title: Candid Interaction: Revealing Hidden Mobile and Wearable Computing Activities

abstract: The growth of mobile and wearable technologies has made it often difficult to understand what people in our surroundings are doing with their technology. In this paper, we introduce the concept of candid interaction: techniques for providing awareness about our mobile and wearable device usage to others in the vicinity. We motivate and ground this exploration through a survey on current attitudes toward device usage during interpersonal encounters. We then explore a design space for candid interaction through seven prototypes that leverage a wide range of technological enhancements, such as Augmented Reality, shape memory muscle wire, and wearable projection. Preliminary user feedback of our prototypes highlights the trade-offs between the benefits of sharing device activity and the need to protect user privacy.


title: Nasal receptors responding to noxious chemical irritants.

abstract: Abstract This study was performed to investigate the chemoreception of trigeminal afferents in the nose. Single unit activity was recorded from the anterior ethmoidal nerve in the anesthetized guinea pig breathing through a tracheostomy during nasal instillation of capsaicin (0.3 mM), nicotine (6 mM) and ammonia (1.5 M) solutions or with distilled water. Out of 36 fibers recorded, nineteen were stimulated by capsaicin, six by nicotine and seventeen by ammonia. Among those of fibers, two were stimulated by both capsaicin and nicotine, six by both capsaicin and ammonia and one nicotine-responsive fiber was also stimulated by ammonia. A large proportion of capsaicin- and nicotine-responsive fibers exhibited long lasting discharges (170.4 ± 17.7 sec and 120.7 ± 29.3 sec, respectively), and were not stimulated by the second application of the same substance. However, fibers responding to ammonia discharged for a shorter time (31.5 ± 6.5 sec), indicating a rapid adaptation. These results indicate that the ethmoidal nerve possesses a well-developed responsiveness to noxious stimuli. The nociceptive component of this nerve may be related to the various cardiorespiratory responses that can be elicited from the nasal cavity and also to local axonal reflexes (neurogenic inflammation) due to the release of chemical mediators from C-fiber endings.


title: Development of real-time ECG signal monitoring system for telemedicine application

abstract: This work presents the development of low cost, portable real-time Electrocardiogram (ECG) signal monitoring system with abnormality detection capability. The 3-lead Electrocardiogram device takes the physical pulse input using electrodes stuck to the arms and right leg of the patient under observation. The ECG signal is processed by the microcontroller AT89S52 in the portable wireless unit to count the heart beat for a duration of one minute and the Heart Rate is displayed on LCD display. The amplitude and intervals of some critical components are obtained, processed and displayed in a graphical user interface. If an abnormality is detected in the ECG signal, an alert SMS is sent by the system to the doctor through GSM modem to enable him to take appropriate protective measures. The system is intended to use for telemedicine application. Further, the system can be improved to monitor multiple physiological signals and to allow patient mobility by transmitting signals wirelessly.


title: Body enhancement through female genital cosmetic surgery creates ethical and rights dilemmas

abstract: Abstract Female genital cosmetic surgery is surgery performed on a woman within a normal range of variation of human anatomy. The issues are heightened by a lack of long-term and substantive evidence-based literature, conflict of interest from personal financial gain through performing these procedures, and confusion around macroethical and microethical domains. It is a source of conflict and controversy globally because the benefit and harm of offering these procedures raise concerns about harmful cultural views, education, and social vulnerability of women with regard to both ethics and human rights. The rights issues of who is defining normal female anatomy and function, as well as the economic vulnerability of women globally, bequeath the profession a greater responsibility to ensure that there is adequate health and general education—not just among patients but broadly in society—that there is neither limitation nor interference in the decision being made, and that there are no psychological disorders that could be influencing such choices.


title: A review on applications of activity recognition systems with regard to performance and evaluation

abstract: Activity recognition systems are a large field of research and development, currently with a focus on advanced machine learning algorithms, innovations in the field of hardware architecture, and on...


title: Scalable Nearest Neighbor Algorithms for High Dimensional Data

abstract: For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbor matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this paper, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular data set. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper. All this research has been released as an open source library called fast library for approximate nearest neighbors (FLANN), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbor matching.


title: development project for supporting training needs

abstract: The UK Department of Trade and Industry Technology Program has funded a 4-year research and development program to analyze the the potential effectiveness of serious games to increase the effectiveness of training and learning. The program is making significant efforts to develop a series of game demonstrators and evaluation techniques to measure the effectiveness of game-based learning. The Serious Games-Engaging Training Solutions (SG-ETS) is one of the first projects to bring game developers and pedagogic expertise together and include experts in game-based learning and human factors at three leading universities in the country, including the Universities of Birmingham, London, and Sheffield, and two leading computer game developing companies, such as Trusim, VEGA Group, PLC. Some of the significant objectives of the (SG-ETS) project include producing an effective and efficient process for selecting and developing serious games, and publish research output related to games.


title: Design of 6-slot 2-pole high-speed permanent magnet synchronous machines with Tooth-Coil windings

abstract: This paper investigates the advantages and drawbacks of High-Speed Permanent Magnet Synchronous Machines (HSPMSM) having a special topology. The analysis is made by using 3.5 kW and 11 kW machines prototypes. The machines under consideration have 6 slots and 2 poles. The rotor is manufactured as a full cylindrical magnet inside a retaining sleeve. Non-overlapping Tooth-Coil (TC) windings were implemented to improve the manufacturing process. The similarities and differences of the prototypes are discussed. Machines' performances are verified by test measurements.


Blockchain Technology, Cognitive Computing, and Healthcare Innovations


title: Evaluating Evaluation: Assessing Progress in Computational Creativity Research

abstract: Computational creativity research has produced many computational systems that are described as creative. A comprehensive literature survey reveals that although such systems are labelled as creative, there is a distinct lack of evaluation of the creativity of creative systems. As a research community, we should adopt a more scientific approach to evaluation of the creativity of our systems if we are to progress in understanding creativity and modelling it computationally. A methodology for creativity evaluation should accommodate different manifestations of creativity but also require a clear, definitive statement of the standards used for evaluation. This paper proposes Evaluation Guidelines, a standard but flexible approach to evaluation of the creativity of computational systems and argues that this approach should be taken up as standard practice in computational creativity research. The approach is outlined and discussed, then illustrated through a comparative evaluation of the creativity of jazz improvisation systems.


title: Paralanguage and human-computer interaction. Part 2: comprehension of synthesized vocal segregates

abstract: Abstract Abstract. The comprehension of synthesized vocal segregates was studied using a paired-associate learning task. Twenty-one naive subjects attempted to learn associations between six vocal segregates and assigned meanings, where the segregates were synthesized using the simple sound facilities common to all PC-compatible machines. In the natural condition sounds were paired with the correct meanings, whereas in the random condition sounds were randomly assigned to incorrect meanings. Subjects in the natural condition showed a significant initial advantage over those in the random condition and significantly better overall performance across learning trials. The results show that uninformed subjects are predisposed to make the correct sound-meaning associations for segregates synthesized with inexpensive hardware, and that associations which are not initially identified are quickly learned. Potential applications are discussed.


title: Equivalence of Virtual Synchronous Machines and Frequency-Droops for Converter-Based MicroGrids

abstract: Over the last decade, frequency-droop-based control schemes have become the preferred solution in microgrids dominated by power electronic converters. More recently, the concept of virtual synchronous machines (VSMs) has emerged as an effective method for adding virtual inertia to the power system through the control of power electronic converters. These two approaches have been developed in two separate contexts, but present strong similarities. In fact, they are equivalent under certain conditions, as demonstrated in this letter. Analysis of this equivalence provides additional physics-based insight into the tuning and operation of both types of controllers.


title: Jena: implementing the semantic web recommendations

abstract: The new Semantic Web recommendations for RDF, RDFS and OWL have, at their heart, the RDF graph. Jena2, a second-generation RDF toolkit, is similarly centered on the RDF graph. RDFS and OWL reasoning are seen as graph-to-graph transforms, producing graphs of virtual triples. Rich APIs are provided. The Model API includes support for other aspects of the RDF recommendations, such as containers and reification. The Ontology API includes support for RDFS and OWL, including advanced OWL Full support. Jena includes the de facto reference RDF/XML parser, and provides RDF/XML output using the full range of the rich RDF/XML grammar. N3 I/O is supported. RDF graphs can be stored in-memory or in databases. Jena's query language, RDQL, and the Web API are both offered for the next round of standardization.


title: A Large Dataset for Improving Patch Matching

abstract: We propose a new dataset for learning local image descriptors which can be used for significantly improved patch matching. Our proposed dataset consists of an order of magnitude more number of scenes, images, and positive and negative correspondences compared to the currently available Multi-View Stereo (MVS) dataset from Brown et al. The new dataset also has better coverage of the overall viewpoint, scale, and lighting changes in comparison to the MVS dataset. Our dataset also provides supplementary information like RGB patches with scale and rotations values, and intrinsic and extrinsic camera parameters which as shown later can be used to customize training data as per application. We train an existing state-of-the-art model on our dataset and evaluate on publicly available benchmarks such as HPatches dataset and Strecha et al.\cite{strecha} to quantify the image descriptor performance. Experimental evaluations show that the descriptors trained using our proposed dataset outperform the current state-of-the-art descriptors trained on MVS by 8%, 4% and 10% on matching, verification and retrieval tasks respectively on the HPatches dataset. Similarly on the Strecha dataset, we see an improvement of 3-5% for the matching task in non-planar scenes.


