title: Analysing the Relationship between IT Governance and Business/IT Alignment Maturity

abstract: IT governance is one of these concepts that suddenly emerged and became an important issue in the information technology area. Some organisations started with the implementation of IT governance in order to achieve a better alignment between business and IT. This paper interprets important existing theories, models and practices in the IT governance domain and derives research questions from it. Next, multiple research strategies are triangulated in order to understand how organisations are implementing IT governance in practice and to analyse the relationship between these implementations and business/IT alignment. Major finding is that organisations with more mature IT governance practices likely obtain a higher degree of business/IT alignment maturity.


title: Nonlinear adaptive flight control using neural networks

abstract: The effectiveness of a controller architecture, which combines adaptive feedforward neural networks with feedback linearization, has been demonstrated on a variety of flight vehicles. The boundedness of tracking error and control signals is guaranteed. The architecture can accommodate both linear-in-the-parameters networks, as well as single-hidden-layer perceptron neural networks. Both theoretical and experimental research is planned to expand and improve the applicability of the approach, and to demonstrate practical utility in the areas of cost reduction and improved flight safety.


title: UnixMan Corpus: A Resource for Language Learning in the Unix Domain

abstract: We present a new resource, the UnixMan Corpus, for studying language learning it the domain of Unix utility manuals. The corpus is built by mining Unix (and other Unix related) man pages for parallel example entries, consisting of English textual descriptions with corresponding command examples. The commands provide a grounded and ambiguous semantics for the textual descriptions, making the corpus of interest to work on Semantic Parsing and Grounded Language Learning. In contrast to standard resources for Semantic Parsing, which tend to be restricted to a small number of concepts and relations, the UnixMan Corpus spans a wide variety of utility genres and topics, and consists of hundreds of command and domain entity types. The semi-structured nature of the manuals also makes it easy to exploit other types of relevant information for Grounded Language Learning. We describe the details of the corpus and provide preliminary classification results.


title: Using information scent and need for cognition to understand online search behavior

abstract: The purpose of this study is to investigate the extent to which two theories, Information Scent and Need for Cognition, explain people's search behaviors when interacting with search engine results pages (SERPs). Information Scent, the perception of the value of information sources, was manipulated by varying the number and distribution of relevant results on the first SERP. Need for Cognition (NFC), a personality trait that measures the extent to which a person enjoys cognitively effortful activities, was measured by a standardized scale. A laboratory experiment was conducted with forty-eight participants, who completed six open-ended search tasks. Results showed that while interacting with SERPs containing more relevant documents, participants examined more documents and clicked deeper in the search result list. When interacting with SERPs that contained the same number of relevant results distributed across different ranks, participants were more likely to abandon their queries when relevant documents appeared later on the SERP. With respect to NFC, participants with higher NFC paginated less frequently and paid less attention to results at lower ranks than those with lower NFC. The interaction between NFC and the number of relevant results on the SERP affected the time spent on searching and a participant's likelihood to reformulate, paginate and stop. Our findings suggest evaluating system effectiveness based on the first page of results, even for tasks that require the user to view multiple documents, and varying interface features based on NFC.


title: Automatic brain tumor segmentation in MRI: Hybridized multilevel thresholding and level set

abstract: Segmentation of tumor from magnetic resonance image (MRI) brain images is an emergent research area in the field of medical image segmentation. As segmentation of brain tumor plays an important role for necessary treatment and planning of tumor surgery. However, segmentation of the brain tumor is still a great challenge in clinics, specially automatic segmentation. In this paper we proposed hybridized multilevel thresholding and level set method for automatic segmentation of brain tumor. The innovation for this paper is to interface the initial segmentation from multilevel thresholding and extract a fine portrait using level set method with morphological operations. The results are compared with the existing method and also with radiologist manual segmentation which confirm the effectiveness of this hybridized paradigm for brain tumor segmentation.


title: On the parameter choice for the Non-Local Means

abstract: This paper deals with the parameter choice for the NL-Means algorithm. Starting with basic computations on toy models, we study the bias-variance trade-off of this filter using a simple notion of regularity in the patch space. We show that this regularity is necessarily local and so should be the parameters of the filter. Relying on Stein's Unbiased Risk Estimate, we then propose a way to locally set these parameters, and we compare this method with the Non-Local Means with optimal global parameter.


title: Prediction of Sea Surface Temperature using Long Short-Term Memory

abstract: This letter adopts long short-term memory (LSTM) to predict sea surface temperature (SST), and makes short-term prediction, including one day and three days, and long-term prediction, including weekly mean and monthly mean. The SST prediction problem is formulated as a time series regression problem. The proposed network architecture is composed of two kinds of layers: an LSTM layer and a full-connected dense layer. The LSTM layer is utilized to model the time series relationship. The full-connected layer is utilized to map the output of the LSTM layer to a final prediction. The optimal setting of this architecture is explored by experiments and the accuracy of coastal seas of China is reported to confirm the effectiveness of the proposed method. The prediction accuracy is also tested on the SST anomaly data. In addition, the model’s online updated characteristics are presented.


title: Effects of Pitch Area-Restrictions on Tactical Behavior, Physical, and Physiological Performances in Soccer Large-Sided Games

abstract: The aim of this study was to identify how pitch area-restrictions affect the tactical behavior, physical, and physiological performances of players during soccer large-sided games. A 10 vs. 9 large-sided game was performed under 3 experimental conditions: (a) restricted-spacing, the pitch was divided into specific areas where players were assigned and they should not leave it; (b) contiguous-spacing, the pitch was divided into specific areas where the players were only allowed to move to a neighboring one; (c) free-spacing, the players had no restrictions in space occupation. The positional data were used to compute players' spatial exploration index and also the distance, coefficient of variation, approximate entropy, and frequency of near-in-phase displacements synchronization of players' dyads formed by the outfield teammates. Players' physical and physiological performances were assessed by the distance covered at different speed categories, game pace, and heart rate. Most likely higher values were found in players' spatial exploration index under free-spacing conditions. The synchronization between dyads' displacements showed higher values for contiguous-spacing and free-spacing conditions. In contrast, for the jogging and running intensity zones, restricted-spacing demanded a moderate effect and most likely decrease compared with other scenarios (~20–50% to jogging and ~60–90% to running). Overall, the effects of limiting players' spatial exploration greatly impaired the coadaptation between teammates' positioning while decreasing the physical and physiological performances. These results allow for a better understanding of players' decision-making process according to specific task rules and can be relevant to enrich practice task design, such that coaches acknowledge the differential effect by using specific pitch-position area restrictions.


title: SIGNet: Scalable Embeddings for Signed Networks

abstract: Recent successes in word embedding and document embedding have motivated researchers to explore similar representations for networks and to use such representations for tasks such as edge prediction, node label prediction, and community detection. Such network embedding methods are largely focused on finding distributed representations for unsigned networks and are unable to discover embeddings that respect polarities inherent in edges. We propose SIGNet, a fast scalable embedding method suitable for signed networks. Our proposed objective function aims to carefully model the social structure implicit in signed networks by reinforcing the principles of social balance theory. Our method builds upon the traditional word2vec family of embedding approaches and adds a new targeted node sampling strategy to maintain structural balance in higher-order neighborhoods. We demonstrate the superiority of SIGNet over state-of-the-art methods proposed for both signed and unsigned networks on several real world datasets from different domains. In particular, SIGNet offers an approach to generate a richer vocabulary of features of signed networks to support representation and reasoning.


title: Content-Aware Concurrent Multipath Transfer for High-Definition Video Streaming over Heterogeneous Wireless Networks

abstract: Delivering high-definition (HD) wireless video under stringent delay constraint is challenging with regard to the limited network resources and high transmission rate. Concurrent multipath transfer (CMT) using stream control transmission protocol (SCTP) exploits the multihoming feature of mobile devices to establish associations with different access networks. In this paper, we study the multihomed HD video communication with SCTP over heterogeneous wireless networks. The existing CMT schemes mainly treat the traffic data in a content-agnostic fashion, and thus cannot effectively leverage the scarce wireless resources to maximize the perceived video quality. To address this critical issue, we propose a content-aware CMT (CMT-CA) solution that featured by the unequal frame-level scheduling based on estimated video parameters and feedback channel status. First, we develop an analytical framework to model the total distortion of parallel video transmission over multiple wireless access networks. Second, we introduce a joint congestion control and data distribution scheme to minimize the total distortion based on online quality evaluation and Markov decision process (MDP). The performance of CMT-CA is evaluated through extensive semi-physical emulations in Exata involving HD video encoded with H.264 codec. Experimental results show that CMT-CA outperforms the reference schemes in terms of video peak signal-to-noise ratio (PSNR), end-to-end delay, and goodput. Or conversely, CMT-CA achieves the same video quality with $20$ percent bandwidth conservation.


title: Microstrip bandstop filter using open stub and spurline

abstract: Microstrip filters are widely used in microwave circuit, This paper briefly describes the design principle of microstrip bandstop filter(BSF). A compact wide band high rejection BSF is presented. This filter combines two traditional BSFs: open stub filter and spurline filter. Due to the inherently compact characteristics of the spurline, the proposed filter shows a better rejection performance than open stub BSF without increasing the circuit size. The results of simulation and optimization given by CST2010 prove the correctness of the design.


