected Bellman residual. Algorithms under the actor-critic framework are presented, and shown to converge. The advantage of such an adaptive basis is demonstrated in simulations.


THE INNERVATION OF STRIATED MUSCLE


title: Prioritizing test cases for regression testing

abstract: Test case prioritization techniques schedule test cases for execution in an order that attempts to increase their effectiveness at meeting some performance goal. Various goals are possible; one involves rate of fault detection, a measure of how quickly faults are detected within the testing process. An improved rate of fault detection during testing can provide faster feedback on the system under test and let software engineers begin correcting faults earlier than might otherwise be possible. One application of prioritization techniques involves regression testing, the retesting of software following modifications; in this context, prioritization techniques can take advantage of information gathered about the previous execution of test cases to obtain test case orderings. We describe several techniques for using test execution information to prioritize test cases for regression testing, including: 1) techniques that order test cases based on their total coverage of code components; 2) techniques that order test cases based on their coverage of code components not previously covered; and 3) techniques that order test cases based on their estimated ability to reveal faults in the code components that they cover. We report the results of several experiments in which we applied these techniques to various test suites for various programs and measured the rates of fault detection achieved by the prioritized test suites, comparing those rates to the rates achieved by untreated, randomly ordered, and optimally ordered suites.


title: The data protection framework decision of 27 November 2008 regarding police and judicial cooperation in criminal matters – A modest achievement however not the improvement some have hoped for

abstract: After more than three years in the making, that have witnessed much controversy, several working texts and at least two altogether different versions, the Data Protection Framework Decision “on the protection of personal data processed in the framework of police and judicial cooperation in criminal matters” (hereafter, the DPFD) was finally adopted on 27 November 2008. The DPFD was supposed to be celebrated as the Data Protection Directive equivalent in European law enforcement (Third Pillar) processing. However, since its formal adoption, and even before that, data protection proponents (the European Data Protection Supervisor, the Article 29 Working Party, national Data Protection Commissioners, NGOs) lamented its adoption as the result of changes that ultimately compromised data protection. Is the DPFD a disappointment to the great expectations that accompanied its first draft, back in 2006? An attempt to address this question shall be undertaken in this paper.


Link Weight Prediction in Signed Networks


title: Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding

abstract: We present a model of pragmatic referring expression interpretation in a grounded communication task (identifying colors from descriptions) that draws upon predictions from two recurrent neural network classifiers, a speaker and a listener, unified by a recursive pragmatic reasoning framework. Experiments show that this combined pragmatic model interprets color descriptions more accurately than the classifiers from which it is built, and that much of this improvement results from combining the speaker and listener perspectives. We observe that pragmatic reasoning helps primarily in the hardest cases: when the model must distinguish very similar colors, or when few utterances adequately express the target color. Our findings make use of a newly-collected corpus of human utterances in color reference games, which exhibit a variety of pragmatic behaviors. We also show that the embedded speaker model reproduces many of these pragmatic behaviors.


title: Embedded sign language interpreter system for deaf and dumb people

abstract: Deaf and dumb conversation is hinged on sign language which renders words through hand and finger symbols. Main objective of the project is building a device that assist deaf and dumb people to convey their messages to normal people. The system consists of two main parts. The first part is hand sign recognition and the second one is speech recognition. This ensures a bi directional communication avoiding the assistance of an interpreter. A webcam for image recognition, a microcontroller unit, and a display device associated with the controller and a Bluetooth module constitutes the hand sign recognition part. In the speech recognition part, digitized signal after processing undergoes statistical modeling and matching for recognition. Speech recognition part is integrated on an easy to use android application. Since the system is deployed in both mobile and embedded platforms, a high performance and power efficient ARM Cortex A8 Processor board is used. For image classification Haar classifier is used while 1-D HMM is used for Speech conversion. Sign language recognition has established its importance in many areas such as Human Computer Interactions (HCIs), robotic controls, home automation etc.


title: Focal colors are universal after all

abstract: It is widely held that named color categories in the world's languages are organized around universal focal colors and that these focal colors tend to be chosen as the best examples of color terms across languages. However, this notion has been supported primarily by data from languages of industrialized societies. In contrast, recent research on a language from a nonindustrialized society has called this idea into question. We examine color-naming data from languages of 110 nonindustrialized societies and show that (i) best-example choices for color terms in these languages cluster near the prototypes for English white, black, red, green, yellow, and blue, and (ii) best-example choices cluster more tightly across languages than do the centers of category extensions, suggesting that universal best examples (foci) may be the source of universal tendencies in color naming.


Using ANOVA to Examine the Relationship between Safety & Security and Human Development


title: Learning Image Representations by Completing Damaged Jigsaw Puzzles

abstract: In this paper, we explore methods of complicating self-supervised tasks for representation learning. That is, we do severe damage to data and encourage a network to recover them. First, we complicate each of three powerful self-supervised task candidates: jigsaw puzzle, inpainting, and colorization. In addition, we introduce a novel complicated self-supervised task called"Completing damaged jigsaw puzzles"which is puzzles with one piece missing and the other pieces without color. We train a convolutional neural network not only to solve the puzzles, but also generate the missing content and colorize the puzzles. The recovery of the aforementioned damage pushes the network to obtain robust and general-purpose representations. We demonstrate that complicating the self-supervised tasks improves their original versions and that our final task learns more robust and transferable representations compared to the previous methods, as well as the simple combination of our candidate tasks. Our approach achieves state-of-the-art performance in transfer learning on PASCAL classification and semantic segmentation.