title: OrthoNoC: A Broadcast-Oriented Dual-Plane Wireless Network-on-Chip Architecture

abstract: On-chip communication remains as a key research issue at the gates of the manycore era. In response to this, novel interconnect technologies have opened the door to new Network-on-Chip (NoC) solutions towards greater scalability and architectural flexibility. Particularly, wireless on-chip communication has garnered considerable attention due to its inherent broadcast capabilities, low latency, and system-level simplicity. This work presents OrthoNoC , a wired-wireless architecture that differs from existing proposals in that both network planes are decoupled and driven by traffic steering policies enforced at the network interfaces. With these and other design decisions, OrthoNoC seeks to emphasize the ordered broadcast advantage offered by the wireless technology. The performance and cost of OrthoNoC are first explored using synthetic traffic, showing substantial improvements with respect to other wired-wireless designs with a similar number of antennas. Then, the applicability of OrthoNoC in the multiprocessor scenario is demonstrated through the evaluation of a simple architecture that implements fast synchronization via ordered broadcast transmissions. Simulations reveal significant execution time speedups and communication energy savings for 64-threaded benchmarks, proving that the value of OrthoNoC goes beyond simply improving the performance of the on-chip interconnect.


title: Converting Basic D3 Charts into Reusable Style Templates

abstract: We present a technique for converting a basic D3 chart into a reusable style template. Then, given a new data source we can apply the style template to generate a chart that depicts the new data, but in the style of the template. To construct the style template we first deconstruct the input D3 chart to recover its underlying structure: the data, the marks and the mappings that describe how the marks encode the data. We then rank the perceptual effectiveness of the deconstructed mappings. To apply the resulting style template to a new data source we first obtain importance ranks for each new data field. We then adjust the template mappings to depict the source data by matching the most important data fields to the most perceptually effective mappings. We show how the style templates can be applied to source data in the form of either a data table or another D3 chart. While our implementation focuses on generating templates for basic chart types (e.g., variants of bar charts, line charts, dot plots, scatterplots, etc.), these are the most commonly used chart types today. Users can easily find such basic D3 charts on the Web, turn them into templates, and immediately see how their own data would look in the visual style (e.g., colors, shapes, fonts, etc.) of the templates. We demonstrate the effectiveness of our approach by applying a diverse set of style templates to a variety of source datasets.


title: Hand Gesture Recognition System

abstract: Gestures are a major form of human communication. Hence gestures can be found to be an appealing way to interact with computers, since they are already a natural part of how people communicate. A primary goal of gesture recognition is to create a system which can identify specific human gestures and use them to convey information for controlling device and by implementing real time gesture recognition a user can control a computer by doing a specific gesture in front of a video camera which is linked to the computer. A primary goal of this gesture recognition research is to create a system which can identify specific human gestures for the control the traffic signals and mouse. This project also covers various issues like what are gesture, their classification, their role in implementing a gesture recognition system for traffic and mouse control, system architecture concepts for implementing a gesture recognition system, major issues involved in implementing gesture recognition system, and future scope of gesture recognition system. For implementation of this system real-time hand tracking and extraction algorithm, and feature extraction are used.


title: NO control of mitochondrial function in normal and transformed cells

abstract: Abstract Nitric oxide (NO) is a signaling molecule with multiple facets and involved in numerous pathological process, including cancer. Among the different pathways where NO has a functionally relevant participation, is the control of mitochondrial respiration and biogenesis. NO is able to inhibit the electron transport chain, mainly at Complex IV, regulating oxygen consumption and ATP generation, but at the same time, can also induce increase in reactive oxygen and nitrogen species. The presence of reactive species can induce oxidative damage or participate in redox signaling. In this review, we discuss how NO affects mitochondrial respiration and mitochondrial biogenesis, and how it influences the development of mitochondrial deficiency and cancer. This article is part of a Special Issue entitled Mitochondria in Cancer, edited by Giuseppe Gasparre, Rodrigue Rossignol and Pierre Sonveaux.


title: User preferences of software documentation genres

abstract: Today's technical software users find a wide variety of content online, from videos to forum posts to online articles. This study examines the extent to which the genre of this content matters to the users searching for it. It presents the findings of a new exploratory study that addresses the significance of genre preferences to the users, given that genre is secondary to their actual business goals. It looks at documentation users' usage of, and attitudes towards, documentation genres, and how factors such as work role and past experience come into play. Our conclusions can help individuals who formulate content strategies to understand the extent and strength of genre usage. This in turn can help them determine a strategic mix of documentation genres.


title: Encoding Predictive Reward Value in Human Amygdala and Orbitofrontal Cortex

abstract: Adaptive behavior is optimized in organisms that maintain flexible representations of the value of sensory-predictive cues. To identify central representations of predictive reward value in humans, we used reinforcer devaluation while measuring neural activity with functional magnetic resonance imaging. We presented two arbitrary visual stimuli, both before and after olfactory devaluation, in a paradigm of appetitive conditioning. In amygdala and orbitofrontal cortex, responses evoked by a predictive target stimulus were decreased after devaluation, whereas responses to the nondevalued stimulus were maintained. Thus, differential activity in amygdala and orbitofrontal cortex encodes the current value of reward representations accessible to predictive cues.


title: Partitioning sparse matrices with eigenvectors of graphs

abstract: The problem of computing a small vertex separator in a graph arises in the context of computing a good ordering for the parallel factorization of sparse, symmetric matrices. An algebraic approach for computing vertex separators is considered in this paper. It is, shown that lower bounds on separator sizes can be obtained in terms of the eigenvalues of the Laplacian matrix associated with a graph. The Laplacian eigenvectors of grid graphs can be computed from Kronecker products involving the eigenvectors of path graphs, and these eigenvectors can be used to compute good separators in grid graphs. A heuristic algorithm is designed to compute a vertex separator in a general graph by first computing an edge separator in the graph from an eigenvector of the Laplacian matrix, and then using a maximum matching in a subgraph to compute the vertex separator. Results on the quality of the separators computed by the spectral algorithm are presented, and these are compared with separators obtained from other algorith...


Who Uses Social Networking Sites? Exploring Associations among Personality and the Relationship with Facebook, Twitter, and Instagram


title: Leveraging the schema in latent factor models for knowledge graph completion

abstract: We focus on the problem of predicting missing links in large Knowledge Graphs (KGs), so to discover new facts. Over the last years, latent factor models for link prediction have been receiving an increasing interest: they achieve state-of-the-art accuracy in link prediction tasks, while scaling to very large KGs. However, KGs are often endowed with additional schema knowledge, describing entity classes, their sub-class relationships, and the domain and range of each predicate: the schema is actually not used by latent factor models proposed in the literature. In this work, we propose an unified method for leveraging additional schema knowledge in latent factor models, with the aim of learning more accurate link prediction models. Our experimental evaluations show the effectiveness of the proposed method on several KGs.


title: AC 2007-1950: ENGINEERING ENTREPRENEURSHIP EDUCATIONAL EXPERIENCE (E4) INITIATIVE: A NEW MODEL FOR SUCCESS

abstract: Recognizing the effect of entrepreneurial activities on student motivation and excitement, the Electronics and Telecommunications Engineering Technology (EET/TET) Programs at Texas A&M University have begun a unique initiative in the area of entrepreneurship. The Engineering Entrepreneurship Education Experience (E4) model brings entrepreneurship from the private sector into the undergraduate environment. It begins with the idea/concept process. At brainstorming sessions with private industry, faculty, and beginning capstone students, new ideas for products and systems are identified for development. Student teams select ideas that have strong industry support, and through two semesters of planning and implementation transform them into fully functional prototypes. At the end of the second semester, the E4 student teams deliver a combined technical/business/marketing presentation to invited private sector and business development representatives. Selected projects may follow one of two paths: the new venture path (regional start-ups) or the technology transfer path (licenses). The most promising products/systems take the new venture path, and the student developers who have now graduated are offered the opportunity to continue in the development of a new business through an undergraduate-dedicated technology incubator located at Texas A&M University. To develop and grow its business, each company receives one full year of support in the incubator. After this, the company leaves the incubator and local economic development councils work with these start-up businesses by offering incentives to keep them in local area . If the project is not selected for incubator support, the student team may follow the technology transfer path. In this case, the University will work as the agent for the inventors (students and faculty), and negotiate with interested companies to license and transition the product know-how to the private sector for production and commercialization. This paper will discuss the new E4 model and present specific examples of student success.


abstract: We present Deep Generalized Canonical Correlation Analysis (DGCCA) -- a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally informative of each other. While methods for nonlinear two-view representation learning (Deep CCA, (Andrew et al., 2013)) and linear many-view representation learning (Generalized CCA (Horst, 1961)) exist, DGCCA is the first CCA-style multiview representation learning technique that combines the flexibility of nonlinear (deep) representation learning with the statistical power of incorporating information from many independent sources, or views. We present the DGCCA formulation as well as an efficient stochastic optimization algorithm for solving it. We learn DGCCA representations on two distinct datasets for three downstream tasks: phonetic transcription from acoustic and articulatory measurements, and recommending hashtags and friends on a dataset of Twitter users. We find that DGCCA representations soundly beat existing methods at phonetic transcription and hashtag recommendation, and in general perform no worse than standard linear many-view techniques.


