title: Kargus: a highly-scalable software-based intrusion detection system

abstract: As high-speed networks are becoming commonplace, it is increasingly challenging to prevent the attack attempts at the edge of the Internet. While many high-performance intrusion detection systems (IDSes) employ dedicated network processors or special memory to meet the demanding performance requirements, it often increases the cost and limits functional flexibility. In contrast, existing software-based IDS stacks fail to achieve a high throughput despite modern hardware innovations such as multicore CPUs, manycore GPUs, and 10 Gbps network cards that support multiple hardware queues. We present Kargus, a highly-scalable software-based IDS that exploits the full potential of commodity computing hardware. First, Kargus batch processes incoming packets at network cards and achieves up to 40 Gbps input rate even for minimum-sized packets. Second, it exploits high processing parallelism by balancing the pattern matching workloads with multicore CPUs and heterogeneous GPUs, and benefits from extensive batch processing of multiple packets per each IDS function call. Third, Kargus adapts its resource usage depending on the input rate, significantly saving the power in a normal situation. Our evaluation shows that Kargus on a 12-core machine with two GPUs handles up to 33 Gbps of normal traffic and achieves 9 to 10 Gbps even when all packets contain attack signatures, a factor of 1.9 to 4.3 performance improvements over the existing state-of-the-art software IDS. We design Kargus to be compatible with the most popular software IDS, Snort.


title: Effects of Transformational Leadership Training on Attitudinal and Financial Outcomes: A Field Experiment

abstract: A pretest-posttest control-group design (N = 20) was used to assess the effects of transformational leadership training, with 9 and 11 managers assigned randomly to training and control groups, respectively. Training consisted of a 1-day group session and 4 individual booster sessions thereafter on a monthly basis. Multivariate analyses of covariance, with pretest scores as the covariate, showed that the training resulted in significant effects on subordinates' perceptions of leaders' transformational leadership, subordinates' own organizational commitment, and 2 aspects of branch-level financial performance.


title: Goods recommendation based on retail knowledge in a Neo4j graph database combined with an inference mechanism implemented in jess

abstract: Along with the extensive use of ontologies as a well-established means for knowledge representation, there is a pressing need for methods that can transform ontology information into knowledge stored in a Graph Database (GDB) which is considered human-like thinking in terms of objects and their relations. In this paper, we describe a two-layer knowledge graph database: a concept layer and an instance layer. The concept layer is the resulting graph representation transferred from an ontology representation. The instance layer is the instance data associated with concept nodes. In this research, we apply the two-layer approach to a retail business transaction data for business information query and reasoning. The two-layer structure is implemented in Neo4j GDB platform and information query and recommendation is implemented with a Jess reasoning engine. The query and recommendation results are represented and visualized in knowledge graph structures. The performance of the system is evaluated in terms of the time efficiency of answering queries of retail data using the GDB and the novelty of recommendations.


title: Multidimensional appraisal of customer relationship management: integrating balanced scorecard and multi criteria decision making approaches

abstract: This paper presents a comprehensive integrative framework for measuring the performance of customer relationship management (CRM) system based on a detailed empirical study of 32 Iranian internet service provider (ISP) firms. At first, by an extensive literature review and experts who have real practical experiences in the field of CRM, appropriate key performance indicators (KPIs) based on four perspectives of balanced scorecard have been extracted. Then, due to the interdependency and feedback among these KPIs, multiple criteria decision making techniques are used to evaluate CRM performance. Specifically, this study first applies the decision making trial and evaluation laboratory approach to determine the interrelated relationships among criteria, and to find the crucial central and influential factors. Then, the analytical network process method is used to obtain the criterion weights. Finally, according to these previous results, the technique for order of preference by similarity to ideal solution method is adopted to analyze the CRM performance of 32 Iranian ISP firms. The results of this study illustrated that learning and growth is the most crucial influential perspective and it would influence on the other perspectives much more. Furthermore, indicators including organization capital, human capital, customer retention process, customer perceived value, and customer expansion process play an essential role in succeeding of CRM. The results of this study can provide a comprehensive insight for managers into discerning how and through which mechanisms CRM can create merits for the organizations.


title: An intelligent event-driven approach for efficient energy consumption in commercial buildings: smart office use case

abstract: In this paper we present a use case related to the intelligent processing of events coming from the conventional ("cheap") sensors in order to support better energy consumption in commercial buildings. The approach has been implemented using our iCEP framework and deployed in the office space of a real working environment. This research is a kind of the proof of the concept for a new technology that the industry partner would like to exploit. The results are very encouraging: smart decisions for the efficient usage of energy can be made by the intelligent processing of "cheap" sensor events.


title: The Superjunction Insulated Gate Bipolar Transistor Optimization and Modeling

abstract: In this paper, we present a detailed analysis and optimization of the superjunction (SJ) insulated gate bipolar transistor (IGBT). The SJ IGBT is a new device that breaks the IGBT limits, i.e., it delivers performance that is dramatically better. More specifically, we demonstrate here that the optimized SJ IGBT can deliver turn-off losses that are at least 50% lower than those of the state-of-art IGBT while maintaining a similarly low on-state performance, both at room temperature and at higher temperatures. The presence of alternating p- and n-pillars in the drift region gives rise to unique characteristics that when optimized can deliver superior performance. This paper also presents a SPICE model of the SJ IGBT under optimized conditions. Its results are in good agreement with the DESSIS simulation results under direct current conditions. This model consists of an intrinsic MOSFET and a parallel combination of wide- and narrow-base p-n-p bipolar junction transistors.


An overview of traffic sign detection methods


title: Encoding Cryptographic Functions to SAT Using Transalg System

abstract: In this paper we propose the technology for constructing propositional encodings of discrete functions. It is aimed at solving inversion problems of considered functions using state-of-the-art SAT solvers. We implemented this technology in the form of the software system called Transalg, and used it to construct SAT encodings for a number of cryptanalysis problems. By applying SAT solvers to these encodings we managed to invert several cryptographic functions. In particular, we used the SAT encodings produced by Transalg to construct the family of two-block MD5 collisions in which the first 10 bytes are zeros. Also we used Transalg encoding for the widely known A5/1 keystream generator to solve several dozen of its cryptanalysis instances in a distributed computing environment. In the paper we compare in detail the functionality of Transalg with that of similar software systems.


title: A Game-Theoretic Approach to Energy Trading in the Smart Grid

abstract: Electric storage units constitute a key element in the emerging smart grid system. In this paper, the interactions and energy trading decisions of a number of geographically distributed storage units are studied using a novel framework based on game theory. In particular, a noncooperative game is formulated between storage units, such as plug-in hybrid electric vehicles, or an array of batteries that are trading their stored energy. Here, each storage unit's owner can decide on the maximum amount of energy to sell in a local market so as to maximize a utility that reflects the tradeoff between the revenues from energy trading and the accompanying costs. Then in this energy exchange market between the storage units and the smart grid elements, the price at which energy is traded is determined via an auction mechanism. The game is shown to admit at least one Nash equilibrium and a novel algorithm that is guaranteed to reach such an equilibrium point is proposed. Simulation results show that the proposed approach yields significant performance improvements, in terms of the average utility per storage unit, reaching up to 130.2% compared to a conventional greedy approach.


Survey of Deep Learning Applications to Medical Image Analysis


title: A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues

abstract: With the widespread adoption of the Internet of Things (IoT), the number of connected devices is growing at an exponential rate, which is contributing to ever-increasing, massive data volumes. Real-time analytics on the massive IoT data, referred to as the “real-time IoT analytics” in this paper, is becoming the mainstream with an aim to provide an immediate or non-immediate actionable insights and business intelligence. However, the analytics network of the existing IoT systems does not adequately consider the requirements of the real-time IoT analytics. In fact, most researchers overlooked an appropriate design of the IoT analytics network while focusing much on the sensing and delivery networks of the IoT system. Since much of the IoT analytics network has often been taken as granted, the survey, in this paper, we aim to review the state-of-the-art of the analytics network methodologies, which are suitable for real-time IoT analytics. In this vein, we first describe the basics of the real-time IoT analytics, use cases, and software platforms, and then explain the shortcomings of the network methodologies to support them. To address those shortcomings, we then discuss the relevant network methodologies which may support the real-time IoT analytics. Also, we present a number of prospective research problems and future research directions focusing on the network methodologies for the real-time IoT analytics.


title: Gender Identity Disorder in Twins: A Review of the Case Report Literature

abstract: ABSTRACT Introduction The etiology of gender identity disorder (GID) remains largely unknown. In recent literature, increased attention has been attributed to possible biological factors in addition to psychological variables. Aim To review the current literature on case studies of twins concordant or discordant for GID. Methods A systematic, comprehensive literature review. Results Of 23 monozygotic female and male twins, nine (39.1%) were concordant for GID; in contrast, none of the 21 same‐sex dizygotic female and male twins were concordant for GID, a statistically significant difference ( P = 0.005). Of the seven opposite‐sex twins, all were discordant for GID. Conclusions These findings suggest a role for genetic factors in the development of GID. Heylens G, De Cuypere G, Zucker KJ, Schelfaut C, Elaut E, Vanden Bossche H, De Baere E, and T'Sjoen G. Gender identity disorder in twins: A review of the case report literature. J Sex Med 2012;9:751–757.