title: Multiple-instance active learning

abstract: We present a framework for active learning in the multiple-instance (MI) setting. In an MI learning problem, instances are naturally organized into bags and it is the bags, instead of individual instances, that are labeled for training. MI learners assume that every instance in a bag labeled negative is actually negative, whereas at least one instance in a bag labeled positive is actually positive. We consider the particular case in which an MI learner is allowed to selectively query unlabeled instances from positive bags. This approach is well motivated in domains in which it is inexpensive to acquire bag labels and possible, but expensive, to acquire instance labels. We describe a method for learning from labels at mixed levels of granularity, and introduce two active query selection strategies motivated by the MI setting. Our experiments show that learning from instance labels can significantly improve performance of a basic MI learning algorithm in two multiple-instance domains: content-based image retrieval and text classification.


title: Scalable multi-domain dialogue state tracking

abstract: Dialogue state tracking (DST) is a key component of task-oriented dialogue systems. DST estimates the user's goal at each user turn given the interaction until then. State of the art approaches for state tracking rely on deep learning methods, and represent dialogue state as a distribution over all possible slot values for each slot present in the ontology. Such a representation is not scalable when the set of possible values are unbounded (e.g., date, time or location) or dynamic (e.g., movies or usernames). Furthermore, training of such models requires labeled data, where each user turn is annotated with the dialogue state, which makes building models for new domains challenging. In this paper, we present a scalable multi-domain deep learning based approach for DST. We introduce a novel framework for state tracking which is independent of the slot value set, and represent the dialogue state as a distribution over a set of values of interest (candidate set) derived from the dialogue history or knowledge. Restricting these candidate sets to be bounded in size addresses the problem of slot-scalability. Furthermore, by leveraging the slot-independent architecture and transfer learning, we show that our proposed approach facilitates quick adaptation to new domains.


title: The Space Object Ontology

abstract: Achieving space domain awareness requires the identification, characterization, and tracking of space objects. Storing and leveraging associated space object data for purposes such as hostile threat assessment, object identification, and collision prediction and avoidance present further challenges. Space objects are characterized according to a variety of parameters including their identifiers, design specifications, components, subsystems, capabilities, vulnerabilities, origins, missions, orbital elements, patterns of life, processes, operational statuses, and associated persons, organizations, or nations. The Space Object Ontology provides a consensus-based realist framework for formulating such characterizations in a computable fashion. Space object data are aligned with classes and relations in the Space Object Ontology and stored in a dynamically updated Resource Description Framework triple store, which can be queried to support space domain awareness and the needs of spacecraft operators. This paper presents the core of the Space Object Ontology, discusses its advantages over other approaches to space object classification, and demonstrates its ability to combine diverse sets of data from multiple sources within an expandable framework. Finally, we show how the ontology provides benefits for enhancing and maintaining long-term space domain awareness.


title: Resting-state brain networks: literature review and clinical applications

abstract: This review focuses on resting-state functional connectivity, a functional MRI technique which allows the study of spontaneous brain activity generated under resting conditions. This approach is useful to explore the brain’s functional organization and to examine if it is altered in neurological or psychiatric diseases. Resting-state functional connectivity has revealed a number of networks which are consistently found in healthy subjects and represent specific patterns of synchronous activity. In this review, we examine the behavioral, physiological and neurological evidences relevant to this coherent brain activity and, in particular, to each network. The investigation of functional connectivity appears promising from a clinical perspective, considering the amount of evidence regarding the importance of spontaneous activity and that resting-state paradigms are inherently simple to implement. We also discuss some examples of existing clinical applications, such as in Alzheimer’s disease, and emerging possibilities such as in pre-operative mapping and disorders of consciousness.


title: Lean Crowdsourcing: Combining Humans and Machines in an Online System

abstract: We introduce a method to greatly reduce the amount of redundant annotations required when crowdsourcing annotations such as bounding boxes, parts, and class labels. For example, if two Mechanical Turkers happen to click on the same pixel location when annotating a part in a given image–an event that is very unlikely to occur by random chance–, it is a strong indication that the location is correct. A similar type of confidence can be obtained if a single Turker happened to agree with a computer vision estimate. We thus incrementally collect a variable number of worker annotations per image based on online estimates of confidence. This is done using a sequential estimation of risk over a probabilistic model that combines worker skill, image difficulty, and an incrementally trained computer vision model. We develop specialized models and algorithms for binary annotation, part keypoint annotation, and sets of bounding box annotations. We show that our method can reduce annotation time by a factor of 4-11 for binary filtering of websearch results, 2-4 for annotation of boxes of pedestrians in images, while in many cases also reducing annotation error. We will make an end-to-end version of our system publicly available.


title: Smart Cities Built on Resilient Cloud Computing and Secure Internet of Things

abstract: Cloud Computing and Internet of Things (IoT) are currently two of the most popular ICT paradigms that are expected to shape the next era of computing. The convergence between cloud computing and IoT has become a hot topic over the last few years because of the benefits that IoT could have from the distributed nature of cloud computing infrastructures. This paper proposes a new platform for using cloud computing capacities for provision and support of ubiquitous connectivity and real-time applications and services for smart cities' needs. We present a framework for data procured from highly distributed, heterogeneous, decentralized, real and virtual devices (sensors, actuators, smart devices) that can be automatically managed, analyzed and controlled by distributed cloud-based services.


title: An Open Source Solution for Testing NAT’d and Nested iptables Firewalls

abstract: As firewalls have increased in power and flexibility, the complexity of configuring them correctly has grown significantly. An error in the firewall configuration can compromise the security of the system or interfere with normal network activity. The chance of an error increases when coordinating multiple firewalls, because the interaction between filters may hide errors more easily noticed on a single firewall. Firewalls on many networks use network address translation, which further increases the complexity of the firewall policy and creates additional opportunities for errors. Because errors in the firewall configuration are often extremely costly in time and security, system administrators need tools for verifying and debugging their firewall policy. ITVal is a tool for analyzing iptables-based firewalls that provides a plain English query language for simple firewall analysis. In this work, we describe extensions to ITVal that allow it to process network address translation rules and analyze multiple firewalls connected sequentially.