title: Leydig Cell Testicular Tumour Presenting as Isosexual Precocious Pseudopuberty in a 5 Year-old Boy with No Palpable Testicular Mass

abstract: Leydig cell testicular tumors are very rare in children and cause isosexual precocious puberty. Palpable testicular mass or asymmetric testes are common findings on routine examination. We report on a 5-yr-old boy with a Leydig cell tumor of the testis presented with isosexual precocious puberty but no scrotal palpable mass. To our knowledge, this is the first reported Leydig cell tumor in a boy without palpable scrotal mass.


title: Towards an interoperable mobile wallet service

abstract: Advancement in mobile technology, coupled with a market wide growth in mobile subscriber base, is encouraging financial, telecom and even third party operators to offer mobile payment services. In presence of a plethora of such services, an imminent problem is lack of interoperability across them. In this work, we address the challenge of interoperability by designing a wallet service built upon a concept of token. A token encapsulates a payment instruction, which can be of different types, such as instant, dated or installment payment. A token is represented by a Info-Matrix code, viz. Quick-Response (QR) code, thereby bringing in additional advantages in terms of user interactions with the wallet service. We present the lifecycle management of a token, beginning with token generation on user request, transfer of token, and acknowledged receipt and encashment of a token. This work presents an architecture for interoperable wallet service, along with the implementation of a system that demonstrates the use of QR-codes as standardized tokens for transactions.


devices and apps for health care professionals : uses and benefits table 1 uses for Mobile devices and apps by health care professionals


title: Word Sense Discrimination By Clustering Contexts In Vector And Similarity Spaces

abstract: This paper systematically compares unsupervised word sense discrimination techniques that cluster instances of a target word that occur in raw text using both vector and similarity spaces. The context of each instance is represented as a vector in a high dimensional feature space. Discrimination is achieved by clustering these context vectors directly in vector space and also by finding pairwise similarities among the vectors and then clustering in similarity space. We employ two different representations of the context in which a target word occurs. First order context vectors represent the context of each instance of a target word as a vector of features that occur in that context. Second order context vectors are an indirect representation of the context based on the average of vectors that represent the words that occur in the context. We evaluate the discriminated clusters by carrying out experiments using sense–tagged instances of 24 SENSEVAL2 words and the well known Line, Hard and Serve sense–tagged corpora.


title: A novel learning based segmentation method for rodent brain structures using MRI

abstract: This paper reports a novel method for fully automated segmentation of rodent brain volume by extending the robust active shape models to incorporate an automatic prior shape selection process. This automatic prior shape selection process using support vector machines provides an automatic shape initialization method for further segmentation of rodent brain structures such as Cerebellum, Neocortex, Corpus Callosum, External Capsule, Caudate Putamen, Hippocampus and Ventricles with the robust active shape model framework in magnetic resonance images (MRI). The mean successful rate of this classification method shows 92.2% accuracy compared to the expert-defined ground truth. We also demonstrate the very promising segmentation results of the robust active shape model framework in rodent brain volume.


title: SecureUML : A UML-Based Modeling Language for Model-Driven Security ?

abstract: We present a modeling language for the model-driven development of secure, distributed systems based on the Unified Modeling Language (UML). Our approach is based on role-based access control with additional support for specifying authorization constraints. We show how UMLcan be used to specify information related to access control in the overall design of an application and how this information can be used to automatically generate complete access control infrastructures. Our approach can be used to improve productivity during the development of secure distributed systems and the quality of the resulting systems.


title: Dynamic modeling and analysis of an omnidirectional mobile robot

abstract: This paper presents the dynamic modeling and analysis of a three-wheeled omnidirectional mobile robot with MY wheels-II, whose dynamics is nonlinear and piecewise-smooth. Firstly, the detailed dynamic model of the robot is derived, which shows that the robot is actually a switched nonlinear system. Analysis of the robot dynamic properties based on the detailed dynamic model is presented in detail. Then to facilitate the controller design for the switched nonlinear system, based on the detailed dynamic model, an average dynamic model is proposed by simply averaging the wheel contact radius. The resulting average dynamic model is nonlinear and smooth, which may then be used as one solution for the model-based control design. Open-loop simulation results show the dynamic properties of the mobile robot. In addition, the effectiveness of the proposed average model in predicting characteristics of the detailed dynamic model is also illustrated through open-loop simulations.


title: A Low-Voltage 5-GHz Quadrature Up-Conversion Mixer for Wireless Transmitter

abstract: In this paper, a low-voltage CMOS quadrature up-conversion mixer for 5-GHz wireless communication applications is designed. The simulation results demonstrate the mixer can reach a high conversion gain (CG), a low noise figure (NT), and a high linearity (IIP3). A miniature lumped- element microwave broadband rat-race hybrid and RLC shift network are used for the LO and the IF port design, respectively. The mixer exhibits a 3 dB improvement in noise and conversion gain, and the image rejection is -35 dBc. The mixer achieves noise figure of 12.8 dB, a conversion gain of 15 dB, an output third intercept point at 0 dBm, while dissipating 15 mW for operating voltage at 1 V. The mixer is designed with TSMC 0.18 um process.


title: The Experience Factor in Foreign Market Entry Behavior of Service Firms

abstract: The paper examines the effect of international experience on service firms' selection of foreign markets and entry modes. The investigation utilizes survey date from 151 United States-based service firms. Results on market selection suggest that, as their experience increases and becomes geographically more diversified, service firms tend to choose markets that are culturally less similar to their home country. On entry mode choice, the paper departs from traditional linear conceptualizations and hypothesizes a U-shaped relationship between experience and propensity for integrated entry modes. Results generally support the hypothesis. The paper explains these findings and describes how service firms resemble and differ from manufacturing firms in their foreign market entry behavior.


title: Intelligent Carpool Routing for Urban Ridesharing by Mining GPS Trajectories

abstract: To support an efficient carpooling service in heavy urban traffic, we propose an intelligent routing scheme based on mining Global Position System trajectories from shared rid- ers. The carpooling system provides many-to-many services with multiple pickup and dropping points. To join a daily carpooling group, the riders must accept a compromised route that is efficient after merging the routes that are preferred by all qualified riders. We developed three frequency-correlated algorithms for route mining, rider selection, and route merging in an urban carpool service. Our approach can cope with the traffic dynamics to yield a suboptimal shared route. Our scheme was successfully tested under heavy Beijing traffic over hundreds of riders. We developed performance metrics to measure the service cost and mileage saved. The ultimate goal is to minimize the riding distances and the transportation costs, and thus alleviate urban traffic jams.


Towards error annotation in a learner corpus of Portuguese


title: The Multiple Facets of Influence Identifying Political Influentials and Opinion Leaders on Twitter

abstract: This study compares six metrics commonly used to identify influential players in two of Canada’s largest political Twitter communities based on the users, and ranking order of users, identified by ...


title: Bi-directional DC/DC converters for plug-in hybrid electric vehicle (PHEV) applications

abstract: Power electronic DC to DC converters in plug-in hybrid and electric automotive applications demand high power bidirectional power flow capability, with wide input voltage range. Output voltage of energy storage devices like ultra capacitor or battery varies with the change in load. The converter needs to provide a successful voltage regulation on the load side for a wide range of input voltage. An isolated half bridge based converter is proposed in this project, with bidirectional power flow and minimum peak current for wide input voltage range through duty cycle, and phase shift control. The proposed converter has competitive total device rating with the conventional isolated bidirectional power converters. The converter uses the transformer leakage inductance as the primary energy transfer element. Proof of concept is realized through a small prototype.


title: Interpreting CNN knowledge via an Explanatory Graph

abstract: This paper learns a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside a pre-trained CNN. Considering that each filter in a conv-layer of a pre-trained CNN usually represents a mixture of object parts, we propose a simple yet efficient method to automatically disentangles different part patterns from each filter, and construct an explanatory graph. In the explanatory graph, each node represents a part pattern, and each edge encodes co-activation relationships and spatial relationships between patterns. More importantly, we learn the explanatory graph for a pre-trained CNN in an unsupervised manner, i.e., without a need of annotating object parts. Experiments show that each graph node consistently represents the same object part through different images. We transfer part patterns in the explanatory graph to the task of part localization, and our method significantly outperforms other approaches.


title: Large-Scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex

abstract: Dual decomposition methods are the current state-of-the-art for training multiclass formulations of Support Vector Machines (SVMs). At every iteration, dual decomposition methods update a small subset of dual variables by solving a restricted optimization problem. In this paper, we propose an exact and efficient method for solving the restricted problem. In our method, the restricted problem is reduced to the well-known problem of Euclidean projection onto the positive simplex, which we can solve exactly in expected O(k) time, where k is the number of classes. We demonstrate that our method empirically achieves state-of-the-art convergence on several large-scale high-dimensional datasets.


title: Powder-based additive manufacturing – a review of types of defects, generation mechanisms, detection, property evaluation and metrology

abstract: Powder-based additive manufacturing (AM) technologies have been evaluated for use in different fields of application (aerospace, medical, etc.). Ideally, AM parts should be at least equivalent, or preferably better quality than conventionally produced parts. Manufacturing defects and their effects on the quality and performance of AM parts are a currently a major concern. It is essential to understand the defect types, their generation mechanisms, and the detection methodologies for mechanical properties evaluation and quality control. We consider the various types of microstructural features or defects, their generation mechanisms, their effect on bulk properties and the capability of existing characterisation methodologies for powder based AM parts in this work. Methods of in-situ non-destructive evaluation and the influence of defects on mechanical properties and design considerations are also reviewed. Together, these provide a framework to understand the relevant machine and material parameters, optimise the process and production, and select appropriate characterisation methods.


DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive


title: Cell Range Expansion Using Distributed Q-Learning in Heterogeneous Networks

abstract: Cell range expansion (CRE) is a technique to expand a pico cell range virtually by adding a bias value to the pico received power, instead of increasing transmit power of pico base station (PBS), so that coverage, cell-edge throughput, and overall network throughput are improved. Many studies have focused on inter-cell interference coordination (ICIC) in CRE, because macro base station’s (MBS’s) strong transmit power harms the expanded region (ER) user equipments (UEs) that select PBSs by bias value. Optimal bias value that minimizes the number of outage UEs depends on several factors such as the dividing ratio of radio resources between MBSs and PBSs. In addition it varies from UE to another. Thus, most articles use the common bias value among all UEs determined by trial-and-error method. In this article, we propose a scheme to determine the bias value of each UE by using Q-learning algorithm where each UE learns its bias value that minimizes the number of outage UEs from its past experience independently. Simulation results show that, compared to the scheme using optimal common bias value, the proposed scheme reduces the number of outage UEs and improves network throughput.


title: Charagram: Embedding Words and Sentences via Character n-grams

abstract: We present Charagram embeddings, a simple approach for learning character-based compositional models to embed textual sequences. A word or sentence is represented using a character n-gram count vector, followed by a single nonlinear transformation to yield a low-dimensional embedding. We use three tasks for evaluation: word similarity, sentence similarity, and part-of-speech tagging. We demonstrate that Charagram embeddings outperform more complex architectures based on character-level recurrent and convolutional neural networks, achieving new state-of-the-art performance on several similarity tasks.


title: Dual-band Wilkinson power divider based on composite right/left-handed transmission lines

abstract: In this paper, an interdigital transmission line (ITL) based on the concept of composite right/left handed (CRLH) transmission line is introduced for the first time. By cascading interdigital capacitors, the ITL is created. The equivalent circuit of the interdigital capacitor and interdigital transmission line are expressed. Moreover, by utilizing the interdigital transmission line, a dual-band Wilkinson power divider is proposed.


title: Detection of SQL injection and XSS attacks in three tier web applications

abstract: Web applications are used on a large scale worldwide, which handles sensitive personal data of users. With web application that maintains data ranging from as simple as telephone number to as important as bank account information, security is a prime point of concern. With hackers aimed to breakthrough this security using various attacks, we are focusing on SQL injection attacks and XSS attacks. SQL injection attack is very common attack that manipulates the data passing through web application to the database servers through web servers in such a way that it alters or reveals database contents. While Cross Site Scripting (XSS) attacks focuses more on view of the web application and tries to trick users that leads to security breach. We are considering three tier web applications with static and dynamic behavior, for security. Static and dynamic mapping model is created to detect anomalies in the class of SQL Injection and XSS attacks.


title: Experiments with security and privacy in IoT networks

abstract: We explore the risks to security and privacy in IoT networks by setting up an inexpensive home automation network and performing a set of experiments intended to study attacks and defenses. We focus on privacy preservation in home automation networks but our insights can extend to other IoT applications. Privacy preservation is fundamental to achieving the promise of IoT, Industrial Internet and M2M. We look at both simple cryptographic techniques and information manipulation to protect a user against an adversary inside the IoT network or an adversary that has compromised remote servers. We show how user data can be masked or selectively leaked and manipulated. We provide a blueprint for inexpensive study of IoT security and privacy using COTS products and services.


title: China's Dilemma on Iran: between energy security and a responsible rising power

abstract: As international pressure on China's policy concerning its increasing investments in Iran grows, the Chinese leadership has to painfully balance its impulse toward energy and economic cooperation with Iran against other important interests, including enhancing its image as a responsible big country. After analyzing the evolution of China–Iran energy relations, this paper tries to answer the question of how China has responded to the intensification of US-led sanctions against Iran, what merits and shortcomings China's attempts to balance or reconcile its interests in Iran might create, and what options China can take to deal with a possible oil crisis.


title: Bypassing Antivirus Detection with Encryption

abstract: Bypassing an antivirus is a common issue among ethical hackers and penetration testers. Several techniques have been -and are being-used to bypass antivirus software; an effective and efficient one is to encrypt the malware by using special purpose tools, called crypters. In this paper, a novel crypter, which is based on the latest techniques, and can bypass antivirus software is described. The crypter is based on a new architecture that enables it to provide a unique output every time it is used. Testing results indicate that the proposed crypter evades detection by all antivirus in all runs.


title: Fuzzy -Means Clustering of Incomplete Data

abstract: The problem of clustering a real s-dimensional data set X={x(1 ),,,,,x(n)} subset R(s) is considered. Usually, each observation (or datum) consists of numerical values for all s features (such as height, length, etc.), but sometimes data sets can contain vectors that are missing one or more of the feature values. For example, a particular datum x(k) might be incomplete, having the form x(k)=(254.3, ?, 333.2, 47.45, ?)(T), where the second and fifth feature values are missing. The fuzzy c-means (FCM) algorithm is a useful tool for clustering real s-dimensional data, but it is not directly applicable to the case of incomplete data. Four strategies for doing FCM clustering of incomplete data sets are given, three of which involve modified versions of the FCM algorithm. Numerical convergence properties of the new algorithms are discussed, and all approaches are tested using real and artificially generated incomplete data sets.


title: An algorithm enabling blind users to find and read barcodes

abstract: Most camera-based systems for finding and reading barcodes are designed to be used by sighted users (e.g. the Red Laser iPhone app), and assume the user carefully centers the barcode in the image before the barcode is read. Blind individuals could benefit greatly from such systems to identify packaged goods (such as canned goods in a supermarket), but unfortunately in their current form these systems are completely inaccessible because of their reliance on visual feedback from the user. To remedy this problem, we propose a computer vision algorithm that processes several frames of video per second to detect barcodes from a distance of several inches; the algorithm issues directional information with audio feedback (e.g. “left,” “right”) and thereby guides a blind user holding a webcam or other portable camera to locate and home in on a barcode. Once the barcode is detected at sufficiently close range, a barcode reading algorithm previously developed by the authors scans and reads aloud the barcode and the corresponding product information. We demonstrate encouraging experimental results of our proposed system implemented on a desktop computer with a webcam held by a blindfolded user; ultimately the system will be ported to a camera phone for use by visually impaired users.


title: The Coherent Heart Heart-Brain Interactions, Psychophysiological Coherence, and the Emergence of System-Wide Order

abstract: This article presents theory and research on the scientific study of emotion that emphasizes the importance of coherence as an optimal psychophysiological state. A dynamic systems view of the interrelations between psychological, cognitive and emotional systems and neural communication networks in the human organism provides a foundation for the view presented. These communication networks are examined from an information processing perspective and reveal a fundamental order in heart-brain interactions and a harmonious synchronization of physiological systems associated with positive emotions. The concept of coherence is drawn on to understand optimal functioning which is naturally reflected in the heart's rhythmic patterns. Research is presented identifying various psychophysiological states linked to these patterns, with neurocardiological coherence emerging as having significant impacts on well being. These include psychophysiological as well as improved cognitive performance. From this, the central role of the heart is explored in terms of biochemical, biophysical and energetic interactions. Appendices provide further details and research on; psychophysiological functioning, reference previous research in this area, details on research linking coherence with optimal cognitive performance, heart brain synchronization and the energetic signature of the various psychophysiological modes.


title: Studies on the mechanism of action of antifertile PG in animal models.