Exploring word Mover's distance and semantic-aware embedding techniques for extractive broadcast news summarization


title: Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Social Network

abstract: Language in social media is extremely dynamic: new words emerge, trend and disappear, while the meaning of existing words can fluctuate over time. Such dynamics are especially notable during a period of crisis. This work addresses several important tasks of measuring, visualizing and predicting short term text representation shift, i.e. the change in a word's contextual semantics, and contrasting such shift with surface level word dynamics, or concept drift, observed in social media streams. Unlike previous approaches on learning word representations from text, we study the relationship between short-term concept drift and representation shift on a large social media corpus - VKontakte posts in Russian collected during the Russia-Ukraine crisis in 2014-2015. Our novel contributions include quantitative and qualitative approaches to (1) measure short-term representation shift and contrast it with surface level concept drift; (2) build predictive models to forecast short-term shifts in meaning from previous meaning as well as from concept drift; and (3) visualize short-term representation shift for example keywords to demonstrate the practical use of our approach to discover and track meaning of newly emerging terms in social media. We show that short-term representation shift can be accurately predicted up to several weeks in advance. Our unique approach to modeling and visualizing word representation shifts in social media can be used to explore and characterize specific aspects of the streaming corpus during crisis events and potentially improve other downstream classification tasks including real-time event detection.


title: Bayesian Policy Gradient and Actor-Critic Algorithms Bayesian Policy Gradient and Actor-Critic Algorithms

abstract: Policy gradient methods are reinforcement learning algorithms that adapt a parameterized policy by following a performance gradient estimate. Many conventional policy gradient methods use Monte-Carlo techniques to estimate this gradient. The policy is improved by adjusting the parameters in the direction of the gradient estimate. Since Monte-Carlo methods tend to have high variance, a large number of samples is required to attain accurate estimates, resulting in slow convergence. In this paper, we first propose a Bayesian framework for policy gradient, based on modeling the policy gradient as a Gaussian process. This reduces the number of samples needed to obtain accurate gradient estimates. Moreover, estimates of the natural gradient as well as a measure of the uncertainty in the gradient estimates, namely, the gradient covariance, are provided at little extra cost. Since the proposed Bayesian framework considers system trajectories as its basic observable unit, it does not require the dynamics within trajectories to be of any particular form, and thus, can be easily extended to partially observable problems. On the downside, it cannot take advantage of the Markov property when the system is Markovian. ::: ::: To address this issue, we proceed to supplement our Bayesian policy gradient framework with a new actor-critic learning model in which a Bayesian class of non-parametric critics, based on Gaussian process temporal difference learning, is used. Such critics model the action-value function as a Gaussian process, allowing Bayes' rule to be used in computing the posterior distribution over action-value functions, conditioned on the observed data. Appropriate choices of the policy parameterization and of the prior covariance (kernel) between action-values allow us to obtain closed-form expressions for the posterior distribution of the gradient of the expected return with respect to the policy parameters. We perform detailed experimental comparisons of the proposed Bayesian policy gradient and actor-critic algorithms with classic Monte-Carlo based policy gradient methods, as well as with each other, on a number of reinforcement learning problems.


title: Learning Analytics: Enhancing the Quality of Higher Education

abstract: The regular gathering of student information has created a high level of complexity, and also an incredible opportunity for teachers to enhance student learning experience. The digital information that learners leave online about their interests, engagement and their preferences gives significant measures of information that can be mined to customise their learning experience better. The motivation behind this article is to inspect the quickly developing field of Learning Analytics and to study why and how enormous information will benefit teachers, institutes, online course developers and students as a whole. The research will discuss the advancement in Big Data and how is it useful in education, along with an overview of the importance of various stakeholders and the challenges that lie ahead. We also look into the tools and techniques that are put into practice to realize the benefits of Analytics in Education. Our results suggest that this field has the immense scope of development but ethical and privacy issues present a challenge.


title: Building IoT-Based Applications for Smart Cities: How Can Ontology Catalogs Help?

abstract: The Internet of Things (IoT) plays an ever-increasing role in enabling smart city applications. An ontology-based semantic approach can help improve interoperability between a variety of IoT-generated as well as complementary data needed to drive these applications. While multiple ontology catalogs exist, using them for IoT and smart city applications require significant amount of work. In this paper, we demonstrate how can ontology catalogs be more effectively used to design and develop smart city applications? We consider four ontology catalogs that are relevant for IoT and smart cities: 1) READY4SmartCities; 2) linked open vocabulary (LOV); 3) OpenSensingCity (OSC); and 4) LOVs for IoT (LOV4IoT). To support semantic interoperability with the reuse of ontology-based smart city applications, we present a methodology to enrich ontology catalogs with those ontologies. Our methodology is generic enough to be applied to any other domains as is demonstrated by its adoption by OSC and LOV4IoT ontology catalogs. Researchers and developers have completed a survey-based evaluation of the LOV4IoT catalog. The usefulness of ontology catalogs ascertained through this evaluation has encouraged their ongoing growth and maintenance. The quality of IoT and smart city ontologies have been evaluated to improve the ontology catalog quality. We also share the lessons learned regarding ontology best practices and provide suggestions for ontology improvements with a set of software tools.


title: Information Leaks Without Memory Disclosures: Remote Side Channel Attacks on Diversified Code

abstract: Code diversification has been proposed as a technique to mitigate code reuse attacks, which have recently become the predominant way for attackers to exploit memory corruption vulnerabilities. As code reuse attacks require detailed knowledge of where code is in memory, diversification techniques attempt to mitigate these attacks by randomizing what instructions are executed and where code is located in memory. As an attacker cannot read the diversified code, it is assumed he cannot reliably exploit the code. In this paper, we show that the fundamental assumption behind code diversity can be broken, as executing the code reveals information about the code. Thus, we can leak information without needing to read the code. We demonstrate how an attacker can utilize a memory corruption vulnerability to create side channels that leak information in novel ways, removing the need for a memory disclosure vulnerability. We introduce seven new classes of attacks that involve fault analysis and timing side channels, where each allows a remote attacker to learn how code has been diversified.


title: Collective annotation of Wikipedia entities in web text

abstract: To take the first step beyond keyword-based search toward entity-based search, suitable token spans ("spots") on documents must be identified as references to real-world entities from an entity catalog. Several systems have been proposed to link spots on Web pages to entities in Wikipedia. They are largely based on local compatibility between the text around the spot and textual metadata associated with the entity. Two recent systems exploit inter-label dependencies, but in limited ways. We propose a general collective disambiguation approach. Our premise is that coherent documents refer to entities from one or a few related topics or domains. We give formulations for the trade-off between local spot-to-entity compatibility and measures of global coherence between entities. Optimizing the overall entity assignment is NP-hard. We investigate practical solutions based on local hill-climbing, rounding integer linear programs, and pre-clustering entities followed by local optimization within clusters. In experiments involving over a hundred manually-annotated Web pages and tens of thousands of spots, our approaches significantly outperform recently-proposed algorithms.


Understanding and Using Patterns in Software Development


title: The commercial use of segmentation and predictive modeling techniques for database marketing in the Netherlands

abstract: Although the application of segmentation and predictive modeling is an important topic in the database marketing (DBM) literature, no study has yet investigated the extent of adoption of these techniques. We present the results of a Dutch survey involving 228 database marketing companies. We find that managers tend to rely on intuition and on the long-standing methods RFM and cross-tabulation. Our results indicate that the application of segmentation and response modeling is positively related to company and database size, frequency of customer contact, and the use of a direct channel of distribution. The respondents indicate that future research should focus on models applicable for Internet marketing, long-term effects of direct marketing, irritation from direct marketing offers, and segmentation and predictive modeling techniques.


title: MICE: Multivariate Imputation by Chained Equations in R

abstract: The R package mice imputes incomplete multivariate data by chained equations. The software mice 1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. mice 1.0 introduced predictor selection, passive imputation and automatic pooling. This article documents mice, which extends the functionality of mice 1.0 in several ways. In mice, the analysis of imputed data is made completely general, whereas the range of models under which pooling works is substantially extended. mice adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling routines, model selection tools, and diagnostic graphs. Imputation of categorical data is improved in order to bypass problems caused by perfect prediction. Special attention is paid to transformations, sum scores, indices and interactions using passive imputation, and to the proper setup of the predictor matrix. mice can be downloaded from the Comprehensive R Archive Network. This article provides a hands-on, stepwise approach to solve applied incomplete data problems.


title: Synthetic Biology for Space Exploration: Promises and Societal Implications

abstract: Synthetic biology can greatly accelerate the development of human space exploration, to the point of allowing permanent human bases on Mars within our lifetime. Among the technological issues to be tackled is the need to provide the consumables required to sustain crews, and using biological systems for the on-site production of resources is an attractive approach. However, all organisms we currently know have evolved on Earth and most extraterrestrial environments stress the capabilities of even terrestrial extremophiles. Two challenges consequently arise: organisms should survive in a metabolically active state with minimal maintenance requirements, and produce compounds of interest while relying only on inputs found in the explored areas. A solution could come from the tools and methods recently developed within the field of synthetic biology. The societal implications are complex: there are implications with synthetic biology and human space colonization independently, and together there are potentially more issues. Establishing colonies relying to a large extent on modified organisms and transferring the developed technologies to terrestrial applications raises a wide range of critical ethical questions and unprecedented societal impacts, on Earth as well as on colonized planetary bodies. The scenario of humans as a multi-planet species should be addressed now, as technologies aimed at making it happen are already under development. Here we give a brief overview of the synthetic biology technologies that are being developed to aid human space exploration, before discussing the impacts of proposed medium-term scenarios on the evolution of our society.


title: A CMOS front end for a laser rangefinder with resonance-based timing discrimination

abstract: An integrated receiver channel for a pulsed time-of-flight laser rangefinder has been designed and simulated. Timing errors are minimized by using a resonance-based timing discrimination at the input of the receiver channel. The front end of the receiver has been implemented using CMOS transistors. The noise of the front end has been analyzed, optimized, and compared to earlier receiver channel realizations. The simulations show that the receiver can use a minimum input pulse amplitude of 2.0 μA. The dynamic range is 1:2500 and the walk error is 165 ps.


