title: Implicit ReasoNet: Modeling Large-Scale Structured Relationships with Shared Memory

abstract: Recent studies on knowledge base completion, the task of recovering missing relationships based on recorded relations, demonstrate the importance of learning embeddings from multi-step relations. However, due to the size of knowledge bases, learning multi-step relations directly on top of observed instances could be costly. In this paper, we propose Implicit ReasoNets (IRNs), which is designed to perform large-scale inference implicitly through a search controller and shared memory. Unlike previous work, IRNs use training data to learn to perform multi-step inference through the shared memory, which is also jointly updated during training. While the inference procedure is not operating on top of observed instances for IRNs, our proposed model outperforms all previous approaches on the popular FB15k benchmark by more than 5.7%.


title: Automatic Image Orientation Detection

abstract: We present an algorithm for automatic image orientation estimation using a Bayesian learning framework. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a learning vector quantizer (LVQ) can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. We further show how principal component analysis (PCA) and linear discriminant analysis (LDA) can be used as a feature extraction mechanism to remove redundancies in the high-dimensional feature vectors used for classification. The proposed method is compared with four different commonly used classifiers, namely k-nearest neighbor, support vector machine (SVM), a mixture of Gaussians, and hierarchical discriminating regression (HDR) tree. Experiments on a database of 16 344 images have shown that our proposed algorithm achieves an accuracy of approximately 98% on the training set and over 97% on an independent test set. A slight improvement in classification accuracy is achieved by employing classifier combination techniques.


title: Challenges and Opportunities of Applying Natural Language Processing in Business Process Management

abstract: The Business Process Management (BPM) field focuses in the coordination of labor so that organizational processes are smoothly executed in a way that products and services are properly delivered. At the same time, NLP has reached a maturity level that enables its widespread application in many contexts, thanks to publicly available frameworks. In this position paper, we show how NLP has potential in raising the benefits of BPM practices at different levels. Instead of being exhaustive, we show selected key challenges were a successful application of NLP techniques would facilitate the automation of particular tasks that nowadays require a significant effort to accomplish. Finally, we report on applications that consider both the process perspective and its enhancement through NLP.


title: Explaining unplanned online media behaviors: Dual system theory models of impulsive use and swearing on social networking sites:

abstract: Unplanned (i.e. spontaneous) online behaviors such as impulsive use of social networking sites (SNS) and swearing on SNS are prevalent and can adversely affect users and society. Drawing on dual system theory, this article conceptualizes and empirically investigates the etiology of such behaviors. Results of two studies (n1 = 295 SNS users, focuses on impulsive use; n2 = 336 SNS users, focuses on swearing) show that both of these online behaviors are driven, in part, by cognitive-emotional preoccupation with the SNS and inhibited, in part, by cognitive-behavioral control over using the SNS. The inhibition effect is both direct and indirect, through the suppression of the cognitive-emotional preoccupation effects. The findings provide a theoretical lens of analysis through which impulsive and unitneded media use behaviors can be explained.


title: KB-Enabled Query Recommendation for Long-Tail Queries

abstract: In recent years, query recommendation algorithms have been designed to provide related queries for search engine users. Most of these solutions, which perform extensive analysis of users' search history (or query logs), are largely insufficient for long-tail queries that rarely appear in query logs. To handle such queries, we study a new solution, which makes use of a knowledge base (or KB), such as YAGO and Freebase. A KB is a rich information source that describes how real-world entities are connected. We extract entities from a query, and use these entities to explore new ones in the KB. Those discovered entities are then used to suggest new queries to the user. As shown in our experiments, our approach provides better recommendation results for long-tail queries than existing solutions.


X-by-Wire, New Technologies for 42V Bus


title: Tlale, “Improved Mecanum Wheel Design for Omni-directional Robots

abstract: Omni-directional is used to describe the ability of a system to move instantaneously in any direction from any configuration. Omnidirectional robotic platforms have vast advantages over a conventional design in terms of mobility in congested environments. They are capable of easily performing tasks in environments congested with static and dynamic obstacles and narrow aisles. These environments are commonly found in factory workshops offices, warehouses, hospitals and elderly care facilities.


title: Miniaturized slot PIFA antenna for tripleband implantable biomedical applications

abstract: This paper presents the design of a triple-band implantable miniaturised slot PIFA antenna for Medical Implant Communication Service (MICS) band at 433MHz, Wireless Medical Telemetry Service (WMTS) band at 1430 MHz and Industrial, Scientific, and Medical (ISM) band at 2.4GHz. Simulations based on homogeneous and inhomogeneous phantoms are employed to design the proposed small antenna suitable for implanting in the arm or under the chest. The measured results are obtained by immersing a prototype of the proposed antenna in a phantom. The antenna occupies a volume less than 1cm3 and its size reduction is about 50% compared to the standard E-shaped patch antenna reported in the literature.


title: Strongly Typed Genetic Programming

abstract: Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection (Koza, 1992). However, in its standard form, there is no way to restrict the programs it generates to those where the functions operate on appropriate data types. In the case when the programs manipulate multiple data types and contain functions designed to operate on particular data types, this can lead to unnecessarily large search times and/or unnecessarily poor generalization performance. Strongly typed genetic programming (STGP) is an enhanced version of genetic programming that enforces data-type constraints and whose use of generic functions and generic data types makes it more powerful than other approaches to type-constraint enforcement. After describing its operation, we illustrate its use on problems in two domains, matrix/vector manipulation and list manipulation, which require its generality. The examples are (1) the multidimensional least-squares regression problem, (2) the multidimensional Kalman filter, (3) the list manipulation function NTH, and (4) the list manipulation function MAPCAR.


title: AIR-MODELLING: A tool for gesture-based solid modelling in context during early design stages in AR environments

abstract: HighlightsWe implemented a modelling tool for product conceptualization over the real context.We carried out a test with 21 users to prove our tool against a traditional CAD tool.Total time for product conceptualization using our tool was smaller in most cases.The users made less use of measurements tool during the sessions with our tool. Augmented reality (AR) technologies are just being used as interface in CAD tools allowing the user to perceive 3D models over a real environment. The influence of the use of AR in the conceptualization of products whose configuration, shape and dimensions depend mainly on the context remains unexplored. We aimed to prove that modelling in AR environments allows to use the context in real-time as an information input for making the iterative design process more efficient. In order to prove that, we developed a tool called AIR-MODELLING in which the designer is able to create virtual conceptual products by hand gestures meanwhile he/she is interacting directly with the real scenario. We conducted a test for comparing designers' performance using AIR-MODELLING and a traditional CAD system. We obtained an average reduction of 44% on the modeling time in 76% of the cases. We found that modelling in AR environments using the hands as interface allows the designer to quickly and efficiently conceptualize potential solutions using the spatial restrictions of the context as an information input in real-time. Additionally, modelling in a natural scale, directly over the real scene, prevents the designer from drawing his/her attention on dimensional details and allows him/her to focus on the product itself and its relation with the environment.


title: Sentiment classification using sentence-level semantic orientation of opinion terms from blogs

abstract: Sentiment analysis is the procedure by which information is extracted from the opinions, appraisals and emotions of people in regards to entities, events and their attributes. In decision making, the opinions of others have a significant effect on customers ease in making choices regards to online shopping, choosing events, products, entities. In this paper, the rule based domain independent sentiment analysis method is proposed. The proposed method classifies subjective and objective sentences from reviews and blog comments. The semantic score of subjective sentences is extracted from SentiWordNet to calculate their polarity as positive, negative or neutral based on the contextual sentence structure. The results show the effectiveness of the proposed method and it outperforms the machine learning methods. The proposed method achieves an accuracy of 87% at the feedback level and 83% at the sentence level.


title: Poly(thioester)s

abstract: Syntheses and properties of aliphatic and aromatic polythioesters (PTEs) were reviewed including polythiocarbonates and polythiourethanes. The content is subdivided into the following sections: PTEs of aliphatic α‐mercapto carboxylic acid, PTEs of ω‐mercapto carboxylic acids, PTEs derived from α,ω‐dimercapto alkanes, aromatic poly(thioester)s, aliphatic poly(thiocarbonate)s, aliphatic poly(thiourethane)s and aromatic polythiocarbonates. The synthetic strategies reviewed in this article include anionic and cationic ring‐opening polymerizations, polycondensations in bulk, polycondensations in solutions, interfacial polycondensations and in vitro enzymatic polycondensations.


title: Divergence Measures and Message Passing

abstract: This paper presents a unifying view of messagepassing algorithms, as methods to approximate a complex Bayesian network by a simpler network with minimum information divergence. In this view, the difference between mean-field methods and belief propagation is not the amount of structure they model, but only the measure of loss they minimize (‘exclusive’ versus ‘inclusive’ Kullback-Leibler divergence). In each case, message-passing arises by minimizing a localized version of the divergence, local to each factor. By examining these divergence measures, we can intuit the types of solution they prefer (symmetry-breaking, for example) and their suitability for different tasks. Furthermore, by considering a wider variety of divergence measures (such as alpha-divergences), we can achieve different complexity and performance goals.


title: Lifelong Learning for Sentiment Classification

abstract: This paper proposes a novel lifelong learning (LL) approach to sentiment classification. LL mimics the human continuous learning process, i.e., retaining the knowledge learned from past tasks and use it to help future learning. In this paper, we first discuss LL in general and then LL for sentiment classification in particular. The proposed LL approach adopts a Bayesian optimization framework based on stochastic gradient descent. Our experimental results show that the proposed method outperforms baseline methods significantly, which demonstrates that lifelong learning is a promising research direction.