Science of Elephants in Kauṭilya’s Arthaśāstra


Sentiment analysis of Twitter data using emoticons and emoji ideograms


title: The knowledge level in cognitive architectures: Current limitations and possible developments

abstract: Abstract In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build artificial agents able to exhibit intelligent behaviors in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs’ knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges.


title: Mediated parent-child contact in work-separated families

abstract: Parents and children in families living with regular separation due to work develop strategies to manage being apart. We interviewed 14 pairs of parents and children (ages 7 -- 13) from work-separated families to understand their experiences and the strategies that they use to keep their family together. Parents focus on combining scheduled synchronous and spontaneous asynchronous communication to maintain a constant presence in the life of the child. Children, on the other hand, focus on other sources of support, on other activities, and on the eventual reunion. Both the remote parent and the child rely heavily on a collocated adult to maintain aware-ness and contact. We compare work-separated families with other types of separation and highlight opportunities for new designs.


title: An open IoT framework based on microservices architecture

abstract: With the continuous development and evolvement of Internet of Things (IoT), monolithic application becomes much larger in scale and even more complex in structure. This leads to poor scalability, extensibility and maintainability. In response to those challenges, microservice architecture has been introduced in the field of IoT application, due to its flexibility, lightweight and loose coupling. However, the existing IoT framework of microservice mainly focus on a specific domain, therefore, this greatly limits its application. In this paper, we propose a general microservice system framework for the IoT application, which is a better scalable, extendable and maintainable architecture. We introduce its system design and related microservices, and emphasize on core service and device communication from service layer to physical layer. It has better capacity to support interoperability and accommodate heterogeneous objects. In addition, this framework can easily achieve more application integration such as automation, intelligence, Geo service and Big Data.


title: Chronic Stress and C-Reactive Protein in Mothers During the First Postpartum Year

abstract: ABSTRACTObjectiveElevated levels of C-reactive protein (CRP) are associated with increased risk of cardiovascular and metabolic disease. The current study tested associations between psychosocial stress and CRP in a large sample of women during the first postpartum year.MethodsWe analyzed data colle


Characterizing Group-Level User Behavior in Major Online Social Networks


title: Toward an architecture for never-ending language learning

abstract: We consider here the problem of building a never-ending language learner; that is, an intelligent computer agent that runs forever and that each day must (1) extract, or read, information from the web to populate a growing structured knowledge base, and (2) learn to perform this task better than on the previous day. In particular, we propose an approach and a set of design principles for such an agent, describe a partial implementation of such a system that has already learned to extract a knowledge base containing over 242,000 beliefs with an estimated precision of 74% after running for 67 days, and discuss lessons learned from this preliminary attempt to build a never-ending learning agent.


title: A real time vehicle's license plate recognition system

abstract: A smart and simple algorithm is presented for a vehicle license plate recognition system. Based on pattern matching, this algorithm can be applied for real time detection of license plates for collecting data for surveying or for some application specific purposes. The proposed system has been prototyped using C++ and the experimental results have been shown for recognition of Alberta license plates.


title: Security Analysis of SHA-256 and Sisters

abstract: This paper studies the security of SHA-256, SHA-384 and SHA-512 against collision attacks and provides some insight into the security properties of the basic building blocks of the structure. It is concluded that neither Chabaud and Joux’s attack, nor Dobbertin-style attacks apply. Differential and linear attacks also don’t apply on the underlying structure. However we show that slightly simplified versions of the hash functions are surprisingly weak : whenever symmetric constants and initialization values are used throughout the computations, and modular additions are replaced by exclusive or operations, symmetric messages hash to symmetric digests. Therefore the complexity of collision search on these modified hash functions potentially becomes as low as one wishes.


title: A Collection for Detecting Triggers of Sentiment Spikes

abstract: The advent of social media has given the opportunity to users to publicly express and share their opinion about any topic. Public opinion is very important for the interested entities that can leverage such information in the process of making decisions. In addition, identifying sentiment changes and the likely causes that have triggered them allows interested parties to adjust their strategies and attract more positive sentiment. With the aim to facilitate research on this problem, we describe a collection of tweets that can be used for detecting and ranking the likely triggers of sentiment spikes towards different entities. To build the collection, we first group tweets by topic which are then manually annotated according to sentiment polarity and strength. We believe that this collection can be useful for further research on detecting sentiment change triggers, sentiment analysis and sentiment prediction.


title: Adsorption behavior of calcined layered double hydroxides towards removal of iodide contaminants

abstract: Abstract Calcined layered double hydroxide (CLDH) as a potential adsorbent to remove iodide contaminants was investigated in batch mode. The uptake capacity of calcined LDH is higher than that of LDHs as precursor, due to their different mechanism which is supported by XRD and TG-MS measurements. The influences of CLDH structure, dosage of CLDH, initial concentration of iodide ion and co-existing anions on the removal of iodide ion have been investigated. The applicability of the Langmuir isotherm shows that the maximum uptake capacity was approximately 376 mg/g, and Gibbs free energy (ΔG 0) for the process is −23.8 kJ/mol. The negative ΔG 0 value shows the spontaneity of the adsorption process.


title: Shape Control of a Snake Robot With Joint Limit and Self-Collision Avoidance

abstract: This paper proposes a shape control method for a snake robot, which maintains head position and orientation, and avoids joint limits and self-collision. We used a passive wheeled snake robot that can switch the grounded/lifted status of its wheels. We derived a kinematic model of the robot that represents its redundancy as both joint angles [the shape controllable points (SCPs)] and the null space of the control input. In the control method, the shape is changed by sequential control of the SCPs, and the null space of the control input is used for joint limit and self-collision avoidance. Jumps in control input do not occur, although the controlled variable and the model are switched. Simulations and an experiment were used to demonstrate the effectiveness of the proposed method.


title: Scheduling for heterogeneous Systems using constrained critical paths

abstract: A complex computing problem may be efficiently solved on a system with multiple processing elements by dividing its implementation code into several tasks or modules that execute in parallel. The modules may then be assigned to and scheduled on the processing elements so that the total execution time is minimum. Finding an optimal schedule for parallel programs is a non-trivial task and is considered to be NP-complete. For heterogeneous systems having processors with different characteristics, most of the scheduling algorithms use greedy approach to assign processors to the modules. This paper suggests a novel approach called constrained earliest finish time (CEFT) to provide better schedules for heterogeneous systems using the concept of the constrained critical paths (CCPs). In contrast to other approaches used for heterogeneous systems, the CEFT strategy takes into account a broader view of the input task graph. Furthermore, the statically generated CCPs may be efficiently scheduled in comparison with other approaches. The experimentation results show that the CEFT scheduling strategy outperforms the well-known HEFT, DLS and LMT strategies by producing shorter schedules for a diverse collection of task graphs.


A COSMIC ARCHIPELAGO: MULTIVERSE SCENARIOS IN THE HISTORY OF MODERN COSMOLOGY


title: Large-Scale Complex Adaptive Systems using Multi-Agent Modeling and Simulation

abstract: Modeling and analysis of large-scale complex adaptive systems (CAS) is critical to understanding their key properties such as self-organization, emergence, and adaptability. These properties are difficult to analyze in real-world scenarios due to performance constraints, metric design, and limitations in existing modeling tools. In our previous work, we proposed the Complex Adaptive Systems Language (CASL) and its associated framework. In this paper, we introduce CASL-SG, a Semantic Group extension for large-scale modeling using relational hierarchies. CASL-SG permits large-scale simulations to be executed on modest hardware by enabling simulations to contain approximately twice as many agents. This achieves a 356% runtime improvement for a Game of Life model with 4 million cells. CASL-SG also enables designers to model behaviors of collectives that drive the system and entities towards self-organization and adaptability.


Impact of Social Media on Mental Health


title: Hierarchical Block Sparse Neural Networks

abstract: Sparse deep neural networks(DNNs) are efficient in both memory and compute when compared to dense DNNs. But due to irregularity in computation of sparse DNNs, their efficiencies are much lower than that of dense DNNs on regular parallel hardware such as TPU. This inefficiency leads to poor/no performance benefits for sparse DNNs. Performance issue for sparse DNNs can be alleviated by bringing structure to the sparsity and leveraging it for improving runtime efficiency. But such structural constraints often lead to suboptimal accuracies. In this work, we jointly address both accuracy and performance of sparse DNNs using our proposed class of sparse neural networks called HBsNN (Hierarchical Block sparse Neural Networks). For a given sparsity, HBsNN models achieve better runtime performance than unstructured sparse models and better accuracy than highly structured sparse models.


title: Implementation design and performance analysis of an IEC 61850 MMS server

abstract: While the communication standard IEC 61850 is already established in the area of substation automation, its use to control and monitor distributed energy resources (DER) is still in the early stages. An open discussion on design and performance issues of IEC 61850 implementations could boost its use for DER. This paper presents the architectural design decisions made in an open implementation of the standard in Java. In addition several performance aspects of IEC 61850 MMS server implementations are analyzed. The overhead caused by the OSI layers in MMS communication is analyzed with respect to PDU size and CPU load. The open implementation is profiled in order to point out where optimization in IEC 61850 implementations are most useful. Finally the performance of this implementation is compared to that of a commercial stack written in the C language.


