title: Relationships in IVF couples 20 years after treatment

abstract: background: Involuntary childlessness is a psychological and social dilemma for at least one but usually both members of the childless couples and is thought to have a lifelong impact on many of these couples. Studies of the long-term effects are scarce and the participation rate in published studies is generally very low. We therefore intended to analyse relationships and family structure in couples 20–23 years after the women in these couples had received IVF treatment. methods: The data are drawn from answers from 788 individuals—81% of the individuals treated. ENRICH has been used to analyse the relationships in those couples who stated that they have remained a couple since their IVF treatment. In total, 412 men and women (206 couples) answered the ENRICH inventory. A total of 14 men and 137 women answered the ENRICH, without their spouse answering the inventory. results: We found that the majority of all couples show a stable relationship 20 years or more after the date of IVF treatment. However, the group of couples who remained childless (9.2% of the total study population) during the 20 years following the IVF treatment differ from the majority on the subscale ‘Children and Parent’ measuring aspects on attitudes and feelings about having and raising children. Another difference seen in the couples that were childless was that men scored significantly higher on ‘Conflict resolution’ and the couples had a higher average score on positive agreement on the issues on ‘Communication’ indicating a skill in communication in the relationship and also an agreement that they are communicating well. conclusions: We found that the majority of IVF couples (90.8%) who had been treated � 20 years prior to follow-up had added at least one biological or adopted child to the family during that time. The relationships in couples who had continued to stay together during that period were generally described as being good, whether the couples had become parents or not.


title: Smart City Pilot Projects Using LoRa and IEEE802.15.4 Technologies

abstract: Information and Communication Technologies (ICTs), through wireless communications and the Internet of Things (IoT) paradigm, are the enabling keys for transforming traditional cities into smart cities, since they provide the core infrastructure behind public utilities and services. However, to be effective, IoT-based services could require different technologies and network topologies, even when addressing the same urban scenario. In this paper, we highlight this aspect and present two smart city testbeds developed in Italy. The first one concerns a smart infrastructure for public lighting and relies on a heterogeneous network using the IEEE 802.15.4 short-range communication technology, whereas the second one addresses smart-building applications and is based on the LoRa low-rate, long-range communication technology. The smart lighting scenario is discussed providing the technical details and the economic benefits of a large-scale (around 3000 light poles) flexible and modular implementation of a public lighting infrastructure, while the smart-building testbed is investigated, through measurement campaigns and simulations, assessing the coverage and the performance of the LoRa technology in a real urban scenario. Results show that a proper parameter setting is needed to cover large urban areas while maintaining the airtime sufficiently low to keep packet losses at satisfactory levels.


title: Predicting direction of stock price index movement using artificial neural networks and support vector machines

abstract: Prediction of stock price index movement is regarded as a challenging task of financial time series prediction. An accurate prediction of stock price movement may yield profits for investors. Due to the complexity of stock market data, development of efficient models for predicting is very difficult. This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index. The models are based on two classification techniques, artificial neural networks (ANN) and support vector machines (SVM). Ten technical indicators were selected as inputs of the proposed models. Two comprehensive parameter setting experiments for both models were performed to improve their prediction performances. Experimental results showed that average performance of ANN model (75.74%) was found significantly better than that of SVM model (71.52%).


title: Game theory based mitigation of Interest flooding in Named Data Network

abstract: The Distributed Denial of Service (DDoS) attacks are a serious threat in today's Internet where packets from large number of compromised hosts block the path to the victim nodes and overload the victim servers. In the newly proposed future Internet Architecture, Named Data Networking (NDN), the architecture itself has prevention measures to reduce the overload to the servers. This on the other hand increases the work and security threats to the intermediate routers. Our project aims at identifying the DDoS attack in NDN which is known as Interest flooding attack, mitigate the consequence of it and provide service to the legitimate users. We have developed a game model for the DDoS attacks and provide possible countermeasures to stop the flooding of interests. Through this game theory model, we either forward or redirect or drop the incoming interest packets thereby reducing the PIT table consumption. This helps in identifying the nodes that send malicious interest packets and eradicate their actions of sending malicious interests further. The main highlight of this work is that we have implemented the Game Theory model in the NDN architecture. It was primarily imposed for the IP internet architecture.


Gender Oppression in Cinematic Content? A Look at Females On-Screen & Behind-the-Camera in Top-Grossing 2007 Films


CACTI-IO Technical Report


title: 29 From Programming to Modeling: Evolving the Contents of a Distributed Software Engineering Course

abstract: Distributed Software Engineering (DSE) concepts in Computer Science (or Engineering) Degrees are commonly introduced using a hands-on approach mainly consisting in teaching a distributed and component-based technology platform (as Java Enterprise Edition or Microsoft .NET) and proposing the students to develop a small distributed software application with it. Though this approach provides some relevant practical knowledge to the students, we believe that it is not the most appropriate one to teach all the specificities of DSE. Thus, in this paper we report on our experience with the redesign of the contents of an initial DSE course following an MDAbased approach. By raising the level of abstraction we gained modularity, separation of concerns and technology independence, while making the course evolve according to the latest trends in software development methods. Our experience was not free from problems but in general the initiative has been positively evaluated and welcomed by the students.


White-Box Cryptography


title: A new approach of a memory efficient huffman tree representation technique

abstract: Huffman compression is a very efficient and effective technique in the field of data compression. It can be more efficient by reducing the memory requirements for Huffman tree. This paper focuses on reducing the size of the tree of Huffman coding and also presents a memory efficient technique to store the Huffman tree where in addition to storing symbols, extra bits are stored to represent tree structure resulting total memory requirements for Huffman tree which are ⌞10.75n−3⌟ bits at worst case and 9n bits at best case where n is the number of symbols. The proposed tree representation technique would be prodigiously efficient among all existing techniques not only for its less memory utilization but also removing crucial limitations of previous works. The proposed memory efficient technique of Huffman tree uses some new terms such as “circular leaf node”, “node with 2 internal son nodes”, and also given some new conception of “upper leaf node” , “external limb”, “internal limb”, and “antenna limb”.


title: Microbiota-Brain-Gut Axis and Neurodegenerative Diseases

abstract: PURPOSE OF REVIEW ::: The purposes of this review were as follows: first, to provide an overview of the gut microbiota and its interactions with the gut and the central nervous system (the microbiota-gut-brain axis) in health, second, to review the relevance of this axis to the pathogenesis of neurodegenerative diseases, such as Parkinson's disease, and, finally, to assess the potential for microbiota-targeted therapies. ::: ::: ::: RECENT FINDINGS ::: Work on animal models has established the microbiota-gut-brain axis as a real phenomenon; to date, the evidence for its operation in man has been limited and has been confronted by considerable logistical challenges. Animal and translational models have incriminated a disturbed gut microbiota in a number of CNS disorders, including Parkinson's disease; data from human studies is scanty. While a theoretical basis can be developed for the use of microbiota-directed therapies in neurodegenerative disorders, support is yet to come from high-quality clinical trials. In theory, a role for the microbiota-gut-brain axis is highly plausible; clinical confirmation is awaited.


title: Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.

abstract: Abstract Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection.


title: Real-time self-localization from panoramic images on mobile devices

abstract: Self-localization in large environments is a vital task for accurately registered information visualization in outdoor Augmented Reality (AR) applications. In this work, we present a system for self-localization on mobile phones using a GPS prior and an online-generated panoramic view of the user's environment. The approach is suitable for executing entirely on current generation mobile devices, such as smartphones. Parallel execution of online incremental panorama generation and accurate 6DOF pose estimation using 3D point reconstructions allows for real-time self-localization and registration in large-scale environments. The power of our approach is demonstrated in several experimental evaluations.


title: An encryption-based access control framework for content-centric networking

abstract: This paper proposes a comprehensive encryption-based access control framework for content centric networking (CCN), called CCN-AC. This framework is both flexible and extensible, enabling the specification, implementation, and enforcement of a variety of access control policies for sensitive content in the network. The design of CCN-AC heavily relies on the concept of secure content object manifests and leverages them to decouple encrypted content from access policy and specifications for minimum communication overhead and maximum utilization of in-network caches. To demonstrate the flexibility of framework, we also describe how to implement two sample access control schemes, group-based access control and broadcast access control, within CCN-AC framework.


Determinants of Lighting Quality II: Research and Recommendations


title: An Impact Analysis of Features in a Classification Approach to Irony Detection in Product Reviews

abstract: Irony is an important device in human communication, both in everyday spoken conversations as well as in written texts including books, websites, chats, reviews, and Twitter messages among others. Specific cases of irony and sarcasm have been studied in different contexts but, to the best of our knowledge, only recently the first publicly available corpus including annotations about whether a text is ironic or not has been published by Filatova (2012). However, no baseline for classification of ironic or sarcastic reviews has been provided. With this paper, we aim at closing this gap. We formulate the problem as a supervised classification task and evaluate different classifiers, reaching an F1-measure of up to 74 % using logistic regression. We analyze the impact of a number of features which have been proposed in previous research as well as combinations of them.