title: Solving constraint satisfaction problems with SAT modulo theories

abstract: Due to significant advances in SAT technology in the last years, its use for solving constraint satisfaction problems has been gaining wide acceptance. Solvers for satisfiability modulo theories (SMT) generalize SAT solving by adding the ability to handle arithmetic and other theories. Although there are results pointing out the adequacy of SMT solvers for solving CSPs, there are no available tools to extensively explore such adequacy. For this reason, in this paper we introduce a tool for translating FLATZINC (MINIZINC intermediate code) instances of CSPs to the standard SMT-LIB language. We provide extensive performance comparisons between state-of-the-art SMT solvers and most of the available FLATZINC solvers on standard FLATZINC problems. The obtained results suggest that state-of-the-art SMT solvers can be effectively used to solve CSPs.


title: Multi-Scale Context Attention Network for Stereo Matching

abstract: Recently, many works for stereo matching with convolutional neural networks have gained satisfactory performance. However, it is still an urgent challenge to deal with ill-posed regions and improve details in disparity maps. To address these problems, we propose a multi-scale context attention network with three main modules: atrous spatial pyramid pooling attention, richer convolutional features, and attention mechanism. First, we propose an atrous spatial pyramid pooling attention module to capture context information by the aggregating context in different scales, meanwhile take advantage of the attention mechanism to selectively emphasize informative features and suppress fewer ones. Then, the richer convolutional module is proposed to bring useful detail information for the network. Additionally, attention mechanism is used to pick out informative features for disparity refinement sub-network. Furthermore, we design a point-specific loss function strategy to perform online hard point mining, which helps the network to improve the accuracy of disparity maps. The experiments on the FlyingThings3D and KITTI 2015 benchmark demonstrate that the proposed method can achieve state-of-the-art performance.


title: Real-Time Driver's Stress Event Detection

abstract: In this paper, a real-time methodology for the detection of stress events while driving is presented. The detection is based on the use of physiological signals, i.e., electrocardiogram, electrodermal activity, and respiration, as well as past observations of driving behavior. Features are calculated over windows of specific length and are introduced in a Bayesian network to detect driver's stress events. The accuracy of the stress event detection based only on physiological features, evaluated on a data set obtained in real driving conditions, resulted in an accuracy of 82%. Enhancement of the stress event detection model with the incorporation of driving event information has reduced false positives, yielding an increased accuracy of 96%. Furthermore, our methodology demonstrates good adaptability due to the application of online learning of the model parameters.


title: A review of ontology based query expansion

abstract: This paper examines the meaning of context in relation to ontology based query expansion and contains a review of query expansion approaches. The various query expansion approaches include relevance feedback, corpus dependent knowledge models and corpus independent knowledge models. Case studies detailing query expansion using domain-specific and domain-independent ontologies are also included. The penultimate section attempts to synthesise the information obtained from the review and provide success factors in using an ontology for query expansion. Finally the area of further research in applying context from an ontology to query expansion within a newswire domain is described.


title: Ten questions about terminology for children with unexplained language problems

abstract: Background: In domains other than language, there is fairly consistent diagnostic terminology to refer to children’s developmental difficulties. For instance, the terms ‘dyslexia’, ‘attention deficit hyperactivity disorder’ and ‘autistic spectrum disorder’ are used for difficulties with reading, attention or social cognition, respectively. There is no agreed label, however, for children with unexplained language problems. Aims: To consider whether we need labels for unexplained language problems in children, and if so, what terminology is appropriate. Main Contribution: There are both advantages and disadvantages to labels, but they are important to ensure children receive services, and to increase our knowledge of the nature and causes of such problems. A survey of labels in current use found 132 different terms, 33 of which had 600 or more returns on Google Scholar between 1994 and 2013. Many of these labels were too general to be useful. Of the remainder, the term ‘specific language impairment’ was the most commonly used. Conclusions: The current mayhem in diagnostic labels is unsustainable; it causes confusion and impedes research progress and access to appropriate services. We need to achieve consensus on diagnostic criteria and terminology. The DSM-5 term ‘language disorder’ is problematic because it identifies too wide a range of conditions on an internet search. One solution is to retain specific language impairment, with the understanding that ‘specific’ means idiopathic (i.e., of unknown origin) rather than implying there are no other problems beyond language. Other options are the terms ‘primary language impairment’, ‘developmental language disorder’ or ‘language learning impairment’.


title: An ERP study of regular and irregular English past tense inflection

abstract: Compositionality is a critical and universal characteristic of human language. It is found at numerous levels, including the combination of morphemes into words and of words into phrases and sentences. These compositional patterns can generally be characterized by rules. For example, the past tense of most English verbs (“regulars”) is formed by adding an -ed suffix. However, many complex linguistic forms have rather idiosyncratic mappings. For example, “irregular” English verbs have past tense forms that cannot be derived from their stems in a consistent manner. Whether regular and irregular forms depend on fundamentally distinct neurocognitive processes (rule-governed combination vs. lexical memorization), or whether a single processing system is sufficient to explain the phenomena, has engendered considerable investigation and debate. We recorded event-related potentials while participants read English sentences that were either correct or had violations of regular past tense inflection, irregular past tense inflection, syntactic phrase structure, or lexical semantics. Violations of regular past tense and phrase structure, but not of irregular past tense or lexical semantics, elicited left-lateralized anterior negativities (LANs). These seem to reflect neurocognitive substrates that underlie compositional processes across linguistic domains, including morphology and syntax. Regular, irregular, and phrase structure violations all elicited later positivities that were maximal over right parietal sites (P600s), and which seem to index aspects of controlled syntactic processing of both phrase structure and morphosyntax. The results suggest distinct neurocognitive substrates for processing regular and irregular past tense forms: regulars depending on compositional processing, and irregulars stored in lexical memory.


title: Studies of wall painting fragments from Kaiping Diaolou by SEM/EDX, micro Raman and FT-IR spectroscopy

abstract: Abstract The multi-technique analytical approach has proved to be a very effective tool for the analysis of artwork, as demonstrated by various studies. In this work, four micro-analysis methods were used to analyze the wall painting fragments in Kaiping Diaolou, a world cultural heritage enlisted in 2007. Field-emission scanning electron microscopy (FE-SEM), energy dispersive X-ray micro-analysis (EDX), combined with micro Raman and Fourier transform infrared (FT-IR) spectroscopy, provided a vast amount of information concerning the raw materials present in the pigments, organic binder, plasters and mortars of the wall painting. Four types of pigments (goethite, lazurite, chromium green and calcite) were identified on the surface layer of the wall paintings. The substrate under the pigment layer was found to be composed of cubic-like calcite (CaCO 3 ), micro-rod bundle-shaped syngenite (K 2 Ca(SO 4 ) 2 ·H 2 O), gypsum (CaSO 4 ·2H 2 O) and silica (SiO 2 ). The organic binder can be attributed to animal glue (such as egg) and drying oil by micro FT-IR spectroscopy. These analysis results can provide important information for the conservation and restoration of the Kaiping Diaolou.


title: Board Recommendation in Pinterest

abstract: In this paper we describe preliminary approaches for contentbased recommendation of Pinterest boards to users. We describe our representation and features for Pinterest boards and users, together with a supervised recommendation model. We observe that features based on latent topics lead to better performance than features based on userassigned Pinterest categories. We also find that using social signals (repins, likes, etc.) can improve recommendation quality.


title: Computed tomography of the abdomen in Saanen goats: III. kidneys, ureters and urinary bladder

abstract: This study describes the findings of computed tomography (CT) of the kidneys, ureters, urinary bladder and adrenal glands in 28 healthy female Saanen goats. CT examination and anatomical slice preparation postmortem was performed as described in the first communication. After subjective evaluation of the CT images, various variables including the size, volume and density of the kidneys, the diameter of the ureters and the size of the adrenal glands were measured. The targeted organs could be accurately visualised using CT and there was very good topographical agreement between the CT images and the anatomical preparations. The kidneys, renal vessels, ureters, urinary bladder and adrenal glands were seen in all goats.


title: Arbitrary-Norm Separating Plane

abstract: A plane separating two point sets in n-dimensional real space is constructed such that it minimizes the sum of arbitrary-norm distances of misclassified points to the plane. In contrast to previous approaches that used surrogates for distance-minimization, the present work is based on a precise norm-dependent explicit closed form for the projection of a point on a plane. This projection is used to formulate the separating-plane problem as a minimization of a convex function on a unit sphere in a norm dual to that of the arbitrary norm used. For the 1-norm, the problem can be solved in polynomial time by solving 2n linear programs or by solving a bilinear program. For a general p-norm, the minimization problem can be transformed via an exact penalty formulation to minimizing the sum ofa convex function and a bilinear function on a convex set. For the one and infinity norms, a finite successive linearization algorithm can be used for solving the exact penalty formulation.


title: Iris Center Corneal Reflection Method for Gaze Tracking Using Visible Light

abstract: Very few attempts, if any, have been made to use visible light in corneal reflection approaches to the problem of gaze tracking. The reasons usually given to justify the limited application of this type of illumination are that the required image features are less accurately depicted, and that visible light may disturb the user. The aim of this paper is to show that it is possible to overcome these difficulties and build an accurate and robust gaze tracker under these circumstances. For this purpose, visible light is used to obtain the corneal reflection or glint in a way analogous to the well-known pupil center corneal reflection technique. Due to the lack of contrast, the center of the iris is tracked instead of the center of the pupil. The experiments performed in our laboratory have shown very satisfactory results, allowing free-head movement and no need of recalibration.