title: Comparing ISO/IEC 12207 and CMMI-DEV: Towards a mapping of ISO/IEC 15504-7

abstract: Software process improvement is a planned, managed and controlled effort which aims to enhance the capability of the software development processes of an organization. In particular, SPI often involves process reference models, process assessment methods and models that guide process improvement though specific standards such as the ISO and CMMI families. Recently, growing interest has been shown towards the need for harmonization of different improvement technologies with the aim of presenting an integrated vision about the standards. In this sense this paper presents a comparison between the process areas of CMMI-DEV and the processes described in the latest version of ISO/IEC 12207:2008. Based on these results we investigate the relationship between the CMMI-DEV and ISO/IEC 15504-7 models with the aim of identifying the degree of coverage of CMMI-DEV maturity levels in relation to ISO/IEC 15504-7.


title: Structure and Overlaps of Ground-Truth Communities in Networks

abstract: One of the main organizing principles in real-world networks is that of network communities, where sets of nodes organize into densely linked clusters. Even though detection of such communities is of great interest, understanding the structure communities in large networks remains relatively limited. In particular, due to the unavailability of labeled ground-truth data, it was traditionally very hard to develop accurate models of network community structure. Here we use six large social, collaboration, and information networks where nodes explicitly state their ground-truth community memberships. For example, nodes in social networks join into explicitly defined interest based groups, and we use such groups as explicitly labeled ground-truth communities. We use such ground-truth communities to study their structural signatures by analyzing how ground-truth communities emerge in networks and how they overlap. We observe some surprising phenomena. First, ground-truth communities contain high-degree hub nodes that reside in community overlaps and link to most of the members of the community. Second, the overlaps of communities are more densely connected than the non-overlapping parts of communities. We show that this in contrast to the conventional wisdom that community overlaps are more sparsely connected than the non-overlapping parts themselves. We then show that many existing models of network communities do not capture dense community overlaps. This in turn means that most present models and community detection methods confuse overlaps as separate communities. In contrast, we present the community-affiliation graph model (AGM), a conceptual model of network community structure. We demonstrate that AGM reliably captures the overall structure of networks as well as the overlapping and hierarchical nature of network communities.


title: Snap-together visualization: can users construct and operate coordinated visualizations?

abstract: Multiple coordinated visualizations enable users to rapidly explore complex information. However, users often need unforeseen combinations of coordinated visualizations. Snap-together visualization (Snap) enables users to rapidly and dynamically construct coordinated?visualization interfaces, customized for their data, without programming. Users U001load data into desired visualizations, then construct coordinations between them for brushing and linking, overview and detail view, drill down, etc. Snap formalizes a conceptual model of visualization coordination based on the relational data model. Visualization developers can easily Snap-enable their independent visualizations using a simple API.Empirical evaluation reveals benefits, cognitive issues and usability concerns with coordination concepts and Snap. Two user studies explore coordination construction and operation. Data-savvy users successfully, enthusiastically and rapidly constructed powerful coordinated?visualization interfaces of their own. Operating an overview-and-detail coordination reliably improved user performance by 30?80% over detail-only and uncoordinated interfaces for most tasks.


title: Recommender systems in e-commerce

abstract: Recommender systems are changing from novelties used by a few E-commerce sites, to serious business tools that are re-shaping the world of E-commerce. Many of the largest commerce Web sites are already using recommender systems to help their customers find products to purchase. A recommender system learns from a customer and recommends products that she will find most valuable from among the available products. In this paper we present an explanation of how recommender systems help E-commerce sites increase sales, and analyze six sites that use recommender systems including several sites that use more than one recommender system. Based on the examples, we create a taxonomy of recommender systems, including the interfaces they present to customers, the technologies used to create the recommendations, and the inputs they need from customers. We conclude with ideas for new applications of recommender systems to E-commerce.


title: Machine learning based network intrusion detection

abstract: Network security has become a very important issue and attracted a lot of study and practice. To detect or prevent network attacks, a network intrusion detection (NID) system may be equipped with machine learning algorithms to achieve better accuracy and faster detection speed. One of the major advantages of applying machine learning to network intrusion detection is that we don't need expert knowledge as much as the black or white list model. In this paper, we apply the equality constrained-optimization-based extreme learning machine to network intrusion detection. An adaptively incremental learning strategy is proposed to derive the optimal number of hidden neurons. The optimization criteria and a way of adaptively increasing hidden neurons with binary search are developed. The proposed approach is applied to network intrusion detection to examine its capability. Experimental results show our proposed approach is effective in building models with good attack detection rates and fast learning speed.


title: Unsupervised Multi-Class Co-Segmentation via Joint-Cut Over $L_{1}$ -Manifold Hyper-Graph of Discriminative Image Regions

abstract: This paper systematically advocates a robust and efficient unsupervised multi-class co-segmentation approach by leveraging underlying subspace manifold propagation to exploit the cross-image coherency. It can combat certain image co-segmentation difficulties due to viewpoint change, partial occlusion, complex background, transient illumination, and cluttering texture patterns. Our key idea is to construct a powerful hyper-graph joint-cut framework, which incorporates mid-level image regions-based intra-image feature representation and $L_{1}$ -manifold graph-based inter-image coherency exploration. For local image region generation, we propose a bi-harmonic distance distribution difference metric to govern the super-pixel clustering in a bottom-up way. It not only affords drastic data reduction but also gives rise to discriminative and structure meaningful feature representation. As for the inter-image coherency, we leverage multi-type features involved $L_{1}$ -graph to detect the underlying local manifold from cross-image regions. As a result, the implicit supervising information could be encoded into the unsupervised hyper-graph joint-cut framework. We conduct extensive experiments and make comprehensive evaluations with other state-of-the-art methods over various benchmarks, including iCoseg, MSRC, and Oxford flower. All the results demonstrate the superiorities of our method in terms of accuracy, robustness, efficiency, and versatility.


title: Affect Intensity Analysis of Dark Web Forums

abstract: Affects play an important role in influencing people's perceptions and decision making. Affect analysis is useful for measuring the presence of hate, violence, and the resulting propaganda dissemination across extremist groups. In this study we performed affect analysis of U.S. and Middle Eastern extremist group forum postings. We constructed an affect lexicon using a probabilistic disambiguation technique to measure the usage of violence and hate affects. These techniques facilitate in depth analysis of multilingual content. The proposed approach was evaluated by applying it across 16 U.S. supremacist and Middle Eastern extremist group forums. Analysis across regions reveals that the Middle Eastern test bed forums have considerably greater violence intensity than the U.S. groups. There is also a strong linear relationship between the usage of hate and violence across the Middle Eastern messages.


title: WOUND HEALING: AN OVERVIEW OF ACUTE, FIBROTIC AND DELAYED HEALING

abstract: Abstract Acute wounds normally heal in a very orderly and efficient manner characterized by four distinct, but overlapping phases: hemostasis, inflammation, proliferation and remodeling. Specific biological markers characterize healing of acute wounds. Likewise, unique biologic markers also characterize pathologic responses resulting in fibrosis and chronic non-healing ulcers. This review describes the major biological processes associated with both normal and pathologic healing. The normal healing response begins the moment the tissue is injured. As the blood components spill into the site of injury, the platelets come into contact with exposed collagen and other elements of the extracellular matrix. This contact triggers the platelets to release clotting factors as well as essential growth factors and cytokines such as platelet-derived growth factor (PDGF) and transforming growth factor beta (TGF-beta). Following hemostasis, the neutrophils then enter the wound site and begin the critical task of phagocytosis to remove foreign materials, bacteria and damaged tissue. As part of this inflammatory phase, the macrophages appear and continue the process of phagocytosis as well as releasing more PDGF and TGF beta. Once the wound site is cleaned out, fibroblasts migrate in to begin the proliferative phase and deposit new extracellular matrix. The new collagen matrix then becomes cross-linked and organized during the final remodeling phase. In order for this efficient and highly controlled repair process to take place, there are numerous cell-signaling events that are required. In pathologic conditions such as non-healing pressure ulcers, this efficient and orderly process is lost and the ulcers are locked into a state of chronic inflammation characterized by abundant neutrophil infiltration with associated reactive oxygen species and destructive enzymes. Healing proceeds only after the inflammation is controlled. On the opposite end of the spectrum, fibrosis is characterized by excessive matrix deposition and reduced remodeling. Often fibrotic lesions are associated with increased densities of mast cells. By understanding the functional relationships of these biological processes of normal compared to abnormal wound healing, hopefully new strategies can be designed to treat the pathological conditions.


title: Migrating Multi-page Web Applications to Single-page AJAX Interfaces

abstract: Recently, a new web development technique for creating interactive web applications, dubbed AJAX, has emerged. In this new model, the single-page web interface is composed of individual components which can be updated/replaced independently. With the rise of AJAX web applications classical multi-page web applications are becoming legacy systems. If until a year ago, the concern revolved around migrating legacy systems to web-based settings, today we have a new challenge of migrating web applications to single-page AJAX applications. Gaining an understanding of the navigational model and user interface structure of the source application is the first step in the migration process. In this paper, we explore how reverse engineering techniques can help analyze classic web applications for this purpose. Our approach, using a schema-based clustering technique, extracts a navigational model of web applications, and identifies candidate user interface components to be migrated to a single-page AJAX interface. Additionally, results of a case study, conducted to evaluate our tool, are presented.