Biometric cryptosystems: authentication, encryption and signature for biometric identities


title: Inferencing in information extraction: Techniques and applications

abstract: Information extraction at Web scale has become one of the most important research topics in data management since major commercial search engines started incorporating knowledge in their search results a couple of years ago [1]. Users increasingly expect structured knowledge as answers to their search needs. Using Bing as an example, the result page for “Lionel Messi” is full of structured knowledge facts, such as his birthday and awards. The research efforts towards improving the accuracy and coverage of such knowledge bases have led to significant advances in Information Extraction techniques [2], [3]. As the initial challenge of accurately extracting facts for popular entities are being addressed, more difficult challenges have emerged such as extending knowledge coverage to long tail entities and domains, understanding interestingness and usefulness of facts within a given context, and addressing information-seeking needs more directly and accurately. In this tutorial, we will survey the recent research efforts and provide an introduction to the techniques that address those challenges, and the applications that benefit from the adoption of those techniques. In particular, this tutorial will focus on a variety of techniques that can be broadly viewed as knowledge inferencing, i.e., combining multiple data sources and extraction techniques to verify existing knowledge and derive new knowledge. More specifically, we focus on four main categories of inferencing techniques: 1) deep natural language processing using machine learning techniques, 2) data cleaning using integrity constraints, 3) large-scale probabilistic reasoning, and 4) leveraging human expertise for domain knowledge extraction.


title: A SURVEY OF TEXT SUMMARIZATION TECHNIQUES

abstract: Numerous approaches for identifying important content for automatic text summarization have been developed to date. Topic representation approaches first derive an intermediate representation of the text that captures the topics discussed in the input. Based on these representations of topics, sentences in the input document are scored for importance. In contrast, in indicator representation approaches, the text is represented by a diverse set of possible indicators of importance which do not aim at discovering topicality. These indicators are combined, very often using machine learning techniques, to score the importance of each sentence. Finally, a summary is produced by selecting sentences in a greedy approach, choosing the sentences that will go in the summary one by one, or globally optimizing the selection, choosing the best set of sentences to form a summary. In this chapter we give a broad overview of existing approaches based on these distinctions, with particular attention on how representation, sentence scoring or summary selection strategies alter the overall performance of the summarizer. We also point out some of the peculiarities of the task of summarization which have posed challenges to machine learning approaches for the problem, and some of the suggested solutions.


title: Earliest deadline scheduling for real-time database systems

abstract: A new priority assignment algorithm called adaptive earliest deadline (AED) is given that stabilizes the overload performance of the earliest deadline policy in a real-time database system (RTDBS) environment. The AED algorithm uses a feedback control mechanism to achieve this objective and does not require knowledge of transaction characteristics. Using a detailed simulation model, the authors compare the performance of AED with respect to earliest deadline and other fixed priority schemes. They also present and evaluate an extension of the AED algorithm called hierarchical earliest deadline (HED), which is designed to handle applications that assign different values to transactions and where the goal is to maximize the total value of the in-time transactions. >


title: LCL-T Resonant Converter With Clamp Diodes: A Novel Constant-Current Power Supply With Inherent Constant-Voltage Limit

abstract: The LCL-T resonant converter behaves as a constant-current (CC) source when operated at the resonant frequency. The output voltage of a CC power supply increases linearly with the load resistance. Therefore, a constant-voltage (CV) limit must be incorporated in the converter for its use in practical applications wherein the open-load condition is commonly experienced by a CC power supply, such as in an arc welding power supply. A novel LCL-T resonant converter with clamp diodes is proposed in this paper, which has built-in CC-CV characteristics. Since the CC-CV characteristics are inherent to the converter, and complex feedback control is not required, the proposed converter is rugged and reliable. The principle of operation of the converter is explained. Experimental results on a 500-W prototype are presented to demonstrate the inherent CC-CV behavior of the converter. Simple extensions of the topology featuring variable CV limits are described


title: Linear and Deformable Image Registration with 3D Convolutional Neural Networks

abstract: Image registration and in particular deformable registration methods are pillars of medical imaging. Inspired by the recent advances in deep learning, we propose in this paper, a novel convolutional neural network architecture that couples linear and deformable registration within a unified architecture endowed with near real-time performance. Our framework is modular with respect to the global transformation component, as well as with respect to the similarity function while it guarantees smooth displacement fields. We evaluate the performance of our network on the challenging problem of MRI lung registration, and demonstrate superior performance with respect to state of the art elastic registration methods. The proposed deformation (between inspiration&expiration) was considered within a clinically relevant task of interstitial lung disease (ILD) classification and showed promising results.


title: A Unified Batch Online Learning Framework for Click Prediction

abstract: We present a unified framework for Batch Online Learning (OL) for Click Prediction in Search Advertisement. Machine Learning models once deployed, show non-trivial accuracy and calibration degradation over time due to model staleness. It is therefore necessary to regularly update models, and do so automatically. This paper presents two paradigms of Batch Online Learning, one which incrementally updates the model parameters via an early stopping mechanism, and another which does so through a proximal regularization. We argue how both these schemes naturally trade-off between old and new data. We then theoretically and empirically show that these two seemingly different schemes are closely related. Through extensive experiments, we demonstrate the utility of of our OL framework; how the two OL schemes relate to each other and how they trade-off between the new and historical data. We then compare batch OL to full model retrains, and show how online learning is more robust to data issues. We also demonstrate the long term impact of Online Learning, the role of the initial Models in OL, the impact of delays in the update, and finally conclude with some implementation details and challenges in deploying a real world online learning system in production. While this paper mostly focuses on application of click prediction for search advertisement, we hope that the lessons learned here can be carried over to other problem domains.


title: Dynamic Obstacle Avoidance for an Omnidirectional Mobile Robot

abstract: We have established a novel method of obstacle-avoidance motion planning for mobile robots in dynamic environments, wherein the obstacles are moving with general velocities and accelerations, and their motion profiles are not preknown. A hybrid system is presented in which a global deliberate approach is applied to determine the motion in the desired path line (DPL), and a local reactive approach is used for moving obstacle avoidance. A machine vision system is required to sense obstacle motion. Through theoretical analysis, simulation, and experimental validation applied to the Ohio University RoboCup robot, we show the method is effective to avoid collisions with moving obstacles in a dynamic environment.


title: Keyphrase extraction in scientific publications

abstract: We present a keyphrase extraction algorithm for scientific publications. Different from previous work, we introduce features that capture the positions of phrases in document with respect to logical sections found in scientific discourse. We also introduce features that capture salient morphological phenomena found in scientific keyphrases, such as whether a candidate keyphrase is an acronyms or uses specific terminologically productive suffixes. We have implemented these features on top of a baseline feature set used by Kea [1]. In our evaluation using a corpus of 120 scientific publications multiply annotated for keyphrases, our system significantly outperformed Kea at the p < .05 level. As we know of no other existing multiply annotated keyphrase document collections, we have also made our evaluation corpus publicly available. We hope that this contribution will spur future comparative research.


title: Multiple-input, multiple-output system identification for characterization of limb stiffness dynamics

abstract: This study presents time-domain and frequency-domain, multiple-input, multiple-output (MIMO) linear system identification techniques that can be used to estimate the dynamic endpoint stiffness of a multijoint limb. The stiffness of a joint or limb arises from a number of physiological mechanisms and is thought to play a fundamental role in the control of posture and movement. Estimates of endpoint stiffness can therefore be used to characterize its modulation during physiological tasks and may provide insight into how the nervous system normally controls motor behavior. Previous MIMO stiffness estimates have focused upon the static stiffness components only or assumed simple parametric models with elastic, viscous, and inertial components. The method presented here captures the full stiffness dynamics during a relatively short experimental trial while assuming only that the system is linear for small perturbations. Simulation studies were performed to investigate the performance of this approach under typical experimental conditions. It was found that a linear MIMO description of endpoint stiffness dynamics was sufficient to describe the displacement responses to small stochastic force perturbations. Distortion of these linear estimates by nonlinear centripetal and Coriolis forces was virtually undetectable for these perturbations. The system identification techniques were also found to be robust in the presence of significant output measurement noise and input coupling. These results indicate that the approach described here will allow the estimation of endpoint stiffness dynamics in an experimentally efficient manner with minimal assumptions about the specific form of these properties.