title: Introduction to Principal Components Analysis

abstract: Principal components analysis (PCA) is a powerful statistical tool that can help researchers analyze datasets with many highly related predictors. PCA is a data reduction technique— that is, it reduces a larger set of predictor variables to a smaller set with minimal loss of information. PCA may be applied before running regression analyses or for exploratory purposes to help researchers understand relationships among their variables or discover patterns in their data. Although it is widely used in certain domains, such as dietary studies and questionnaire development, it is underused in other domains. In this article I first provide examples that illustrate the utility of PCA and then delve into greater detail about how it works.


title: Diagnosis of Trichomonas vaginalis infection by PCR using vaginal swab samples.

abstract: Trichomonas vaginalis infection is the most prevalent nonviral sexually transmitted disease (STD) in the world. A PCR test using vaginal swab samples for the detection of T. vaginalis was developed to add T. vaginalis infection to the growing list of STDs that can be detected by DNA amplification techniques. A primer set, BTUB 9/2, was designed to target a well-conserved region in the beta-tubulin genes of T. vaginalis. All strains (15 of 15) of T. vaginalis tested were successfully detected by PCR giving a single predicted product of 112 bp in gel electrophoresis. No such targeted product was amplified with DNA from Trichomonas tenax, Trichomonas gallinae, Chlamydia trachomatis, Neisseria gonorrhoeae, Giardia lamblia, Chilomastix sulcatus, Dientamoeba fragilis, and Entamoeba histolytica. An optimal analytical sensitivity of one T. vaginalis organism per PCR was achieved. Culture, performed with the Inpouch TV culture system, was examined daily with a light microscope to identify T. vaginalis. Twenty-three of 350 (6.6%) vaginal swab samples from women attending an army medical clinic were culture positive for T. vaginalis. Of these culture positive specimens, PCR detected 22 of 23 (96%) with primer set BTUB 9/2, and wet preparation detected only 12 of 23 (52%). Seventeen specimens were BTUB 9/2-PCR positive and culture negative. Ten of these discordant specimens were determined to be as true positive by PCR using primer sets TVA 5-1/6 and/or AP65 A/B, which target different regions in the T. vaginalis genome, and seven were determined to be false positive. The sensitivity of BTUB 9/2-PCR was 97% and the specificity was 98%. The sensitivities of culture and wet preparation were 70 and 36%, respectively. The diagnosis of T. vaginalis infection by PCR is a sensitive and specific method that could be incorporated into a joint strategy for the screening of multiple STDs by using molecular amplification methods.


Wear tolerance of turfgrass species.


title: A Reduced feature based neural network approach to classify the category of students

abstract: To ensure more effectiveness in the learning process in educational institutions, categorization of students is a very interesting method to enhance student's learning capabilities by identifying the factors that affect their performance and use their categories to design targeted inventions for improving their quality. Many research works have been conducted on student performances, to improve their grades and to stop them from dropping out from school by using a data driven approach [1] [2]. In this paper, we have proposed a new model to categorize students into 3 categories to determine their learning capabilities and to help them to improve their studying techniques. We have chosen the state of the art of machine learning approach to classify student's nature of study by selecting prominent features of their activity in their academic field. We have chosen a data driven approach where key factors that determines the base of student and classify them into high, medium and low ranks. This process generates a system where we can clearly identify the crucial factors for which they are categorized. Manual construction of student labels is a difficult approach. Therefore, we have come up with a student categorization model on the basis of selected features which are determined by the preprocessing of Dataset and implementation of Random Forest Importance; Chi2 algorithm; and Artificial Neural Network algorithm. For the research we have used Python's Machine Learning libraries: Scikit-Learn [3]. For Deep Learning paradigm we have used Tensor-Flow, Keras. For data processing Pandas library and Matplotlib and Pyplot has been used for graph visualization purpose.


title: Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs

abstract: Even though end-to-end supervised learning has shown promising results for sensorimotor control of self-driving cars, its performance is greatly affected by the weather conditions under which it was trained, showing poor generalization to unseen conditions. In this paper, we show how knowledge can be transferred using semantic maps to new weather conditions without the need to obtain new ground truth data. To this end, we propose to divide the task of vehicle control into two independent modules: a control module which is only trained on one weather condition for which labeled steering data is available, and a perception module which is used as an interface between new weather conditions and the fixed control module. To generate the semantic data needed to train the perception module, we propose to use a generative adversarial network (GAN)-based model to retrieve the semantic information for the new conditions in an unsupervised manner. We introduce a master-servant architecture, where the master model (semantic labels available) trains the servant model (semantic labels not available). We show that our proposed method trained with ground truth data for a single weather condition is capable of achieving similar results on the task of steering angle prediction as an end-to-end model trained with ground truth data of 15 different weather conditions.


title: Memetic evolution of deep neural networks

abstract: Deep neural networks (DNNs) have proven to be effective at solving challenging problems, but their success relies on finding a good architecture to fit the task. Designing a DNN requires expert knowledge and a lot of trial and error, especially as the difficulty of the problem grows. This paper proposes a fully automatic method with the goal of optimizing DNN topologies through memetic evolution. By recasting the mutation step as a series of progressively refined educated local-search moves, this method achieves results comparable to best human designs. Our extensive experimental study showed that the proposed memetic algorithm supports building a real-world solution for segmenting medical images, it exhibits very promising results over a challenging CIFAR-10 benchmark, and works very fast. Given the ever growing availability of data, our memetic algorithm is a very promising avenue for hands-free DNN architecture design to tackle emerging classification tasks.