title: A centralized trust model approach for cloud computing

abstract: In the IT world of corporate networking, how businesses store and compute data is starting to shift from in-house servers to the cloud. However, some enterprises are still hesitant to make this leap to the cloud because of their information security and data privacy concerns. Enterprises that want to invest into this service need to feel confident that the information stored on the cloud is secure. Due to this need for confidence, trust is one of the major qualities that cloud service providers (CSPs) must build for cloud service users (CSUs). To do this, a model that all CSPs can follow must exist to establish a trust standard in the industry. If no concrete model exists, the future of cloud computing will be stagnant. This paper presents a new trust model that involves all the cloud stakeholders such as CSU, CSP, and third-party auditors. Our proposed trust model is objective since it involves third-party auditors to develop unbiased trust between the CSUs and the CSPs. Furthermore, to support the implementation of the proposed trust model, we rank CSPs according to the trust-values obtained from the trust model. The final score for each participating CSP will be determined based on the third-party assessment and the feedback received from the CSUs.


title: 380Vdc/48Vdc/ 3kw DC/DC converter with 98.2% efficiency

abstract: According to the Emerge Alliance, the interest for 380Vdc power distribution for data centres is growing. The DC distribution reduces the number of power conversion steps by utilizing a DC voltage of 380V; it provides a significant reduction of cable dimensions and associated costs. Some server board vendors have recently begun to initialize a move from a supply voltage of 12Vdc to 48Vdc. In order to make use of the new preferred voltage used by these vendors, DC/DC converters are required. In regards to the high and increasing power consumption of data centres, it is essential that such power converters are highly efficient. This paper presents a new topology of a 3kW DC/DC converter (380Vdc–48Vdc) that boasts a conversion peak efficiency of 98.2%. The DC/DC converter is designed with a boost stage and an LLC DC/DC stage. As the converter is typically connected to a battery system consisting of 168 cells in a series, the converter can exploit the fact that the battery system provides 381Vdc in float operation. In normal float operation the DC input voltage is stable. This allows for the input voltage to be used directly as the DC link input to the LLC stage. That means that the BOOST-stage is not required in normal operation. However, during charge or discharge situations, the battery voltage may drop down to about 260Vdc, which would be too low as input voltage for an efficient LLC-stage. Therefore a BOOST stage is required to maintain a narrow band for voltage variations at the DC link from the BOOST-stage to the LLC-stage, in case of charge/discharge. The novelty of the proposed topology is therefore a built-in bypass switch, using a MOSFET, where the BOOST stage is bypassed in normal operation, but actively used in the case of a battery voltage lower than a certain threshold.


title: Mobile node localization in cellular networks

abstract: Location information is the major component in location based applications. This information is used in different safety and service oriented applications to provide users with services according to their Geolocation. There are many approaches to locate mobile nodes in indoor and outdoor environments. In this paper, we are interested in outdoor localization particularly in cellular networks of mobile nodes and presented a localization method based on cell and user location information. Our localization method is based on hello message delay (sending and receiving time) and coordinate information of Base Transceiver Station (BTSs). To validate our method across cellular network, we implemented and simulated our method in two scenarios i.e. maintaining database of base stations in centralize and distributed system. Simulation results show the effectiveness of our approach and its implementation applicability in telecommunication systems .


title: Dynamically Modeling Patient's Health State from Electronic Medical Records: A Time Series Approach

abstract: In this paper, we present a method to dynamically estimate the probability of mortality inside the Intensive Care Unit (ICU) by combining heterogeneous data. We propose a method based on Generalized Linear Dynamic Models that models the probability of mortality as a latent state that evolves over time. This framework allows us to combine different types of features (lab results, vital signs readings, doctor and nurse notes, etc) into a single state, which is updated each time new patient data is observed. In addition, we include the use of text features, based on medical noun phrase extraction and Statistical Topic Models. These features provide context about the patient that cannot be captured when only numerical features are used. We fill out the missing values using a Regularized Expectation Maximization based method assuming temporal data. We test our proposed approach using 15,000 Electronic Medical Records (EMRs) obtained from the MIMIC II public dataset. Experimental results show that the proposed model allows us to detect an increase in the probability of mortality before it occurs. We report an AUC 0.8657. Our proposed model clearly outperforms other methods of the literature in terms of sensitivity with 0.7885 compared to 0.6559 of Naive Bayes and F-score with 0.5929 compared to 0.4662 of Apache III score after 24 hours.


title: Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model

abstract: We show how the spellings of known words can help us deal with unknown words in open-vocabulary NLP tasks. The method we propose can be used to extend any closedvocabulary generative model, but in this paper we specifically consider the case of neural language modeling. Our Bayesian generative story combines a standard RNN language model (generating the word tokens in each sentence) with an RNNbased spelling model (generating the letters in each word type). These two RNNs respectively capture sentence structure and word structure, and are kept separate as in linguistics. By invoking the second RNN to generate spellings for novel words in context, we obtain an open-vocabulary language model. For known words, embeddings are naturally inferred by combining evidence from type spelling and token context. Comparing to baselines (including a novel strong baseline), we beat previous work and establish state-of-the-art results on multiple datasets.


title: Internal Capital Allocation and Firm Performance

abstract: We introduce a novel concept of the activeness of internal capital allocations across industries. We derive a measure of this activeness and use it to compare the performance of firms with different capital allocation styles. We find that firms that actively change their capital allocation across industries have a lower average industry-adjusted profitability than firms that follow passive strategies. Moreover, we find that active firms obtain lower valuation and lower excess stock returns in subsequent periods. Our findings suggest that conglomerate firms that actively allocate resources across industries do not do so efficiently, and that the stock market does not fully incorporate the information revealed through the internal capital allocation process.


title: Effectiveness of high-intensity laser therapy and splinting in lateral epicondylitis; a prospective, randomized, controlled study

abstract: Lateral epicondylitis (LE) is a common disorder that causes pain on the outside of the elbow, as well as pain and weakness during gripping. In this prospective, randomized, controlled, assessor-blinded trial, we planned to investigate the effects of high-intensity laser therapy (HILT) in patients with LE and to compare these results with those of a brace and placebo HILT. Patients were randomly assigned to three treatment groups. The first group was treated with HILT. The second group (sham therapy group) received placebo HILT, while the third group (brace group) used the lateral counterforce brace for LE. The patients were assessed for grip strength, pain, disability, and quality of life. Outcome measurements and ultrasonographic examination of the patients were performed before treatment (week 0) and after treatment (after 4 and 12 weeks). HILT and brace groups showed significant improvements for most evaluation parameters (pain scores, grip strength, disability scores, and several subparts of the short-form 36 health survey (physical function, role limitations due to physical functioning, bodily pain, general health, and vitality)) after treatment (after 4 and 12 weeks). However, the improvements in evaluation parameters of the patients with LE in HILT and brace groups were not reflected to ultrasonographic findings. Furthermore, comparison of the percentage changes of the parameters after treatment relative to pretreatment values did not show a significant difference between HILT and brace groups. We conclude that HILT and splinting are effective physical therapy modalities for patients with LE in reducing pain and improving disability, quality of life, and grip strength.


title: Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas

abstract: This paper addresses digital communication in a Rayleigh fading environment when the channel characteristic is unknown at the transmitter but is known (tracked) at the receiver. Inventing a codec architecture that can realize a significant portion of the great capacity promised by information theory is essential to a standout long-term position in highly competitive arenas like fixed and indoor wireless. Use (n T , n R ) to express the number of antenna elements at the transmitter and receiver. An (n, n) analysis shows that despite the n received waves interfering randomly, capacity grows linearly with n and is enormous. With n = 8 at 1% outage and 21-dB average SNR at each receiving element, 42 b/s/Hz is achieved. The capacity is more than 40 times that of a (1, 1) system at the same total radiated transmitter power and bandwidth. Moreover, in some applications, n could be much larger than 8. In striving for significant fractions of such huge capacities, the question arises: Can one construct an (n, n) system whose capacity scales linearly with n, using as building blocks n separately coded one-dimensional (1-D) subsystems of equal capacity? With the aim of leveraging the already highly developed 1-D codec technology, this paper reports just such an invention. In this new architecture, signals are layered in space and time as suggested by a tight capacity bound.


title: Simulation and Experiment of a Compact Wideband 90$^{\circ}$ Differential Phase Shifter

abstract: A compact wideband 90° differential phase shifter is developed by modifying ports termination in the Abbosh phase-shifter configuration. This novel phase-shifter arrangement consists of a 3-dB directional coupler with the coupled and transmission ports terminated with reactive loads. The proper reactance is found at the input of the coupled line section in which the remaining ports are open circuited. Both coupled sections utilize a multilayer broadside coupling microstrip-slot-microstrip tight coupler. A theoretical model is presented to explain the performance of the proposed phase shifter and design procedure. Further, the phase shifter was designed and manufactured. Results of calculation and measurement show that the developed circuit provides a 90° differential phase shift with deviation less than ±4° across the 3-7-GHz frequency band.