Pair-Wise: Automatic Essay Evaluation using Word Mover’s Distance


title: Social comparisons on social media: The impact of Facebook on young women's body image concerns and mood

abstract: The present study experimentally investigated the effect of Facebook usage on women’s mood and body image, whether these effects differ from an online fashion magazine, and whether appearance comparison tendency moderates any of these effects. Female participants (n = 112) were randomly assigned to spend 10 minutes browsing their Facebook account, a magazine website, or an appearance-neutral control website before completing state measures of mood, body dissatisfaction, and appearance discrepancies (weight-related, and face, hair, and skin-related). Participants also completed a trait measure of appearance comparison tendency. Participants who spent time on Facebook reported being in a more negative mood than those who spent time on the control website. Furthermore, women high in appearance comparison tendency reported more facial, hair, and skin-related discrepancies after Facebook exposure than exposure to the control website. Given its popularity, more research is needed to better understand the impact that Facebook has on appearance concerns.


title: Meeting Deadlines of Scientific Workflows in Public Clouds with Tasks Replication

abstract: The elasticity of Cloud infrastructures makes them a suitable platform for execution of deadline-constrained workflow applications, because resources available to the application can be dynamically increased to enable application speedup. Existing research in execution of scientific workflows in Clouds either try to minimize the workflow execution time ignoring deadlines and budgets or focus on the minimization of cost while trying to meet the application deadline. However, they implement limited contingency strategies to correct delays caused by underestimation of tasks execution time or fluctuations in the delivered performance of leased public Cloud resources. To mitigate effects of performance variation of resources on soft deadlines of workflow applications, we propose an algorithm that uses idle time of provisioned resources and budget surplus to replicate tasks. Simulation experiments with four well-known scientific workflows show that the proposed algorithm increases the likelihood of deadlines being met and reduces the total execution time of applications as the budget available for replication increases.


title: Robot Learning from Human Teachers

abstract: Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers. We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system. Chapter 2 gives a brief survey of the psychology literature that provides insights from human social learning that are relevant to designing robotic social learners. Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work. First, is the choice of input, how the human teacher interacts with the robot to provide demonstrations. Next, is the choice of modeling technique. Currently, there is a dichotomy in the field between approaches that model low-level motor skills and those that model high-level tasks composed of primitive actions. We devote a chapter to each of these. Chapter 7 is devoted to interactive and active learning approaches that allow the robot to refine an existing task model. And finally, Chapter 8 provides best practices for evaluation of LfD systems, with a focus on how to approach experiments with human subjects in this domain.


title: Generic Text Summarization Using Probabilistic Latent Semantic Indexing

abstract: This paper presents a strategy to generate generic summary of documents using Probabilistic Latent Semantic Indexing. Generally a document contains several topics rather than a single one. Summaries created by human beings tend to cover several topics to give the readers an overall idea about the original document. Hence we can expect that a summary containing sentences from better part of the topic spectrum should make a better summary. PLSI has proven to be an effective method in topic detection. In this paper we present a method for creating extractive summary of the document by using PLSI to analyze the features of document such as term frequency and graph structure. We also show our results, which was evaluated using ROUGE, and compare the results with other techniques, proposed in the past.


title: Parallel auto-encoder for efficient outlier detection

abstract: Detecting outliers from big data plays an important role in network security. Previous outlier detection algorithms are generally incapable of handling big data. In this paper we present an parallel outlier detection method for big data, based on a new parallel auto-encoder method. Specifically, we build a replicator model of the input data to obtain the representation of sample data. Then, the replicator model is used to measure the replicability of test data, where records having higher reconstruction errors are classified as outliers. Experimental results show the performance of the proposed parallel algorithm.


title: Sampling for Scalable Visual Analytics

abstract: Sampling is becoming an essential tool for scalable interactive visual analysis. After outlining prior work by the database community on sampling for visualization of aggregation queries, this article considers how these results might be improved and extended to a broader setting. The goal is to better understand how users interact with sampling to enable wider adoption of sampling for scalable visual analytics.


title: Of passwords and people: measuring the effect of password-composition policies

abstract: Text-based passwords are the most common mechanism for authenticating humans to computer systems. To prevent users from picking passwords that are too easy for an adversary to guess, system administrators adopt password-composition policies (e.g., requiring passwords to contain symbols and numbers). Unfortunately, little is known about the relationship between password-composition policies and the strength of the resulting passwords, or about the behavior of users (e.g., writing down passwords) in response to different policies. We present a large-scale study that investigates password strength, user behavior, and user sentiment across four password-composition policies. We characterize the predictability of passwords by calculating their entropy, and find that a number of commonly held beliefs about password composition and strength are inaccurate. We correlate our results with user behavior and sentiment to produce several recommendations for password-composition policies that result in strong passwords without unduly burdening users.


title: Mahimahi: accurate record-and-replay for HTTP

abstract: This paper presents Mahimahi, a framework to record traffic from HTTP-based applications, and later replay it under emulated network conditions. Mahimahi improves upon prior record-and-replay frameworks in three ways. First, it is more accurate because it carefully emulates the multi-server nature of Web applications, present in 98% of the Alexa US Top 500 Web pages. Second, it isolates its own network traffic, allowing multiple Mahimahi instances emulating different networks to run concurrently without mutual interference. And third, it is designed as a set of composable shells, providing ease-of-use and extensibility. ::: ::: We evaluate Mahimahi by: (1) analyzing the performance of HTTP/1.1, SPDY, and QUIC on a corpus of 500 sites, (2) using Mahimahi to understand the reasons why these protocols are suboptimal, (3) developing Cumulus, a cloud-based browser designed to overcome these problems, using Mahimahi both to implement Cumulus by extending one of its shells, and to evaluate it, (4) using Mahimahi to evaluate HTTP multiplexing protocols on multiple performance metrics (page load time and speed index), and (5) describing how others have used Mahimahi.


title: Attack countermeasure trees (ACT): towards unifying the constructs of attack and defense trees

abstract: Attack tree (AT) is one of the widely used non-state-space models for security analysis. The basic formalism of AT does not take into account defense mechanisms. Defense trees (DTs) have been developed to investigate the effect of defense mechanisms using measures such as attack cost, security investment cost, return on attack (ROA), and return on investment (ROI). DT, however, places defense mechanisms only at the leaf nodes and the corresponding ROI/ROA analysis does not incorporate the probabilities of attack. In attack response tree (ART), attack and response are both captured but ART suffers from the problem of state-space explosion, since solution of ART is obtained by means of a state-space model. In this paper, we present a novel attack tree paradigm called attack countermeasure tree (ACT) which avoids the generation and solution of a state-space model and takes into account attacks as well as countermeasures (in the form of detection and mitigation events). In ACT, detection and mitigation are allowed not just at the leaf node but also at the intermediate nodes while at the same time the state-space explosion problem is avoided in its analysis. We study the consequences of incorporating countermeasures in the ACT using three case studies (ACT for BGP attack, ACT for a SCADA attack and ACT for malicious insider attacks). Copyright © 2011 John Wiley & Sons, Ltd.


title: The biomechanics of running

abstract: This review article summarizes the current literature regarding the analysis of running gait. It is compared to walking and sprinting. The current state of knowledge is presented as it fits in the context of the history of analysis of movement. The characteristics of the gait cycle and its relationship to potential and kinetic energy interactions are reviewed. The timing of electromyographic activity is provided. Kinematic and kinetic data (including center of pressure measurements, raw force plate data, joint moments, and joint powers) and the impact of changes in velocity on these findings is presented. The status of shoewear literature, alterations in movement strategies, the role of biarticular muscles, and the springlike function of tendons are addressed. This type of information can provide insight into injury mechanisms and training strategies. © 1998 Elsevier Science B.V.