title: OR-PCA with MRF for Robust Foreground Detection in Highly Dynamic Backgrounds

abstract: Accurate and efficient foreground detection is an important task in video surveillance system. The task becomes more critical when the background scene shows more variations, such as water surface, waving trees, varying illumination conditions, etc. Recently, Robust Principal Components Analysis (RPCA) shows a very nice framework for moving object detection. The background sequence is modeled by a low-dimensional subspace called low-rank matrix and sparse error constitutes the foreground objects. But RPCA presents the limitations of computational complexity and memory storage due to batch optimization methods, as a result it is difficult to apply for real-time system. To handle these challenges, this paper presents a robust foreground detection algorithm via Online Robust PCA (OR-PCA) using image decomposition along with continuous constraint such as Markov Random Field (MRF). OR-PCA with good initialization scheme using image decomposition approach improves the accuracy of foreground detection and the computation time as well. Moreover, solving MRF with graph-cuts exploits structural information using spatial neighborhood system and similarities to further improve the foreground segmentation in highly dynamic backgrounds. Experimental results on challenging datasets such as Wallflower, I2R, BMC 2012 and Change Detection 2014 dataset demonstrate that our proposed scheme significantly outperforms the state of the art approaches and works effectively on a wide range of complex background scenes.


title: Demystifying differentiable programming: shift/reset the penultimate backpropagator

abstract: Deep learning has seen tremendous success over the past decade in computer vision, machine translation, and gameplay. This success rests crucially on gradient-descent optimization and the ability to “learn” parameters of a neural network by backpropagating observed errors. However, neural network architectures are growing increasingly sophisticated and diverse, which motivates an emerging quest for even more general forms of differentiable programming, where arbitrary parameterized computations can be trained by gradient descent. In this paper, we take a fresh look at automatic differentiation (AD) techniques, and especially aim to demystify the reverse-mode form of AD that generalizes backpropagation in neural networks. We uncover a tight connection between reverse-mode AD and delimited continuations, which permits implementing reverse-mode AD purely via operator overloading and without managing any auxiliary data structures. We further show how this formulation of AD can be fruitfully combined with multi-stage programming (staging), leading to an efficient implementation that combines the performance benefits of deep learning frameworks based on explicit reified computation graphs (e.g., TensorFlow) with the expressiveness of pure library approaches (e.g., PyTorch).


title: A Comparative Study for Predicting Heart Diseases Using Data Mining Classification Methods

abstract: Improving the precision of heart diseases detection has been investigated by many researchers in the literature. Such improvement induced by the overwhelming health care expenditures and erroneous diagnosis. As a result, various methodologies have been proposed to analyze the disease factors aiming to decrease the physicians practice variation and reduce medical costs and errors. In this paper, our main motivation is to develop an effective intelligent medical decision support system based on data mining techniques. In this context, five data mining classifying algorithms, with large datasets, have been utilized to assess and analyze the risk factors statistically related to heart diseases in order to compare the performance of the implemented classifiers (e.g., Naive Bayes, Decision Tree, Discriminant, Random Forest, and Support Vector Machine). To underscore the practical viability of our approach, the selected classifiers have been implemented using MATLAB tool with two datasets. Results of the conducted experiments showed that all classification algorithms are predictive and can give relatively correct answer. However, the decision tree outperforms other classifiers with an accuracy rate of 99.0% followed by Random forest. That is the case because both of them have relatively same mechanism but the Random forest can build ensemble of decision tree. Although ensemble learning has been proved to produce superior results, but in our case the decision tree has outperformed its ensemble version.


title: Managing stakeholder relations when developing sustainable business models: the case of the Brazilian energy sector

abstract: Abstract Although theoretically the importance of sustainable business models has become widely accepted in the literature, little is known about how managers deal with practical issues such as differences in stakeholder interests. In this paper, we focus on the question: What strategies can help decision-makers to better connect with stakeholders and overcome the challenges of conflicting interests when considering sustainability in their business models? Our research draws on two case studies in the Brazilian energy sector: the national oil company Petrobras and its attempts to develop biodiesel, and the national electrical utility Eletrobras and its attempts to provide electricity to impoverished communities. This paper is based on original qualitative data derived frominterviews with 138 key informants conducted between 2003 and 2010 with supply chain members and other stakeholders, as well as secondary sources. We draw from a number of literature streams to develop our paper. One stream relates to sustainable supply chain and the recognition of the relationship among economic, environmental and social values rather than only focusing on economic and/or environmental aspects. Another stream suggests that BOP (Bottom of the Pyramid) settings present potential ideal opportunities to connect business with sustainability. Finally, we use stakeholder theory to analyze how sustainable values can drive the operationalization of sustainable business models. We suggest that a combination of approaches promoting the participation of a diverse number of local stakeholders, encouraging both learning and capability building and shifting stakeholder values from single to multiple objectives are critical to overcome the challenges of stakeholders conflicting interests.


title: Task-Driven Dictionary Learning

abstract: Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience and signal processing. For signals such as natural images that admit such sparse representations, it is now well established that these models are well suited to restoration tasks. In this context, learning the dictionary amounts to solving a large-scale matrix factorization problem, which can be done efficiently with classical optimization tools. The same approach has also been used for learning features from data for other purposes, e.g., image classification, but tuning the dictionary in a supervised way for these tasks has proven to be more difficult. In this paper, we present a general formulation for supervised dictionary learning adapted to a wide variety of tasks, and present an efficient algorithm for solving the corresponding optimization problem. Experiments on handwritten digit classification, digital art identification, nonlinear inverse image problems, and compressed sensing demonstrate that our approach is effective in large-scale settings, and is well suited to supervised and semi-supervised classification, as well as regression tasks for data that admit sparse representations.