title: Efficient support for complex numbers in Java

abstract: One glaring weakness of Java for numerical programming is its lack of support for complex numbers. Simply creating a Complex number class leads to poor performance relative to Fortran. We show in this paper, however, that the combination of stich a Complex class and a compiler that understands its semantics does indeed lead to Fortran-like performance. This performance gain is achieved while leaving the Java language completely unchanged and maintaining full compatibility with existing Java Virtual Machines. We quantify the effectiveness of our approach through experiments with linear algebra, electromagnetics, and computational fluid-dynamics kernels.


title: Competition and innovation for smart and creative society (CISCS)

abstract: The globalization of world economy has changed many aspects of economic structure, speed of technology development, patterns of innovation, competition between nations as well as between companies. In addition, humanity is facing a new historical crisis of global climate, which has prompted varied perspectives of the impact and implication of this crisis on socio-economic horizon of societies. However, despite these various perspectives, there seems to be a converging view that global forces of technology and innovation have strengthened globalization of economic processes. Given this positivist view, it seems that the globalization process will be further intensified regardless of neo-liberalist groups or political progressive groups. (Dicken 2011; Stern et al. 2006). While the intensification of the globalization process has made the world economy more integrated than ever, it has generated a harsh competition environment for smalland medium-sized corporations (SMEs) and multinational corporations (MNCs) in domestic and global markets. Such a severe competition pushes companies, regional and national governments, and universities to collaborate with one another for creating technological innovation and development that arise from R & D activities. In order to be competitive, many advanced nations and newly industrialized nations have built a system of triple helix collaboration that is corporatism between industry, academia, and government. The platform of the triple helix is an innovative cluster in a region, representing a symbol of regional competitiveness as well as that of a national competitiveness in a nation. (Park 2012; Shienstock 2007; Porter 1998). Although there may still be gaps in our understanding of the innovation processes, it is clear that the technological innovation has made a major contribution to the world economy, as well as to the globalization processes. In particular, information and communication technologies (ICT) and transportation technologies have played pivotal roles in improving productivity based on automatization and flexibility, as well as in global logistics based on a rapid transportation system. Additionally, frequent innovation shortens product life cycles that increase competition between companies on national and global markets. (Dicken 2011; Florida 2008). The expanding globalization process based on economic competition between major economic actors has lead to the emergence of global environmental issues that we have to tackle in the twenty-first century. As a result, sustainable development has become the uppermost important issue in the global community. To create sustainable development, proper energy consumption and protecting environment have become key parameters of the global environmental agenda. Therefore, many nations are keen to integrate technology innovation into low-carbon-based green growth strategies that can be the first step towards building a smart and creative society. (Kang 2012). Furthermore, the global economic environment has been changed fundamentally since the global financial crisis starting with subprime mortgage crisis in the US housing market in 2008. Since then the major world economies such as the USA, the EU, and Japan have been sluggish, while the new emerging markets such as China and India continue to generate a relatively high economic growths. Due to the global economic downturn, national economies have been facing challenges how to recover their economic S.-C. Park (&) Graduate School of Knowledge Based Technology and Energy, Korea Polytechnic University, Seoul, South Korea e-mail: scpark@kpu.ac.kr


title: A new dynamic control model with stability analysis for omnidirectional mobile robot

abstract: This paper proposes a new dynamic control model with stability analysis for an omnidirectional mobile robot. In particular, the dynamical model uses a feedback linearization for the control in order to follow a given trajectory. To do this, a PID controller is practically considered for controlling the motion of the mobile robot. In order to prove the stability of the proposed system, Lyapunov stability function is used which gives a guarantee for the asymptotical stability. The simulations and experimental results are carried out with the dynamic control method which leads the stability of the mobile robot during circular path navigation. A three-wheels omnidirectional mobile robot is considered for the purpose of simulation and experimental studies.


Large-scale Correlation of Accounts Across Social Networks


title: The Reliability and Validity of the Perceived Dietary Adherence Questionnaire for People with Type 2 Diabetes

abstract: Nutrition therapy is essential for diabetes treatment, and assessment of dietary intake can be time consuming. The purpose of this study was to develop a reliable and valid instrument to measure diabetic patients’ adherence to Canadian diabetes nutrition recommendations. Specific information derived from three, repeated 24-h dietary recalls of 64 type 2 diabetic patients, aged 59.2 ± 9.7 years, was correlated with a total score and individual items of the Perceived Dietary Adherence Questionnaire (PDAQ). Test-retest reliability was completed by 27 type 2 diabetic patients, aged 62.8 ± 8.4 years. The correlation coefficients for PDAQ items versus 24-h recalls ranged from 0.46 to 0.11. The intra-class correlation (0.78) was acceptable, indicating good reliability. The results suggest that PDAQ is a valid and reliable measure of diabetes nutrition recommendations. Because it is quick to administer and score, it may be useful as a screening tool in research and as a clinical tool to monitor dietary adherence.


title: A new version of ant system for subset problems

abstract: Early applications of Ant Colony Optimization (ACO) have been mainly concerned with solving ordering problems (e.g., traveling salesman problem). We introduce a new version of Ant System-an ACO algorithm for solving subset problems. The computational study involves the Multiple Knapsack Problem (MKP); the reported results show the potential power of the ACO approach for solving this type of subset problem.


title: Connectivity Optimization for Wireless Sensor Networks Applied to Forest Monitoring

abstract: Device Deployment plays a key role in the performance of any large-scale Wireless Sensor Network (WSN) application. WSN device deployment (i.e. the numbers and positions of the devices) must consider several design factors, viz. coverage, connectivity, lifetime, etc. However, connectivity remains the most fundamental factor especially in a large scale harsh environment. In this paper, we explore the problem of Relay Node (RN) placement in 3D forestry space. We formulate a generalized RN deployment optimization problem aimed at maximizing the network connectivity with constraints on RNs count. We investigate how the number of RNs can affect the connectivity of a WSN in a harsh environment. Based on quantitative analysis of such effects, the paper sets a threshold on the minimum number of required RNs.


title: Aesthetics of Interaction Design: A Literature Review

abstract: This literature review focuses on aesthetics of interaction design with further goal of outlining a study towards prediction model of aesthetic value. The review covers three main issues, tightly related to aesthetics of interaction design: evaluation of aesthetics, relations between aesthetics and interaction qualities and implementation of aesthetics in interaction design. Analysis of previous models is carried out according to definition of interaction aesthetics: holistic approach to aesthetic perception considering its' action- and appearance-related components. As a result the empirical study is proposed for investigating the relations between attributes of interaction and users' aesthetic experience.


title: Robust Model-Based 3D Head Pose Estimation

abstract: We introduce a method for accurate three dimensional head pose estimation using a commodity depth camera. We perform pose estimation by registering a morphable face model to the measured depth data, using a combination of particle swarm optimization (PSO) and the iterative closest point (ICP) algorithm, which minimizes a cost function that includes a 3D registration and a 2D overlap term. The pose is estimated on the fly without requiring an explicit initialization or training phase. Our method handles large pose angles and partial occlusions by dynamically adapting to the reliable visible parts of the face. It is robust and generalizes to different depth sensors without modification. On the Biwi Kinect dataset, we achieve best-in-class performance, with average angular errors of 2.1, 2.1 and 2.4 degrees for yaw, pitch, and roll, respectively, and an average translational error of 5.9 mm, while running at 6 fps on a graphics processing unit.


title: CRISPR-Cas: Adapting to change

abstract: Bacteria and archaea are engaged in a constant arms race to defend against the ever-present threats of viruses and invasion by mobile genetic elements. The most flexible weapons in the prokaryotic defense arsenal are the CRISPR-Cas adaptive immune systems. These systems are capable of selective identification and neutralization of foreign DNA and/or RNA. CRISPR-Cas systems rely on stored genetic memories to facilitate target recognition. Thus, to keep pace with a changing pool of hostile invaders, the CRISPR memory banks must be regularly updated with new information through a process termed CRISPR adaptation. In this Review, we outline the recent advances in our understanding of the molecular mechanisms governing CRISPR adaptation. Specifically, the conserved protein machinery Cas1-Cas2 is the cornerstone of adaptive immunity in a range of diverse CRISPR-Cas systems.


title: Wi-Fi Gesture Recognition on Existing Devices

abstract: This paper introduces the first wireless gesture recognition system that operates using existingWi-Fi signals and devices. To achieve this, we first identify limitations of existing wireless gesture recognition approaches that limit their applicability to Wi-Fi. We then introduce algorithms that can classify gestures using information that is readily available on Wi-Fi devices. We demonstrate the feasibility of our design using a prototype implementation on off-the-shelf Wi-Fi devices. Our results show that we can achieve a classification accuracy of 91% while classifying four gestures across six participants, without the need for per-participant training. Finally, we show the feasibility of gesture recognition in non-line-ofsight situations with the participants interacting with a Wi-Fi device placed in a backpack.