title: Investigating the determinants and age and gender differences in the acceptance of mobile learning

abstract: With the proliferation of mobile computing technology, mobile learning (m-learning) will play a vital role in the rapidly growing electronic learning market. M-learning is the delivery of learning to students anytime and anywhere through the use of wireless Internet and mobile devices. However, acceptance of m-learning by individuals is critical to the successful implementation of m-learning systems.Thus, there is a need to research the factors that affect user intention to use m-learning. Based on the unified theory of acceptance and use of technology (UTAUT), which integrates elements across eight models of information technology use, this study was to investigate the determinants of m-learning acceptance and to discover if there exist either age or gender differences in the acceptance of m-learning, or both. Data collected from 330 respondents inTaiwan were tested against the research model using the structural equation modelling approach. The results indicate that performance expectancy, effort expectancy, social influence, perceived playfulness, and self-management of learning were all significant determinants of behavioural intention to use m-learning. We also found that age differences moderate the effects of effort expectancy and social influence on m-learning use intention, and that gender differences moderate the effects of social influence and self-management of learning on m-learning use intention.These findings provide several important implications for m-learning acceptance, in terms of both research and practice.


title: A new algorithm for identity verification based on the analysis of a handwritten dynamic signature

abstract: Graphical abstractDisplay Omitted HighlightsWe propose a new algorithm for the dynamic signature verification.The algorithm implements a new way of signatures division - hybrid partitioning.Hybrid partitions are associated with time and dynamics of the signing process.The algorithm compares test signatures to the reference ones in interpretable way.The algorithm works independently for each signer. Identity verification based on authenticity assessment of a handwritten signature is an important issue in biometrics. There are many effective methods for signature verification taking into account dynamics of a signing process. Methods based on partitioning take a very important place among them. In this paper we propose a new approach to signature partitioning. Its most important feature is the possibility of selecting and processing of hybrid partitions in order to increase a precision of the test signature analysis. Partitions are formed by a combination of vertical and horizontal sections of the signature. Vertical sections correspond to the initial, middle, and final time moments of the signing process. In turn, horizontal sections correspond to the signature areas associated with high and low pen velocity and high and low pen pressure on the surface of a graphics tablet. Our previous research on vertical and horizontal sections of the dynamic signature (created independently) led us to develop the algorithm presented in this paper. Selection of sections, among others, allows us to define the stability of the signing process in the partitions, promoting signature areas of greater stability (and vice versa). In the test of the proposed method two databases were used: public MCYT-100 and paid BioSecure.


title: Conformalized density- and distance-based anomaly detection in time-series data

abstract: Anomalies (unusual patterns) in time-series data give essential, and often actionable information in critical situations. Examples can be found in such fields as healthcare, intrusion detection, finance, security and flight safety. In this paper we propose new conformalized density- and distance-based anomaly detection algorithms for a one-dimensional time-series data. The algorithms use a combination of a feature extraction method, an approach to assess a score whether a new observation differs significantly from a previously observed data, and a probabilistic interpretation of this score based on the conformal paradigm.


title: Design of a robot for in-pipe inspection using omnidirectional wheels and active stabilization

abstract: This paper discusses the design of a vehicle for in-pipe inspection using omnidirectional wheels and active stabilizing control. A novel propulsion mechanism is discussed using omnidirectional wheels (or omni-wheels) is presented which allows direct control of the orientation in the pipe. This paper will show the development and evaluation of a prototype model. Rapid prototyping techniques have been used in this proof-of-principle.


title: Pokeweed antiviral protein (PAP) increases plant systemic resistance to Tobacco mosaic virus infection in Nicotiana benthamiana

abstract: Pokeweed antiviral protein (PAP) isoforms are members of the type I ribosome inactivating proteins (RIPs) that are isolated from the extracts of pokeweed plant leaves (Phytolacca americana) and show many biological activities. However, the antiviral potential of the PAP in plants and its potential role in plant systemic resistance has been rarely reported. Therefore, in this report, we investigated the role of PAP in plant systemic resistance response against virus infection in Nicotiana benthamiana. N. benthamiana plants pre-treated with 0.1 mg/mL PAP three days before inoculation with green fluorescent protein (GFP)-tagged tobacco mosaic virus (TMV) showed less symptoms and less reactive oxygen species (ROS) accumulation compared to PBS-treated plants inoculated with TMV-GFP. Exogenous application of PAP yielded a significant reduction in GFP fluorescence in non-inoculated upper leaves. Furthermore, quantitative real-time PCR analysis revealed that the accumulation levels of TMV were significantly reduced in the systemic leaves of PAP-treated N. benthamiana plants compared with the levels observed in PBS-treated plants. In addition, some antioxidant enzymes including superoxide dismutase (SOD), catalase (CAT), peroxidase (POD) were activated after PAP treatment. Taken together, our results suggest that exogenous application of PAP enhances N. benthamiana systemic resistance to TMV infection by regulating ROS levels. Furthermore, we concluded that PAP might be acting as a strong antioxidant that does not allow the induction/accumulation of harmful ROS on virus infection.


title: Global Localization Using Dead-Reckoning and Kinect Sensors for Robots with Omnidirectional Mecanum Wheels

abstract: This paper presents a low-cost global pose localization method using least-squares method and extended Kalman filter (EKF) for an anthropomorphous dual-arm mobile robot (ADAMR) driven by omnidirectional Mecanum wheels in indoor environments. This method is developed by fusing measurements from the KINECT sensor and four encoders mounted on the omnidirectional Mecanum wheels. The KINECT sensor is used to recognize landmarks in the working environments of the robot, and then obtain the azimuth angles and distances between these landmarks and the robot. Based on these measured information, a static global pose initialization algorithm using least-squares method is applied to estimate both unknown start-up position and orientation of the robot. Once the initial pose has been roughly determined, an EKF approach is proposed to fuse the odometric measurements from the four encoders and the azimuth angles and distances from the KINECT sensor to corresponding landmarks. Simulations are conducted to show performance of the proposed method. Experimental results are used to illustrate that the proposed method provides accurate static estimation of both unknown initial poses of the robot.


title: Classifying relationships between object-oriented design patterns

abstract: Since the publication of the Design Patterns book (E. Gamma et al., 1994), a large number of object-oriented design patterns have been identified and codified. As part of the pattern form, object-oriented design patterns must indicate their relationships with other patterns, but these relationships are typically described very briefly, and different collections of patterns describe different relationships in different ways. In this paper, we describe and classify the common relationships between object-oriented design patterns. Practitioners can use these relationships to help them identity those patterns which may be applicable to a particular problem, and pattern writers can use these relationships to help them integrate new patterns into the body of the patterns literature.


title: Google's PageRank and Beyond: The Science of Search Engine Rankings

abstract: Why doesn't your home page appear on the first page of search results, even when you query your own name? How do other web pages always appear at the top? What creates these powerful rankings? And how? The first book ever about the science of web page rankings, Google's PageRank and Beyond supplies the answers to these and other questions and more. The book serves two very different audiences: the curious science reader and the technical computational reader. The chapters build in mathematical sophistication, so that the first five are accessible to the general academic reader. While other chapters are much more mathematical in nature, each one contains something for both audiences. For example, the authors include entertaining asides such as how search engines make money and how the Great Firewall of China influences research. The book includes an extensive background chapter designed to help readers learn more about the mathematics of search engines, and it contains several MATLAB codes and links to sample web data sets. The philosophy throughout is to encourage readers to experiment with the ideas and algorithms in the text. Any business seriously interested in improving its rankings in the major search engines can benefit from the clear examples, sample code, and list of resources provided. Many illustrative examples and entertaining asides MATLAB code Accessible and informal style Complete and self-contained section for mathematics review


title: Eigenvarieties for classical groups and complex conjugations in Galois representations

abstract: The goal of this paper is to remove the irreducibility hypothesis in a theorem of Richard Taylor describing the image of complex conjugations by $p$-adic Galois representations associated with regular, algebraic, essentially self-dual, cuspidal automorphic representations of $\GL_{2n+1}$ over a totally real number field $F$. We also extend it to the case of representations of $\GL_{2n}/F$ whose multiplicative character is"odd". We use a $p$-adic deformation argument, more precisely we prove that on the eigenvarieties for symplectic and even orthogonal groups, there are"many"points corresponding to (quasi-)irreducible Galois representations. The recent work of James Arthur describing the automorphic spectrum for these groups is used to define these Galois representations, and also to transfer self-dual automorphic representations of the general linear group to these classical groups.


title: “Exercise to be fit, not skinny”: The effect of fitspiration imagery on women's body image

abstract: Abstract Fitspiration is an online trend designed to inspire viewers towards a healthier lifestyle by promoting exercise and healthy food. The present study aimed to experimentally investigate the impact of fitspiration images on women's body image. Participants were 130 female undergraduate students who were randomly assigned to view either a set of Instagram fitspiration images or a control set of travel images presented on an iPad. Results showed that acute exposure to fitspiration images led to increased negative mood and body dissatisfaction and decreased state appearance self-esteem relative to travel images. Importantly, regression analyses showed that the effects of image type were mediated by state appearance comparison. Thus it was concluded that fitspiration can have negative unintended consequences for body image. The results offer support to general sociocultural models of media effects on body image, and extend these to “new” media.


title: Information systems outsourcing: A literature analysis

abstract: Outsourcing has become one of the strategies adopted by businesses to manage their IS. During the last few years, the use of outsourcing has resulted in an increase in the volume of literature devoted to it. We decided therefore to analyse the literature with the aim of identifying the main topics, the methodologies most often applied and the authors and countries that have contributed most to the area of IS outsourcing. We also intended to offer suggestions on improving research in this field. The paper thus provides a review of articles about IS outsourcing published in the most prestigious journals of the IS area and journals of Management or Business.