title: Generative mixture of networks

abstract: A generative model based on training deep architectures is proposed. The model consists of K networks that are trained together to learn the underlying distribution of a given data set. The process starts with dividing the input data into K clusters and feeding each of them into a separate network. After few iterations of training networks separately, we use an EM-like algorithm to train the networks together and update the clusters of the data. We call this model Mixture of Networks. The provided model is a platform that can be used for any deep structure and be trained by any conventional objective function for distribution modeling. As the components of the model are neural networks, it has high capability in characterizing complicated data distributions as well as clustering data. We apply the algorithm on MNIST hand-written digits and Yale face datasets. We also demonstrate the clustering ability of the model using some real-world and toy examples.


title: Design of Permanent Magnet Machines for Hybrid Vehicles

abstract: Summary This paper deals with the analysis of different PM machines for hybrid electric vehicles application. The main machine parameters, electromagnetic torque and the skewing effect is investigated using ANSYS program. The model parameters of the PM machines are derived with Fixed Permeability Method (FPM). With this method the components of the PM machine can be calculated with high accuracy. Different FE calculation methods (Maxwell’s stress tensor method and the new mathematical model for the PM machine) are used to calculate the electromagnetic torque of the machine. Also the skewing effect on the torque performance of the PM machine is investigated.


title: A Comparative Study on Maximum Power Point Tracking Techniques for Photovoltaic Power Systems

abstract: This paper provides a comprehensive review of the maximum power point tracking (MPPT) techniques applied to photovoltaic (PV) power system available until January, 2012. A good number of publications report on different MPPT techniques for a PV system together with implementation. But, confusion lies while selecting a MPPT as every technique has its own merits and demerits. Hence, a proper review of these techniques is essential. Unfortunately, very few attempts have been made in this regard, excepting two latest reviews on MPPT [Salas, 2006], [Esram and Chapman, 2007]. Since, MPPT is an essential part of a PV system, extensive research has been revealed in recent years in this field and many new techniques have been reported to the list since then. In this paper, a detailed description and then classification of the MPPT techniques have made based on features, such as number of control variables involved, types of control strategies employed, types of circuitry used suitably for PV system and practical/commercial applications. This paper is intended to serve as a convenient reference for future MPPT users in PV systems.


title: Enhancing Relational Database Security by Metadata Segregation

abstract: Abstract Although many prominent Relational Database Management Systems provides inbuilt security controls and mechanisms, the information resided in the data-store are at great risk. This research aims to reduce the risk of unauthorized data access by providing an extra layer of security. This research proposes a novel method for incorporating information security while designing the relational database by segregating the information on the basis of its sensitivity level and creating referential integrity constraints dynamically at run time. Different techniques to identify and quantify sensitive attributes and restructuring database architecture have been discussed for the proposed approach. The primary keys of the restructured tables and most critical information attributes were secured using Transparent Data Encryption utility provided by Oracle 11 g to prohibit illegitimate use of information. The performance of the proposed architecture was evaluated with 1,000,000 records which shows that by increasing the number of records, the response time of Select statement increased dramatically whereas it increased gradually for Insert, Update and Delete operations.


title: Aligning movies with scripts by exploiting temporal ordering constraints

abstract: Scripts provide rich textual annotation of movies, including dialogs, character names, and other situational descriptions. Exploiting such rich annotations requires aligning the sentences in the scripts with the corresponding video frames. Previous work on aligning movies with scripts predominantly relies on time-aligned closed-captions or subtitles, which are not always available. In this paper, we focus on automatically aligning faces in movies with their corresponding character names in scripts without requiring closed-captions/subtitles. We utilize the intuition that faces in a movie generally appear in the same sequential order as their names are mentioned in the script. We first apply standard techniques for face detection and tracking, and cluster similar face tracks together. Next, we apply a generative Hidden Markov Model (HMM) and a discriminative Latent Conditional Random Field (LCRF) to align the clusters of face tracks with the corresponding character names. Our alignment models (especially LCRF) significantly outperform the previous state-of-the-art on two different movie datasets and for a wide range of face clustering algorithms.


title: Analysis and Development of Novel Three-Phase Hybrid Magnetic Paths Switched Reluctance Motors Using Modular and Segmental Structures for EV Applications

abstract: The classical switched reluctance motors (SRMs) often suffer from drawbacks such as low power and torque densities, high torque ripple, mutual coupling, etc., which limit their industrial applications. This paper presents the analysis and development of two novel three-phase SRMs with hybrid magnetic paths comprising six E-shaped modular stators and three segmental common rotors, termed as the modular SRMs (MSRMs), for electric vehicle applications. The machine topologies with different winding arrangements are described. The voltage and output power equations are analytically derived, and some design particularities and parameters are discussed. The field distributions and static magnetic characteristics of an MSRM with double coil are analyzed by using 3-D finite-element method. After that, two MSRMs with different winding arrangements, namely a double-coil MSRM and single-coil MSRM, are analyzed and compared to evaluate the distinct features of this novel MSRM, accompanied with a classical three-phase 6/4 SRM. The comparison includes static magnetic characteristics, mass of iron core, normal dynamic, and fault-tolerant performances. It is shown that the double-coil MSRM appears to have better characteristics such as higher torque production capability, lower torque ripple and cost, higher torque and output power densities, and higher reliability and fault tolerance. For experimental verification, laboratory testing of a double-coil MSRM is developed, and the simulated and measured static inductance characteristics and dynamic performances correlate well.