Determination of Heavy Metals in Roach ( Rutilus rutilus ) and Bleak ( Alburnus alburnus ) in Zhrebchevo Dam Lake


title: Learning from Millions of 3D Scans for Large-scale 3D Face Recognition

abstract: Deep networks trained on millions of facial images are believed to be closely approaching human-level performance in face recognition. However, open world face recognition still remains a challenge. Although, 3D face recognition has an inherent edge over its 2D counterpart, it has not benefited from the recent developments in deep learning due to the unavailability of large training as well as large test datasets. Recognition accuracies have already saturated on existing 3D face datasets due to their small gallery sizes. Unlike 2D photographs, 3D facial scans cannot be sourced from the web causing a bottleneck in the development of deep 3D face recognition networks and datasets. In this backdrop, we propose a method for generating a large corpus of labeled 3D face identities and their multiple instances for training and a protocol for merging the most challenging existing 3D datasets for testing. We also propose the first deep CNN model designed specifically for 3D face recognition and trained on 3.1 Million 3D facial scans of 100K identities. Our test dataset comprises 1,853 identities with a single 3D scan in the gallery and another 31K scans as probes, which is several orders of magnitude larger than existing ones. Without fine tuning on this dataset, our network already outperforms state of the art face recognition by over 10%. We fine tune our network on the gallery set to perform end-to-end large scale 3D face recognition which further improves accuracy. Finally, we show the efficacy of our method for the open world face recognition problem.


title: BodyScan: Enabling Radio-based Sensing on Wearable Devices for Contactless Activity and Vital Sign Monitoring

abstract: Wearable devices are increasingly becoming mainstream consumer products carried by millions of consumers. However, the potential impact of these devices is currently constrained by fundamental limitations of their built-in sensors. In this paper, we introduce radio as a new powerful sensing modality for wearable devices and propose to transform radio into a mobile sensor of human activities and vital signs. We present BodyScan, a wearable system that enables radio to act as a single modality capable of providing whole-body continuous sensing of the user. BodyScan overcomes key limitations of existing wearable devices by providing a contactless and privacy-preserving approach to capturing a rich variety of human activities and vital sign information. Our prototype design of BodyScan is comprised of two components: one worn on the hip and the other worn on the wrist, and is inspired by the increasingly prevalent scenario where a user carries a smartphone while also wearing a wristband/smartwatch. This prototype can support daily usage with one single charge per day. Experimental results show that in controlled settings, BodyScan can recognize a diverse set of human activities while also estimating the user's breathing rate with high accuracy. Even in very challenging real-world settings, BodyScan can still infer activities with an average accuracy above 60% and monitor breathing rate information a reasonable amount of time during each day.


title: Corrosion of a hip stem with a modular neck taper junction: a retrieval study of 16 cases.

abstract: Abstract Since the early 1990s, there has been a dramatic increase in modular total hip designs ranging from a stem with a proximal taper and modular head, to a distal stem, double taper proximal neck, and modular head. Clinical advantages of the modular neck include intraoperative adjustment of leg length via the neck-head taper and femoral anteversion via the neck-stem taper. Sixteen cases of a double tapered cone, Margron hip prosthesis, were presented for retrieval analysis. Macroscopic inspection, corrosion testing, light microscopy, and scanning electron microscopy were conducted to elucidate mechanisms of failure. In this regard, 6 neck components showed significant fretting, and crevice corrosion of the neck-stem taper with an average implantation time of 39 months compared with the remaining retrievals, which showed no corrosion with and average time in situ of 2.7 months. This retrieval study demonstrates that even with a modern taper design and corrosion-resistant materials, increased modularity can lead to fretting and crevice corrosion, metal ion generation, and particulate debris that may contribute to periprosthetic osteolysis and loosening.


title: Student behavior in a web-based educational system

abstract: The behavior of users over the web is one of the most relevant and research topic nowadays. Not only mining the user's behavior in order to provide better content is popular, but the prediction of the user's behavior is interesting and can increase user experience. Moreover, the business clearly desires such information to improve their services. In this paper we focus to the education domain as it belongs to the most dynamically transforming areas. Web based e-learning systems are nowadays reaching still greater popularity, because of possibilities they offer to students. We analyze various sources of "e-students" feedback and discuss today's challenges from the logging and feedback collecting point of view. Next, we focus on the prediction of student's next action within an e-learning application (in the mean of "stay or leave?" question). Such information can improve students' attrition rate by introducing various personalized approaches. We proposed the classifier based on polynomial regression and stochastic gradient descent to learn the attributes importance. In this way we are able to process a stream of data in one single iteration and thus we are able to reflect dynamic users' behavior changes. Our experiments are based on the log data collected from our web-based education system ALEF during three-year period. We found that there is an extensive heterogeneity in the users' (student) behavior which we were able to handle by using individual weights calculated for every user. We proposed a classifier predicting if the student ends the session in next action.Proposed classifier processes the dynamically changing data streams of logs.List of most important attributes for the session end prediction is identified.Analysis of students' behavior in a web-based educational system is provided.


title: Discovering work prioritisation patterns from event logs

abstract: Business process improvement initiatives typically employ various process analysis techniques, including evidence-based analysis techniques such as process mining, to identify new ways to streamline current business processes. While plenty of process mining techniques have been proposed to extract insights about the way in which activities within processes are conducted, techniques to understand resource behaviour are limited. At the same time, an understanding of resources behaviour is critical to enable intelligent and effective resource management - an important factor which can significantly impact overall process performance. The presence of detailed records kept by today's organisations, including data about who, how, what, and when various activities were carried out by resources, open up the possibility for real behaviours of resources to be studied. This paper proposes an approach to analyse one aspect of resource behaviour: the manner in which a resource prioritises his/her work. The proposed approach has been formalised, implemented, and evaluated using a number of synthetic and real datasets.


title: Towards cyclic fabrication systems for modular robotics

abstract: A cyclic fabrication system (CFS) is a network of materials, tools, and manufacturing processes that can produce all or most of its constituent components. This paper proposes an architecture for a robotic CFS based on modular components. The proposed system is intended to self-replicate via producing necessary components for replica devices. Some design challenges unique to self-replicating machines are discussed. Results from several proof-of-principle experiments are presented, including a manipulator designed to handle and assemble modules of the same type it is constructed from, a DC brush motor fabricated largely from raw materials, and basic manufacturing tools made with a simple CFS.


title: Type II Diabetes Mellitus in Arabic-Speaking Countries

abstract: The global epidemic of diabetes has not spared the Arabic-speaking countries, which have some of the highest prevalence of type II diabetes. This is particularly true of the Arab Gulf, a conglomerate of high income, oil-producing countries where prevalence rates are the highest. The prevalence rates among adults of the Arabic speaking countries as a whole range between 4%-21%, with the lowest being in Somalia and the highest in Kuwait. As economic growth has accelerated, so has the movement of the populations to urban centers where people are more likely to adopt lifestyles that embrace increased high-calorie food consumption and sedentary lifestyles. These factors likely contribute to the increased prevalence of obesity and diabetes in the Arabic speaking countries.


title: A 1.2-ns16×16-Bit Binary Multiplier Using

abstract: � Abstract—For higher order multiplications, a huge number of adders or compressors are to be used to perform the partial product addition. We have reduced the number of adders by introducing special kind of adders that are capable to add five/six/seven bits per decade. These adders are called compressors. Binary counter property has been merged with the compressor property to develop high order compressors. Uses of these compressors permit the reduction of the vertical critical paths. A 16×16 bit multiplier has been developed using these compressors. These compressors make the multipliers faster as compared to the conventional design that have been used 4-2 compressors and 3-2 compressors. Keywords—Binary multiplier, Compressors, Counter, Column adder, Low power.


title: Integrating rich document representations for text classification

abstract: This paper involves deriving high quality information from unstructured text data through the integration of rich document representations to improve machine learning text classification problems. Previous research has applied Neural Network Language Models (NNLMs) to document classification performance, and word vector representations have been used to measure semantics among text. Never have they been combined together and shown to have improved text classification performance. Our belief is that the inference and clustering abilities of word vectors coupled with the power of a neural network can create more accurate classification predictions. The first phase our work focused on word vector representations for classification purposes. This approach included analyzing two distinct text sources with pre-marked binary outcomes for classification, creating a benchmark metric, and comparing against word vector representations within the feature space as a classifier. The results showed promise, obtaining an area under the curve of 0.95 utilizing word vectors, relative to the benchmark case of 0.93. The second phase of the project focused on utilizing an extension of the neural network model used in phase one to represent a document in its entirety as opposed to being represented word by word. Preliminary results indicated a slight improvement over the baseline model of approximately 2–3 percent.


title: Remus: High Availability via Asynchronous Virtual Machine Replication. (Best Paper)

abstract: Allowing applications to survive hardware failure is an expensive undertaking, which generally involves reengineering software to include complicated recovery logic as well as deploying special-purpose hardware; this represents a severe barrier to improving the dependability of large or legacy applications. We describe the construction of a general and transparent high availability service that allows existing, unmodified software to be protected from the failure of the physical machine on which it runs. Remus provides an extremely high degree of fault tolerance, to the point that a running system can transparently continue execution on an alternate physical host in the face of failure with only seconds of downtime, while completely preserving host state such as active network connections. Our approach encapsulates protected software in a virtual machine, asynchronously propagates changed state to a backup host at frequencies as high as forty times a second, and uses speculative execution to concurrently run the active VM slightly ahead of the replicated system state.