On the effect of data set size on bias and variance in classification learning


title: XML family of specifications : a practical guide

abstract: A system and method for controlling a lean-burning internal combustion engine. The system and method enable a starter carburetor to operate at cold but not at hot temperature, to provide gulp air and counterbalance means for the starter carburetor, together with means to inhibit them at startup and for a brief time thereafter, and to inhibit exhaust gas recirculation when it would be deleterious to smooth operation. The system also provides for continuous accelerator pump operation during acceleration except at high temperatures. The objective is to secure smooth operation at startup, cold, warm or hot, and during transient conditions during engine warmup.


title: Collaborative approach on mitigating spectrum sensing data hijack attack and dynamic spectrum allocation based on CASG modeling in wireless cognitive radio networks

abstract: Cognitive radio networks being a promising technology among the wireless devices where dynamic spectrum access by secondary users to utilize the scarce spectrum among the Primary spectrum owners (PSO). The spectrum is being masqueraded and secondary users occupants are attacked, one among the vulnerability issue is spectrum sensing data hijack (SSDH). We have proposed a combinatorial auction based schema to allocate the spectrum to secondary users based on combined auction multi stage stackelberg game has been invoked to analyse the spectrum, the winner has been allocated the spectrum based on greedy approach. The SSDH attack has been mitigated using the neighbor monitoring approach and the revealed approach makes the prone free allocation and improves the secrecy rate of the Spectrum. The competitors of secondary users are allocated with ideal spectrum based greedy winning approach. The Primary Spectrum owners perform a recall approach if the residing primary users inside PSO have wide scarcity towards the spectrum. The allotted spectrum of winner from the corresponding secondary users will be recalled for bidding and the spectrum will be allocated to the primary user in this recall based scenario.


title: Modelling urban growth and landscape changes in the Atlanta metropolitan area

abstract: We use an urban growth model, closely coupled with a land transition model, to simulate future urban growth in the Atlanta metropolitan area, one of the fastest growing metropolises in the United States during the past three decades. We calibrate the model with historical data that are extracted from a time series of satellite images. We design three specific scenarios to simulate the spatial consequences of urban growth under different environmental conditions. The first scenario is to simulate the continued growth trend by maintaining unchanged the current conditions. The second scenario is to project the growth trend by taking into consideration road development and environmental protection. The third scenario is to simulate the development trend by slowing down growth and changing growth pattern. The first two scenarios demonstrate that unchecked urban growth would result in the displacement of almost the entire natural vegetation and all of the open space in the metro area. In contrast, the result from the third scenario shows that much more greenness and open space, including buffer zones of large streams and lakes, could be preserved. Accordingly, the last scenario should be the most desirable for the future urban growth of Atlanta. We also examine the model's effectiveness as applied to the Atlanta area and suggest future research directions for more accurate simulations.


title: SIMOnt: A Security Information Management Ontology Framework

abstract: In this paper, we have proposed the design of a Security Information Management Ontology (SIMOnto) framework, which utilizes natural language processing and statistical analysis to mine an exhaustive list of concepts and their relationships in an automatic way. Concepts are extracted using TF-IDF and LSA techniques whereas, relations between them are mined using semantic and co-occurrence based analyses. The mined concepts and relations are presented to domain experts for validation before creation of ontology using Protege.


title: m-Polar Fuzzy Sets: An Extension of Bipolar Fuzzy Sets

abstract: Recently, bipolar fuzzy sets have been studied and applied a bit enthusiastically and a bit increasingly. In this paper we prove that bipolar fuzzy sets and [0,1]2-sets (which have been deeply studied) are actually cryptomorphic mathematical notions. Since researches or modelings on real world problems often involve multi-agent, multi-attribute, multi-object, multi-index, multi-polar information, uncertainty, or/and limit process, we put forward (or highlight) the notion of m-polar fuzzy set (actually, [0,1]m-set which can be seen as a generalization of bipolar fuzzy set, where m is an arbitrary ordinal number) and illustrate how many concepts have been defined based on bipolar fuzzy sets and many results which are related to these concepts can be generalized to the case of m-polar fuzzy sets. We also give examples to show how to apply m-polar fuzzy sets in real world problems.


title: Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks

abstract: The visual attributes of cells, such as the nuclear morphology and chromatin openness, are critical for histopathology image analysis. By learning cell-level visual representation, we can obtain a rich mix of features that are highly reusable for various tasks, such as cell-level classification, nuclei segmentation, and cell counting. In this paper, we propose a unified generative adversarial networks architecture with a new formulation of loss to perform robust cell-level visual representation learning in an unsupervised setting. Our model is not only label-free and easily trained but also capable of cell-level unsupervised classification with interpretable visualization, which achieves promising results in the unsupervised classification of bone marrow cellular components. Based on the proposed cell-level visual representation learning, we further develop a pipeline that exploits the varieties of cellular elements to perform histopathology image classification, the advantages of which are demonstrated on bone marrow datasets.


title: How to start a training program in your growing business

abstract: Increased sales of 69 per cent. Increased profits of 700 per cent. What causes such dramatic growth? And how can small business owners achieve it? "How to Start A Training Program in Your Growing Business" provides the guidelines for getting a training program going, and examples of companies that have done it with dramatic results. Readers will learn how to create a business plan for training; develop training standards and write a training policy; set budgets; introduce training into the organization; deliver training; and monitor program results. Even the smallest one-person training operation can implement a successful, cost-effective plan. The key lies in customizing training to a company's specific needs - making it an integral part of business, not merely an abstract concept unrelated to profits. Such an approach not only helps small businesses train better employees, it makes company growth a shared commitment.


title: What do we owe to intelligent robots?

abstract: Great technological advances in such areas as computer science, artificial intelligence, and robotics have brought the advent of artificially intelligent robots within our reach within the next century. Against this background, the interdisciplinary field of machine ethics is concerned with the vital issue of making robots “ethical” and examining the moral status of autonomous robots that are capable of moral reasoning and decision-making. The existence of such robots will deeply reshape our socio-political life. This paper focuses on whether such highly advanced yet artificially intelligent beings will deserve moral protection (in the form of being granted moral rights) once they become capable of moral reasoning and decision-making. I argue that we are obligated to grant them moral rights once they have become full ethical agents, i.e., subjects of morality. I present four related arguments in support of this claim and thereafter examine four main objections to the idea of ascribing moral rights to artificial intelligent robots.


title: Non-Negative Residual Matrix Factorization with Application to Graph Anomaly Detection ∗

abstract: Given an IP source-destination traffic network, how do we spot mis-behavioral IP sources (e.g., port-scanner)? How do we find strange users in a user-movie rating graph? Moreover, how can we present the results intuitively so that it is relatively easier for data analysts to interpret? We propose NrMF, a non-negative residual matrix factorization framework, to address such challenges. We present an optimization formulation as well as an effective algorithm to solve it. Our method can naturally capture abnormal behaviors on graphs. In addition, the proposed algorithm is linear wrt the size of the graph therefore it is suitable for large graphs. The experimental results on several data sets validate its effectiveness as well as efficiency.


title: Fast Edge Detection Using Structured Forests

abstract: Edge detection is a critical component of many vision systems, including object detectors and image segmentation algorithms. Patches of edges exhibit well-known forms of local structure, such as straight lines or T-junctions. In this paper we take advantage of the structure present in local image patches to learn both an accurate and computationally efficient edge detector. We formulate the problem of predicting local edge masks in a structured learning framework applied to random decision forests. Our novel approach to learning decision trees robustly maps the structured labels to a discrete space on which standard information gain measures may be evaluated. The result is an approach that obtains realtime performance that is orders of magnitude faster than many competing state-of-the-art approaches, while also achieving state-of-the-art edge detection results on the BSDS500 Segmentation dataset and NYU Depth dataset. Finally, we show the potential of our approach as a general purpose edge detector by showing our learned edge models generalize well across datasets.