title: Assessing capacity scalability policies in RMS using system dynamics

abstract: This paper presents a model for assessing different capacity scalability policies in Reconfigurable Manufacturing System (RMS) for different changing demand scenarios. The novelty of this approach is two fold: (1) it is the first attempt to explore different capacity scalability policies in RMS based on multiple performance measures, mainly scaling rate, Work In Process level, inventory level and backlog level; and (2) the dynamic scalability process in RMS is modeled for the first time using System Dynamics. Different policies for capacity scalability for various demand scenarios were assessed. Numerical simulation results obtained using the developed capacity scalability model showed that the best capacity scalability policy to be adopted for RMS is dependent on the anticipated demand pattern as well as the various manufacturing objectives. The presented assessment results will help the capacity scalability planners better decide the different tradeoffs between the competing strategic and operational objectives of the manufacturing enterprise, before setting the suitable capacity scalability plan parameters.


title: Multimodal Hierarchical Dirichlet Process-based Active Perception

abstract: In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory, and haptic information, which can be observed by performing actions on an object. However, performing many actions on a target object requires a long time. In a real-time scenario, i.e., when the time is limited, the robot has to determine the set of actions that is most effective for recognizing a target object. We propose an MHDP-based active perception method that uses the information gain (IG) maximization criterion and lazy greedy algorithm. We show that the IG maximization criterion is optimal in the sense that the criterion is equivalent to a minimization of the expected Kullback--Leibler divergence between a final recognition state and the recognition state after the next set of actions. However, a straightforward calculation of IG is practically impossible. Therefore, we derive an efficient Monte Carlo approximation method for IG by making use of a property of the MHDP. We also show that the IG has submodular and non-decreasing properties as a set function because of the structure of the graphical model of the MHDP. Therefore, the IG maximization problem is reduced to a submodular maximization problem. This means that greedy and lazy greedy algorithms are effective and have a theoretical justification for their performance. We conducted an experiment using an upper-torso humanoid robot and a second one using synthetic data. The experimental results show that the method enables the robot to select a set of actions that allow it to recognize target objects quickly and accurately. The results support our theoretical outcomes.


title: Implementing it Service Management: A Case Study Focussing on Critical Success Factors

abstract: Queensland Health, a large Australian government agency, implemented a centralised IT service management model based on the ITIL framework. This paper presents an in-depth case study of the implementation. It sheds light on the challenges and breakthroughs, confirms a set of factors that contributed to the project’s success and offers a learning opportunity for other organisations. The study indicates that the commitment of senior management is crucial to the project’s success as is a project champion and the recognition of the need for an appropriate change management strategy to transform the organisational culture to a service-oriented focus. Maintaining close and forthright relationships with multiple vendors facilitates technology transfer to in-house staff while a benefits realisation plan is a valuable tool for tracking and communicating tangible and intangible project benefits to the project stakeholders. An effective project governance and execution process further contributes to the implementation success.


title: Smooth Vertical Surface Climbing With Directional Adhesion

abstract: Stickybot is a bioinspired robot that climbs smooth vertical surfaces such as glass, plastic, and ceramic tile at 4 cm/s. The robot employs several design principles adapted from the gecko including a hierarchy of compliant structures, directional adhesion, and control of tangential contact forces to achieve control of adhesion. We describe the design and fabrication methods used to create underactuated, multimaterial structures that conform to surfaces over a range of length scales from centimeters to micrometers. At the finest scale, the undersides of Stickybot's toes are covered with arrays of small, angled polymer stalks. Like the directional adhesive structures used by geckos, they readily adhere when pulled tangentially from the tips of the toes toward the ankles; when pulled in the opposite direction, they release. Working in combination with the compliant structures and directional adhesion is a force control strategy that balances forces among the feet and promotes smooth attachment and detachment of the toes.


title: Unsupervised Learning for Color Constancy

abstract: Most digital camera pipelines use color constancy methods to reduce the influence of illumination and camera sensor on the colors of scene objects. The highest accuracy of color correction is obtained with learning-based color constancy methods, but they require a significant amount of calibrated training images with known ground-truth illumination. Such calibration is time consuming, preferably done for each sensor individually, and therefore a major bottleneck in acquiring high color constancy accuracy. Statistics-based methods do not require calibrated training images, but they are less accurate. In this paper an unsupervised learning-based method is proposed that learns its parameter values after approximating the unknown ground-truth illumination of the training images, thus avoiding calibration. In terms of accuracy the proposed method outperforms all statistics-based and many learning-based methods. An extension of the method is also proposed, which learns the needed parameters from non-calibrated images taken with one sensors and which can then be successfully applied to images taken with another sensor. This effectively enables inter-camera unsupervised learning for color constancy. Additionally, a new high quality color constancy benchmark dataset with 1365 calibrated images is created, used for testing, and made publicly available. The results are presented and discussed. The source code and the dataset are available at this http URL


Improvement of Illumination-Insensitive Feathers for Face Recognition under Complex Illumination Conditions


title: Protocol-oblivious forwarding: unleash the power of SDN through a future-proof forwarding plane

abstract: A flexible and programmable forwarding plane is essential to maximize the value of Software-Defined Networks (SDN). In this paper, we propose Protocol-Oblivious Forwarding (POF) as a key enabler for highly flexible and programmable SDN. Our goal is to remove any dependency on protocol-specific configurations on the forwarding elements and enhance the data-path with new stateful instructions to support genuine software defined networking behavior. A generic flow instruction set (FIS) is defined to fulfill this purpose. POF helps to lower network cost by using commodity forwarding elements and to create new value by enabling numerous innovative network services. We built both hardware-based and open source software-based prototypes to demonstrate the feasibility and advantages of POF. We report the preliminary evaluation results and the insights we learnt from the experiments. POF is future-proof and expressive. We believe it represents a promising direction to evolve the OpenFlow protocol and the future SDN forwarding elements.