title: Generating High Fidelity 3-D Thermograms With a Handheld Real-Time Thermal Imaging System

abstract: Infrared thermography is a widely used technique to measure and portray the surface temperature of an object in form of thermal images. Two-dimensional images, however, have some inherent limitations with regard to the fidelity with which they can depict the surface temperature of a three dimensional object. In the past two decades, there have been several works describing different techniques to generate 3-D models textured with thermal information using various combinations of sensors in order to address some of these limitations. Most of these approaches generate 3-D thermograms of an object from a single perspective with bulky measurement systems and therefore do not address problems that arise when scanning objects in a continuous manner from multiple perspectives. But reductions in cost, size, and weight of infrared and depth-sensing cameras as well as a significant increase in computational power of personal computers have enabled the development of low cost, handheld, real-time 3-D thermal imaging systems. This paper elaborates through a series of experiments on the main factors that affect the real-time generation of 3-D thermograms with such a system and demonstrates how taking these factors into consideration significantly improves the appearance and fidelity of the generated 3-D thermogram. Most of the insight gained in this paper can be transferred to 3-D thermal imaging systems based on other combination of sensors.


title: Coevolution of Fitness Predictors

abstract: We present an algorithm that coevolves fitness predictors, optimized for the solution population, which reduce fitness evaluation cost and frequency, while maintaining evolutionary progress. Fitness predictors differ from fitness models in that they may or may not represent the objective fitness, opening opportunities to adapt selection pressures and diversify solutions. The use of coevolution addresses three fundamental challenges faced in past fitness approximation research: 1) the model learning investment; 2) the level of approximation of the model; and 3) the loss of accuracy. We discuss applications of this approach and demonstrate its impact on the symbolic regression problem. We show that coevolved predictors scale favorably with problem complexity on a series of randomly generated test problems. Finally, we present additional empirical results that demonstrate that fitness prediction can also reduce solution bloat and find solutions more reliably.


title: Optimization of Computer Simulation Models with Rare Events

abstract: Discrete event simulation systems (DESS) are widely used in many diverse areas such as computer-communication networks, flexible manufacturing systems, project evaluation and review techniques (PERT), and flow networks. Because of their complexity, such systems are typically analyzed via Monte Carlo simulation methods. This paper deals with optimization of complex computer simulation models involving rare events. A classic example is to find an optimal (s, S) policy in a multi-item, multicommodity inventory system, when quality standards require the backlog probability to be extremely small. Our approach is based on change of the probability measure techniques, also called likelihood ratio (LR) and importance sampling (IS) methods. Unfortunately, for arbitrary probability measures the LR estimators and the resulting optimal solution often tend to be unstable and may have large variances. Therefore, the choice of the corresponding importance sampling distribution and in particular its parameters in an optimal way is an important task. We consider the case where the IS distribution comes from the same parametric family as the original (true) one and use the stochastic counterpart method to handle simulation based optimization models. More specifically, we use a two-stage procedure: at the first stage we identify (estimate) the optimal parameter vector at the IS distribution, while at the second stage we estimate the optimal solution of the underlying constrained optimization problem. Particular emphasis will be placed on estimation of rare events and on integration of the associated performance function into stochastic optimization programs. Supporting numerical results are provided as well.


title: Specifying Software Requirements for Complex Systems: New Techniques and Their Application

abstract: This paper concerns new techniques for making requirements specifications precise, concise, unambiguous, and easy to check for completeness and consistency. The techniques are well-suited for complex real-time software systems; they were developed to document the requirements of existing flight software for the Navy's A-7 aircraft. The paper outlines the information that belongs in a requirements document and discusses the objectives behind the techniques. Each technique is described and illustrated with examples from the A-7 document. The purpose of the paper is to introduce the A-7 document as a model of a disciplined approach to requirements specification; the document is available to anyone who wishes to see a fully worked-out example of the approach.


title: Scan Line Based Road Marking Extraction from Mobile LiDAR Point Clouds†

abstract: Mobile Mapping Technology (MMT) is one of the most important 3D spatial data acquisition technologies. The state-of-the-art mobile mapping systems, equipped with laser scanners and named Mobile LiDAR Scanning (MLS) systems, have been widely used in a variety of areas, especially in road mapping and road inventory. With the commercialization of Advanced Driving Assistance Systems (ADASs) and self-driving technology, there will be a great demand for lane-level detailed 3D maps, and MLS is the most promising technology to generate such lane-level detailed 3D maps. Road markings and road edges are necessary information in creating such lane-level detailed 3D maps. This paper proposes a scan line based method to extract road markings from mobile LiDAR point clouds in three steps: (1) preprocessing; (2) road points extraction; (3) road markings extraction and refinement. In preprocessing step, the isolated LiDAR points in the air are removed from the LiDAR point clouds and the point clouds are organized into scan lines. In the road points extraction step, seed road points are first extracted by Height Difference (HD) between trajectory data and road surface, then full road points are extracted from the point clouds by moving least squares line fitting. In the road markings extraction and refinement step, the intensity values of road points in a scan line are first smoothed by a dynamic window median filter to suppress intensity noises, then road markings are extracted by Edge Detection and Edge Constraint (EDEC) method, and the Fake Road Marking Points (FRMPs) are eliminated from the detected road markings by segment and dimensionality feature-based refinement. The performance of the proposed method is evaluated by three data samples and the experiment results indicate that road points are well extracted from MLS data and road markings are well extracted from road points by the applied method. A quantitative study shows that the proposed method achieves an average completeness, correctness, and F-measure of 0.96, 0.93, and 0.94, respectively. The time complexity analysis shows that the scan line based road markings extraction method proposed in this paper provides a promising alternative for offline road markings extraction from MLS data.


title: Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Model

abstract: We adopted an approach based on an LSTM neural network to monitor and detect faults in industrial multivariate time series data. To validate the approach we created a Modelica model of part of a real gasoil plant. By introducing hacks into the logic of the Modelica model, we were able to generate both the roots and causes of fault behavior in the plant. Having a self-consistent data set with labeled faults, we used an LSTM architecture with a forecasting error threshold to obtain precision and recall quality metrics. The dependency of the quality metric on the threshold level is considered. An appropriate mechanism such as "one handle" was introduced for filtering faults that are outside of the plant operator field of interest.


ON PREDICTION AND THE POWER TRANSPORMATION FAMILY


title: A Compact Frequency Reconfigurable Rectenna for 5.2- and 5.8-GHz Wireless Power Transmission

abstract: A compact reconfigurable rectifying antenna (rectenna) has been proposed for 5.2- and 5.8- GHz microwave power transmission. The proposed rectenna consists of a frequency reconfigurable microstrip antenna and a frequency reconfigurable rectifying circuit. Here, the use of the odd-symmetry mode has significantly cut down the antenna size by half. By controlling the switches installed in the antenna and the rectifying circuit, the rectenna is able to switch operation between 5.2 and 5.8 GHz. Simulated conversion efficiencies of 70.5% and 69.4% are achievable at the operating frequencies of 5.2 and 5.8 GHz, respectively, when the rectenna is given with an input power of 16.5 dBm. Experiment has been conducted to verify the design idea. Due to fabrication tolerances and parametric deviation of the actual diode, the resonant frequencies of the rectenna are measured to be 4.9 and 5.9 GHz. When supplied with input powers of 16 and 15 dBm, the measured maximum conversion efficiencies of the proposed rectenna are found to be 65.2% and 64.8% at 4.9 and 5.9 GHz, respectively, which are higher than its contemporary counterparts.


title: Locality Exists in Graph Processing: Workload Characterization on an Ivy Bridge Server

abstract: Graph processing is an increasingly important application domain and is typically communication-bound. In this work, we analyze the performance characteristics of three high-performance graph algorithm codebases using hardware performance counters on a conventional dual-socket server. Unlike many other communication-bound workloads, graph algorithms struggle to fully utilize the platform's memory bandwidth and so increasing memory bandwidth utilization could be just as effective as decreasing communication. Based on our observations of simultaneous low compute and bandwidth utilization, we find there is substantial room for a different processor architecture to improve performance without requiring a new memory system.


title: Dynamic Data-Driven Estimation of Non-Parametric Choice Models

abstract: We study non-parametric estimation of consumer choice models. Non-parametric approaches were introduced to alleviate unreasonable assumptions and issues of suboptimal model fit/selection present in traditional parametric approaches, and are prevalent in several application areas. We present two convex optimization-based frameworks, a primal approach and a dual approach, to efficiently learn a non-parametric choice model from data that is close to the best-fitting one. As opposed to the existing literature, both approaches enjoy provable convergence guarantees and extend naturally to the dynamic observation setting. Our computational study on the dynamic setting reveals the true impact of how much data are needed and at what rate to achieve the best trade-off in terms of estimation accuracy and model simplicity. In the static setup, we also compare our non-parametric approach with existing parametric approaches.