A Japanese Chess Commentary Corpus.


title: The field of cultural production, or: The economic world reversed☆

abstract: To be fully understood, literary production has to be approached in relational terms, by constructing the literary field, i.e. the space of literary prises de position that are possible in a given period in a given society. Prises de position arise from the encounter between particular agents' dispositions (their habitus, shaped by their social trajectory) and their position in a field of positions which is defined by the distribution of a specific form of capital. This specific literary (or artistic, or philosophical, etc.) capital functions within an ‘economy’ whose logic is an inversion of the logic of the larger economy of the society. The ‘interest in distinterestedness’ can be understood by examining the structural relations between the field of literary production and the field of class relations. A number of effects within the literary field arise from the homologies between positions within the two fields. This model is then used to analyze the particular case of the literary field in late 19th century France.


title: Gender Salience and the Use of Linguistic Qualifiers and Intensifiers in Online Course Discussions

abstract: Past studies of online discourse found social presence to be a critical element in the learning process. Social presence connotes the extent to which students perceive themselves as intellectually connected to the other participants in an online dialogue. Impediments to the development of social presence can disrupt knowledge formation as a group activity. This study analyzed differences in the language styles of men and women as one such impediment. When students self-categorize by gender, the expository speaking style of men may crowd out the epistolary style of women, leading women to cognitively disengage from the group learning process. However, in online course environments, where self-categorization is by shared identity, such crowding out is less likely to occur. This study found no evidence of a male crowding-out effect.


title: Human Identification in Information Systems : Management Challenges and Public Policy Issues

abstract: Many information systems involve data about people. In order reliably to associate data with particular individuals, it is necessary that an effective and efficient identification scheme be established and maintained. There is remarkably little in the information technology literature concerning human identification. Seeks to overcome that deficiency by undertaking a survey of human identity and human identification. Discusses techniques including names, codes, knowledge‐based and token‐based identification, and biometrics. Identifies the key challenge to management as being to devise a scheme which is practicable and economic, and of sufficiently high integrity to address the risks the organization confronts in its dealings with people. Proposes that much greater use be made of schemes which are designed to afford people anonymity, or which enable them to use multiple identities or pseudonyms, while at the same time protecting the organization′s own interest. Describes multi‐purpose and inhabitant regist...


title: Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

abstract: Deep learning (DL) based semantic segmentation methods have been providing state-of-the-art performance in the last few years. More specifically, these techniques have been successfully applied to medical image classification, segmentation, and detection tasks. One deep learning technique, U-Net, has become one of the most popular for these applications. In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively. The proposed models utilize the power of U-Net, Residual Network, as well as RCNN. There are several advantages of these proposed architectures for segmentation tasks. First, a residual unit helps when training deep architecture. Second, feature accumulation with recurrent residual convolutional layers ensures better feature representation for segmentation tasks. Third, it allows us to design better U-Net architecture with same number of network parameters with better performance for medical image segmentation. The proposed models are tested on three benchmark datasets such as blood vessel segmentation in retina images, skin cancer segmentation, and lung lesion segmentation. The experimental results show superior performance on segmentation tasks compared to equivalent models including U-Net and residual U-Net (ResU-Net).


title: Hierarchical Reinforcement Learning with Hindsight

abstract: Reinforcement Learning (RL) algorithms can suffer from poor sample efficiency when rewards are delayed and sparse. We introduce a solution that enables agents to learn temporally extended actions at multiple levels of abstraction in a sample efficient and automated fashion. Our approach combines universal value functions and hindsight learning, allowing agents to learn policies belonging to different time scales in parallel. We show that our method significantly accelerates learning in a variety of discrete and continuous tasks.


title: Modeling and validating self-adaptive service-oriented applications

abstract: Self-adaptive and autonomous behaviors are becoming more and more important in the context of service-oriented applications, and formal modeling self-adaptive service-oriented components is highly required to assure quality properties. This paper enhances the formal framework SCA-ASM for modeling and validating distributed self-adaptive service-oriented applications. We explain how modeling an SCA-ASM component able to monitor and react to environmental changes (context-awareness) and to internal changes (self-awareness), and present the operators for expressing and coordinating self-adaptive behaviors in a distributed setting. We also support techniques for validating adaptation scenarios, and getting feedback of the correctness of the adaptation logic as implemented by the managing SCA-ASM components over the managed ones. As a proof-of-concepts, we use self-adaptive SCA-ASMs for modeling and validating a decentralized traffic monitoring system.


Abstract Implementing GrabCut


title: ER2OWL: Generating OWL Ontology from ER Diagram

abstract: Ontology is the fundamental part of Semantic Web. The goal of W3C is to bring the web into (its full potential) a semantic web with reusing previous systems and artifacts. Most legacy systems have been documented in structural analysis and structured design (SASD), especially in simple or Extended ER Diagram (ERD). Such systems need up-gradation to become the part of semantic web. In this paper, we present ERD to OWL-DL ontology transformation rules at concrete level. These rules facilitate an easy and understandable transformation from ERD to OWL. The set of rules for transformation is tested on a structured analysis and design example. The framework provides OWL ontology for semantic web fundamental. This framework helps software engineers in upgrading the structured analysis and design artifact ERD, to components of semantic web. Moreover our transformation tool, ER2OWL, reduces the cost and time for building OWL ontologies with the reuse of existing entity relationship models.


title: Concrete Dropout

abstract: Dropout is used as a practical tool to obtain uncertainty estimates in large vision models and reinforcement learning (RL) tasks. But to obtain well-calibrated uncertainty estimates, a grid-search over the dropout probabilities is necessary—a prohibitive operation with large models, and an impossible one with RL. We propose a new dropout variant which gives improved performance and better calibrated uncertainties. Relying on recent developments in Bayesian deep learning, we use a continuous relaxation of dropout’s discrete masks. Together with a principled optimisation objective, this allows for automatic tuning of the dropout probability in large models, and as a result faster experimentation cycles. In RL this allows the agent to adapt its uncertainty dynamically as more data is observed. We analyse the proposed variant extensively on a range of tasks, and give insights into common practice in the field where larger dropout probabilities are often used in deeper model layers.


title: Future of Health: Findings from a survey of stakeholders on the future of health and healthcare in England

abstract: This article presents findings from a survey conducted by RAND Europe at the request of the National Institute for Health Research (NIHR) to gather and synthesise stakeholder views on the future of health and healthcare in England in 20 to 30 years' time. The aim of the research was to generate an evidenced-based picture of the future health and healthcare needs, and how it might differ from today, in order to inform strategic discussions about the future priorities of the NIHR and the health and social care research communities more broadly. The survey provided a rich and varied dataset based on responses from 300 stakeholders in total. A wide range of fields were represented, including public health, social care, primary care, cancer, genomics, mental health, geriatrics, child health, patient advocacy and health policy. The respondent group also included a number of professional and private stakeholder categories, such as clinicians, policy experts, academics and patient and public representatives. The study findings validate a number of prominent health research priorities currently visible in England, such as antimicrobial resistance, the burden of dementia and age-related multi-morbidity, digital health and genomics. Interest in these areas and other themes, such as mental health, health inequalities and transforming health service models, cut across multiple disciplinary boundaries. However, it is clear that there are a variety of views among stakeholders on the relative importance of these areas of focus, and the best approach to manage their emergence in the coming decades. The full dataset of survey responses, for which permission to share was given, is a useful resource for those seeking to engage with a particular issue in more depth. The dataset can be found on NIHR's website at: http://nihr.ac.uk/news-and-events/documents/quotes.xls.


title: Aggregate information, common knowledge and agreeing not to bet

abstract: This note considers gambles that take place even if only some—but not all—individuals agree to participate. I show that the bet cannot take place if it is commonly known how many individuals are willing to participate.


title: Adaptive Kalman Filtering for anomaly detection in software appliances

abstract: Availability and reliability are often important features of key software appliances such as firewalls, web servers, etc. In this paper we seek to go beyond the simple heartbeat monitoring that is widely used for failover control. We do this by integrating more fine grained measurements that are readily available on most platforms to detect possible faults or the onset of failures. In particular, we evaluate the use of adaptive Kalman Filtering for automated CPU usage prediction that is then used to detect abnormal behaviour. Examples from experimental tests are given.


title: Learning Thermodynamics with Boltzmann Machines

abstract: A Boltzmann machine is a stochastic neural network that has been extensively used in the layers of deep architectures for modern machine learning applications. In this paper, we develop a Boltzmann machine that is capable of modelling thermodynamic observables for physical systems in thermal equilibrium. Through unsupervised learning, we train the Boltzmann machine on data sets constructed with spin configurations importance-sampled from the partition function of an Ising Hamiltonian at different temperatures using Monte Carlo (MC) methods. The trained Boltzmann machine is then used to generate spin states, for which we compare thermodynamic observables to those computed by direct MC sampling. We demonstrate that the Boltzmann machine can faithfully reproduce the observables of the physical system. Further, we observe that the number of neurons required to obtain accurate results increases as the system is brought close to criticality.


title: Testing of network security systems through DoS attacks

abstract: Cyber attacks are becoming more and more sophisticated, obtaining access to sensitive data. These attacks are causing major damages to various organizations, such as financial losses, service disruption etc. In this paper, we will use Penetration testing technique in order to test network security. This technique uses the same tools and methods as malicious attackers use, in order to take control of different systems, with one difference; These tests have been officially authorized and sanctioned. The practical part of this paper will analyze firewalls and other protective systems and they role in overall security during these attacks. Firewalls that will be analyzed in this paper are Threat Management Gateway (TMG 2010), Adaptive Security Appliance (ASA) as well as Next Generation Firewalls. For testing such systems, we will simulate DoS (Denial of Service) attacks.