title: Law and logic: A review from an argumentation perspective

abstract: This article reviews legal applications of logic, with a particularly marked concern for logical models of legal argument. We argue that the law is a rich test bed and important application field for logic-based AI research. First applications of logic to the representation of legal regulations are reviewed, where the main emphasis is on representation and where the legal conclusions follow from that representation as a matter of deduction. This includes the representation of deontic concepts, normative positions, legal ontologies, time and change. Then legal applications of logic are reviewed where legal rules are not just applied but are the object of reasoning and discourse. This includes arguing about applying statutory rules in unforeseen circumstances, interpretative reasoning in light of the facts of a case, and evidential reasoning to establish the facts of a case. This part of the review has special emphasis on argumentation-based approaches. This also holds for the final part, which discusses formal models of legal procedure and of multi-agent interaction in legal proceedings. The review concludes with identifying some of the main open research problems. The review shows that modern legal applications of logic confirm the recent trend of widening the scope of logic from deduction to information flow, argumentation and interaction.


title: Robust throughput and routing for mobile ad hoc wireless networks

abstract: Flows transported across mobile ad hoc wireless networks suffer from route breakups caused by nodal mobility. In a network that aims to support critical interactive real-time data transactions, to provide for the uninterrupted execution of a transaction, or for the rapid transport of a high value file, it is essential to identify robust routes across which such transactions are transported. Noting that route failures can induce long re-routing delays that may be highly interruptive for many applications and message/stream transactions, it is beneficial to configure the routing scheme to send a flow across a route whose lifetime is longer, with sufficiently high probability, than the estimated duration of the activity that it is selected to carry. We evaluate the ability of a mobile ad hoc wireless network to distribute flows across robust routes by introducing the robust throughput measure as a performance metric. The utility gained by the delivery of flow messages is based on the level of interruption experienced by the underlying transaction. As a special case, for certain applications only transactions that are completed without being prematurely interrupted may convey data to their intended users that is of acceptable utility. We describe the mathematical calculation of a network's robust throughput measure, as well as its robust throughput capacity. We introduce the robust flow admission and routing algorithm (RFAR) to provide for the timely and robust transport of flow transactions across mobile ad hoc wireless network systems.


title: Multinomial goodness-of-fit tests for logistic regression models

abstract: We examine the properties of several tests for goodness-of-fit for multinomial logistic regression. One test is based on a strategy of sorting the observations according to the complement of the estimated probability for the reference outcome category and then grouping the subjects into g equal-sized groups. A g × c contingency table, where c is the number of values of the outcome variable, is constructed. The test statistic, denoted as Cg, is obtained by calculating the Pearson χ2 statistic where the estimated expected frequencies are the sum of the model-based estimated logistic probabilities. Simulations compare the properties of Cg with those of the ungrouped Pearson χ2 test (X2) and its normalized test (z). The null distribution of Cg is well approximated by the χ2 distribution with (g−2) × (c−1) degrees of freedom. The sampling distribution of X2 is compared with a χ2 distribution with n × (c−1) degrees of freedom but shows erratic behavior. With a few exceptions, the sampling distribution of z adheres reasonably well to the standard normal distribution. Power simulations show that Cg has low power for a sample of 100 observations, but satisfactory power for a sample of 400. The tests are illustrated using data from a study of cytological criteria for the diagnosis of breast tumors. Copyright © 2008 John Wiley & Sons, Ltd.


title: A Service-Oriented Approach to Crowdsensing for Accessible Smart Mobility Scenarios

abstract: This work presents an architecture to help designing and deploying smart mobility applications. The proposed solution builds on the experience already matured by the authors in different fields: crowdsourcing and sensing done by users to gather data related to urban barriers and facilities, computation of personalized paths for users with special needs, and integration of open data provided by bus companies to identify the actual accessibility features and estimate the real arrival time of vehicles at stops. In terms of functionality, the first “monolithic” prototype fulfilled the goal of composing the aforementioned pieces of information to support citizens with reduced mobility (users with disabilities and/or elderly people) in their urban movements. In this paper, we describe a service-oriented architecture that exploits the microservices orchestration paradigm to enable the creation of new services and to make the management of the various data sources easier and more effective. The proposed platform exposes standardized interfaces to access data, implements common services to manage metadata associated with them, such as trustworthiness and provenance, and provides an orchestration language to create complex services, naturally mapping their internal workflow to code. The manuscript demonstrates the effectiveness of the approach by means of some case studies.


title: Exploiting the Capture Effect on DSC and BSS Color in Dense IEEE 802.11ax Deployments

abstract: Future wireless local area networks (WLANs) are expected to serve thousands of users in diverse environments. To address the new challenges that WLANs will face, and to overcome the limitations that previous IEEE standards introduced, a new IEEE 802.11 amendment is under development. IEEE 802.11ax aims to enhance spectrum efficiency in a dense deployment; hence system throughput improves. Dynamic Sensitivity Control (DSC) and BSS Color are the main schemes under consideration in IEEE 802.11ax for improving spectrum efficiency In this paper, we evaluate DSC and BSS Color schemes when physical layer capture (PLC) is modelled. PLC refers to the case that a receiver successfully decodes the stronger frame when collision occurs. It is shown, that PLC could potentially lead to fairness issues and higher throughput in specific cases. We study PLC in a small and large scale scenario, and show that PLC could also improve fairness in specific scenarios.