Microsoft Word - 01 Sensors and Signals Introduction.doc


title: Discovering temporal regularities in retail customers’ shopping behavior

abstract: In this paper we investigate the regularities characterizing the temporal purchasing behavior of the customers of a retail market chain. Most of the literature studying purchasing behavior focuses on what customers buy while giving few importance to the temporal dimension. As a consequence, the state of the art does not allow capturing which are the temporal purchasing patterns of each customers. These patterns should describe the customer’s temporal habits highlighting when she typically makes a purchase in correlation with information about the amount of expenditure, number of purchased items and other similar aggregates. This knowledge could be exploited for different scopes: set temporal discounts for making the purchases of customers more regular with respect the time, set personalized discounts in the day and time window preferred by the customer, provide recommendations for shopping time schedule, etc. To this aim, we introduce a framework for extracting from personal retail data a temporal purchasing profile able to summarize whether and when a customer makes her distinctive purchases. The individual profile describes a set of regular and characterizing shopping behavioral patterns, and the sequences in which these patterns take place. We show how to compare different customers by providing a collective perspective to their individual profiles, and how to group the customers with respect to these comparable profiles. By analyzing real datasets containing millions of shopping sessions we found that there is a limited number of patterns summarizing the temporal purchasing behavior of all the customers, and that they are sequentially followed in a finite number of ways. Moreover, we recognized regular customers characterized by a small number of temporal purchasing behaviors, and changing customers characterized by various types of temporal purchasing behaviors. Finally, we discuss on how the profiles can be exploited both by customers to enable personalized services, and by the retail market chain for providing tailored discounts based on temporal purchasing regularity.


title: From preprocessor-constrained parse graphs to preprocessor-constrained control flow

abstract: Preprocessor-aware static analysis tools are needed for C Code to gain sound knowledge about the interference among all conditionally compiled program parts. We provide formal descriptions and algorithms to construct a preprocessor-aware control flow graph from preprocessor-aware parse graphs of SuperC. Based on the structure of parse graphs capturing the syntax nodes constrained by preprocessor constraints, we show how to model, formalize, and compute preprocessor-aware intra-procedural control-flow graphs. Such preprocessor-aware control-flow graphs may serve as the basis for subsequent preprocessor-aware control and data flow analyses.


title: Image quality measures and their performance

abstract: A number of quality measures are evaluated for gray scale image compression. They are all bivariate, exploiting the differences between corresponding pixels in the original and degraded images. It is shown that although some numerical measures correlate well with the observers' response for a given compression technique, they are not reliable for an evaluation across different techniques. A graphical measure called Hosaka plots, however, can be used to appropriately specify not only the amount, but also the type of degradation in reconstructed images.


title: Differences of English and Chinese as Written Languages and Strategies in English Writing Teaching

abstract: The size of both the United States and China guarantees that many people will communicate in English and Chinese. But those two common languages do not guarantee perfect communication in writing practice for several reasons. This paper explores the factors resulting in non-understandable communication of English and Chinese as written language, and some strategies applied by English teachers when training students’ English writing.


title: Internal model based PID control of shell and tube heat exchanger system

abstract: Shell and tube heat exchanger system is widely used in chemical plants because it can sustain wide range of temperature and pressure. The main purpose of a heat exchanger system is to transfer heat from a hot fluid to a cooler fluid, so temperature control of outlet fluid is of prime importance. To control the temperature of outlet fluid of the shell and tube heat exchanger system a conventional PID controller can be used, for better disturbance rejection and more optimal control a feed forward controller can also be employed. Due to inherent disadvantages of conventional control techniques, model based control technique is employed and an internal model based PID controller is developed to control the temperature of outlet fluid of the shell and tube heat exchanger system. The designed controller regulates the temperature of the outgoing fluid to a desired set point in the shortest possible time irrespective of load and process disturbances, equipment saturation and nonlinearity. The internal model based PID controller provides a satisfactory performance in both steady state and transient state. The developed internal model based PID controller has demonstrated 84% improvement in the overshoot and 44.6% improvement in settling time as compared to the classical controller.


title: Axial flux segmental rotor flux-switching synchronous motor

abstract: A novel three-phase, concentrated winding, axial flux, segmental rotor, flux-switching synchronous motor is presented. The proposed topology has rotor segments made of only iron laminations and operates with flux-switching principle. Compared to existing axial flux permanent magnet (AFPM) topologies, the proposed machine contains reduced amount of permanent magnets located only in its stator. This approach makes the assembly easier and reduce the cost compared to conventional AFPM. Moreover, the proposed topologies have higher torque density and torque-to-weight ratio compared to reported axial flux SRMs (AFSRMs). Detailed design and operation of the proposed machine is presented; the performance of the machine is evaluated for an in-wheel traction application.


Improving retrieval performance by relevance feedback


title: Feature extraction and classification for EMG signals using linear discriminant analysis

abstract: Analysis of EMG signal has been an interested topic in recent years for classifying surface myoelectric signal patterns. Myoelectric control is an unconventional method to control the upper limb prostheses, human-assisting robots and rehabilitation devices. The aim of present work is to assess the time-domain features of EMG signal for myoelectric control of upper extremity prostheses by utilizing scatter plot. Classification accuracy is calculated using linear discriminant classifier for different combination of feature vectors using principal component analysis (PCA) and uncorrelated linear discriminant analysis (ULDA) feature reduction techniques. Results show that willison amplitude and waveform length are the best features for separating the distinct upper-limb motions.


title: Neuroinformatics of the Allen Mouse Brain Connectivity Atlas

abstract: The Allen Mouse Brain Connectivity Atlas is a mesoscale whole brain axonal projection atlas of the C57Bl/6J mouse brain. Anatomical trajectories throughout the brain were mapped into a common 3D space using a standardized platform to generate a comprehensive and quantitative database of inter-areal and cell-type-specific projections. This connectivity atlas has several desirable features, including brain-wide coverage, validated and versatile experimental techniques, a single standardized data format, a quantifiable and integrated neuroinformatics resource, and an open-access public online database (http://connectivity.brain-map.org/). Meaningful informatics data quantification and comparison is key to effective use and interpretation of connectome data. This relies on successful definition of a high fidelity atlas template and framework, mapping precision of raw data sets into the 3D reference framework, accurate signal detection and quantitative connection strength algorithms, and effective presentation in an integrated online application. Here we describe key informatics pipeline steps in the creation of the Allen Mouse Brain Connectivity Atlas and include basic application use cases.


title: Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes

abstract: Background:Application of novel machine learning approaches to electronic health record (EHR) data could provide valuable insights into disease processes. We utilized this approach to build predictive models for progression to prediabetes and type 2 diabetes (T2D).Methods:Using a novel analytical platform (Reverse Engineering and Forward Simulation [REFS]), we built prediction model ensembles for progression to prediabetes or T2D from an aggregated EHR data sample. REFS relies on a Bayesian scoring algorithm to explore a wide model space, and outputs a distribution of risk estimates from an ensemble of prediction models. We retrospectively followed 24 331 adults for transitions to prediabetes or T2D, 2007-2012. Accuracy of prediction models was assessed using an area under the curve (AUC) statistic, and validated in an independent data set.Results:Our primary ensemble of models accurately predicted progression to T2D (AUC = 0.76), and was validated out of sample (AUC = 0.78). Models of progression to T2D ...


title: Automatic Paper Sliceform Design from 3D Solid Models

abstract: A paper sliceform or lattice-style pop-up is a form of papercraft that uses two sets of parallel paper patches slotted together to make a foldable structure. The structure can be folded flat, as well as fully opened (popped-up) to make the two sets of patches orthogonal to each other. Automatic design of paper sliceforms is still not supported by existing computational models and remains a challenge. We propose novel geometric formulations of valid paper sliceform designs that consider the stability, flat-foldability and physical realizability of the designs. Based on a set of sufficient construction conditions, we also present an automatic algorithm for generating valid sliceform designs that closely depict the given 3D solid models. By approximating the input models using a set of generalized cylinders, our method significantly reduces the search space for stable and flat-foldable sliceforms. To ensure the physical realizability of the designs, the algorithm automatically generates slots or slits on the patches such that no two cycles embedded in two different patches are interlocking each other. This guarantees local pairwise assembility between patches, which is empirically shown to lead to global assembility. Our method has been demonstrated on a number of example models, and the output designs have been successfully made into real paper sliceforms.


title: Design and Implementation of a Microgrid Energy Management System

abstract: A microgrid is characterized by the integration of distributed energy resources and controllable loads in a power distribution network. Such integration introduces new, unique challenges to microgrid management that have never been exposed to traditional power systems. To accommodate these challenges, it is necessary to redesign a conventional Energy Management System (EMS) so that it can cope with intrinsic characteristics of microgrids. While many projects have shown excellent research outcomes, they have either tackled portions of the characteristics or validated their EMSs only via simulations. This paper proposes a Microgrid Platform (MP), an advanced EMS for efficient microgrid operations. We design the MP by taking into consideration (i) all the functional requirements of a microgrid EMS (i.e., optimization, forecast, human–machine interface, and data analysis) and (ii) engineering challenges (i.e., interoperability, extensibility, and flexibility). Moreover, a prototype system is developed and deployed in two smart grid testbeds: UCLA Smart Grid Energy Research Center and Korea Institute of Energy Research. We then conduct experiments to verify the feasibility of the MP design in real-world settings. Our testbeds and experiments demonstrate that the MP is able to communicate with various energy devices and to perform an energy management task efficiently.