Unifying Instance-Based and Rule-Based Induction


title: Dynamic trajectory planning for mobile robot navigation in crowded environments

abstract: This paper describes a trajectory planning algorithm for mobile robot navigation in crowded environments; the aim is to solve the problem of planning a valid path through moving people.


title: External Genital Warts: Diagnosis, Treatment, and Prevention

abstract: External genital warts (EGWs) are visible warts that occur in the perigenital and perianal regions. They are due primarily to non-oncogenic human papillomavirus (HPV) types, usually types 6 and 11. Physical examination assisted by bright light and magnification is the recommended approach for primary diagnosis. Biopsy is indicated when EGWs are fixed to underlying structures or discolored or when standard therapies are not effective. Recurrences are common, and there is no single treatment that is superior to others. Among women with atypical squamous cells, molecular HPV testing may be useful in determining who should be referred for colposcopy. Condoms may provide some protection against HPV-related diseases and thus are recommended in new sexual relationships and when partnerships are not mutually monogamous. Because the efficacy of cesarean section in preventing vertical transmission of HPV infection from women with EGWs to their progeny has not been proved, it is not recommended.


Application of a novel deformable image registration technique to facilitate classification, tracking and targeting of tumor and normal tissue


title: LI, SONG, GONG: SKETCH RECOGNITION BY ENSEMBLE MATCHING 1 Sketch Recognition by Ensemble Matching of Structured Features

abstract: Sketch recognition aims to automatically classify human hand sketches of objects into known categories. This has become increasingly a desirable capability due to recent advances in human computer interaction on portable devices. The problem is nontrivial because of the sparse and abstract nature of hand drawings as compared to photographic images of objects, compounded by a highly variable degree of details in human sketches. To this end, we present a method for the representation and matching of sketches by exploiting not only local features but also global structures of sketches, through a star graph based ensemble matching strategy. Different local feature representations were evaluated using the star graph model to demonstrate the effectiveness of the ensemble matching of structured features. We further show that by encapsulating holistic structure matching and learned bag-of-features models into a single framework, notable recognition performance improvement over the state-of-the-art can be observed. Extensive comparative experiments were carried out using the currently largest sketch dataset released by Eitz et al. [15], with over 20,000 sketches of 250 object categories generated by AMT (Amazon Mechanical Turk) crowd-sourcing.


title: A Feature-Reduction Fuzzy Clustering Algorithm Based on Feature-Weighted Entropy

abstract: Fuzzy clustering algorithms generally treat data points with feature components under equal importance. However, there are various datasets with irrelevant features involved in clustering process that may cause bad performance for fuzzy clustering algorithms. That is, different feature components should take different importance. In this paper, we present a novel method for improving fuzzy clustering algorithms that can automatically compute individual feature weight, and simultaneously reduce these irrelevant feature components. In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the best known. We first consider the FCM objective function with feature-weighted entropy, and construct a learning schema for parameters, and then reduce these irrelevant feature components. We call it a feature-reduction FCM (FRFCM). During FRFCM processes, a new procedure for eliminating irrelevant feature(s) with small weight(s) is created for feature reduction. The computational complexity of FRFCM is also analyzed. Some numerical and real datasets are used to compare FRFCM with various feature-weighted FCM methods in the literature. Experimental results and comparisons actually demonstrate these good aspects of FRFCM with its effectiveness and usefulness in practice.


title: Beyond market baskets: generalizing association rules to correlations

abstract: One of the most well-studied problems in data mining is mining for association rules in market basket data. Association rules, whose significance is measured via support and confidence, are intended to identify rules of the type, “A customer purchasing item A often also purchases item B.” Motivated by the goal of generalizing beyond market baskets and the association rules used with them, we develop the notion of mining rules that identify correlations (generalizing associations), and we consider both the absence and presence of items as a basis for generating rules. We propose measuring significance of associations via the chi-squared test for correlation from classical statistics. This leads to a measure that is upward closed in the itemset lattice, enabling us to reduce the mining problem to the search for a border between correlated and uncorrelated itemsets in the lattice. We develop pruning strategies and devise an efficient algorithm for the resulting problem. We demonstrate its effectiveness by testing it on census data and finding term dependence in a corpus of text documents, as well as on synthetic data.


title: Finding "It": Weakly-Supervised Reference-Aware Visual Grounding in Instructional Videos

abstract: Grounding textual phrases in visual content with standalone image-sentence pairs is a challenging task. When we consider grounding in instructional videos, this problem becomes profoundly more complex: the latent temporal structure of instructional videos breaks independence assumptions and necessitates contextual understanding for resolving ambiguous visual-linguistic cues. Furthermore, dense annotations and video data scale mean supervised approaches are prohibitively costly. In this work, we propose to tackle this new task with a weakly-supervised framework for reference-aware visual grounding in instructional videos, where only the temporal alignment between the transcription and the video segment are available for supervision. We introduce the visually grounded action graph, a structured representation capturing the latent dependency between grounding and references in video. For optimization, we propose a new reference-aware multiple instance learning (RA-MIL) objective for weak supervision of grounding in videos. We evaluate our approach over unconstrained videos from YouCookII and RoboWatch, augmented with new reference-grounding test set annotations. We demonstrate that our jointly optimized, reference-aware approach simultaneously improves visual grounding, reference-resolution, and generalization to unseen instructional video categories.


title: Supplier strategies to compensate for knowledge asymmetries in buyer-supplier relationships: implications for economic upgrading

abstract: This paper explores a special form of international outsourcing relationship in which suppliers make recurrent discrete transactions with the same buyers over a long period of time without the existence of any original legally binding written agreement. The study examines three research questions: (1) Can suppliers in such relationships access any of their buyers' tacit knowledge? (2) What implications does their access or the lack thereof have for their economic upgrading? (3) What strategies do suppliers adopt to compensate for existing knowledge asymmetries? The case analysis of three small Bangladeshi garment manufacturers reveals the following key findings. The studied firms only have access to their buyers' explicit/codified knowledge. Notwithstanding this, they have successfully developed relevant knowledge that has allowed them to engage in process upgrading.


Advancement in the Mobile APP Review System to Enhance Quality of Mobile Applications


title: Deep convolutional recurrent neural network with attention mechanism for robust speech emotion recognition

abstract: We present a deep convolutional recurrent neural network for speech emotion recognition based on the log-Mel filterbank energies, where the convolutional layers are responsible for the discriminative feature learning. Based on the hypothesis that a better understanding of the internal configuration within an utterance would help reduce misclassification, we further propose a convolutional attention mechanism to learn the utterance structure relevant to the task. In addition, we quantitatively measure the performance gain contributed by each module in our model in order to characterize the nature of emotion expressed in speech. The experimental results on the eNTERFACE'05 emotion database validate our hypothesis and also demonstrate an absolute improvement by 4.62% compared to the state-of-the-art approach.


title: Simultaneous Administration of the Rosenberg Self-Esteem Scale in 53 Nations: Exploring the Universal and Culture-Specific Features of Global Self-Esteem

abstract: The Rosenberg Self-Esteem Scale (RSES) was translated into 28 languages and administered to 16,998 participants across 53 nations. The RSES factor structure was largely invariant across nations. RSES scores correlated with neuroticism, extraversion, and romantic attachment styles within nearly all nations, providing additional support for cross-cultural equivalence of the RSES. All nations scored above the theoretical midpoint of the RSES, indicating generally positive self-evaluation may be culturally universal. Individual differences in self-esteem were variable across cultures, with a neutral response bias prevalent in more collectivist cultures. Self-competence and self-liking subscales of the RSES varied with cultural individualism. Although positively and negatively worded items of the RSES were correlated within cultures and were uniformly related to external personality variables, differences between aggregates of positive and negative items were smaller in developed nations. Because negatively worded items were interpreted differently across nations, direct cross-cultural comparisons using the RSES may have limited value.


title: Visual and Haptic Perception Training to Improve Handwriting Skills in Children With Dysgraphia

abstract: We investigated a visual-perceptual and haptic-perceptual training program to enhance motor skills and Chinese handwriting performance among children with handwriting difficulties. The participants were 28 first- and second-grade children with handwriting difficulties. They were matched according to age and then randomly assigned into a control group or an experimental group. Participants in the experimental group received 12 sessions of a training program, whereas those in the control group received conventional handwriting training. The Test of Visual Perceptual Skills-Third Edition (TVPS-3), Tactual Performance Test (TPT), and Battery of Chinese Basic Literacy (BCBL) were all administered before and after 6 wk of intervention. Repeated-measures analysis of variance revealed that the experimental group showed significant improvement on the TVPS-3 but not on the TPT. Along with the improved visual-perceptual skills, the experimental group showed a significant difference in far-point copy speed and handwriting accuracy, as reflected in the BCBL.


title: The First Workshop on Knowledge Graphs and Semantics for Text Retrieval and Analysis (KG4IR)

abstract: Knowledge graphs have been used throughout the history of information retrieval for a variety of tasks. Technological advances in knowledge acquisition and alignment technology from the last few years gave rise to a body of new approaches for utilizing knowledge graphs in text retrieval tasks. It is therefore time to consolidate the community efforts in studying how knowledge graph technology can be employed in information retrieval systems in the most effective way. It is also time to start a dialogue with researchers working on knowledge acquisition and alignment to ensure that resulting technologies and algorithms meet the demands posed by information retrieval tasks. The goal of this workshop is to bring together a community of researchers and practitioners who are interested in using, aligning, and constructing knowledge graphs and similar semantic resources for information retrieval applications.