Multi-Language Multi-Speaker Acoustic Modeling for LSTM-RNN Based Statistical Parametric Speech Synthesis


title: Platform-independent modeling of explicitly parallel programs

abstract: We propose a model-driven approach to parallel programming of SPMD-style, explicitly parallel computations. We define an executable, platform-independent modeling language with explicitly parallel control and data flow for an abstract parallel computer with shared address space, and implement it as an extension of UML2 activity diagrams and a generator for Fork source code that can be compiled and executed on a high-level abstract parallel machine simulator. We also sketch how to refine the modeling language to address more realistic parallel platforms.


title: Performance analysis in soccer: a Cartesian coordinates based approach using RoboCup data

abstract: In soccer, like in business, results are often the best indicator of a team’s performance in a certain competition but insufficient to a coach to asses his team performance. As a consequence, measurement tools play an important role in this particular field. In this research work, a performance tool for soccer, based only in Cartesian coordinates is presented. Capable of calculating final game statistics, suisber of shots, the calculus methodology analyzes the game in a sequential manner, starting with the identification of the kick event (the basis for detecting all events), which is related with a positive variation in the ball’s velocity vector. The achieved results were quite satisfactory, mainly due to the number of successfully detected events in the validation process (based on manual annotation). For the majority of the statistics, these values are above 92% and only in the case of shots do these values drop to numbers between 74 and 85%. In the future, this methodology could be improved, especially regarding the shot statistics, integrated with a real-time localization system, or expanded for other collective sports games, such as hockey or basketball.


1 Photoplethysmography ( PPG ) system


title: Estimating software project effort using analogies

abstract: Accurate project effort prediction is an important goal for the software engineering community. To date most work has focused upon building algorithmic models of effort, for example COCOMO. These can be calibrated to local environments. We describe an alternative approach to estimation based upon the use of analogies. The underlying principle is to characterize projects in terms of features (for example, the number of interfaces, the development method or the size of the functional requirements document). Completed projects are stored and then the problem becomes one of finding the most similar projects to the one for which a prediction is required. Similarity is defined as Euclidean distance in n-dimensional space where n is the number of project features. Each dimension is standardized so all dimensions have equal weight. The known effort values of the nearest neighbors to the new project are then used as the basis for the prediction. The process is automated using a PC-based tool known as ANGEL. The method is validated on nine different industrial datasets (a total of 275 projects) and in all cases analogy outperforms algorithmic models based upon stepwise regression. From this work we argue that estimation by analogy is a viable technique that, at the very least, can be used by project managers to complement current estimation techniques.


Artificial Intelligence in Medical Diagnosis


title: Using Scalable Data Mining for Predicting Flight Delays

abstract: Flight delays are frequent all over the world (about 20% of airline flights arrive more than 15min late) and they are estimated to have an annual cost of billions of dollars. This scenario makes the prediction of flight delays a primary issue for airlines and travelers. The main goal of this work is to implement a predictor of the arrival delay of a scheduled flight due to weather conditions. The predicted arrival delay takes into consideration both flight information (origin airport, destination airport, scheduled departure and arrival time) and weather conditions at origin airport and destination airport according to the flight timetable. Airline flight and weather observation datasets have been analyzed and mined using parallel algorithms implemented as MapReduce programs executed on a Cloud platform. The results show a high accuracy in predicting delays above a given threshold. For instance, with a delay threshold of 15min, we achieve an accuracy of 74.2% and 71.8% recall on delayed flights, while with a threshold of 60min, the accuracy is 85.8% and the delay recall is 86.9%. Furthermore, the experimental results demonstrate the predictor scalability that can be achieved performing data preparation and mining tasks as MapReduce applications on the Cloud.


title: node2vec: Scalable Feature Learning for Networks

abstract: Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.


title: Image collection summarization via dictionary learning for sparse representation

abstract: In this paper, a novel framework is developed to achieve effective summarization of large-scale image collection by treating the problem of automatic image summarization as the problem of dictionary learning for sparse representation, e.g., the summarization task can be treated as a dictionary learning task (i.e., the given image set can be reconstructed sparsely with this dictionary). For image set of a specific category or a mixture of multiple categories, we have built a sparsity model to reconstruct all its images by using a subset of most representative images (i.e., image summary); and we adopted the simulated annealing algorithm to learn such sparse dictionary by minimizing an explicit optimization function. By investigating their reconstruction ability under sparsity constrain and diversity constrain, we have quantitatively measure the performance of various summarization algorithms. Our experimental results have shown that our dictionary learning for sparse representation algorithm can obtain more accurate summary as compared with other baseline algorithms.


title: F-CNN: An FPGA-based framework for training Convolutional Neural Networks

abstract: This paper presents a novel reconfigurable framework for training Convolutional Neural Networks (CNNs). The proposed framework is based on reconfiguring a streaming datapath at runtime to cover the training cycle for the various layers in a CNN. The streaming datapath can support various parameterized modules which can be customized to produce implementations with different trade-offs in performance and resource usage. The modules follow the same input and output data layout, simplifying configuration scheduling. For different layers, instances of the modules contain different computation kernels in parallel, which can be customized with different layer configurations and data precision. The associated models on performance, resource and bandwidth can be used in deriving parameters for the datapath to guide the analysis of design trade-offs to meet application requirements or platform constraints. They enable estimation of the implementation specifications given different layer configurations, to maximize performance under the constraints on bandwidth and hardware resources. Experimental results indicate that the proposed module design targeting Maxeler technology can achieve a performance of 62.06 GFLOPS for 32-bit floating-point arithmetic, outperforming existing accelerators. Further evaluation based on training LeNet-5 shows that the proposed framework achieves about 4 times faster than CPU implementation of Caffe and about 7.5 times more energy efficient than the GPU implementation of Caffe.