title: On Data-Driven Saak Transform

abstract: Being motivated by the multilayer RECOS (REctified-COrrelations on a Sphere) transform, we develop a data-driven Saak (Subspace approximation with augmented kernels) transform in this work. The Saak transform consists of three steps: 1) building the optimal linear subspace approximation with orthonormal bases using the second-order statistics of input vectors, 2) augmenting each transform kernel with its negative, 3) applying the rectified linear unit (ReLU) to the transform output. The Karhunen-Lo\'eve transform (KLT) is used in the first step. The integration of Steps 2 and 3 is powerful since they resolve the sign confusion problem, remove the rectification loss and allow a straightforward implementation of the inverse Saak transform at the same time. Multiple Saak transforms are cascaded to transform images of a larger size. All Saak transform kernels are derived from the second-order statistics of input random vectors in a one-pass feedforward manner. Neither data labels nor backpropagation is used in kernel determination. Multi-stage Saak transforms offer a family of joint spatial-spectral representations between two extremes; namely, the full spatial-domain representation and the full spectral-domain representation. We select Saak coefficients of higher discriminant power to form a feature vector for pattern recognition, and use the MNIST dataset classification problem as an illustrative example.


title: Force Modeling, Identification, and Feedback Control of Robot-Assisted Needle Insertion: A Survey of the Literature

abstract: Robot-assisted surgery is of growing interest in the surgical and engineering communities. The use of robots allows surgery to be performed with precision using smaller instruments and incisions, resulting in shorter healing times. However, using current technology, an operator cannot directly feel the operation because the surgeon-instrument and instrument-tissue interaction force feedbacks are lost during needle insertion. Advancements in force feedback and control not only help reduce tissue deformation and needle deflection but also provide the surgeon with better control over the surgical instruments. The goal of this review is to summarize the key components surrounding the force feedback and control during robot-assisted needle insertion. The literature search was conducted during the middle months of 2017 using mainstream academic search engines with a combination of keywords relevant to the field. In total, 166 articles with valuable contents were analyzed and grouped into five related topics. This survey systemically summarizes the state-of-the-art force control technologies for robot-assisted needle insertion, such as force modeling, measurement, the factors that influence the interaction force, parameter identification, and force control algorithms. All studies show force control is still at its initial stage. The influence factors, needle deflection or planning remain open for investigation in future.


title: Promising cannabinoid-based therapies for Parkinson’s disease: motor symptoms to neuroprotection

abstract: Parkinson’s disease (PD) is a slow insidious neurological disorder characterized by a loss of dopaminergic neurons in the midbrain. Although several recent preclinical advances have proposed to treat PD, there is hardly any clinically proved new therapeutic for its cure. Increasing evidence suggests a prominent modulatory function of the cannabinoid signaling system in the basal ganglia. Hence, use of cannabinoids as a new therapeutic target has been recommended as a promising therapy for PD. The elements of the endocannabinoid system are highly expressed in the neural circuit of basal ganglia wherein they bidirectionally interact with dopaminergic, glutamatergic, and GABAergic signaling systems. As the cannabinoid signaling system undergoes a biphasic pattern of change during progression of PD, it explains the motor inhibition typically observed in patients with PD. Cannabinoid agonists such as WIN-55,212-2 have been demonstrated experimentally as neuroprotective agents in PD, with respect to their ability to suppress excitotoxicity, glial activation, and oxidative injury that causes degeneration of dopaminergic neurons. Additional benefits provided by cannabinoid related compounds including CE-178253, oleoylethanolamide, nabilone and HU-210 have been reported to possess efficacy against bradykinesia and levodopa-induced dyskinesia in PD. Despite promising preclinical studies for PD, use of cannabinoids has not been studied extensively at the clinical level. In this review, we reassess the existing evidence suggesting involvement of the endocannabinoid system in the cause, symptomatology, and treatment of PD. We will try to identify future threads of research that will help in the understanding of the potential therapeutic benefits of the cannabinoid system for treating PD.


title: A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder

abstract: highlights abstract Objective: The problem of identifying, in advance, the most effective treatment agent for various psychi- atric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD). Methods: A relatively small number of most discriminating features are selected from a large group of candidate features extracted from the subject's pre-treatment EEG, using a machine learning procedure for feature selection. The selected features are fed into a classifier, which was realized as a mixture of fac- tor analysis (MFA) model, whose output is the predicted response in the form of a likelihood value. This likelihood indicates the extent to which the subject belongs to the responder vs. non-responder classes. The overall method was evaluated using a ''leave-n-out'' randomized permutation cross-validation pro- cedure. Results: A list of discriminating EEG biomarkers (features) was found. The specificity of the proposed method is 80.9% while sensitivity is 94.9%, for an overall prediction accuracy of 87.9%. There is a 98.76% confidence that the estimated prediction rate is within the interval (75%, 100%). Conclusions: These results indicate that the proposed ML method holds considerable promise in predict- ing the efficacy of SSRI antidepressant therapy for MDD, based on a simple and cost-effective pre-treat- ment EEG. Significance: The proposed approach offers the potential to improve the treatment of major depression and to reduce health care costs.


title: Dialectical behavior therapy and domains of functioning over two years

abstract: Individuals diagnosed with borderline personality disorder (BPD) tend to have a significant degree of functional impairment across a range of social and occupational spheres including difficulty finding and maintaining satisfying employment, housing, or relationships. Understanding what factors are associated with functional impairment will enable treatment providers to move those diagnosed with BPD beyond symptomatic recovery and toward a life worth living. This paper investigated the trajectories and predictors of functional outcomes for suicidal women with BPD (N = 99) during a treatment outcome study of Dialectical Behavior Therapy (DBT). Results revealed that participants had statistical and clinical improvements in functioning. Individuals with high emotion dysregulation displayed poorer psychosocial functioning at the subsequent assessment period and slower rates of change, which was also seen in reverse for one psychosocial functioning variable. Skills use was not related to individual trajectories in functioning. This study highlights the relationship of emotion dysregulation to functioning within a sample of suicidal women with BPD as well as the importance researching multiple domains in functioning.


title: Learning to target: what works for behavioral targeting

abstract: Understanding what interests and delights users is critical to effective behavioral targeting, especially in information-poor contexts. As users interact with content and advertising, their passive behavior can reveal their interests towards advertising. Two issues are critical for building effective targeting methods: what metric to optimize for and how to optimize. More specifically, we first attempt to understand what the learning objective should be for behavioral targeting so as to maximize advertiser's performance. While most popular advertising methods optimize for user clicks, as we will show, maximizing clicks does not necessarily imply maximizing purchase activities or transactions, called conversions, which directly translate to advertiser's revenue. In this work we focus on conversions which makes a more relevant metric but also the more challenging one. Second is the issue of how to represent and combine the plethora of user activities such as search queries, page views, ad clicks to perform the targeting. We investigate several sources of user activities as well as methods for inferring conversion likelihood given the activities. We also explore the role played by the temporal aspect of user activities for targeting, e.g., how recent activities compare to the old ones. Based on a rigorous offline empirical evaluation over 200 individual advertising campaigns, we arrive at what we believe are best practices for behavioral targeting. We deploy our approach over live user traffic to demonstrate its superiority over existing state-of-the-art targeting methods.


title: Finding community structure in networks using the eigenvectors of matrices. Phys

abstract: We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as "modularity" over possible divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in networks and a new centrality measure that identifies those vertices that occupy central positions within the communities to which they belong. The algorithms and measures proposed are illustrated with applications to a variety of real-world complex networks.


title: Do age and gender differences exist in selfie-related behaviours?

abstract: Recently scholars have started examining selfie-related behaviour, with an emphasis on young women. However, little is known about age and gender differences in selfie-taking and posting patterns. To address this gap, an online survey of a sample of 3763 Norwegian social media users was carried out. This study provides the first empirical evidence on how adolescents (aged 12 to 19), young adults (20-30) and adults (31-50) differ in terms of selfie behaviour. Females were more likely to take personal and group selfies, post personal selfies, crop photos and use photographic filters compared to males. Adolescents were found to be more likely than young adults to take own and group selfies, post own selfies, and use photographic filters. Similarly, young adults were more likely to take own and group selfies, post and edit photos than older adults. The predictive effect of age was stronger among women than among men regarding selfie taking, posting and editing behaviour. The interpretations and implications of the study findings are discussed in the light of previous literature. An online survey of 3763 Norwegian social media users was carried out.First empirical study to investigate differences among adolescents' and adults' selfie behaviour.Females are more likely to take and share selfies and edit photos than males.Age differences in selfie-related behaviour were observed.Predictive effect of age was stronger on selfie behaviour among women than men.


title: Deep Learning Control for Complex and Large Scale Cloud Systems

abstract: AbstractDeep learning attempts to model high level perceptions in data using deep graph representations and creating models to learn these representations from large-scale unlabeled signals. Efficient unsupervised feature learning is extracted by deep learning algorithms and with multiple processing layers, composed of multiple linear and non-linear transformations. Actual systems become more and more complex with huge numbers of state variables and control of such large and complex systems with chaotic behavior, which needs more information about systems. Deep learning control by discovering continoiusly almost all possible information seems to be a reasonable approach to model and control largescale and complex systems. Recent advancements in machine learning algorithms and platforms are leading to deep learning controllers in real-time applications. The goal of this paper is to describe the concept of deep learning control and explain how cloud fog computing and edge analytics could handle massive amou...