abstract: Antifertile effects of PGF2 alpha, PGE2, PGE1, sulprostone and other PGs were evaluated in different pregnancy models in rats, guinea pigs and rhesus monkeys and the underlying mechanisms of action were investigated. Quantitative and qualitative species differences and pregnancy stage dependency were recorded. Basic regulatory differences of the pregnant uterus seem to exist in these species. In early pregnant rats, abortifacient effects were based on luteolytic effects, independent of the PG used. The myometrium was found to be refractory to the injected PG as long as serum progesterone levels were kept high. By contrast, in guinea pigs after the luteoplacental shift of progesterone secretion (tested after day 40 p.c.) and in rhesus monkeys even before this shift (tested day 20 p.c.) abortifacient effects were found to be exerted by direct stimulation of the myometrium. Uterine stimulation was possible in the presence of any level of serum progesterone. The induction of uterine PG synthesis was probably of importance supporting the expulsion. The role of obvious tissue damage within the conceptus remained uncertain. In contrast to rats there seems to be a pre-existing PG-sensitivity of the pregnant myometrium in guinea pigs and primates. In guinea pigs sensitivity slightly increased for E- but not for F-type PG toward term. Oxytocin sensitivity was found to increase by a factor of more than 100 between days 23-63 of pregnancy. Time dependent changes in uterine receptivity to PG and oxytocin may be considered as a regulatory principle which might permit parturition to occur in the presence of progesterone as an evolutionary adaptation to a placental progesterone secretion which cannot be abolished. It was concluded that in the presence of already established gradual uterine responsiveness to PG (and Oxytocin) during gestation efficient blocking mechanisms for uterine PG-formation must exist in order to explain uterine quiescence. Almost complete resistance of pregnancy to oestrogen which exists in humans, monkeys and guinea pigs was considered as to be pharmacological evidence of such a mechanism. The principles of endocrine control of the myometrium and its pharmacology seem similar in guinea pig and primate pregnancy. The guinea pig might therefore provide a relevant model to study potential drug effects on the regulatory balance of the pregnant uterus and also to achieve a better understanding of human uterine physiology.


title: Liquid biopsy for cancer screening, patient stratification and monitoring

abstract: Molecular characterization of a patient’s tumor to guide treatment decisions is increasingly being applied in clinical care and can have a significant impact on disease outcome. These molecular analyses, including mutation characterization, are typically performed on tissue acquired through a biopsy at diagnosis. However, tumors are highly heterogeneous and sampling in its entirety is challenging. Furthermore, tumors evolve over time and can alter their molecular genotype, making clinical decisions based on historical biopsy data suboptimal. Personalized medicine for cancer patients aims to tailor the best treatment options for the individual at diagnosis and during treatment. To fully enable personalized medicine it is desirable to have an easily accessible, minimally invasive way to determine and follow the molecular makeup of a patient’s tumor longitudinally. One such approach is through a liquid biopsy, where the genetic makeup of the tumor can be assessed through a biofluid sample. Liquid biopsies have the potential to help clinicians screen for disease, stratify patients to the best treatment and monitor treatment response and resistance mechanisms in the tumor. A liquid biopsy can be used for molecular characterization of the tumor and its non-invasive nature allows repeat sampling to monitor genetic changes over time without the need for a tissue biopsy. This review will summarize three approaches in the liquid biopsy field: circulating tumor cells (CTCs), cell free DNA (cfDNA) and exosomes. We also outline some of the analytical challenges encountered using liquid biopsy techniques to detect rare mutations in a background of wild-type sequences.


title: New Constructions for Forward and Backward Private Symmetric Searchable Encryption

abstract: We study the problem of dynamic symmetric searchable encryption. In that setting, it is crucial to minimize the information revealed to the server as a result of update operations (insertions and deletions). Two relevant privacy properties have been defined in that context: forward and backward privacy. The first makes it hard for the server to link an update operation with previous queries and has been extensively studied in the literature. The second limits what the server can learn about entries that were deleted from the database, from queries that happen after the deletion. Backward privacy was formally studied only recently (Bost et al., CCS 2017) in a work that introduced a formal definition with three variable types of leakage (Type-I to Type-III ordered from most to least secure), as well as the only existing schemes that satisfy this property. In this work, we introduce three novel constructions that improve previous results in multiple ways. The first scheme achieves Type-II backward privacy and our experimental evaluation shows it has 145-253X faster search computation times than previous constructions with the same leakage. Surprisingly, it is faster even than schemes with Type-III leakage which makes it the most efficient implementation of a forward and backward private scheme so far. The second one has search time that is asymptotically within a polylogarithmic multiplicative factor of the theoretical optimal (i.e., the result size of a search), and it achieves the strongest level of backward privacy (Type-I). All previous Type-I constructions require time that is at least linear in the total number of updates for the requested keywords, even the (arbitrarily many) previously deleted ones. Our final scheme improves upon the second one by reducing the number of roundtrips for a search at the cost of extra leakage (Type-III).


title: A spiral antenna over a high-impedance surface consisting of fan-shaped patch cells

abstract: Radiation characteristics of a spiral antenna over a high-impedance surface (HIS) are analyzed. The HIS consists of fan-shaped patch cells. The fan-shaped patches are arranged homogeneously in the circumferential direction but are set non-homogeneously in the radial direction. The analysis is performed using method of moment. Radiation characteristics of a spiral antenna with a perfect electric conductor (PEC) reflector are analyzed. It is reaffirmed that wideband radiation characteristics, including input impedance and axial ratio are deteriorate. Subsequently, Radiation characteristics of a spiral antenna with a fan-shaped HIS reflector are analyzed. It is revealed that input impedance and axial ratio are mitigated by replacing the PEC reflector with the fan-shaped HIS reflector.


title: Passive Investment Strategies and Efficient Markets

abstract: This paper presents the case for and the evidence in favour of passive investment strategies and examines the major criticisms of the technique. I conclude that the evidence strongly supports passive investment management in all markets—small–capitalisation stocks as well as large–capitalisation equities, US markets as well as international markets, and bonds as well as stocks. Recent attacks on the efficient market hypothesis do not weaken the case for indexing.


A novel dry friction modeling and its impact on differential equations computation and Lyapunov exponents estimation


title: A new exact method for the two-dimensional orthogonal packing problem

abstract: Abstract The two-dimensional orthogonal packing problem (2 OPP ) consists in determining if a set of rectangles (items) can be packed into one rectangle of fixed size (bin). In this paper we propose two exact algorithms for solving this problem. The first algorithm is an improvement on a classical branch&bound method, whereas the second algorithm is based on a new relaxation of the problem. We also describe reduction procedures and lower bounds which can be used within enumerative methods. We report computational experiments for randomly generated benchmarks which demonstrate the efficiency of both methods: the second method is competitive compared to the best previous methods. It can be seen that our new relaxation allows an efficient detection of non-feasible instances.


title: A quantitative meta-analysis and review of motor learning in the human brain

abstract: Neuroimaging studies have improved our understanding of which brain structures are involved in motor learning. Despite this, questions remain regarding the areas that contribute consistently across paradigms with different task demands. For instance, sensorimotor tasks focus on learning novel movement kinematics and dynamics, while serial response time task (SRTT) variants focus on sequence learning. These differing task demands are likely to elicit quantifiably different patterns of neural activity on top of a potentially consistent core network. The current study identified consistent activations across 70 motor learning experiments using activation likelihood estimation (ALE) meta-analysis. A global analysis of all tasks revealed a bilateral cortical–subcortical network consistently underlying motor learning across tasks. Converging activations were revealed in the dorsal premotor cortex, supplementary motor cortex, primary motor cortex, primary somatosensory cortex, superior parietal lobule, thalamus, putamen and cerebellum. These activations were broadly consistent across task specific analyses that separated sensorimotor tasks and SRTT variants. Contrast analysis indicated that activity in the basal ganglia and cerebellum was significantly stronger for sensorimotor tasks, while activity in cortical structures and the thalamus was significantly stronger for SRTT variants. Additional conjunction analyses then indicated that the left dorsal premotor cortex was activated across all analyses considered, even when controlling for potential motor confounds. The highly consistent activation of the left dorsal premotor cortex suggests it is a critical node in the motor learning network.


title: Real-time performance evaluation of a genetic-algorithm-based fuzzy logic controller for IPM motor drives

abstract: This work presents a novel speed control scheme using a genetic-based fuzzy logic controller (GFLC) for an interior permanent-magnet synchronous motor (IPMSM) drive. The proposed GFLC is developed to have less computational burden, which makes it suitable for real-time implementation. The parameters for the GFLC are tuned by genetic algorithm (GA). The complete drive incorporating the GFLC is successfully implemented in real-time using a digital signal processor board DS 1102 for a laboratory 1-hp interior permanent magnet motor. The efficacy of the proposed GFLC-based IPMSM drive is verified by simulation as well as experimental results at various operating conditions. A performance comparison with a conventional proportional-integral controller is also provided to show the superiority of the proposed controller. The proposed GFLC is found to be robust for high-performance industrial drive applications.


title: DropConnected neural network trained with diverse features for classifying heart sounds

abstract: A fully-connected, two-hidden-layer neural network trained by error backpropagation, and regularized with DropConnect is used to classify heart sounds as normal or abnormal. The heart sounds are segmented using an open-source algorithm based on a hidden semi-Markov model. Features are extracted from the heart sounds using a wavelet transform, mel-frequency cepstral coefficients, inter-beat properties, and signal complexity. Features are normalized by subtracting by their means and dividing by their standard deviations across the whole training set. Any feature which is not significantly different between normal and abnormal recordings in the training data is removed, as are highly-correlated features. The dimensionality of the features vector is reduced by projecting it onto its first 70 principal components. A 10 fold cross-validation study gives a mean classification score of 84.1% with a variance of 2.9%. The final score on the test data was 85.2%.