title: NUTRIENT CLAIMS ON PACKAGING

abstract: Food labels provide important information to consumers and assist them in making informed purchase decisions. Over time, food labels have evolved from basic product identity to potentially complex labels. The labels can include specific nutrient claims as well as detailed nutrient composition data. To remain competitive in the global marketplace, the meat industry must understand the role of meat in the diet, the perceived and real risks associated with consumption of meat, methods for improving the nutritional compositions, and the regulatory requirements for properly labeling meat. This article will highlight key areas related to meat intake and labeling.


title: Distribution of genome-wide linkage disequilibrium based on microsatellite loci in the Samoan population

abstract: Whole genome-wide scanning for susceptibility loci based on linkage disequilibrium (LD) has been proposed as a powerful strategy for mapping common complex diseases, especially in isolated populations. We recruited 389 individuals from 175 families in the US territory of American Samoa, and 96 unrelated individuals from American Samoa and the independent country of Samoa in order to examine background LD by using a 10 centimorgan (cM) map containing 381 autosomal and 18 X-linked microsatellite markers. We tested the relationship between LD and recombination fraction by fitting a regression model. We estimated a slope of -0.021 (SE 0.00354; p < 0.0001). Based on our results, LD in the Samoan population decays steadily as the recombination fraction between autosomal markers increases. The patterns of LD observed in the Samoan population are quite similar to those previously observed in Palau but markedly contrast with those observed in a non-isolated Caucasian sample, where there is essentially no marker-to-marker LD. Our analyses support the hypothesis of a recent bottleneck, which is consistent with the known demographic history of the Samoan population. Furthermore, population substructure tests support the hypothesis that self-identified Samoans represent one homogenous genetic population.


title: Deeply-Recursive Convolutional Network for Image Super-Resolution

abstract: We propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN). Our network has a very deep recursive layer (up to 16 recursions). Increasing recursion depth can improve performance without introducing new parameters for additional convolutions. Albeit advantages, learning a DRCN is very hard with a standard gradient descent method due to exploding/ vanishing gradients. To ease the difficulty of training, we propose two extensions: recursive-supervision and skip-connection. Our method outperforms previous methods by a large margin.


title: Chinese Whispers: Cooperative Paraphrase Acquisition

abstract: We present a framework for the acquisition of sentential paraphrases based on crowdsourcing. The proposed method maximizes the lexical divergence between an original sentence s and its valid paraphrases by running a sequence of paraphrasing jobs carried out by a crowd of non-expert workers. Instead of collecting direct paraphrases of s, at each step of the sequence workers manipulate semantically equivalent reformulations produced in the previous round. We applied this method to paraphrase English sentences extracted from Wikipedia. Our results show that, keeping at each round n the most promising paraphrases (i.e. the more lexically dissimilar from those acquired at round n-1), the monotonic increase of divergence allows to collect good-quality paraphrases in a cost-effective manner.


A Literature Review of Spreadsheet Technology


title: CT image segmentation methods for bone used in medical additive manufacturing

abstract: Abstract Aim of the study The accuracy of additive manufactured medical constructs is limited by errors introduced during image segmentation. The aim of this study was to review the existing literature on different image segmentation methods used in medical additive manufacturing. Methods Thirty-two publications that reported on the accuracy of bone segmentation based on computed tomography images were identified using PubMed, ScienceDirect, Scopus, and Google Scholar. The advantages and disadvantages of the different segmentation methods used in these studies were evaluated and reported accuracies were compared. Results The spread between the reported accuracies was large (0.04 mm – 1.9 mm). Global thresholding was the most commonly used segmentation method with accuracies under 0.6 mm. The disadvantage of this method is the extensive manual post-processing required. Advanced thresholding methods could improve the accuracy to under 0.38 mm. However, such methods are currently not included in commercial software packages. Statistical shape model methods resulted in accuracies from 0.25 mm to 1.9 mm but are only suitable for anatomical structures with moderate anatomical variations. Conclusions Thresholding remains the most widely used segmentation method in medical additive manufacturing. To improve the accuracy and reduce the costs of patient-specific additive manufactured constructs, more advanced segmentation methods are required.


title: Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg?

abstract: Twitter is a new web application playing dual roles of online social networking and microblogging. Users communicate with each other by publishing text-based posts. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots, which appear to be a double-edged sword to Twitter. Legitimate bots generate a large amount of benign tweets delivering news and updating feeds, while malicious bots spread spam or malicious contents. More interestingly, in the middle between human and bot, there has emerged cyborg referred to either bot-assisted human or human-assisted bot. To assist human users in identifying who they are interacting with, this paper focuses on the classification of human, bot, and cyborg accounts on Twitter. We first conduct a set of large-scale measurements with a collection of over 500,000 accounts. We observe the difference among human, bot, and cyborg in terms of tweeting behavior, tweet content, and account properties. Based on the measurement results, we propose a classification system that includes the following four parts: 1) an entropy-based component, 2) a spam detection component, 3) an account properties component, and 4) a decision maker. It uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot, or cyborg. Our experimental evaluation demonstrates the efficacy of the proposed classification system.


title: Modelling fire probability in the Brazilian Amazon using the maximum entropy method

abstract: Fires are both a cause and consequence of important changes in the Amazon region. The development and implementation of better fire management practices and firefighting strategies are important steps to reduce the Amazon ecosystems’ degradation and carbon emissions from land-use change in the region. We extended the application of the maximum entropy method (MaxEnt) to model fire occurrence probability in the Brazilian Amazon on a monthly basis during the 2008 and 2010 fire seasons using fire detection data derived from satellite images. Predictor variables included climatic variables, inhabited and uninhabited protected areas and land-use change maps. Model fit was assessed using the area under the curve (AUC) value (threshold-independent analysis), binomial tests and model sensitivity and specificity (threshold-dependent analysis). Both threshold-independent (AUC = 0.919 ± 0.004) and threshold-dependent evaluation indicate satisfactory model performance. Pasture, annual deforestation and secondary vegetation are the most effective variables for predicting the distribution of the occurrence data. Our results show that MaxEnt may become an important tool to guide on-the-ground decisions on fire prevention actions and firefighting planning more effectively and thus to minimise forest degradation and carbon loss from forest fires in Amazonian ecosystems.


title: Improving Neighborhood-Based Collaborative Filtering by Reducing Hubness

abstract: For recommending multimedia items, collaborative filtering (CF) denotes the technique of automatically predicting a user's rating or preference for an item by exploiting item preferences of a (large) group of other users. In traditional memory-based (or neighborhood-based) recommenders, this is accomplished by, first, selecting a number of similar users (or items) and, second, combining their ratings into a single user's predicted rating for an item. Strategies for both defining similarity (i.e., to identify nearest neighbors) and for combining ratings (i.e., to weight their impact) have been extensively studied and even resulted in inconsistent findings. In this paper, we investigate the effects of the high dimensionality of userxitem matrices on the quality of memory-based movie rating prediction. By examining several publicly available real-world CF data sets, we show that the step of nearest neighbor selection is affected by the phenomena of similarity concentration and hub occurrence due to high-dimensional data spaces and the class of similarity measures used. To mitigate this, we adapt a normalization technique called mutual proximity that has been shown to reduce these effects in classification tasks. Finally, we show that removing hubs and incorporating normalized similarity values into the neighbor weighting step leads to increased rating prediction accuracy, observable on all examined data sets in terms of lowered error measure (RMSE).


title: A Novel User Authentication Scheme Based on QR-Code

abstract: User authentication is one of the fundamental procedures to ensure secure communications and share system resources over an insecure public network channel. Thus, a simple and efficient authentication mechanism is required for securing the network system in the real environment. In general, the password-based authentication mechanism provides the basic capability to prevent unauthorized access. Especially, the purpose of the one-time password is to make it more difficult to gain unauthorized access to restricted resources. Instead of using the password file as conventional authentication systems, many researchers have devoted to implement various one-time password schemes using smart cards, time-synchronized token or short message service in order to reduce the risk of tampering and maintenance cost. However, these schemes are impractical because of the far from ubiquitous hardware devices or the infrastructure requirements. To remedy these weaknesses, the attraction of the QR - code technique can be introduced into our one-time password authentication protocol. Not the same as before, the proposed scheme based on QR code not only eliminates the usage of the password verification table, but also is a cost effective solution since most internet users already have mobile phones. For this reason, instead of carrying around a separate hardware token for each security domain, the superiority of handiness benefit from the mobile phone makes our approach more practical and convenient.


title: GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms

abstract: In continuous action domains, standard deep reinforcement learning algorithms like DDPG suffer from inefficient exploration when facing sparse or deceptive reward problems. Conversely, evolutionary and developmental methods focusing on exploration like Novelty Search, Quality-Diversity or Goal Exploration Processes explore more robustly but are less efficient at fine-tuning policies using gradient descent. In this paper, we present the GEP-PG approach, taking the best of both worlds by sequentially combining a Goal Exploration Process and two variants of DDPG. We study the learning performance of these components and their combination on a low dimensional deceptive reward problem and on the larger Half-Cheetah benchmark. We show that DDPG fails on the former and that GEP-PG improves over the best DDPG variant in both environments. Supplementary videos and discussion can be found at http://frama.link/gep_pg, the code at http://github.com/flowersteam/geppg.