title: Crowdsourcing Participatory Evaluation of Medical Pictograms Using Amazon Mechanical Turk

abstract: Background: Consumer and patient participation proved to be an effective approach for medical pictogram design, but it can be costly and time-consuming. We proposed and evaluated an inexpensive approach that crowdsourced the pictogram evaluation task to Amazon Mechanical Turk (MTurk) workers, who are usually referred to as the “turkers”. Objective: To answer two research questions: (1) Is the turkers’ collective effort effective for identifying design problems in medical pictograms? and (2) Do the turkers’ demographic characteristics affect their performance in medical pictogram comprehension? Methods: We designed a Web-based survey (open-ended tests) to ask 100 US turkers to type in their guesses of the meaning of 20 US pharmacopeial pictograms. Two judges independently coded the turkers’ guesses into four categories: correct, partially correct, wrong, and completely wrong. The comprehensibility of a pictogram was measured by the percentage of correct guesses, with each partially correct guess counted as 0.5 correct. We then conducted a content analysis on the turkers’ interpretations to identify misunderstandings and assess whether the misunderstandings were common. We also conducted a statistical analysis to examine the relationship between turkers’ demographic characteristics and their pictogram comprehension performance. Results: The survey was completed within 3 days of our posting the task to the MTurk, and the collected data are publicly available in the multimedia appendix for download. The comprehensibility for the 20 tested pictograms ranged from 45% to 98%, with an average of 72.5%. The comprehensibility scores of 10 pictograms were strongly correlated to the scores of the same pictograms reported in another study that used oral response–based open-ended testing with local people. The turkers’ misinterpretations shared common errors that exposed design problems in the pictograms. Participant performance was positively correlated with their educational level. Conclusions: The results confirmed that crowdsourcing can be used as an effective and inexpensive approach for participatory evaluation of medical pictograms. Through Web-based open-ended testing, the crowd can effectively identify problems in pictogram designs. The results also confirmed that education has a significant effect on the comprehension of medical pictograms. Since low-literate people are underrepresented in the turker population, further investigation is needed to examine to what extent turkers’ misunderstandings overlap with those elicited from low-literate people.


title: An attribute-based access matrix model

abstract: In traditional access control models like MAC, DAC, and RBAC, authorization decisions are determined according to identities of subjects and objects, which are authenticated by a system completely. Modern access control practices, such as DRM, trust management, and usage control, require flexible authorization policies. In such systems, a subject may be only partially authenticated according to one or more attributes. In this paper we propose an attribute-based access matrix model, named ABAM, which extends the access matrix model. We show that ABAM enhances the expressive power of the access matrix model by supporting attribute-based authorizations. Specifically, ABAM is comprehensive enough to encompass traditional access control models as well as some usage control concepts and specifications. On the other side, expressive power and safety are two fundamental but conflictive objectives in an access control model. We study the safety property of ABAM and conclude that the safety problem is decidable for a restricted case where attribute relationships allow no cycles. The restricted case is shown to be reasonable enough to model practical systems.


title: On the Evaluation of RDF Distribution Algorithms Implemented over Apache Spark

abstract: Querying very large RDF data sets in an efficient manner requires a sophisticated distribution strategy. Several innovative solutions have recently been proposed for optimizing data distribution with predefined query workloads. This paper presents an in-depth analysis and experimental comparison of five representative and complementary distribution approaches. For achieving fair experimental results, we are using Apache Spark as a common parallel computing framework by rewriting the concerned algorithms using the Spark API. Spark provides guarantees in terms of fault tolerance, high availability and scalability which are essential in such systems. Our different implementations aim to highlight the fundamental implementation-independent characteristics of each approach in terms of data preparation, load balancing, data replication and to some extent to query answering cost and performance. The presented measures are obtained by testing each system on one synthetic and one real-world data set over query workloads with differing characteristics and different partitioning constraints.


title: Teaching computer science through game design

abstract: Playing computer games is a popular recreational activity for young people. Not surprisingly, many of these enthusiasts dream that one day they will develop computer games themselves. Developing computer games involves many aspects of computing, including computer graphics, artificial intelligence, human-computer interaction, security, distributed programming, simulation, and software engineering. Users who can't code a single line can use game maker to build games and learn object-oriented design.


title: Adaptive routing with end-to-end feedback: distributed learning and geometric approaches

abstract: Minimal delay routing is a fundamental task in networks. Since delays depend on the (potentially unpredictable) traffic distribution, online delay optimization can be quite challenging. While uncertainty about the current network delays may make the current routing choices sub-optimal, the algorithm can nevertheless try to learn the traffic patterns and keep adapting its choice of routing paths so as to perform nearly as well as the best static path. This online shortest path problem is a special case of online linear optimization, a problem in which an online algorithm must choose, in each round, a strategy from some compact set S ⊆ Rd so as to try to minimize a linear cost function which is only revealed at the end of the round. Kalai and Vempala[4] gave an algorithm for such problems in the transparent feedback model, where the entire cost function is revealed at the end of the round. Here we present an algorithm for online linear optimization in the more challenging opaque feedback model, in which only the cost of the chosen strategy is revealed at the end of the round. In the special case of shortest paths, opaque feedback corresponds to the notion that in each round the algorithm learns only the end-to-end cost of the chosen path, not the cost of every edge in the network.We also present a second algorithm for online shortest paths, which solves the shortest-path problem using a chain of online decision oracles, one at each node of the graph. This has several advantages over the online linear optimization approach. First, it is effective against an adaptive adversary, whereas our linear optimization algorithm assumes an oblivious adversary. Second, even in the case of an oblivious adversary, the second algorithm performs better than the first, as measured by their additive regret.


title: A Safe Screening Rule for Sparse Logistic Regression

abstract: The l1-regularized logistic regression (or sparse logistic regression) is a widely used method for simultaneous classification and feature selection. Although many recent efforts have been devoted to its efficient implementation, its application to high dimensional data still poses significant challenges. In this paper, we present a fast and effective sparse logistic regression screening rule (Slores) to identify the "0" components in the solution vector, which may lead to a substantial reduction in the number of features to be entered to the optimization. An appealing feature of Slores is that the data set needs to be scanned only once to run the screening and its computational cost is negligible compared to that of solving the sparse logistic regression problem. Moreover, Slores is independent of solvers for sparse logistic regression, thus Slores can be integrated with any existing solver to improve the efficiency. We have evaluated Slores using high-dimensional data sets from different applications. Experiments demonstrate that Slores outperforms the existing state-of-the-art screening rules and the efficiency of solving sparse logistic regression can be improved by one magnitude.


title: Distributed convergence to nash equilibrium of antagonistic optimization networks

abstract: In this paper, we propose a distributed subgradient-based algorithm for a network consisting of two subnetworks to solve the antagonistic optimization problem. The two subnetworks have the same sum objective function, where one wants to minimize it and the other one wants to maximize it. Then the network is engaged in a zero-sum game scenario. We show that the network can achieve a Nash equilibrium by the proposed algorithm for weight-balanced digraphs under mild connectivity and stepsize conditions.


title: A Federated Edge Cloud-IoT Architecture Enabling semantic-based service provisioning in Future Internet

abstract: The provisioning of powerful decentralized services in the Internet of Things (IoT), as well as the integration of innovative IoT platforms, requires ubiquity, reliability, high-performance, efficiency, and scalability. Towards the accomplishment of the above requirements, the contemporary technological trends leaded to the IoT and Cloud convergence. This combination created the approach of federated Cloud-IoT architectures. The Cloud-IoT architectural vision is paving the way to the vital step, beyond the IoT cognitive capabilities, that refers to the IoT world leveraging cloud principles. For the success of the deployment of such innovative and high-performance solutions, the introduction and the exploitation of 5G technological principles will empower the way forward. Towards this direction, this paper presents a federated architecture combined by distributed Edge Cloud-IoT platforms, that enables the semantic-based service combination and provisioning. The 5G technological features, such as low-latency, zero-losses, high-bitrates are studied and proposed for the empowerment of the federation of the Edge Cloud-IoT architectures. A smart health use case is presented for the evaluation of the proposed solution.


title: Low-Rank Tensor Decomposition-Aided Channel Estimation for Millimeter Wave MIMO-OFDM Systems

abstract: We consider the problem of downlink channel estimation for millimeter wave (mmWave) MIMO-OFDM systems, where both the base station (BS) and the mobile station (MS) employ large antenna arrays for directional precoding/beamforming. Hybrid analog and digital beamforming structures are employed in order to offer a compromise between hardware complexity and system performance. Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems. By exploiting the sparse scattering nature of mmWave channels, we propose a CANDECOMP/PARAFAC (CP) decomposition-based method for channel parameter estimation (including angles of arrival/departure, time delays, and fading coefficients). In our proposed method, the received signal at the MS is expressed as a third-order tensor. We show that the tensor has the form of a low-rank CP, and the channel parameters can be estimated from the associated factor matrices. Our analysis reveals that the uniqueness of the CP decomposition can be guaranteed even when the size of the tensor is small. Hence the proposed method has the potential to achieve substantial training overhead reduction. We also develop Cramer-Rao bound (CRB) results for channel parameters and compare our proposed method with a compressed sensing-based method. Simulation results show that the proposed method attains mean square errors that are very close to their associated CRBs and present a clear advantage over the compressed sensing-based method.