title: Learning Convolutional Text Representations for Visual Question Answering

abstract: Visual question answering is a recently proposed artificial intelligence task that requires a deep understanding of both images and texts. In deep learning, images are typically modeled through convolutional neural networks, and texts are typically modeled through recurrent neural networks. While the requirement for modeling images is similar to traditional computer vision tasks, such as object recognition and image classification, visual question answering raises a different need for textual representation as compared to other natural language processing tasks. In this work, we perform a detailed analysis on natural language questions in visual question answering. Based on the analysis, we propose to rely on convolutional neural networks for learning textual representations. By exploring the various properties of convolutional neural networks specialized for text data, such as width and depth, we present our "CNN Inception + Gate" model. We show that our model improves question representations and thus the overall accuracy of visual question answering models. We also show that the text representation requirement in visual question answering is more complicated and comprehensive than that in conventional natural language processing tasks, making it a better task to evaluate textual representation methods. Shallow models like fastText, which can obtain comparable results with deep learning models in tasks like text classification, are not suitable in visual question answering.


title: Building TensorFlow Applications in Smart City Scenarios

abstract: Nowadays, visual object recognition is one of the key applications for computer vision and deep learning techniques. With the recent development in mobile computing technology, many deep learning framework software support Personal Digital Assistant systems, i.e., smart phones or tablets, allowing developers to conceive innovative applications. In this work, we intend to employ such ICT strategies with the aim of supporting the tourism in an art city: for these reasons, we propose to provide tourists with a mobile application in order to better explore artistic heritage within an urban environment by using just their smartphone's camera. The software solution is based on Google TensorFlow, an innovative deep learning framework mainly designed for pattern recognition tasks. The paper presents our design choices and an early performance evaluation.


title: Two-step parameter extraction procedure with formal optimization for physics-based circuit simulator IGBT and p-i-n diode models

abstract: A practical and accurate parameter extraction method is presented for the Fourier-based-solution physics-based insulated gate bipolar transistor (IGBT) and power diode models. The goal is to obtain a model accurate enough to allow switching loss prediction under a variety of operating conditions. In the first step of the extraction procedure, only one simple clamped inductive load test is needed for the extraction of the six parameters required for the diode model and of the 12 and 15 parameters required for the nonpunch-through (NPT) and punch-through (PT) IGBT models, respectively. The second part of the extraction procedure is an automated formal optimization step that refines the parameter estimation. Validation with experimental results from various structures of IGBT demonstrates the accuracy of the proposed IGBT and diode models and the robustness of the parameter extraction method.


title: Consumer benefits of labels and bans on GMO food-An empirical analysis using Choice Experiments

abstract: Applying a choice experiment on the choice of consumer goods we show that Swedish consumers do not regard GMO food as being equivalent to conventional food. A central argument by proponents of GMO is that the end products are identical to those where GMO has not been used. That respondents in our survey disagree with this argument is supported by two observations. First, a positive significant WTP is found for a mandatory labeling policy. This result confirms previous observations that GMO food can be a credence good causing a market failure. Second, consumers are also willing to pay a significantly higher product price to ensure a total ban on the use of GMO in animal fodder. Even if scientists and politicians argue that most of today’s GMO food is indistinguishable from GMO free food, the consumers disagree.


title: Assistive Clothing Pattern Recognition for Visually Impaired People

abstract: Choosing clothes with complex patterns and colors is a challenging task for visually impaired people. Automatic clothing pattern recognition is also a challenging research problem due to rotation, scaling, illumination, and especially large intraclass pattern variations. We have developed a camera-based prototype system that recognizes clothing patterns in four categories (plaid, striped, patternless, and irregular) and identifies 11 clothing colors. The system integrates a camera, a microphone, a computer, and a Bluetooth earpiece for audio description of clothing patterns and colors. A camera mounted upon a pair of sunglasses is used to capture clothing images. The clothing patterns and colors are described to blind users verbally. This system can be controlled by speech input through microphone. To recognize clothing patterns, we propose a novel Radon Signature descriptor and a schema to extract statistical properties from wavelet subbands to capture global features of clothing patterns. They are combined with local features to recognize complex clothing patterns. To evaluate the effectiveness of the proposed approach, we used the CCNY Clothing Pattern dataset. Our approach achieves 92.55% recognition accuracy which significantly outperforms the state-of-the-art texture analysis methods on clothing pattern recognition. The prototype was also used by ten visually impaired participants. Most thought such a system would support more independence in their daily life but they also made suggestions for improvements.


title: Wireless Network Behavior under ICMP Ping Flood DoS Attack and Mitigation Techniques

abstract: Internet Control Message Protocol (ICMP) is an erro r reporting and diagnostic utility and it is consider ed as a part of Internet Protocol (IP) suite. Although this protoco l is very important for ensuring correct data distribution, i t can be exploited by malicious users for conducting different Denial of Service (DoS) attacks. Due to the broadcast nature of wireless co mmunication, exploitation of this kind of attack is even easier. By sending bogus ICMP redirect packets, a malicious user can either disrupt or intercept communication from a wireless access poin t. In this paper, we present our approach to simulate the ICMP Ping Flood Attack, and to analyze the effects of th is attack on wireless networks using OPNET Modeler. We propose several countermeasures against this type of attack. Simula tion results regarding the effects of link failure recovery mech anism against this type of attack are discussed .


title: Combinative hypergraph learning for semi-supervised image classification

abstract: Recent years have witnessed a surge of interest in hypergraph-based transductive image classification. Hypergraph-based transductive learning models the high-order relationship of samples by using a hyperedge to link multiple samples. In order to extend the high-order relationship of samples, we incorporate linear correlation of sparse representation to hypergraph learning framework to improve learning performance. In this paper, we present a new transductive learning method called combinative hypergraph learning (CHL). CHL captures the similarity between two samples in the same category by adding sparse hypergraph learning to conventional hypergraph learning. And more, we propose two strategies to combine the two hypergraph learning methods. Experimental results on two image datasets have demonstrated the effectiveness of CHL in comparison to the state-of-the-art methods and shown that our proposed method is promising.


title: Invariant image recognition by Zernike moments

abstract: The problem of rotation-, scale-, and translation-invariant recognition of images is discussed. A set of rotation-invariant features are introduced. They are the magnitudes of a set of orthogonal complex moments of the image known as Zernike moments. Scale and translation invariance are obtained by first normalizing the image with respect to these parameters using its regular geometrical moments. A systematic reconstruction-based method for deciding the highest-order Zernike moments required in a classification problem is developed. The quality of the reconstructed image is examined through its comparison to the original one. The orthogonality property of the Zernike moments, which simplifies the process of image reconstruction, make the suggest feature selection approach practical. Features of each order can also be weighted according to their contribution to the reconstruction process. The superiority of Zernike moment features over regular moments and moment invariants was experimentally verified. >


title: Reinforcement Learning in Continuous State and Action Spaces

abstract: Many traditional reinforcement-learning algorithms have been designed for problems with small finite state and action spaces. Learning in such discrete problems can been difficult, due to noise and delayed reinforcements. However, many real-world problems have continuous state or action spaces, which can make learning a good decision policy even more involved. In this chapter we discuss how to automatically find good decision policies in continuous domains. Because analytically computing a good policy from a continuous model can be infeasible, in this chapter we mainly focus on methods that explicitly update a representation of a value function, a policy or both. We discuss considerations in choosing an appropriate representation for these functions and discuss gradient-based and gradient-free ways to update the parameters. We show how to apply these methods to reinforcement-learning problems and discuss many specific algorithms. Amongst others, we cover gradient-based temporal-difference learning, evolutionary strategies, policy-gradient algorithms and (natural) actor-critic methods. We discuss the advantages of different approaches and compare the performance of a state-of-the-art actor-critic method and a state-of-the-art evolutionary strategy empirically.


title: Role of cytokines in preterm labour and brain injury.

abstract: Intrauterine infection induces an intra-amniotic inflammatory response involving the activation of a number of cytokines and chemokines which, in turn, may trigger preterm contractions, cervical ripening and rupture of the membranes. Infection and cytokine-mediated inflammation appear to play a prominent role in preterm birth at early gestations (<30 weeks). The role of infection/inflammation in preterm birth in Europe has been incompletely characterised. The rate of preterm birth in Sweden is lower, and the rate of chorioamnionitis, bacterial vaginosis (BV), neonatal sepsis, and urinary tract infections during pregnancy is lower compared with the USA. In a Swedish population of women with preterm labour or preterm premature rupture of the membranes (PPROM) <34 weeks of gestation, microorganisms were detected in the amniotic fluid in 25% of women with PPROM and in 16% of those in preterm labour. Nearly half of these women had intra-amniotic inflammation defined as elevated interleukin-6 (IL-6) and IL-8, and there was a high degree of correlation between cytokine levels and preterm birth or the presence of microbial colonisation. These data do not support the hypothesis that infection-related preterm birth is less frequent in northern Europe than elsewhere. The intra-amniotic inflammatory response has also been associated with white matter injury and cerebral palsy. We find that in experimental models, induction of a systemic inflammatory response using lipopolysaccharide activates toll-like receptors (TLRs), which produce either white matter lesions or increase brain susceptibility to secondary insults. Recently, IL-18 in umbilical blood was shown to correlate with brain injury in preterm infants and IL-18 deficiency in mice decreases CNS vulnerability.