title: Association mapping of seed quality traits using the Canadian flax (Linum usitatissimum L.) core collection

abstract: Key messageThe identification of stable QTL for seed quality traits by association mapping of a diverse panel of linseed accessions establishes the foundation for assisted breeding and future fine mapping in linseed.AbstractLinseed oil is valued for its food and non-food applications. Modifying its oil content and fatty acid (FA) profiles to meet market needs in a timely manner requires clear understanding of their quantitative trait loci (QTL) architectures, which have received little attention to date. Association mapping is an efficient approach to identify QTL in germplasm collections. In this study, we explored the quantitative nature of seed quality traits including oil content (OIL), palmitic acid, stearic acid, oleic acid, linoleic acid (LIO) linolenic acid (LIN) and iodine value in a flax core collection of 390 accessions assayed with 460 microsatellite markers. The core collection was grown in a modified augmented design at two locations over 3 years and phenotypic data for all seven traits were obtained from all six environments. Significant phenotypic diversity and moderate to high heritability for each trait (0.73–0.99) were observed. Most of the candidate QTL were stable as revealed by multivariate analyses. Nine candidate QTL were identified, varying from one for OIL to three for LIO and LIN. Candidate QTL for LIO and LIN co-localized with QTL previously identified in bi-parental populations and some mapped nearby genes known to be involved in the FA biosynthesis pathway. Fifty-eight percent of the QTL alleles were absent (private) in the Canadian cultivars suggesting that the core collection possesses QTL alleles potentially useful to improve seed quality traits. The candidate QTL identified herein will establish the foundation for future marker-assisted breeding in linseed.


title: End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural Networks

abstract: We investigate sequence machine learning techniques on raw radio signal time-series data. By applying deep recurrent neural networks we learn to discriminate between several application layer traffic types on top of a constant envelope modulation without using an expert demodulation algorithm. We show that complex protocol sequences can be learned and used for both classification and generation tasks using this approach.


title: Communication and Localization for a Cooperative eSafety-System

abstract: The core concept of the project, presented in this paper, is to enable the future availability of a modular cooperative system that will bring together sensor and communication technologies permitting to all road users (vehicles, motorcycles, bicycles, pedestrians) to take an active part in the reduction of the number of accidents that involve vulnerable road users. For this, legacy detection technologies, such as infrared or radar are enhanced by a RF-based communication.


title: A semantic model for actions and events in ambient intelligence

abstract: Event ma nagement and response generation are two essential aspects of systems for ambient intelligence. This work proposes handling these issues through the use of a semantic model for ambient intelligence which, under the umbrella of a philosophical and common-sense optic, describes what actions and events are, how they are connected, and how computational systems should think about their meaning. This model entails an approach with which to both reason about and model context events and generate behavioral responses to those events, when necessary. The model supports this ad hoc response generation by automatically composing services when those which are available do not meet the expected functionality (without requesting user intervention). An evaluation methodology is presented and illustrated with a case scenario, in which synthetic data has been generated to emulate events and analyze the system response. The evaluation of the system response is carried out on the basis of a vector of attributes.


title: Advanced Maintenance Simulation by Means of Hand-Based Haptic Interfaces

abstract: Aerospace industry has been involved in virtual simulation for design and testing since the birth of virtual reality. Today this industry is showing a growing interest in the development of haptic-based maintenance training applications, which represent the most advanced way to simulate maintenance and repair tasks within a virtual environment by means of a visual-haptic approach. The goal is to allow the trainee to experiment the service procedures not only as a workflow reproduced at a visual level but also in terms of the kinaesthetic feedback involved with the manipulation of tools and components. This study, conducted in collaboration with aerospace industry specialists, is aimed to the development of an immersive virtual capable of immerging the trainees into a virtual environment where mechanics and technicians can perform maintenance simulation or training tasks by directly manipulating 3D virtual models of aircraft parts while perceiving force feedback through the haptic interface. The proposed system is based on ViRstperson, a virtual reality engine under development at the Italian Center for Aerospace Research (CIRA) to support engineering and technical activities such as design-time maintenance procedure validation, and maintenance training. This engine has been extended to support haptic-based interaction, enabling a more complete level of interaction, also in terms of impedance control, and thus fostering the development of haptic knowledge in the user. The user's "sense of touch" within the immersive virtual environment is simulated through an Immersion CyberForce® hand-based force-feedback device. Preliminary testing of the proposed system seems encouraging.


title: Learning context-sensitive word embeddings with neural tensor skip-gram model

abstract: Distributed word representations have a rising interest in NLP community. Most of existing models assume only one vector for each individual word, which ignores polysemy and thus degrades their effectiveness for downstream tasks. To address this problem, some recent work adopts multiprototype models to learn multiple embeddings per word type. In this paper, we distinguish the different senses of each word by their latent topics. We present a general architecture to learn the word and topic embeddings efficiently, which is an extension to the Skip-Gram model and can model the interaction between words and topics simultaneously. The experiments on the word similarity and text classification tasks show our model outperforms state-of-the-art methods.


title: A Full-Bridge Resonant Inverter With Modified Phase-Shift Modulation for High-Frequency AC Power Distribution Systems

abstract: The design of a resonant inverter for high-frequency ac (HFAC) power distribution systems is complicated by the following three factors: 1) A number of electronic loads located in different locations are connected to the resonant inverter, the impedance, and the power factor of the equivalent load of which varies over a wider range than a system with a certain load; 2) the resonant inverter is subject to an input-line voltage varying over a wide range; and 3) the characteristics of the resonant inverter depend on the load impedance. It is mandatory to operate the inverter with zero-voltage switching under various load conditions of different power factors and over wide input variations. It is further desirable that multiple resonant inverters can be paralleled with simple current-sharing control (CSC). A phase-shift- modulation (PSM)-controlled full-bridge series-parallel resonant inverter is proposed for the HFAC power distribution architectures. A new PSM method is proposed with which the phase angle of the inverter output voltage is independent of the modulation signal of the phase-shift modulator. Such a feature allows multiple resonant inverters to operate in parallel with a magnitude CSC. The resonant inverter is analyzed with a general nonresistive load model, and the design curves are developed. A prototype resonant inverter system is designed and implemented with an operation frequency of 1 MHz, a rated output power of 150 W, and a sinusoidal output voltage of 1-MHz 28-V rms. The proposed resonant inverter has the advantages of high efficiency over wide input/output line variations, high waveform quality of the output voltage, and phase-angle independence of the voltage-feedback/feed-forward control and CSC.


title: Analysis and Visualization of Discrete Fracture Networks Using a Flow Topology Graph

abstract: We present an analysis and visualization prototype using the concept of a flow topology graph (FTG) for characterization of flow in constrained networks, with a focus on discrete fracture networks (DFN), developed collaboratively by geoscientists and visualization scientists. Our method allows users to understand and evaluate flow and transport in DFN simulations by computing statistical distributions, segment paths of interest, and cluster particles based on their paths. The new approach enables domain scientists to evaluate the accuracy of the simulations, visualize features of interest, and compare multiple realizations over a specific domain of interest. Geoscientists can simulate complex transport phenomena modeling large sites for networks consisting of several thousand fractures without compromising the geometry of the network. However, few tools exist for performing higher-level analysis and visualization of simulated DFN data. The prototype system we present addresses this need. We demonstrate its effectiveness for increasingly complex examples of DFNs, covering two distinct use cases – hydrocarbon extraction from unconventional resources and transport of dissolved contaminant from a spent nuclear fuel repository.


title: Convergence of a block coordinate descent method for nondifferentiable minimization

abstract: We study the convergence properties of a (block) coordinate descent method applied to minimize a nondifferentiable (nonconvex) function f(x1, . . . , x N ) with certain separability and regularity properties. Assuming that f is continuous on a compact level set, the subsequence convergence of the iterates to a stationary point is shown when either f is pseudoconvex in every pair of coordinate blocks from among N-1 coordinate blocks or f has at most one minimum in each of N-2 coordinate blocks. If f is quasiconvex and hemivariate in every coordinate block, then the assumptions of continuity of f and compactness of the level set may be relaxed further. These results are applied to derive new (and old) convergence results for the proximal minimization algorithm, an algorithm of Arimoto and Blahut, and an algorithm of Han. They are applied also to a problem of blind source separation.


title: Evaluation of vehicle fleet maintenance management indicators by application of DEMATEL and ANP

abstract: The paper refers to the importance of maintenance management to increase the vehicle fleet energy efficiency. The fleet maintenance management influences as the vehicle maintenance process itself as well as the primary transport process but also their environment. In order to increase fleet energy efficiency by means of a more efficient maintenance management, it is indispensable to observe maintenance process, transport process and the environment. Since the implementation effects of such measures can be measured by different indicators, this paper analyses the influence of indicators in all three mentioned areas on management decision-making. In this sense, appropriate indicators have been defined and subsequently used in fleet maintenance management. To determine levels and intensities of interdependence as well as relative weight of selected indicators two methods have been combined: Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP). A model was proposed with indicators' interdependence whose relative weights were calculated. The proposed model has been implemented in several companies with road vehicle fleets. Collected results show the perceived evaluation by company managers in view of maintenance management process influence onto their fleet energy efficiency. Besides, by proposed model implementation we have obtained equally managers' evaluation upon effectiveness and efficiency of the maintenance management within studied companies.