title: Low Store Energy, Low VDDmin, 8T2R Nonvolatile Latch and SRAM With Vertical-Stacked Resistive Memory (Memristor) Devices for Low Power Mobile Applications

abstract: Many mobile SoC chips employ a “two-macro” approach including volatile and nonvolatile memory macros (i.e. SRAM and Flash), to achieve high-performance or low-voltage power-on operation with the capability of power-off nonvolatile data storage. However, the two-macro approach suffers from slow store/restore speeds due to word-by-word serial transfer of data between the volatile and nonvolatile memories. Slow store/restore speeds require long power-on/off time and leave the device vulnerable to sudden power failure . This study proposes a resistive memory (memristor) based nonvolatile SRAM (or memristor latch) cell to achieve fast bit-to-bit parallel store/restore operations, low store/restore energy consumption, and a compact cell area. This resistive nonvolatile 8T2R (Rnv8T) cell includes two fast-write memristor (RRAM) devices vertical-stacked over the 8T, and a novel 2T memristor-switch, which provides both memristor control and SRAM write-assist functions. The write assist feature enables the Rnv8T cell to use read favored transistor sizing to prevent read/write failure at lower VDDs. We also fabricated the first macro-level memristor-based (or RRAM-based) nonvolatile SRAM. This 16 Kb Rnv8T macro achieved the lowest store energy and R/W VDDmin (0.45 V) of any nonvolatile SRAM or two-macro solution.


title: A Survey of Snake-Inspired Robot Designs

abstract: Body undulation used by snakes and the physical architecture of a snake body may offer significant benefits over typical legged or wheeled locomotion designs in certain types of scenarios. A large number of research groups have developed snake-inspired robots to exploit these benefits. The purpose of this review is to report different types of snake-inspired robot designs and categorize them based on their main characteristics. For each category, we discuss their relative advantages and disadvantages. This review will assist in familiarizing a newcomer to the field with the existing designs and their distinguishing features. We hope that by studying existing robots, future designers will be able to create new designs by adopting features from successful robots. The review also summarizes the design challenges associated with the further advancement of the field and deploying snake-inspired robots in practice.


title: Coordinating Liquid and Free Air Cooling with Workload Allocation for Data Center Power Minimization.

abstract: Data centers are seeking more efficient cooling techniques to reduce their operating expenses, because cooling can account for 30-40% of the power consumption of a data center. Recently, liquid cooling has emerged as a promising alternative to traditional air cooling, because it can help eliminate undesired air recirculation. Another emerging technology is free air cooling, which saves chiller power by utilizing outside cold air for cooling. Some existing data centers have already started to adopt both liquid and free air cooling techniques for significantly improved cooling efficiency and more data centers are expected to follow. In this paper, we propose SmartCool, a power optimization scheme that effectively coordinates different cooling techniques and dynamically manages workload allocation for jointly optimized cooling and server power. In sharp contrast to the existing work that addresses different cooling techniques in an isolated manner, SmartCool systematically formulates the integration of different cooling systems as a constrained optimization problem. Furthermore, since geo-distributed data centers have different ambient temperatures, SmartCool dynamically dispatches the incoming requests among a network of data centers with heterogeneous cooling systems to best leverage the high efficiency of free cooling. A light-weight heuristic algorithm is proposed to achieve a near-optimal solution with a low run-time overhead. We evaluate SmartCool both in simulation and on a hardware testbed. The results show that SmartCool outperforms two state-of-the-art baselines by having a 38% more power savings.


title: Origin of a folded repeat protein from an intrinsically disordered ancestor

abstract: All life is built upon the chemical activity of proteins. For this activity, proteins need to fold into specific 3D structures. Protein folding is complicated and easily disrupted, and its evolutionary origin remains poorly understood. A possibility is that folded proteins arose through different genetic processes from shorter pieces of protein called peptides, which participated in an ancient, primordial form of life. One of these processes involves the same peptide being repeated within one protein chain. In 2015, researchers identified 40 primordial peptides whose sequences appear in seemingly unrelated proteins. The study suggested that repetition allows peptides that are unable to fold by themselves to yield folded proteins. Now, Zhu et al. – who are members of the same research group who performed the 2015 study – have explored experimentally whether one of these peptides could indeed yield a folded protein by repetition. The studied primordial peptide gave rise to several protein folds seen today, including – by repetition – a type of fold called TPR. Zhu et al. tried to retrace the emergence of the TPR fold by taking a descendant of the primordial peptide from a ribosomal protein, which is unable to fold without the assistance of an RNA scaffold, and repeating it three times within the same protein chain. The ribosome is a central component of all living cells and evolves very slowly, and so the peptide Zhu et al. took from it is likely to retain many properties of its primordial ancestor. Further experiments found that the repeated peptide was indeed able to fold into a TPR-like structure, but needed several mutations to do so. Introducing these mutations back into the ribosomal protein, however, did not affect the survival and growth of the cell. Thus, they could have occurred without adverse effects during evolution. Structure is a prerequisite for chemical activity, but it is activity that is under selection in living beings. Having produced a new protein, Zhu et al. will now explore ways of endowing it with a selectable activity.


title: Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA

abstract: MOTIVATION ::: The variation in community composition between microbiome samples, termed beta diversity, can be measured by pairwise distance based on either presence-absence or quantitative species abundance data. PERMANOVA, a permutation-based extension of multivariate analysis of variance to a matrix of pairwise distances, partitions within-group and between-group distances to permit assessment of the effect of an exposure or intervention (grouping factor) upon the sampled microbiome. Within-group distance and exposure/intervention effect size must be accurately modeled to estimate statistical power for a microbiome study that will be analyzed with pairwise distances and PERMANOVA. ::: ::: ::: RESULTS ::: We present a framework for PERMANOVA power estimation tailored to marker-gene microbiome studies that will be analyzed by pairwise distances, which includes: (i) a novel method for distance matrix simulation that permits modeling of within-group pairwise distances according to pre-specified population parameters; (ii) a method to incorporate effects of different sizes within the simulated distance matrix; (iii) a simulation-based method for estimating PERMANOVA power from simulated distance matrices; and (iv) an R statistical software package that implements the above. Matrices of pairwise distances can be efficiently simulated to satisfy the triangle inequality and incorporate group-level effects, which are quantified by the adjusted coefficient of determination, omega-squared (ω2). From simulated distance matrices, available PERMANOVA power or necessary sample size can be estimated for a planned microbiome study.


title: Data quality analysis and cleaning strategy for wireless sensor networks

abstract: The quality of data in wireless sensor networks has a significant impact on decision support, and data cleaning is an effective way to improve data quality. However, if the data cleaning strategies are not correctly designed, it might result in an unsatisfactory cleaning effect with increased system cleaning costs. Initially, data quality evaluation indicators and their measurement methods in wireless sensor networks were introduced. We then explored the impact of relationship between different indicators which are used in the quality assessment. Finally, data cleaning strategy for wireless sensor networks based on the relationship between data quality indicators was proposed by comparing and analyzing data cleaning schemes with different orders. The experimental results showed that the proposed data cleaning strategy can effectively improve data availability and have a better cleaning effect in wireless sensor networks for the same cleaning cost.


title: Intelligent Intrusion Detection in Low-Power IoTs

abstract: Security and privacy of data are one of the prime concerns in today’s Internet of Things (IoT). Conventional security techniques like signature-based detection of malware and regular updates of a signature database are not feasible solutions as they cannot secure such systems effectively, having limited resources. Programming languages permitting immediate memory accesses through pointers often result in applications having memory-related errors, which may lead to unpredictable failures and security vulnerabilities. Furthermore, energy efficient IoT devices running on batteries cannot afford the implementation of cryptography algorithms as such techniques have significant impact on the system power consumption. Therefore, in order to operate IoT in a secure manner, the system must be able to detect and prevent any kind of intrusions before the network (i.e., sensor nodes and base station) is destabilised by the attackers. In this article, we have presented an intrusion detection and prevention mechanism by implementing an intelligent security architecture using random neural networks (RNNs). The application’s source code is also instrumented at compile time in order to detect out-of-bound memory accesses. It is based on creating tags, to be coupled with each memory allocation and then placing additional tag checking instructions for each access made to the memory. To validate the feasibility of the proposed security solution, it is implemented for an existing IoT system and its functionality is practically demonstrated by successfully detecting the presence of any suspicious sensor node within the system operating range and anomalous activity in the base station with an accuracy of 97.23%. Overall, the proposed security solution has presented a minimal performance overhead.


title: Google Scholar to overshadow them all? Comparing the sizes of 12 academic search engines and bibliographic databases

abstract: Information on the size of academic search engines and bibliographic databases (ASEBDs) is often outdated or entirely unavailable. Hence, it is difficult to assess the scope of specific databases, such as Google Scholar. While scientometric studies have estimated ASEBD sizes before, the methods employed were able to compare only a few databases. Consequently, there is no up-to-date comparative information on the sizes of popular ASEBDs. This study aims to fill this blind spot by providing a comparative picture of 12 of the most commonly used ASEBDs. In doing so, we build on and refine previous scientometric research by counting query hit data as an indicator of the number of accessible records. Iterative query optimization makes it possible to identify a maximum number of hits for most ASEBDs. The results were validated in terms of their capacity to assess database size by comparing them with official information on database sizes or previous scientometric studies. The queries used here are replicable, so size information can be updated quickly. The findings provide first-time size estimates of ProQuest and EbscoHost and indicate that Google Scholar’s size might have been underestimated so far by more than 50%. By our estimation Google Scholar, with 389 million records, is currently the most comprehensive academic search engine.


title: The serendipitous discovery of the brain's default network

abstract: Abstract One of the most unexpected findings by functional neuroimaging has been the discovery of the brain's default network — a set of brain regions that is spontaneously active during passive moments. The default network's discovery was a fortunate accident that occurred due to the inclusion of rest control conditions in early PET and functional MRI studies. At first, the network was ignored. Later, its presence was shunned as evidence of an experimental confound. Finally, it emerged as a mainstream target of focused study. Here, I describe a personal perspective of the default network's serendipitous discovery.


title: An Ensemble Model for Diabetes Diagnosis in Large-scale and Imbalanced Dataset

abstract: Diabetes is becoming a more and more serious health challenge worldwide with the yearly rising prevalence, especially in developing countries. The vast majority of diabetes are type 2 diabetes, which has been indicated that about 80% of type 2 diabetes complications can be prevented or delayed by timely detection. In this paper, we propose an ensemble model to precisely diagnose the diabetic on a large-scale and imbalance dataset. The dataset used in our work covers millions of people from one province in China from 2009 to 2015, which is highly skew. Results on the real-world dataset prove that our method is promising for diabetes diagnosis with a high sensitivity, F3 and G --- mean, i.e, 91.00%, 58.24%, 86.69%, respectively.