title: Improved Representation Learning for Predicting Commonsense Ontologies

abstract: Recent work in learning ontologies (hierarchical and partially-ordered structures) has leveraged the intrinsic geometry of spaces of learned representations to make predictions that automatically obey complex structural constraints. We explore two extensions of one such model, the order-embedding model for hierarchical relation learning, with an aim towards improved performance on text data for commonsense knowledge representation. Our first model jointly learns ordering relations and non-hierarchical knowledge in the form of raw text. Our second extension exploits the partial order structure of the training data to find long-distance triplet constraints among embeddings which are poorly enforced by the pairwise training procedure. We find that both incorporating free text and augmented training constraints improve over the original order-embedding model and other strong baselines.


title: A data augmentation methodology for training machine/deep learning gait recognition algorithms

abstract: There are several confounding factors that can reduce the accuracy of gait recognition systems. These factors can reduce the distinctiveness, or alter the features used to characterise gait, they include variations in clothing, lighting, pose and environment, such as the walking surface. Full invariance to all confounding factors is challenging in the absence of high-quality labelled training data. We introduce a simulation-based methodology and a subject-specific dataset which can be used for generating synthetic video frames and sequences for data augmentation. With this methodology, we generated a multi-modal dataset. In addition, we supply simulation files that provide the ability to simultaneously sample from several confounding variables. The basis of the data is real motion capture data of subjects walking and running on a treadmill at different speeds. Results from gait recognition experiments suggest that information about the identity of subjects is retained within synthetically generated examples. The dataset and methodology allow studies into fully-invariant identity recognition spanning a far greater number of observation conditions than would otherwise be possible.


title: An Analysis on Switching Loss Optimized PWM Strategies for Three Phase PWM Voltage Source Converters

abstract: The pulse width modulation of power converters is an operation parameter that exhibits a great influence on occurring semiconductor losses. Especially the switching losses can be reduced significantly by applying the appropriate PWM method maintaining the value of pulse frequency. To achieve such effect usually a so called discontinuous PWM is applied. But there is still a certain degree of freedom in using such PWM strategy. Thus the question for the optimal adjustment to the corresponding operation point arises. This work derives the basic rules for adjusting the discontinuous PWM appropriately with special focus on the semiconductor switching losses. The theoretical results are validated by experimental evidence that was gained at a calorimetric test set up.


title: An Indicator of Research Front Activity: Measuring Intellectual Organization as Uncertainty Reduction in Document Sets

abstract: When using scientific literature to model scholarly discourse, a research specialty can be operationalized as an evolving set of related documents. Each publication can be expected to contribute to the further development of the specialty at the research front. The specific combinations of title words and cited references in a paper can then be considered as a signature of the knowledge claim in the paper: new words and combinations of words can be expected to represent variation, while each paper is at the same time selectively positioned into the intellectual organization of a field using context-relevant references. Can the mutual information among these three dimensions--title words, cited references, and sequence numbers--be used as an indicator of the extent to which intellectual organization structures the uncertainty prevailing at a research front? The effect of the discovery of nanotubes (1991) on the previously existing field of fullerenes is used as a test case. Thereafter, this method is applied to science studies with a focus on scientometrics using various sample delineations. An emerging research front about citation analysis can be indicated.


A Seventh-Generation x86 Microprocessor


title: An empirical investigation of factors influencing data quality improvement success

abstract: While some research has been done to identify the dimensions of data quality and to develop methodologies of improving particular aspects of data quality, the fundamental questions of these methodologies remain vague. This paper tries to fill this gap by empirically analyzing the factors influencing the success of data quality improvements. Hereto, we develop a model for data quality improvement success. This model is evaluated using survey data from 179 respondents. The significance of the model is computed using the maximum likelihood estimation of AMOS. The results show, that organizational implementation success is positively associated with perceived data quality, whereas no significant contribution of data quality projects to perceived data quality could be observed.


title: Can programming be liberated from the von Neumann style?: a functional style and its algebra of programs

abstract: Conventional programming languages are growing ever more enormous, but not stronger. Inherent defects at the most basic level cause them to be both fat and weak: their primitive word-at-a-time style of programming inherited from their common ancestor—the von Neumann computer, their close coupling of semantics to state transitions, their division of programming into a world of expressions and a world of statements, their inability to effectively use powerful combining forms for building new programs from existing ones, and their lack of useful mathematical properties for reasoning about programs. An alternative functional style of programming is founded on the use of combining forms for creating programs. Functional programs deal with structured data, are often nonrepetitive and nonrecursive, are hierarchically constructed, do not name their arguments, and do not require the complex machinery of procedure declarations to become generally applicable. Combining forms can use high level programs to build still higher level ones in a style not possible in conventional languages. Associated with the functional style of programming is an algebra of programs whose variables range over programs and whose operations are combining forms. This algebra can be used to transform programs and to solve equations whose “unknowns” are programs in much the same way one transforms equations in high school algebra. These transformations are given by algebraic laws and are carried out in the same language in which programs are written. Combining forms are chosen not only for their programming power but also for the power of their associated algebraic laws. General theorems of the algebra give the detailed behavior and termination conditions for large classes of programs. A new class of computing systems uses the functional programming style both in its programming language and in its state transition rules. Unlike von Neumann languages, these systems have semantics loosely coupled to states—only one state transition occurs per major computation.


title: Planning optimal grasps

abstract: The authors address the problem of planning optimal grasps. Two general optimality criteria that consider the total finger force and the maximum finger force are introduced and discussed. Their formalization using various metrics on a space of generalized forces is detailed. The geometric interpretation of the two criteria leads to an efficient planning algorithm. An example of its use in a robotic environment equipped with two-jaw and three-jaw is described. >


title: Improvement on a Trapdoor Hash Function

abstract: By appending some bits to the original signature, a trapdoor hash function converts any signature scheme into secure signature scheme with very efficient online computation. Many of them have been proposed. However, all of them require performing a modular reduction during the online phase. The paper presents a trapdoor hash function to get rid of the modular reduction in online computation.


title: Medical Persona Classification in Social Media

abstract: Identifying medical persona from a social media post is of paramount importance for drug marketing and pharmacovigilance. In this work, we propose multiple approaches to infer the medical persona associated with a social media post. We pose this as a supervised multi-label text classification problem. The main challenge is to identify the hidden cues in a post that are indicative of a particular persona. We first propose a large set of manually engineered features for this task. Further, we propose multiple neural network based architectures to extract useful features from these posts using pre-trained word embeddings. Our experiments on thousands of blogs and tweets show that the proposed approach results in 7% and 5% gain in F-measure over manual feature engineering based approach for blogs and tweets respectively.


Designing Twister 2 : Efficient Programming Environment Toolkit for Big Data


title: Image annotation via deep neural network

abstract: Multilabel image annotation is one of the most important open problems in computer vision field. Unlike existing works that usually use conventional visual features to annotate images, features based on deep learning have shown potential to achieve outstanding performance. In this work, we propose a multimodal deep learning framework, which aims to optimally integrate multiple deep neural networks pretrained with convolutional neural networks. In particular, the proposed framework explores a unified two-stage learning scheme that consists of (i) learning to fune-tune the parameters of deep neural network with respect to each individual modality, and (ii) learning to find the optimal combination of diverse modalities simultaneously in a coherent process. Experiments conducted on the NUS-WIDE dataset evaluate the performance of the proposed framework for multilabel image annotation, in which the encouraging results validate the effectiveness of the proposed algorithms.


title: Information extraction from scientific articles: a survey

abstract: In last few decades, with the advent of World Wide Web (WWW), world is being overloaded with huge data. This huge data carries potential information that once extracted, can be used for betterment of humanity. Information from this data can be extracted using manual and automatic analysis. Manual analysis is not scalable and efficient, whereas, the automatic analysis involves computing mechanisms that aid in automatic information extraction over huge amount of data. WWW has also affected overall growth in scientific literature that makes the process of literature review quite laborious, time consuming and cumbersome job for researchers. Hence a dire need is felt to automatically extract potential information out of immense set of scientific articles to automate the process of literature review. Therefore, in this study, aim is to present the overall progress concerning automatic information extraction from scientific articles. The information insights extracted from scientific articles are classified in two broad categories i.e. metadata and key-insights. As available benchmark datasets carry a significant role in overall development in this research domain, existing datasets against both categories are extensively reviewed. Later, research studies in literature that have applied various computational approaches applied on these datasets are consolidated. Major computational approaches in this regard include Rule-based approaches, Hidden Markov Models, Conditional Random Fields, Support Vector Machines, Naïve-Bayes classification and Deep Learning approaches. Currently, there are multiple projects going on that are focused towards the dataset construction tailored to specific information needs from scientific articles. Hence, in this study, state-of-the-art regarding information extraction from scientific articles is covered. This study also consolidates evolving datasets as well as various toolkits and code-bases that can be used for information extraction from scientific articles.