title: Competing over Networks

abstract: Recent advances in information technology have allowed firms to gather vast amounts of data regarding consumers’ preferences and the structure and intensity of their social interactions. This paper examines a game-theoretic model of competition between firms which can target their marketing budgets to individuals embedded in a social network. We provide a sharp characterization of the optimal targeted marketing strategies and highlight their dependence on the underlying social network structure. Furthermore, we identify network structures for which the returns to targeting are maximized and we provide conditions under which it is optimal for the firms to asymmetrically target a subset of the individuals. Finally, we provide a lower bound on the extent of asymmetry in these asymmetric equilibria and therefore shed light on the effect of the network structure on the outcome of marketing competition between the firms.


title: Design and manufacturing of a 3-D conformal slotted waveguide antenna array in Ku-band based on Direct Metal Laser Sintering

abstract: One of the growing trend in antennas is to address multifunctional systems, providing direct space saving and cost reduction. A consideration for future generation antennas is to replace the classical planar geometry with new functionalities. Three-dimensional (3-D) / conformal antennas offer significant advantages for such system. However, traditional manufacturing processes limit their realization. Additive Manufacturing have been identified as very promising solutions to overcome the dimensional challenge that 3-D components represent. These technologies enable the manufacturing of complex and conformal structures and goes beyond the conventional subtractive manufacturing techniques. In this contribution we propose to evaluate the manufacturing capacities of the Direct Metal Laser Sintering (DMLS). For this purpose, we need an easy-to-test antenna structure containing pertinent features representative of all complication we can run into when designing a complex antenna, including phased array antenna. We propose a conformal slotted waveguide antenna arrays (SWAA) operating in Ku band. The electrical performances of the device have been compared between simulation with ANSYS® HFSS™ software and measurements. Despite a mismatch in the reflection parameter, good results are obtained in gain. They enable us to envision promising future achievements and a better mastering of the global set of parameters.


title: Exploiting machine learning algorithms for cognitive radio

abstract: Cognitive radio is an intelligent radio that has the ability to sense and learn from its environment. Basic core of cognitive radio contains a learning engine and it plays an important role in every application of cognitive radio from spectrum sensing to spectrum management. Learning engine implements different learning algorithms. In this paper we discuss various learning algorithms and their application in solving specific problems of cognitive radio. Some of the prominent learning algorithms discussed in this paper are Genetic Algorithm (GA), Artificial Neural Networks (ANN) and Hidden Markov Model (HMM).


title: Initial quantitative comparison of 940nm and 950nm infrared sensor performance for measuring glucose non-invasively

abstract: This project uses a non-invasive method for measuring the blood glucose concentration levels. By implementing two infrared light with different wavelength; 940nm and 950nm based on the use of light emitting diodes and measure transmittance through solution of distilled water and d-glucose of concentration from 0mg/dL to 200mg/dL by using a 1000nm photodiode. It is observed that the output voltage from the photodiode increased proportionally to the increased of concentration levels. The relation observed was linear. Nine subjects with the same age but different body weight have been used to observe the glucose level during fasting and non-fasting. During fasting the voltage is about 0.13096V to 0.236V and during non-fasting the voltage range is about 0.12V to 0.256V. This method of measuring blood glucose level may become a preferably choice for diabetics because of the non-invasive and may extend to the general public. For having a large majority people able to monitor their blood glucose levels, it may prevent hypoglycemia, hyperglycemia and perhaps the onset of diabetes.


title: Towards Machine Learning-Based Auto-tuning of MapReduce

abstract: MapReduce, which is the de facto programming model for large-scale distributed data processing, and its most popular implementation Hadoop have enjoyed widespread adoption in industry during the past few years. Unfortunately, from a performance point of view getting the most out of Hadoop is still a big challenge due to the large number of configuration parameters. Currently these parameters are tuned manually by trial and error, which is ineffective due to the large parameter space and the complex interactions among the parameters. Even worse, the parameters have to be re-tuned for different MapReduce applications and clusters. To make the parameter tuning process more effective, in this paper we explore machine learning-based performance models that we use to auto-tune the configuration parameters. To this end, we first evaluate several machine learning models with diverse MapReduce applications and cluster configurations, and we show that support vector regression model (SVR) has good accuracy and is also computationally efficient. We further assess our auto-tuning approach, which uses the SVR performance model, against the Starfish auto tuner, which uses a cost-based performance model. Our findings reveal that our auto-tuning approach can provide comparable or in some cases better performance improvements than Starfish with a smaller number of parameters. Finally, we propose and discuss a complete and practical end-to-end auto-tuning flow that combines our machine learning-based performance models with smart search algorithms for the effective training of the models and the effective exploration of the parameter space.


title: Cross-modal deep metric learning with multi-task regularization

abstract: DNN-based cross-modal retrieval has become a research hotspot, by which users can search results across various modalities like image and text. However, existing methods mainly focus on the pairwise correlation and reconstruction error of labeled data. They ignore the semantically similar and dissimilar constraints between different modalities, and cannot take advantage of unlabeled data. This paper proposes Cross-modal Deep Metric Learning with Multi-task Regularization (CDMLMR), which integrates quadruplet ranking loss and semi-supervised contrastive loss for modeling cross-modal semantic similarity in a unified multi-task learning architecture. The quadruplet ranking loss can model the semantically similar and dissimilar constraints to preserve cross-modal relative similarity ranking information. The semi-supervised contrastive loss is able to maximize the semantic similarity on both labeled and unlabeled data. Compared to the existing methods, CDMLMR exploits not only the similarity ranking information but also unlabeled cross-modal data, and thus boosts cross-modal retrieval accuracy.


title: Deep Stereo Fusion: Combining Multiple Disparity Hypotheses with Deep-Learning

abstract: Stereo matching is a popular technique to infer depth from two or more images and wealth of methods have been proposed to deal with this problem. Despite these efforts, finding accurate stereo correspondences is still an open problem. The strengths and weaknesses of existing methods are often complementary and in this paper, motivated by recent trends in this field, we exploit this fact by proposing Deep Stereo Fusion, a Convolutional Neural Network capable of combining the output of multiple stereo algorithms in order to obtain more accurate result with respect to each input disparity map. Deep Stereo Fusion process a 3D features vector, encoding both spatial and cross-algorithm information, in order to select the best disparity hypothesis among those proposed by the single stereo matchers. To the best of our knowledge, our proposal is the first i) to leverage on deep learning and ii) able to predict the optimal disparity assignments by taking only as input cue the disparity maps. This second feature makes our method suitable for deployment even when other cues (e.g., confidence) are not available such as when dealing with disparity maps provided by off-the-shelf 3D sensors. We thoroughly evaluate our proposal on the KITTI stereo benchmark with respect state-of-the-art in this field.


title: Towards personalised automated driving: Prediction of preferred ACC behaviour based on manual driving

abstract: More and more Advanced Driver Assistance Systems (ADASs) are entering the market for improving both safety and comfort. Adaptive Cruise Control (ACC) is an ADAS application that has high interaction with the driver. ACC systems use limited sensor input and have only few configuration possibilities. This may result in the behaviour of the ACC not matching user's preferences in all cases, resulting in lower acceptance of the system. In this work, we examine the possibilities for a Personalised ACC (PACC), which adapts the ACC settings such that it matches the driver preference in order to increase the acceptance. The driver preferred ACC behaviour is predicted using machine learning techniques and manual driving data. On-road experiments showed that the method is promising as it is able to discriminate between two preference clusters with an accuracy of 85%.


title: Therapeutic Drug Monitoring of Kidney Transplant Recipients Using Profiled Support Vector Machines

abstract: This paper proposes a twofold approach for therapeutic drug monitoring (TDM) of kidney recipients using support vector machines (SVMs), for both predicting and detecting Cyclosporine A (CyA) blood concentrations. The final goal is to build useful, robust, and ultimately understandable models for individualizing the dosage of CyA. We compare SVMs with several neural network models, such as the multilayer perceptron (MLP), the Elman recurrent network, finite/infinite impulse response networks, and neural network ARMAX approaches. In addition, we present a profile-dependent SVM (PD-SVM), which incorporates a priori knowledge in both tasks. Models are compared numerically, statistically, and in the presence of additive noise. Data from 57 renal allograft recipients were used to develop the models. Patients followed a standard triple therapy, and CyA trough concentration was the dependent variable. The best results for the CyA blood concentration prediction were obtained using the PD-SVM (mean error of 0.36 ng/mL and root-mean-square error of 52.01 ng/mL in the validation set) and appeared to be more robust in the presence of additive noise. The proposed PD-SVM improved results from the standard SVM and MLP, specially significant (both numerical and statistically) in the one-against-all scheme. Finally, some clinical conclusions were obtained from sensitivity rankings of the models and distribution of support vectors. We conclude that the PD-SVM approach produces more accurate and robust models than do neural networks. Finally, a software tool for aiding medical decision-making including the prediction models is presented


title: Bi-Fi: An Embedded Sensor/System Architecture for Remote Biological Monitoring

abstract: Wireless-enabled processor modules intended for communicating low-frequency phenomena (i.e., temperature, humidity, and ambient light) have been enabled to acquire and transmit multiple biological signals in real time, which has been achieved by using computationally efficient data acquisition, filtering, and compression algorithms, and interfacing the modules with biological interface hardware. The sensor modules can acquire and transmit raw biological signals at a rate of 32 kb/s, which is near the hardware limit of the modules. Furthermore, onboard signal processing enables one channel, sampled at a rate of 4000 samples/s at 12-bit resolution, to be compressed via adaptive differential-pulse-code modulation (ADPCM) and transmitted in real time. In addition, the sensors can be configured to filter and transmit individual time-referenced ldquospikerdquo waveforms, or to transmit the spike height and width for alleviating network traffic and increasing battery life. The system is capable of acquiring eight channels of analog signals as well as data via an asynchronous serial connection. A back-end server archives the biological data received via networked gateway sensors, and hosts them to a client application that enables users to browse recorded data. The system also acquires, filters, and transmits oxygen saturation and pulse rate via a commercial-off-the-shelf interface board. The system architecture can be configured for performing real-time nonobtrusive biological monitoring of humans or rodents. This paper demonstrates that low-power, computational, and bandwidth-constrained wireless-enabled platforms can indeed be leveraged for wireless biosignal monitoring.