title: Modelling and control of a PVTOL quadrotor carrying a suspended load

abstract: This work proposes the representation of the high level dynamicmodel of a quadrotor UAVwith a suspended load, through Euler-Lagrange equations. The rotorcraft movement is restricted to the XZ plane of the Cartesian space, so that it performs like a PVTOL machine. A nonlinear controller is proposed to stabilize the quadrotor and the load, during positioning and trajectory tracking tasks. Finally, simulated results are presented in order to validate the proposal.


title: Short-term memory stores organized by information domain

abstract: Vision and audition have complementary affinities, with vision excelling in spatial resolution and audition excelling in temporal resolution. Here, we investigated the relationships among the visual and auditory modalities and spatial and temporal short-term memory (STM) using change detection tasks. We created short sequences of visual or auditory items, such that each item within a sequence arose at a unique spatial location at a unique time. On each trial, two successive sequences were presented; subjects attended to either space (the sequence of locations) or time (the sequence of inter item intervals) and reported whether the patterns of locations or intervals were identical. Each subject completed blocks of unimodal trials (both sequences presented in the same modality) and crossmodal trials (Sequence 1 visual, Sequence 2 auditory, or vice versa) for both spatial and temporal tasks. We found a strong interaction between modality and task: Spatial performance was best on unimodal visual trials, whereas temporal performance was best on unimodal auditory trials. The order of modalities on crossmodal trials also mattered, suggesting that perceptual fidelity at encoding is critical to STM. Critically, no cost was attributable to crossmodal comparison: In both tasks, performance on crossmodal trials was as good as or better than on the weaker unimodal trials. STM representations of space and time can guide change detection in either the visual or the auditory modality, suggesting that the temporal or spatial organization of STM may supersede sensory-specific organization.


title: Enhance emotional and social adaptation skills for children with autism spectrum disorder: A virtual reality enabled approach

abstract: Abstract Deficits in social-emotional reciprocity, one of the diagnostic criteria of Autism Spectrum Disorder (ASD), greatly hinders children with ASD from responding appropriately and adapting themselves in various social situations. Although evidences have shown that virtual reality environment is a promising tool for emotional and social adaptation skills training on ASD population, there is a lack of large-scale trials with intensive evaluations to support such findings. This paper presents a virtual reality enabled program for enhancing emotional and social adaptation skills for children with ASD. Six unique learning scenarios, of which one focuses on emotion control and relaxation strategies, four that simulate various social situations, and one that facilitates consolidation and generalization, are designed and developed with corresponding psychoeducation procedures and protocols. The learning scenarios are presented to the children via a 4-side immersive virtual reality environment (a.k.a., half-CAVE) with non-intrusive motion tracking. A total number of 94 children between the ages of 6–12 with clinical diagnosis of ASD participated in the 28-session program that lasted for 14 weeks. By comparing pre- and post-assessments, results reported in this paper show significant improvements in the project's primary measures on children's emotion expression and regulation and social-emotional reciprocity but not on other secondary measures.


title: ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

abstract: A key requirement for leveraging supervised deep learning methods is the availability of large, labeled datasets. Unfortunately, in the context of RGB-D scene understanding, very little data is available – current datasets cover a small range of scene views and have limited semantic annotations. To address this issue, we introduce ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations. To collect this data, we designed an easy-to-use and scalable RGB-D capture system that includes automated surface reconstruction and crowdsourced semantic annotation. We show that using this data helps achieve state-of-the-art performance on several 3D scene understanding tasks, including 3D object classification, semantic voxel labeling, and CAD model retrieval.


title: A Deep Learning Approach for the Prediction of Retail Store Sales

abstract: The purpose of this research is to construct a sales prediction model for retail stores using the deep learning approach, which has gained significant attention in the rapidly developing field of machine learning in recent years. Using such a model for analysis, an approach to store management could be formulated. The present study uses three years' worth of point-of-sale (POS) data from a retail store to construct a sales prediction model that, given the sales of a particular day, predicts the changes in sales on the following day. As a result, a deep learning model that considers the L1 regularization achieved a sale forecasting accuracy rate of 86%. The products at the retail store have been finely categorized. Even if the attributes of the product categories are increased in number from tens to thousands, the predictive accuracy did not fall by more than about 7%. In contrast, the accuracy decreased by around 13% when the logistic regression model was used. These results indicate that deep learning is highly suitable for constructing models that include multi-attribute variables. The present research demonstrates that deep learning is effective for analyzing the POS data of retail stores.


title: Design and Analysis of the Droop Control Method for Parallel Inverters Considering the Impact of the Complex Impedance on the Power Sharing

abstract: This paper investigates the characteristics of the active and reactive power sharing in a parallel inverters system under different system impedance conditions. The analyses conclude that the conventional droop method cannot achieve efficient power sharing for the case of a system with complex impedance condition. To achieve the proper power balance and minimize the circulating current in the different impedance situations, a novel droop controller that considers the impact of complex impedance is proposed in this paper. This controller can simplify the coupled active and reactive power relationships, which are caused by the complex impedance in the parallel system. In addition, a virtual complex impedance loop is included in the proposed controller to minimize the fundamental and harmonic circulating current that flows in the parallel system. Compared to the other methods, the proposed controller can achieve accurate power sharing, offers efficient dynamic performance, and is more adaptive to different line impedance situations. Simulation and experimental results are presented to prove the validity and the improvements achieved by the proposed controller.


title: Detecting Gang-Involved Escalation on Social Media Using Context

abstract: Gang-involved youth in cities such as Chicago have increasingly turned to social media to post about their experiences and intents online. In some situations, when they experience the loss of a loved one, their online expression of emotion may evolve into aggression towards rival gangs and ultimately into real-world violence. In this paper, we present a novel system for detecting Aggression and Loss in social media. Our system features the use of domain-specific resources automatically derived from a large unlabeled corpus, and contextual representations of the emotional and semantic content of the user's recent tweets as well as their interactions with other users. Incorporating context in our Convolutional Neural Network (CNN) leads to a significant improvement.


title: Multi-Objective Decision Making

abstract: Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. To illustrate this, we employ the popular problem classes of multi-objective Markov decision processes (MOMDPs) and multi-objective coordination graphs (MO-CoGs). First, we discuss different use cases for multi-objective decision making, and why they often necessitate explicitly multi-objective algorithms. We advocate a utility-based approach to multi-objective decision making, i.e., that what constitutes an optimal solution to a multi-objective decision problem should be derived from the available information about user utility. We show how different assumptions about user utility and what types of policies are allowed lead to different solution concepts, which we outline in a taxonomy of multi-objective decision problems. Second, we show how to create new methods for multi-objective decision making using existing single-objective methods as a basis. Focusing on planning, we describe two ways to creating multi-objective algorithms: in the inner loop approach, the inner workings of a single-objective method are adapted to work with multi-objective solution concepts; in the outer loop approach, a wrapper is created around a single-objective method that solves the multi-objective problem as a series of single-objective problems. After discussing the creation of such methods for the planning setting, we discuss how these approaches apply to the learning setting. Next, we discuss three promising application domains for multi-objective decision making algorithms: energy, health, and infrastructure and transportation. Finally, we conclude by outlining important open problems and promising future directions.


title: Implementation of PID controller and pre-filter to control non-linear ball and plate system

abstract: In this paper, the authors try to make PID controller with Pre-filter that is implemented at ball and plate system. Ball and plate system will control the position of ball's axis in pixels value by using servo motor as its actuator and webcam as its sensor of position. PID controller with Pre-filter will have a better response than conventional PID controller. Eventhough the response of PID with Pre-filter is slower than conventional PID, the effect of Pre-filter in the system will give the less overshoot response.


title: GCN: GPU-Based Cube CNN Framework for Hyperspectral Image Classification

abstract: Hyperspectral image classification has been proved significant in remote sensing field. Traditional classification methods have meet bottlenecks due to the lack of remote sensing background knowledge or high dimensionality. Deep learning based methods, such as deep convolutional neural network (CNN), can effectively extract high level features from raw data. But the training of deep CNN is rather time-consuming. The general purpose graphic processing units (GPUs) have been considered as one of the most common co-processors that can help accelerate deep learning applications. In this paper we propose a GPU-based Cube CNN (GCN) framework for hyperspectral image classification. First, a Parallel Neighbor Pixels Extraction (PNPE) algorithm is designed to enable the framework directly loading raw hyperspectral image into GPU's global memory, and extracting samples into data cube. Then, based on the peculiarity of hyperspectral image and cube convolution, we propose a novel Cube CNN-to-GPU mapping mechanism that transfers the training of Cube CNN to GPU effectively. Finally, the mini-batch gradient descent(MBGD) algorithm is improved with Computing United Device Architecture(CUDA) multi-streaming technique, which further speeds up network training in GCN framework. Experiments on KSC dataset, PU dataset and SA dataset show that, compared with state-of-art framework Caffe, we achieve up to 83% and 67% reduction in network training time without losing accuracy, when using SGD (Stochastic Gradient Descent) and MBGD algorithm respectively. Experiments across different GPUs show the same performance trend, which demonstrates the good extendibility of GCN framework.