title: Correlation between bullying and clinical depression in adolescent patients

abstract: A literature review of the associations between involvement in bullying and depression is presented. Many studies have demonstrated a concurrent association between involvement in bullying and depression in adolescent population samples. Not only victims but also bullies display increased risk of depression, although not all studies have confirmed this for the bullies. Retrospective studies among adults support the notion that victimization is followed by depression. Prospective follow-up studies have suggested both that victimization from bullying may be a risk factor for depression and that depression may predispose adolescents to bullying. Research among clinically referred adolescents is scarce but suggests that correlations between victimization from bullying and depression are likely to be similar in clinical and population samples. Adolescents who bully present with elevated numbers of psychiatric symptoms and psychiatric and social welfare treatment contacts.


title: Laboratory Medicine Practice Guidelines: Recommendations for the Use of Laboratory Tests to Support Poisoned Patients Who Present to the Emergency Department

abstract: BACKGROUND ::: Exposure to drugs and toxins is a major cause for patients' visits to the emergency department (ED). ::: ::: ::: METHODS ::: Recommendations for the use of clinical laboratory tests were prepared by an expert panel of analytical toxicologists and ED physicians specializing in clinical toxicology. These recommendations were posted on the world wide web and presented in open forum at several clinical chemistry and clinical toxicology meetings. ::: ::: ::: RESULTS ::: A menu of important stat serum and urine toxicology tests was prepared for clinical laboratories who provide clinical toxicology services. For drugs-of-abuse intoxication, most ED physicians do not rely on results of urine drug testing for emergent management decisions. This is in part because immunoassays, although rapid, have limitations in sensitivity and specificity and chromatographic assays, which are more definitive, are more labor-intensive. Ethyl alcohol is widely tested in the ED, and breath testing is a convenient procedure. Determinations made within the ED, however, require oversight by the clinical laboratory. Testing for toxic alcohols is needed, but rapid commercial assays are not available. The laboratory must provide stat assays for acetaminophen, salicylates, co-oximetry, cholinesterase, iron, and some therapeutic drugs, such as lithium and digoxin. Exposure to other heavy metals requires laboratory support for specimen collection but not for emergent testing. ::: ::: ::: CONCLUSIONS ::: Improvements are needed for immunoassays, particularly for amphetamines, benzodiazepines, opioids, and tricyclic antidepressants. Assays for new drugs of abuse must also be developed to meet changing abuse patterns. As no clinical laboratory can provide services to meet all needs, the National Academy of Clinical Biochemistry Committee recommends establishment of regional centers for specialized toxicology testing.


title: It's worth the hassle!: the added value of evaluating the usability of mobile systems in the field

abstract: The distinction between field and laboratory is classical in research methodology. In human-computer interaction, and in usability evaluation in particular, it has been a controversial topic for several years. The advent of mobile devices has revived this topic. Empirical studies that compare evaluations in the two settings are beginning to appear, but they provide very different results. This paper presents results from an experimental comparison of a field-based and a lab-based usability evaluation of a mobile system. The two evaluations were conducted in exactly the same way. The conclusion is that it is definitely worth the hassle to conduct usability evaluations in the field. In the field-based evaluation we identified significantly more usability problems and this setting revealed problems with interaction style and cognitive load that were not identified in the laboratory.


title: Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference

abstract: This paper presents a new deep learning architecture for Natural Language Inference (NLI). Firstly, we introduce a new architecture where alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning. Secondly, we adopt factorization layers for efficient and expressive compression of alignment vectors into scalar features, which are then used to augment the base word representations. The design of our approach is aimed to be conceptually simple, compact and yet powerful. We conduct experiments on three popular benchmarks, SNLI, MultiNLI and SciTail, achieving competitive performance on all. A lightweight parameterization of our model also enjoys a $\approx 3$ times reduction in parameter size compared to the existing state-of-the-art models, e.g., ESIM and DIIN, while maintaining competitive performance. Additionally, visual analysis shows that our propagated features are highly interpretable.


title: Anytime algorithm development tools

abstract: Anytime algorithms are playing an increasingly important role in the construction of effective reasoning and planning systems. Early work on anytime algorithms concentrated on the construction of applications in such areas as medical diagnosis and mobile robot navigation. In this paper we describe a programming environment to support the development of such applications as well as larger applications in which several anytime algorithms are used. The widespread use of anytime algorithms depends largely on the availability of such programming tools for algorithm construction, performance measurement, composition of anytime algorithms, and monitoring of their execution. We present a prototype system that meets these needs. Created in lisp, this library of functions, graphical tools and monitoring modules will accelerate and simplify the process of programming with anytime algorithms.


title: Our Princess Is in Another Castle: A Review of Trends in Serious Gaming for Education

abstract: Do video games show demonstrable relationships to academic achievement gains when used to support the K-12 curriculum? In a review of literature, we identified 300+ articles whose descriptions related to video games and academic achievement. We found some evidence for the effects of video games on language learning, history, and physical education (specifically exergames), but little support for the academic value of video games in science and math. We summarize the trends for each subject area and supply recommendations for the nascent field of video games research. Many educationally interesting games exist, yet evidence for their impact on student achievement is slim. We recommend separating simulations from games and refocusing the question onto the situated nature of game-player-context interactions, including meta-game social collaborative elements.


title: A Multichannel High-Precision CMOS Time-to-Digital Converter for Laser-Scanner-Based Perception Systems

abstract: A multichannel time-to-digital converter (TDC) implemented with 0.35-μm complementary metal-oxide-semiconductor technology that uses a low-frequency crystal as reference and measures the time intervals with counter and delay line interpolation techniques is described. The multichannel measurement architecture provides information on the time intervals between several timing signals. The circuit can be used for laser time-of-flight distance measurements, e.g., where it can determine time intervals between a transmitted laser pulse and several reflected pulses and also pulsewidths or rise times, to compensate for the timing walk error. This paper shows how several measurement channels can be integrated into one TDC without losing the measurement performance. The circuit offers a measurement precision that is better than 8 ps and a measurement range of up to 74 μs. In terms of laser distance measurement, its performance is equivalent to millimeter-level precision within an 11-km range.


title: The ND2DB attack: Database content extraction using timing

abstract: In this paper we present a new attack technique that allows extraction of selected database content relying merely on the attacker's ability to perform database transactions (INSERTs or UPDATEs) that are usually available to any anonymous database user. Our attack technique uses a side-channel timing attack in the realm of database indexing algorithms and data structures. We prove that by exploiting the inherent characteristics of the most commonly used indexing data structures and algorithms in today's commercial database management systems it is possible to extract privacy-sensitive data from a database. In particular we prove, both in theory and practice that it is feasible to do so if the B-tree data structure is used and the attacker is able to insert records with chosen data that is used as the search key of one of the table's indexes. We present experimental results of a successful attack implementation against MySQL and provide conclusions and ideas for further research.


title: Wearable Medical Monitoring Systems Based on Wireless Networks: A Review

abstract: The last decade has witnessed a rapid increase of interest in new sensing and monitoring devices, including wireless wearable mobile devices and sensing networks for medical health applications. This paper reviews current wearable mobile medical monitoring systems (WMMMSs) with emphasis on devices based on textile and wireless sensing networks. These monitoring systems consist of various types of small physiological sensors, transmission modules, and processing capabilities. In addition, WMMMSs have a potential to change the health care model by providing inexpensive wearable unobtrusive solutions for continuous medical health monitoring. It is with the hope that the current survey is able to provide a direction for future research improvements and also serve as a reference for current achievements and their maturity level. In this review, a great number of system implementations are compared according to different classifications. Besides, these systems also act as an effective approach to identify the technological advantages and disadvantages of the current state-of-the-art in wearable devices solutions.


title: Matching as Nonparametric Preprocessing for Reducing Model Dependence

abstract: Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms, and other modeling assumptions, how can researchers ensure that the few estimates presented are accurate or representative? How do readers know that publications are not merely demonstrations that it is possible to find a specification that fits the author's favorite hypothesis? And how do we evaluate or even define statistical properties like unbiasedness or mean squared error when no unique model or estimator even exists? Matching methods, which offer the promise of causal inference with fewer assumptions, constitute one possible way forward, but crucial results in this fast-growing methodological literature are often grossly misinterpreted. We explain how to avoid these misinterpretations and propose a unified approach that makes it possible for researchers to preprocess data with matching (such as with the easy-to-use software we offer) and then to apply the best parametric techniques they would have used anyway. This procedure makes parametric models produce more accurate and considerably less model-dependent causal inferences.


title: Information Security for Electric Power Utilities (EPUs)—CIGRÉ Developments on Frameworks, Risk Assessment, and Technology

abstract: This paper deals with the important issue of proper treatment of information security for electric power utilities. It is based on the efforts of CIGRE Working Group (WG) D2.22 on ldquoTreatment of Information Security for Electric Power Utilities (EPUs)rdquo carried out between 2006 and 2008/2009. The WG produces a Technical Brochure (TB), where the purpose is to emphasize three main issues: security frameworks, risk assessment, and security technology. Here, guidance is given on different security frameworks based on an information security domain model. Also, baseline controls are treated. For risk assessment, a survey has been carried out. Only few commonalities, but several differences, have been found. Here, a methodology must be developed together with practical recommendations. For security technologies, guidance is given for deployment of different solutions, based on a logical diagram using different controls. Last, proposal on further work is given.