title: Concomitant splenectomy for hypersplenic thrombocytopenia in hepatic resection for hepatocellular carcinoma.

abstract: BACKGROUND/AIMS: Resection of hepatocellular carcinoma (HCC) in patients with liver cirrhosis and hypersplenic thrombocytopenia (HSTC) is risky. Controversy exists concerning the role of concomitant splenectomy for HSTC in cirrhotic patients undergoing hepatectomy for HCC. METHODOLOGY: During the past 10 years, 294 patients have undergone hepatic resection for HCC in our department. Among them, 11 cirrhotic patients with severe HSTC (platelet count ≤80000/mm 3 ) underwent splenectomy simultaneously. The clinical outcomes were retrospectively reviewed. RESULTS: The resected spleen weighed 479±242g. The post-operative mortality and morbidity were 9.1% and 27.3%, respectively. In all patients, the platelet count was elevated to above 100000/mm 3 , and serum total bilirubin was significantly lowered within 1 week of operation. The overall 5-year actuarial and disease-free survival rates were 66.7%. None of the patients developed severe infectious complications during the follow-up period. CONCLUSIONS: Concomitant splenectomy for severe HSTC in cirrhotic patients undergoing hepatectomy for HCC is justified as the benefits of concomitant splenectomy by far surpass the adverse effects.


title: Perceptions of performance and satisfaction after relocation to an activity-based office

abstract: Many companies move from open-plan offices (OPO) to activity-based workplaces (ABWs). However, few studies examine the benefits and drawbacks following such a change. The aim of this study was to explore how physical conditions, office use, communication, privacy, territoriality, satisfaction and perceived performance change following a company's relocation from an OPO to an ABW. A mixed methods approach included pre- and post-relocation questionnaires and post-relocation focus groups, individual interviews and observations. The questionnaires enabled comparisons over time (n = 34) and broader analyses based on retrospective ratings of perceived change (n = 66). Results showed that satisfaction with auditory privacy, background noise, air quality, outdoor view and aesthetics increased significantly after relocation. Negative outcomes, such as lack of communication within teams, were perceived as being due to the high people-to-workstation ratio and lack of rules. Overall satisfaction with the physical work environment increased in the ABW compared to the OPO. Perceived performance did not change significantly. Practitioner Summary: Activity-based workplaces (ABWs) are commonly implemented although their effects on performance and well-being are unclear. This case study gives advice to stakeholders involved in office planning. Despite shortcomings with the people-to-workstation ratio and rules, employees showed improved satisfaction with auditory privacy and aesthetics in the ABW compared with the previous open-plan office.


title: Long term load forecasting with hourly predictions based on long-short-term-memory networks

abstract: The conventional methodology for long term load forecasting is mostly restricted to electricity load data with monthly or annual granularity. This leads to forecasts with very low accuracy. In this paper, a novel method for long term load forecasting with hourly resolution is proposed. The model is fundamentally centered on Recurrent Neural Network consisting of Long-Short-Term-Memory (LSTM-RNN) cells. The long term relations in a time series data of electricity load demand are taken into account using LSTM-RNN and hence results in more accurate forecasts. The proposed model is implemented on real time data of ISO New England electricity market. Precisely, publicly available data of twelve years from 2004 to 2015 have been collected to train and validate the model. Electricity demand predictions have been made for a period of five years from 2011 to 2015 on a rolling basis. The proposed model is found to be highly accurate with a Mean Absolute Percentage Error (MAPE) of 6.54 within a confidence interval of 2.25%. Moreover, the model has a computation time of approximately 30 minutes which is favorable for offline training to forecast electricity load for a period of five years.


title: Scalable NDN Forwarding: Concepts, Issues and Principles

abstract: Named Data Networking (NDN) is a recently proposed general- purpose network architecture that leverages the strengths of Internet architecture while aiming to address its weaknesses. NDN names packets rather than end-hosts, and most of NDN's characteristics are a consequence of this fact. In this paper, we focus on the packet forwarding model of NDN. Each packet has a unique name which is used to make forwarding decisions in the network. NDN forwarding differs substantially from that in IP; namely, NDN forwards based on variable-length names and has a read-write data plane. Designing and evaluating a scalable NDN forwarding node architecture is a major effort within the overall NDN research agenda. In this paper, we present the concepts, issues and principles of scalable NDN forwarding plane design. The essential function of NDN forwarding plane is fast name lookup. By studying the performance of the NDN reference implementation, known as CCNx, and simplifying its forwarding structure, we identify three key issues in the design of a scalable NDN forwarding plane: 1) exact string matching with fast updates, 2) longest prefix matching for variable-length and unbounded names and 3) large- scale flow maintenance. We also present five forwarding plane design principles for achieving 1 Gbps throughput in software implementation and 10 Gbps with hardware acceleration.


title: A comparative analysis of machine learning techniques for student retention management

abstract: Student retention is an essential part of many enrollment management systems. It affects university rankings, school reputation, and financial wellbeing. Student retention has become one of the most important priorities for decision makers in higher education institutions. Improving student retention starts with a thorough understanding of the reasons behind the attrition. Such an understanding is the basis for accurately predicting at-risk students and appropriately intervening to retain them. In this study, using five years of institutional data along with several data mining techniques (both individuals as well as ensembles), we developed analytical models to predict and to explain the reasons behind freshmen student attrition. The comparative analyses results showed that the ensembles performed better than individual models, while the balanced dataset produced better prediction results than the unbalanced dataset. The sensitivity analysis of the models revealed that the educational and financial variables are among the most important predictors of the phenomenon.


title: Charging from sampled network usage

abstract: IP flows have heavy-tailed packet and byte size distributions. This make them poor candidates for uniform sampling---i.e. selecting 1 in N flows---since omission or inclusion of a large flow can have a large effect on estimated total traffic. Flows selected in this manner are thus unsuitable for use in usage sensitive billing. We propose instead using a size-dependent sampling scheme which gives priority to the larger contributions to customer usage. This turns the heavy tails to our advantage; we can obtain accurate estimates of customer usage from a relatively small number of important samples.The sampling scheme allows us to control error when charging is sensitive to estimated usage only above a given base level. A refinement allows us to strictly limit the chance that a customers estimated usage will exceed their actual usage. Furthermore, we show that a secondary goal, that of controlling the rate at which samples are produced, can be fulfilled provided the billing cycle is sufficiently long. All these claims are supported by experiments on flow traces gathered from a commercial network.


title: Self-Organizing Roles on Agile Software Development Teams

abstract: Self-organizing teams have been recognized and studied in various forms-as autonomous groups in socio-technical systems, enablers of organizational theories, agents of knowledge management, and as examples of complex-adaptive systems. Over the last decade, self-organizing teams have taken center stage in software engineering when they were incorporated as a hallmark of Agile methods. Despite the long and rich history of self-organizing teams and their recent popularity with Agile methods, there has been little research on the topic within software wngineering. Particularly, there is a dearth of research on how Agile teams organize themselves in practice. Through a Grounded Theory research involving 58 Agile practitioners from 23 software organizations in New Zealand and India over a period of four years, we identified informal, implicit, transient, and spontaneous roles that make Agile teams self-organizing. These roles-Mentor, Coordinator, Translator, Champion, Promoter, and Terminator-are focused toward providing initial guidance and encouraging continued adherence to Agile methods, effectively managing customer expectations and coordinating customer collaboration, securing and sustaining senior management support, and identifying and removing team members threatening the self-organizing ability of the team. Understanding these roles will help software development teams and their managers better comprehend and execute their roles and responsibilities as a self-organizing team.


title: Comparison of linear regression and a probabilistic neural network to predict porosity from 3‐D seismic attributes in Lower Brushy Canyon channeled sandstones, southeast New Mexico

abstract: The Lower Brushy Canyon Formation of the Delaware Basin, New Mexico, consists of a series of overlying sand‐filled channels and associated fans separated by laterally extensive organic siltstone and carbonate interbeds. This laterally and vertically complex geology creates the need for precise interwell estimation of reservoir properties. In this paper we integrate wireline log and 3‐D seismic data to directly predict porosity in the area of an existing oil field in southeast New Mexico. The 3‐D seismic data were used to interpret the location of major stratigraphic markers between wells, and these seismic horizons were used to constrain a time window for a volume‐based attribute analysis. Stepwise regression and crossvalidation were used to combine seismic attributes to predict porosity in wells where the porosity was known from the well logs. The results of a linear regression porosity model showed good correlation (r2=0.74) between seven seismic attributes and the observed porosity logs at 11 wells in ...