title: HFIM: a Spark-based hybrid frequent itemset mining algorithm for big data processing

abstract: Frequent itemset mining is one of the data mining techniques applied to discover frequent patterns, used in prediction, association rule mining, classification, etc. Apriori algorithm is an iterative algorithm, which is used to find frequent itemsets from transactional dataset. It scans complete dataset in each iteration to generate the large frequent itemsets of different cardinality, which seems better for small data but not feasible for big data. The MapReduce framework provides the distributed environment to run the Apriori on big transactional data. However, MapReduce is not suitable for iterative process and declines the performance. We introduce a novel algorithm named Hybrid Frequent Itemset Mining (HFIM), which utilizes the vertical layout of dataset to solve the problem of scanning the dataset in each iteration. Vertical dataset carries information to find support of each itemsets. Moreover, we also include some enhancements to reduce number of candidate itemsets. The proposed algorithm is implemented over Spark framework, which incorporates the concept of resilient distributed datasets and performs in-memory processing to optimize the execution time of operation. We compare the performance of HFIM with another Spark-based implementation of Apriori algorithm for various datasets. Experimental results show that the HFIM performs better in terms of execution time and space consumption.


title: New Experiment System for the Interaction Between Soft Rock and Water : A Case Study on the Mogao Grottoes Support Rock

abstract: The strength of rock strongly depends on the water content especially when the rocks contain clay materials. The interaction between soft rock and water always threaten the soft rock engineering projects. For this problem, new sets of laboratory experiment systems are developed to simulate the interaction between soft rock and water or vapor. In this paper, the principles of experiment systems are introduced with particular reference to the application on soft rock in Mogao Grottoes, one of the world famous ancient sites in China. Two kinds of rock samples, the sandstone and muddy sandstone, are obtained by insitu sampling system. Then the laboratory experiments are performed under different environment conditions. By the specific boundary conditions design, the physical-chemical effect and mechanic effect on water absorption of rock samples are separated for further mechanism study by the experiment system, and the different hydrological actions of water and vapor under variable experiment environment are obtained. The interaction mechanism is discussed with assistant methods, such as SEM (Scanning Electron Microscope), mercury injection test, X-ray diffraction analysis and etc. With the relation between water content and soft rock strength, the study may provide guidance and basis for the soft rock engineering in the future.


title: A Correlated Worker Model for Grouped, Imbalanced and Multitask Data

abstract: We consider the important crowdsourcing problem of estimating worker confusion matrices, or sensitivities and specificities for binary classification tasks. In addition to providing diagnostic insights into worker performance, such estimates enable robust online task routing for classification tasks exhibiting imbalance and asymmetric costs. However, labeled data is often expensive and hence estimates must be made without much of it. This poses a challenge to existing methods. In this paper, we propose a novel model that captures the correlations between entries in confusion matrices. We applied this model in two practical scenarios: (1) an imbalanced classification task in which workers are known to belong to groups and (2) a multitask scenario in which labels for the same workers are available in more than one labeling task. We derive an efficient variational inference approach that scales to large datasets. Experiments on two real world citizen science datasets (biomedical citation screening and galaxy morphological classification) demonstrate consistent improvement over competitive baselines. We have made our source code available.


title: Rehand: Realistic electric prosthetic hand created with a 3D printer

abstract: Abstract Myoelectric prosthetic hands provide an appearance with five fingers and a grasping function to forearm amputees. However, they have problems in weight, appearance, and cost. This paper reports on the Rehand, a realistic electric prosthetic hand created with a 3D printer. It provides a realistic appearance that is same as the cosmetic prosthetic hand and a grasping function. A simple link mechanism with one linear actuator for grasping and 3D printed parts achieve low cost, light weight, and ease of maintenance. An operating system based on a distance sensor provides a natural operability equivalent to the myoelectric control system. A supporter socket allows them to wear the prosthetic hand easily. An evaluation using the Southampton Hand Assessment Procedure (SHAP) demonstrated that an amputee was able to operate various objects and do everyday activities with the Rehand.


title: Model-based feedback in the language modeling approach to information retrieval

abstract: The language modeling approach to retrieval has been shown to perform well empirically. One advantage of this new approach is its statistical foundations. However, feedback, as one important component in a retrieval system, has only been dealt with heuristically in this new retrieval approach: the original query is usually literally expanded by adding additional terms to it. Such expansion-based feedback creates an inconsistent interpretation of the original and the expanded query. In this paper, we present a more principled approach to feedback in the language modeling approach. Specifically, we treat feedback as updating the query language model based on the extra evidence carried by the feedback documents. Such a model-based feedback strategy easily fits into an extension of the language modeling approach. We propose and evaluate two different approaches to updating a query language model based on feedback documents, one based on a generative probabilistic model of feedback documents and one based on minimization of the KL-divergence over feedback documents. Experiment results show that both approaches are effective and outperform the Rocchio feedback approach.


title: People tracking with human motion predictions from social forces

abstract: For many tasks in populated environments, robots need to keep track of current and future motion states of people. Most approaches to people tracking make weak assumptions on human motion such as constant velocity or acceleration. But even over a short period, human behavior is more complex and influenced by factors such as the intended goal, other people, objects in the environment, and social rules. This motivates the use of more sophisticated motion models for people tracking especially since humans frequently undergo lengthy occlusion events. In this paper, we consider computational models developed in the cognitive and social science communities that describe individual and collective pedestrian dynamics for tasks such as crowd behavior analysis. In particular, we integrate a model based on a social force concept into a multi-hypothesis target tracker. We show how the refined motion predictions translate into more informed probability distributions over hypotheses and finally into a more robust tracking behavior and better occlusion handling. In experiments in indoor and outdoor environments with data from a laser range finder, the social force model leads to more accurate tracking with up to two times fewer data association errors.


Optimal Control of DC motor using Grey Wolf Optimizer Algorithm


title: mNIR: Diversifying Search Results based on a Mixture of Novelty, Intention and Relevance

abstract: Current search engines do not explicitly take different meanings and usages of user queries into consideration when they rank the search results. As a result, they tend to retrieve results that cover the most popular meanings or usages of the query. Consequently, users who want results that cover a rare meaning or usage of query or results that cover all different meanings/usages may have to go through a large number of results in order to find the desired ones. Another problem with current search engines is that they do not adequately take users' intention into consideration. In this paper, we introduce a novel result ranking algorithm (mNIR) that explicitly takes result novelty, user intention-based distribution and result relevancy into consideration and mixes them to achieve better result ranking. We analyze how giving different emphasis to the above three aspects would impact the overall ranking of the results. Our approach builds on our previous method for identifying and ranking possible categories of any user query based on the meanings and usages of the terms and phrases within the query. These categories are also used to generate category queries for retrieving results matching different meanings/usages of the original user query. Our experimental results show that the proposed algorithm can outperform state-of-the-art diversification approaches.


title: Parallelizing Graph Algorithms on GPU for Optimization

abstract: Many practical applications include image processing, space searching, network analysis, graph partitioning etc. in that large graphs having a millions of vertices are commonly used and to process on that vertices is difficult task. Using high-end computers practical-time implementations are reported but are accessible only to a few. Efficient performance of those applications requires fast implementation of graph processing and hence Graphics Processing Units (GPUs) of today having a high computational power of accelerating capacity are deployed. The NVIDIA GPU can be treated as a SIMD processor array using the CUDA programming model. In this paper Breadth-First Search and All Pair shortest path and traveling salesmen problem graph algorithms are performed on GPU capabilities. The algorithms are introduced to optimize such that they can efficiently adopt GPU. Also an optimization technique that reduce data transfer rate CPU to GPU and reduce access of global memory is designed to reduce latency. Analysis of All pair shortest path algorithm by performing on different memories of GPU which shows that using shared memory can reduce execution time and increase speedup over CPU than global memory and coalescing access of data. TSP algorithm shows that increasing number of blocks and iteration obtained optimized tour length. Keyword: Graphics Processing unit, CUDA, BFS, All Pair Shortest path, TSP, Graph processing, optimization.


title: Multi-layer depth peeling via fragment sort

abstract: We present an accelerated depth peeling algorithm for order-independent transparency rendering on graphics hardware. Unlike traditional depth peeling which only peels one layer of transparent pixels per rendering pass, our algorithm peels multiple layers simultaneously per rendering pass. Our acceleration is achieved via our fragment program which sorts and writes multiple fragment colors and depths via MRT. A notable feature of our algorithm is that it is robust against the unreliable parallel read-after-write behavior in current graphics hardware, guaranteeing correct transparency ordering. For ordinary scenes rendered under RGBA8 color precision, we achieve up to 8x speed-up over conventional depth peeling with current generation graphics hardware. Our algorithm is simple to implement on current GPU without any hardware modification. In addition, it does not require applications to perform any pre-sorting of transparent geometry.