title: Improving introductory programming with Scratch?

abstract: Programming is known for its complexity and difficulty. It is believed to be hard to teach and to learn and many pupils and students in programming courses have difficulties to master all required competencies and skills. At introductory level of programming that problem is even more notable. There are different approaches to improve the dropout rate at programming courses and to populate programming. Recently, special visual environments for visual programming are being used. Early research results show that visual programming can be more efficient than classical textual programming, students can be more motivated, less bored and not burden with the syntax of programming languages. This paper discusses the visual programming environment Scratch as one of the newer environments for teaching and learning introductory programming. Although it is primarily aimed for school children aged 8 to 16, it can be used also at faculty level. Scratch was experimentally used in primary school for introductory programming, the results of which are presented in the paper. The emphasis of presented results is on retention of opinion and attitude after some time passed from formal using of Scratch. Results are complemented with the results from the same survey on students that also used Scratch.


title: KNN Matting

abstract: This paper proposes to apply the nonlocal principle to general alpha matting for the simultaneous extraction of multiple image layers; each layer may have disjoint as well as coherent segments typical of foreground mattes in natural image matting. The estimated alphas also satisfy the summation constraint. As in nonlocal matting, our approach does not assume the local color-line model and does not require sophisticated sampling or learning strategies. On the other hand, our matting method generalizes well to any color or feature space in any dimension, any number of alphas and layers at a pixel beyond two, and comes with an arguably simpler implementation, which we have made publicly available. Our matting technique, aptly called KNN matting, capitalizes on the nonlocal principle by using K nearest neighbors (KNN) in matching nonlocal neighborhoods, and contributes a simple and fast algorithm that produces competitive results with sparse user markups. KNN matting has a closed-form solution that can leverage the preconditioned conjugate gradient method to produce an efficient implementation. Experimental evaluation on benchmark datasets indicates that our matting results are comparable to or of higher quality than state-of-the-art methods requiring more involved implementation. In this paper, we take the nonlocal principle beyond alpha estimation and extract overlapping image layers using the same Laplacian framework. Given the alpha value, our closed form solution can be elegantly generalized to solve the multilayer extraction problem. We perform qualitative and quantitative comparisons to demonstrate the accuracy of the extracted image layers.


title: Experiments in Graph-Based Semi-Supervised Learning Methods for Class-Instance Acquisition

abstract: Graph-based semi-supervised learning (SSL) algorithms have been successfully used to extract class-instance pairs from large unstructured and structured text collections. However, a careful comparison of different graph-based SSL algorithms on that task has been lacking. We compare three graph-based SSL algorithms for class-instance acquisition on a variety of graphs constructed from different domains. We find that the recently proposed MAD algorithm is the most effective. We also show that class-instance extraction can be significantly improved by adding semantic information in the form of instance-attribute edges derived from an independently developed knowledge base. All of our code and data will be made publicly available to encourage reproducible research in this area.


title: [Teaching medical humanities through an illness narrative].

abstract: There has been growing interest regarding the 'medical humanities' in most medical schools in Korea. Medical humanities is an interdisciplinary field of humanities, social science, and the arts that aims to have a critical or supplementary role in medical education and practice. Thus, diverse educational methods should be applied to achieve the goals of medical humanities. The illness narrative is one of the most powerful tools in this context. An illness narrative is a patient's story about his illness, including the meaning of the illness in his life. The illness narrative is widely accepted as an effective educational tool in medical humanities. But, in Korea, there has been concern about the nature, theoretical background, and usefulness of the illness narrative. Medical students and doctors can obtain empathy and clinical wisdom through telling, hearing, reading, and writing illness narratives. In this article, I will examine the nature and meaning of illness narratives in teaching medical humanities and discuss several examples of narrative training programs.


title: Adaptive motion artefact reduction in respiration and ECG signals for wearable healthcare monitoring systems

abstract: Wearable healthcare monitoring systems (WHMSs) have received significant interest from both academia and industry with the advantage of non-intrusive and ambulatory monitoring. The aim of this paper is to investigate the use of an adaptive filter to reduce motion artefact (MA) in physiological signals acquired by WHMSs. In our study, a WHMS is used to acquire ECG, respiration and triaxial accelerometer (ACC) signals during incremental treadmill and cycle ergometry exercises. With these signals, performances of adaptive MA cancellation are evaluated in both respiration and ECG signals. To achieve effective and robust MA cancellation, three axial outputs of the ACC are employed to estimate the MA by a bank of gradient adaptive Laguerre lattice (GALL) filter, and the outputs of the GALL filters are further combined with time-varying weights determined by a Kalman filter. The results show that for the respiratory signals, MA component can be reduced and signal quality can be improved effectively (the power ratio between the MA-corrupted respiratory signal and the adaptive filtered signal was 1.31 in running condition, and the corresponding signal quality was improved from 0.77 to 0.96). Combination of the GALL and Kalman filters can achieve robust MA cancellation without supervised selection of the reference axis from the ACC. For ECG, the MA component can also be reduced by adaptive filtering. The signal quality, however, could not be improved substantially just by the adaptive filter with the ACC outputs as the reference signals.


title: Toward controlling a KUKA LBR IIWA for interactive tracking

abstract: In this paper we use KUKA's Fast Robot Interface (FRI) to design and implement a tracking controller on the Lightweight Robot (LBR) IIWA. We seek low latency, accurate and smooth tracking of the link positions to facilitate human interaction tasks. Focusing on a single joint and its low-level series elastic dynamics, we identify the internal torque control structure and its characteristics. Tracking controllers of varying complexity are tested in an optical motion capture system to provide an independent external reference measurement. Using full state feedback of both motor position and sensed joint torque, we achieve smooth and good tracking of the unsensed link positions.


title: The functional role of cross-frequency coupling

abstract: Recent studies suggest that cross-frequency coupling (CFC) might play a functional role in neuronal computation, communication and learning. In particular, the strength of phase-amplitude CFC differs across brain areas in a task-relevant manner, changes quickly in response to sensory, motor and cognitive events, and correlates with performance in learning tasks. Importantly, whereas high-frequency brain activity reflects local domains of cortical processing, low-frequency brain rhythms are dynamically entrained across distributed brain regions by both external sensory input and internal cognitive events. CFC might thus serve as a mechanism to transfer information from large-scale brain networks operating at behavioral timescales to the fast, local cortical processing required for effective computation and synaptic modification, thus integrating functional systems across multiple spatiotemporal scales.


title: Scalable Probabilistic Databases with Factor Graphs and MCMC

abstract: Incorporating probabilities into the semantics of incomplete databases has posed many challenges, forcing systems to sacrifice modeling power, scalability, or treatment of relational algebra operators. We propose an alternative approach where the underlying relational database always represents a single world, and an external factor graph encodes a distribution over possible worlds; Markov chain Monte Carlo (MCMC) inference is then used to recover this uncertainty to a desired level of fidelity. Our approach allows the efficient evaluation of arbitrary queries over probabilistic databases with arbitrary dependencies expressed by graphical models with structure that changes during inference. MCMC sampling provides efficiency by hypothesizing modifications to possible worlds rather than generating entire worlds from scratch. Queries are then run over the portions of the world that change, avoiding the onerous cost of running full queries over each sampled world. A significant innovation of this work is the connection between MCMC sampling and materialized view maintenance techniques: we find empirically that using view maintenance techniques is several orders of magnitude faster than naively querying each sampled world. We also demonstrate our system's ability to answer relational queries with aggregation, and demonstrate additional scalability through the use of parallelization on a real-world complex model of information extraction. This framework is sufficiently expressive to support probabilistic inference not only for answering queries, but also for inferring missing database content from raw evidence.


Weakly Supervised Tweet Stance Classification by Relational Bootstrapping


title: Large-scale supervised multimodal hashing with semantic correlation maximization

abstract: Due to its low storage cost and fast query speed, hashing has been widely adopted for similarity search in multimedia data. In particular, more and more attentions have been payed to multimodal hashing for search in multimedia data with multiple modalities, such as images with tags. Typically, supervised information of semantic labels is also available for the data points in many real applications. Hence, many supervised multimodal hashing (SMH) methods have been proposed to utilize such semantic labels to further improve the search accuracy. However, the training time complexity of most existing SMH methods is too high, which makes them unscalable to large-scale datasets. In this paper, a novel SMH method, called semantic correlation maximization (SCM), is proposed to seamlessly integrate semantic labels into the hashing learning procedure for large-scale data modeling. Experimental results on two real-world datasets show that SCM can significantly outperform the state-of-the-art SMH methods, in terms of both accuracy and scalability.