An Improvement of Label Power Set Method Based on Priority Label Transformation


title: Benchmarking Feed-Forward Neural Networks: Models and Measures

abstract: Existing metrics for the learning performance of feed-forward neural networks do not provide a satisfactory basis for comparison because the choice of the training epoch limit can determine the results of the comparison. I propose new metrics which have the desirable property of being independent of the training epoch limit. The efficiency measures the yield of correct networks in proportion to the training effort expended. The optimal epoch limit provides the greatest efficiency. The learning performance is modelled statistically, and asymptotic performance is estimated. Implementation details may be found in (Hamey, 1992).


title: An Efficient Black-box Technique for Defeating Web Application Attacks

abstract: Over the past few years, injection vulnerabilities have become the primary target for remote exploits. SQL injection, command injection, and cross-site scripting are some of the popular attacks that exploit these vulnerabilities. Taint-tracking has emerged as one of the most promising approaches for defending against these exploits, as it supports accurate detection (and prevention) of popular injection attacks. However, practical deployment of tainttracking defenses has been hampered by a number of factors, including: (a) high performance overheads (often over 100%), (b) the need for deep instrumentation, which has the potential to impact application robustness and stability, and (c) specificity to the language in which an application is written. In order to overcome these limitations, we present a new technique in this paper called taint inference. This technique does not require any source-code or binary instrumentation of the application to be protected; instead, it operates by intercepting requests and responses from this application. For most web applications, this interception may be achieved using network layer interposition or library interposition. We then develop a class of policies called syntaxand taint-aware policies that can accurately detect and/or block most injection attacks. An experimental evaluation shows that our techniques are effective in detecting a broad range of attacks on applications written in multiple languages (including PHP, Java and C), and impose low performance overheads (below 5%).


title: The Grand Tour. Language, National Identity and Masculinity

abstract: The Grand Tour usually refers to the travel of Englishmen to Italy, where they acquired the taste essential to the gentleman and the paintings and other objects of virt[ugrave] to display in their country estates. In this paper, I consider not Englishmen abroad but the meaning of the Grand Tour as a cultural and educational practice for elite males in eighteenth-century England. My argument is that as an educational practice, the Grand Tour was highly contradictory, and that it was especially paradoxical in relation to one of its aims, language learning. Using Bakhtin's concepts of heteroglossia and dialogism as analytical framework, I explore the shifting power relations between the English and French languages throughout the century, and argue that the Grand Tour played a key role in the elaboration of discourses on language and national identity in the late eighteenth century.


title: Turning Trust Around: Smart Contract-Assisted Public Key Infrastructure

abstract: In past, several Certificate Authority (CA) compromise and subsequent mis-issue of certificate raise the importance of certificate transparency and dynamic trust management for certificates. Certificate Transparency (CT) provides transparency for issued certificates, thus enabling corrective measure for a mis-issued certificate by a CA. However, CT and existing mechanisms cannot convey the dynamic trust state for a certificate. To address this weakness, we propose Smart Contract-assisted PKI (SCP) - a smart contract based PKI extension - to manage dynamic trust network for PKI. SCP enables distributed trust in PKI, provides a protocol for managing dynamic trust, assures trust state of a certificate, and provides a better trust experience for end-users.


title: Squadron: Incentivizing Quality-Aware Mission-Driven Crowd Sensing

abstract: Recent years have witnessed the success of mobile crowd sensing systems, which outsource sensory data collection to the public crowd equipped with various mobile devices in a wide spectrum of civilian applications. We envision that crowd sensing could as well be very useful in a whole host of mission-driven scenarios, such as peacekeeping operations, non-combatant evacuations, and humanitarian missions. However, the power of crowd sensing could not be fully unleashed in mission-driven crowd sensing (MiCS) systems, unless workers are effectively incentivized to participate. Therefore, in this paper, taking into consideration workers' diverse quality of information (QoI), we propose Squadron, a quality-aware incentive mechanism for MiCS systems. Squadron adopts the reverse auction framework. It approximately minimizes the platform's total payment for worker recruiting in a computationally efficient manner, and recruits workers who potentially could provide high quality data. Furthermore, it also satisfies the desirable properties of truth-fulness and individual rationality. Through rigorous theoretical analysis, as well as extensive simulations, we validate the various aforementioned desirable properties held by Squadron.


title: A predictive control of class E resonant inverter for ultrasonic cleaners

abstract: This paper presents a model predictive current control method for a class E resonant inverter for piezoelectric ceramic transducer (PCTs) applications. The capacitor voltage is used to predict a future value of the load current of the PCT. The class E resonant inverter is operated at the suboptimal mode to minimize switching losses of the power switch. Power switches operate under zero voltage switching (ZVS) condition. The proposed method is straightforward and can be implemented on a digital controller provided that its physical model is known a priori. The class E resonant inverter hardware is constructed and the control algorithm is implemented using a STM32f4 microcontroller. For verification purpose, an operational region of the class E resonant inverter with the frequency range of 27–33 kHz and output current of 80–130 mA is displayed.


title: Test and Study on Impact of Corrosion on Bond Force between Steel and Concrete

abstract: The corrosion of steel will have a bad impact on the safety of reinforced concrete structure. In severe cases, it may even be disastrous. In order to understand the impact of steel corrosion on the structure, tests are carried out to study corrosion and expansion rules of steel bars as well as the impact rules of corrosion on bond force between steel and concrete. The results show that wet and salty environment will result in steel corrosion; relatively minor corrosion will not cause expansion cracks of protection layers; when steel rust to a certain extent, it will cause cracks along the protection layer; when there exists minor corrosion in steel and the protection layer does not have expansion cracks, the bond force is still large and rapidly decreases as the corrosion rate increases.


title: Inter-Technology Backscatter: Towards Internet Connectivity for Implanted Devices

abstract: We introduce inter-technology backscatter, a novel approach that transforms wireless transmissions from one technology to another, on the air. Specifically, we show for the first time that Bluetooth transmissions can be used to create Wi-Fi and ZigBee-compatible signals using backscatter communication. Since Bluetooth, Wi-Fi and ZigBee radios are widely available, this approach enables a backscatter design that works using only commodity devices. ::: We build prototype backscatter hardware using an FPGA and experiment with various Wi-Fi, Bluetooth and ZigBee devices. Our experiments show we can create 2-11 Mbps Wi-Fi standards-compliant signals by backscattering Bluetooth transmissions. To show the generality of our approach, we also demonstrate generation of standards-complaint ZigBee signals by backscattering Bluetooth transmissions. Finally, we build proof-of-concepts for previously infeasible applications including the first contact lens form-factor antenna prototype and an implantable neural recording interface that communicate directly with commodity devices such as smartphones and watches, thus enabling the vision of Internet connected implanted devices.


title: A synthetic data generator for clustering and outlier analysis

abstract: We present a distribution-based and transformation-based approach to synthetic data generation and demonstrate that the approach is very efficient in generating different types of multi-dimensional numerical datasets for data clustering and outlier analysis. We developed a data generating system that is able to systematically create testing datasets based on user’s requirements such as the number of points, the number of clusters, the size, shapes and locations of clusters, and the density level of either cluster data or noise/outliers in a dataset. Two standard probability distributions are considered in data generation. One is uniform distribution and the other is normal distribution. Since outlier detection, especially local outlier detection, is conducted in the context of clusters of a dataset, our synthetic data generator is suitable for both clustering and outlier analysis. In addition, the data format has been carefully designed so that generated data can be visualized not only by our system but also by some popular statistical rendering tools such as statCrunch [16] and statPoint [17] that display data with standard statistical graphical approaches. To our knowledge, our system is probably the first synthetic data generation system that systematically generates datasets for evaluating the clustering and outlier analysis algorithms. Being an object-oriented system, the current data generator can be easily integrated into other data analysis systems.


title: Establish Internet Student-Assessing of Teaching Quality System to Make the Assessment Perfect

abstract: Evaluation of teaching quality is an important means to control classroom teaching quality in colleges and universities. The essay briefly expounds the key section of teaching quality supervision, student assessment. The essay also analyses the principle that a good assessing system should follow and points out disadvantages of traditional student-assessing teaching quality. The author suggests establishing the student-assessing teaching quality system on internet to improve and perfect the system.