Examining the Effects of Distractive Multitasking with Peripheral Computing in the Classroom


DEVELOPMENT OF A MATLAB GRAPHICAL USER INTERFACE FOR THE MULTI-TENSOR ANALYSIS OF DIFFUSION MAGNETIC RESONANCE IMAGING


title: Mucus Layers in Inflammatory Bowel Disease

abstract: The intestinal epithelium is covered with mucus with the main structural building block being the densely O-glycosylated MUC2 mucin. The intestinal epithelium is exposed to ingested material, our digestive machinery, and large amounts of microorganisms. Mucus is the first line of defense and aids to limit exposure to all these threats to the epithelium. In the small intestine, mucus acts as a matrix, which contains antimicrobial products, such as defensins and immunoglobulin A that limit epithelial exposure to the luminal bacteria. In the colon, the stratified inner mucus layer acts as a physical barrier excluding bacteria from the epithelium. Bacterial penetration of this normally restricted zone is observed in many colitis models and also in patients with ulcerative colitis. Mucus defects that allow bacteria to reach the epithelium and to stimulate an immune system response can lead to the development of intestinal inflammation. The current state of our knowledge concerning the function of the mucus layers and the main mucin component, MUC2, in inflammatory bowel disease is described in this review.


title: A review of penile elongation surgery

abstract: Penile elongation surgery is less commonly performed in the public sector, but involves a collaborative approach between urology and plastic surgery. Congenital and acquired micropenis are the classic surgical indications for penile elongation surgery. The goal of intervention in these patients is to restore a functional penis size in order to allow normal standing micturition, enable satisfying sexual intercourse and improve patient quality of life. Many men seeking elongation actually have normal length penises, but perceive themselves to be small, a psychologic condition termed 'penile dysmorphophobia'. This paper will review the anatomy and embryology of congenital micropenis and discuss both conservative and surgical management options for men seeking penile elongation therapy.


title: When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away)

abstract: In past and recent years, the issues related to managing technical debt received significant attention by researchers from both industry and academia. There are several factors that contribute to technical debt. One of these is represented by code bad smells, i.e., symptoms of poor design and implementation choices. While the repercussions of smells on code quality have been empirically assessed, there is still only anecdotal evidence on when and why bad smells are introduced. To fill this gap, we conducted a large empirical study over the change history of 200 open source projects from different software ecosystems and investigated when bad smells are introduced by developers, and the circumstances and reasons behind their introduction. Our study required the development of a strategy to identify smell-introducing commits, the mining of over 0.5M commits, and the manual analysis of 9,164 of them (i.e., those identified as smell-introducing). Our findings mostly contradict common wisdom stating that smells are being introduced during evolutionary tasks. In the light of our results, we also call for the need to develop a new generation of recommendation systems aimed at properly planning smell refactoring activities.


Recommendations for peer-to-peer support for NICU parents


title: User relationship strength modeling for friend recommendation on Instagram

abstract: Social strength modeling in the social media community has attracted increasing research interest. Different from Flickr, which has been explored by many researchers, Instagram is more popular for mobile users and is conducive to likes and comments but seldom investigated. On Instagram, a user can post photos/videos, follow other users, comment and like other users posts. These actions generate diverse forms of data that result in multiple user relationship views. In this paper, we propose a new framework to discover the underlying social relationship strength. User relationship learning under multiple views and the relationship strength modeling are coupled into one process framework. In addition, given the learned relationship strength, a coarse-to-fine method is proposed for friend recommendation. Experiments on friend recommendations for Instagram are presented to show the effectiveness and efficiency of the proposed framework. As exhibited by our experimental results, it can obtain better performance over other related methods. Although our method has been proposed for Instagram, it can be easily extended to any other social media communities.


title: Electronic Word of Mouth: Motives for and Consequences of Reading Customer Articulations on the Internet

abstract: The Internet makes it possible for consumers to obtain electronic word of mouth from other consumers. Customer comments articulated via the Internet are available to a vast number of other customers, and therefore can be expected to have a significant impact on the success of goods and services. This paper derives several motives that explain why customers retrieve other customers' on-line articulations from Web-based consumer opinion platforms. The relevance of these motives and their impact on consumer buying and communication behavior are tested in a large-scale empirical study. The results illustrate that consumers read on-line articulations mainly to save decision-making time and make better buying decisions. Structural equation modeling shows that their motives for retrieving on-line articulations strongly influence their behavior.


title: MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets

abstract: A recent technical breakthrough in the domain of machine learning is the discovery and the multiple applications of Generative Adversarial Networks (GANs). Those generative models are computationally demanding, as a GAN is composed of two deep neural networks, and because it trains on large datasets. A GAN is generally trained on a single server. In this paper, we address the problem of distributing GANs so that they are able to train over datasets that are spread on multiple workers. MD-GAN is exposed as the first solution for this problem: we propose a novel learning procedure for GANs so that they fit this distributed setup. We then compare the performance of MD-GAN to an adapted version of federated learning to GANs, using the MNIST, CIFAR10 and CelebA datasets. MD-GAN exhibits a reduction by a factor of two of the learning complexity on each worker node, while providing better or identical performances with the adaptation of federated learning. We finally discuss the practical implications of distributing GANs.


title: Bacterial phylogeny structures soil resistomes across habitats

abstract: Functional metagenomic selections for resistance to 18 antibiotics in 18 different soils reveal that bacterial community composition is the primary determinant of soil antibiotic resistance gene content.


title: A Taxonomic Analysis of User-Interface Metaphors in the Microsoft O ce Project Gallery

abstract: User-interface metaphors are not well understood in terms of their relationships and qualities. They are, however, constantly used both consciously and unconsciously in most user-interface designs. This paper demonstrates a taxonomic classification and analysis of the user-interface metaphors in the Microsoft Office Project Gallery. The classification offers insight both into the nature of the metaphors in the user-interface investigated and more generally, and helps validate the use of the taxonomy as an assessment tool.


title: How expensive are free smartphone apps?

abstract: Proliferation of data-enabled mobile devices has fueled the popularity of smartphone applications (apps) at a rapid pace, and the trend is likely to continue for the foreseeable future. Along with the increase in popularity, the characteristics and concept of paid apps and free apps is also changing. In general, paid smartphone apps generate their revenue simply from the cost of downloading the app. On the other hand, free apps rely on advertisements, and/or virtual currencies, for their revenue generation. There are several variants of these generalized approaches. In this work, we focus on identifying the overhead traffic that is generated by the free apps with respect to the paid apps. The overhead traffic is not associated with the operation of the app itself and thus should not impact the usage experience of the apps. Specifically, we consider advertisements, and the transmission of analytic data, as the main components of overhead traffic. With the gradual disappearance of unlimited data plans, the overhead traffic does not come for free. The goal of this paper is quantify the cost of the overhead traffic of the popular free apps and compare it with the paid apps. We have developed an intricate methodology for identifying and measuring the bandwidth requirements of the overheads associated with the free apps. Through comprehensive measurements, we have shown that in most cases, the paid versions of the apps will indeed be a fraction of the cost to the end users when compared to the actual cost of the free versions.


title: Neural Episodic Control

abstract: Deep reinforcement learning methods attain super-human performance in a wide range of environments. Such methods are grossly inefficient, often taking orders of magnitudes more data than humans to achieve reasonable performance. We propose Neural Episodic Control: a deep reinforcement learning agent that is able to rapidly assimilate new experiences and act upon them. Our agent uses a semi-tabular representation of the value function: a buffer of past experience containing slowly changing state representations and rapidly updated estimates of the value function. We show across a wide range of environments that our agent learns significantly faster than other state-of-the-art, general purpose deep reinforcement learning agents.


title: The Role of Pragmatics in Solving the Winograd Schema Challenge

abstract: Different aspects and approaches to commonsense reasoning have been investigated in order to provide solutions for the Winograd Schema Challenge (WSC). The vast complexities of natural language processing (parsing, assigning word sense, integrating context, pragmatics and world-knowledge, ...) give broad appeal to systems based on statistical analysis of corpora. However, solutions based purely on learning from corpora are not currently able to capture the semantics underlying the WSC - which was intended to provide problems whose solution requires knowledge and reasoning, rather than statistical analysis of superficial lexical features. In this paper we consider the WSC as a means for highlighting challenges in the field of commonsense reasoning more generally. We begin by discussing issues with current approaches to the WSC. Following this we outline some key challenges faced, in particular highlighting the importance of dealing with pragmatics. We then argue for an alternative approach which favours the use of knowledge bases where the deep semantics of the different interpretations of commonsense terms are formalised. Furthermore, we suggest using heuristic approaches based on pragmatics to determine appropriate configurations of both reasonable interpretations of terms and necessary assumptions about the world.


title: Effects of sleep deprivation on procedural errors.

abstract: In a large sample (N = 234), we tested effects of 24-hr of sleep deprivation on error rates in a procedural task that requires memory maintenance of task-relevant information. In the evening, participants completed the task under double-blind conditions and then either stayed awake in the lab overnight or slept at home. In the morning, participants completed the task again. Sleep-deprived participants were more likely to suffer a general breakdown in ability (or willingness) to meet a modest accuracy criterion they had met the night before. Among sleep-deprived participants who could still perform the task, error rates were elevated, and errors reflecting memory failures increased with time-on-task. The results suggest that sleep-deprived individuals should not perform procedural tasks associated with interruptions and costly errors-or, if they must, they should perform such tasks only for short periods. (PsycINFO Database Record (c) 2018 APA, all rights reserved).


Registration of Deformable Objects using a Depth Camera


title: People reidentification in surveillance and forensics: A survey

abstract: The field of surveillance and forensics research is currently shifting focus and is now showing an ever increasing interest in the task of people reidentification. This is the task of assigning the same identifier to all instances of a particular individual captured in a series of images or videos, even after the occurrence of significant gaps over time or space. People reidentification can be a useful tool for people analysis in security as a data association method for long-term tracking in surveillance. However, current identification techniques being utilized present many difficulties and shortcomings. For instance, they rely solely on the exploitation of visual cues such as color, texture, and the object’s shape. Despite the many advances in this field, reidentification is still an open problem. This survey aims to tackle all the issues and challenging aspects of people reidentification while simultaneously describing the previously proposed solutions for the encountered problems. This begins with the first attempts of holistic descriptors and progresses to the more recently adopted 2D and 3D model-based approaches. The survey also includes an exhaustive treatise of all the aspects of people reidentification, including available datasets, evaluation metrics, and benchmarking.


title: Particle Video: Long-Range Motion Estimation Using Point Trajectories

abstract: This paper describes a new approach to motion estimation in video. We represent video motion using a set of particles. Each particle is an image point sample with a longduration trajectory and other properties. To optimize these particles, we measure point-based matching along the particle trajectories and distortion between the particles. The resulting motion representation is useful for a variety of applications and cannot be directly obtained using existing methods such as optical flow or feature tracking. We demonstrate the algorithm on challenging real-world videos that include complex scene geometry, multiple types of occlusion, regions with low texture, and non-rigid deformations.


title: Blunted neural response to rewards prospectively predicts depression in adolescent girls

abstract: The prevalence of depression increases substantially during adolescence. Several predictors of major depressive disorder have been established, but their predictive power is limited. In the current study, the feedback negativity (FN), an event-related potential component elicited by feedback indicating monetary gain versus loss, was recorded in 68 never-depressed adolescent girls. Over the following 2 years, 24% of participants developed a major depressive episode (MDE); illness onset was predicted by blunted FN at initial evaluation. Lower FN amplitude predicted more depressive symptoms during the follow-up period, even after controlling for neuroticism and depressive symptoms at baseline. This is the first prospective study to demonstrate a link between a neural measure of reward sensitivity and the first onset of an MDE. The current results suggest that low reward sensitivity may be an important factor in the development of depression. Descriptors: Reward, Depression, Feedback negativity, Feedback-related negativity, Predictors


title: A content-driven reputation system for the wikipedia

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


title: Algorithms for estimating relative importance in networks

abstract: Large and complex graphs representing relationships among sets of entities are an increasingly common focus of interest in data analysis---examples include social networks, Web graphs, telecommunication networks, and biological networks. In interactive analysis of such data a natural query is "which entities are most important in the network relative to a particular individual or set of individuals?" We investigate the problem of answering such queries in this paper, focusing in particular on defining and computing the importance of nodes in a graph relative to one or more root nodes. We define a general framework and a number of different algorithms, building on ideas from social networks, graph theory, Markov models, and Web graph analysis. We experimentally evaluate the different properties of these algorithms on toy graphs and demonstrate how our approach can be used to study relative importance in real-world networks including a network of interactions among September 11th terrorists, a network of collaborative research in biotechnology among companies and universities, and a network of co-authorship relationships among computer science researchers.


title: Incorporating intra-class variance to fine-grained visual recognition

abstract: Fine-grained visual recognition aims to capture discriminative characteristics amongst visually similar categories. The state-of-the-art research work has significantly improved the fine-grained recognition performance by deep metric learning using triplet network. However, the impact of intra-category variance on the performance of recognition and robust feature representation has not been well studied. In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition. Through partitioning training images within each category into a few groups, we form the triplet samples across different categories as well as different groups, which is called Group Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is strengthened by incorporating intra-class variance with GS-TRS, which may contribute to the optimization objective of triplet network. Extensive experiments over benchmark datasets CompCar and VehicleID show that the proposed GS-TRS has significantly outperformed state-of-the-art approaches in both classification and retrieval tasks.


title: Review of TDNN (time delay neural network) architectures for speech recognition

abstract: The TDNN architecture for speech recognition is described, and its recognition performance for Japanese phonemes and phrases is explained. In comparative studies, it is shown that the TDNN yields superior phoneme recognition performance. The TDNN optimized for phoneme recognition, however, does not necessarily result in optimized word or phrase recognition performance, as overfitting to the specific phoneme data or recording conditions may occur. Care must therefore be taken to achieve robust integration, and several studies toward this goal are reported. >


title: Evolution of naval AESA radars

abstract: Modern radar development is driven by an evolving and expanding range of threats. The low end of the threat spectrum is composed of small, slow-moving targets such as UAVs or small surface vessels. At the high end of the threat spectrum there is an increasing number of very fast missiles, up to the level of (Tactical) Ballistic Missiles. Furthermore, the mission environment has evolved as well, which is illustrated on the naval side by increased operations in littoral environments, often in an asymmetric warfare setting. Active Electronically Scanned Array (AESA) radar systems are uniquely suited to operate in these operational and threat environments, where the flexibility and redundancy of AESA systems give them an edge over more conventional solutions. Active array systems have been in use for more than a decade now (e.g., the APAR X-band system). Typically, these systems use RF beamforming or 1D Digital Beamforming (DBF). Recent advances in technology have made it possible to extend these beamforming concepts to full 2D digital beamforming, also known as Dual Axis Multi-beam. This results in additional flexibility and possibilities on a system level. This paper describes the underlying technology evolution and the added functionality and flexibility that is achieved with such systems.


title: A computational analysis of whole-genome expression data reveals chromosomal domains of gene expression

abstract: Chromosome correlation maps display correlations between the expression patterns of genes on the same chromosome. Using these maps, we show here that adjacent pairs of genes, as well as nearby non-adjacent pairs of genes, show correlated expression independent of their orientation. We present specific examples of adjacent pairs with highly correlated expression patterns, in which the promoter of only one of the two genes contains an upstream activating sequence (UAS) known to be associated with that expression pattern. Finally, we show that genes with similar functions tend to occur in adjacent positions along the chromosomes. Our results suggest that, in certain chromosomal expression domains, an UAS can affect the transcription of genes that are not immediately downstream from it.


title: High-Resolution Millimeter-Wave Digitally Controlled Oscillators With Reconfigurable Passive Resonators

abstract: Two new millimeter-wave digitally controlled oscillators (DCOs) that achieve a tuning range >10% and fine frequency resolution 1 MHz simultaneously are described. Switched metal capacitors distributed across a passive resonator tune the oscillation frequency. To obtain sub-MHz frequency resolution, tuning step attenuation techniques exploiting an inductor and a transformer are proposed. Two 60-GHz implementations, a fine-resolution inductor-based DCO (L-DCO) and a transformer-coupled DCO (T-DCO), are demonstrated in 90-nm CMOS. The phase noise of both DCOs is lower than -90.5 dBc/Hz at 1-MHz offset across 56-62 GHz. The T-DCO achieves a fine frequency tuning step of 2.5 MHz, whereas the L-DCO tuning step is over one order of magnitude finer at 160 kHz. The L-DCO and T-DCO consume 10 and 12 mA, respectively, from a 1.2-V supply. The size of each DCO core is 0.4 ×0.4 mm2.


title: An Optimization Framework for QoS-Enabled Adaptive Video Streaming Over OpenFlow Networks

abstract: OpenFlow is a programmable network protocol and associated hardware designed to effectively manage and direct traffic by decoupling control and forwarding layers of routing. This paper presents an analytical framework for optimization of forwarding decisions at the control layer to enable dynamic Quality of Service (QoS) over OpenFlow networks and discusses application of this framework to QoS-enabled streaming of scalable encoded videos with two QoS levels. We pose and solve optimization of dynamic QoS routing as a constrained shortest path problem, where we treat the base layer of scalable encoded video as a level-1 QoS flow, while the enhancement layers can be treated as level-2 QoS or best-effort flows. We provide experimental results which show that the proposed dynamic QoS framework achieves significant improvement in overall quality of streaming of scalable encoded videos under various coding configurations and network congestion scenarios.