title: Experimental Investigation of the Effect of the Driving Voltage of an Electroadhesion Actuator

abstract: This paper investigates the effect of driving voltage on the attachment force of an electroadhesion actuator, as the existing literature on the saturation of the adhesive force at a higher electric field is incomplete. A new type of electroadhesion actuator using normally available materials, such as aluminum foil, PVC tape and a silicone rubber sheet used for keyboard protection, has been developed with a simple layered structure that is capable of developing adhesive force consistently. The developed actuator is subjected to the experiment for the evaluation of various test surfaces; aluminum, brick, ceramic, concrete and glass. The driving high voltage is varied in steps to determine the characteristics of the output holding force. Results show a quadratic relation between F (adhesion force) and V (driving voltage) within the 2 kV range. After this range, the F-V responses consistently show a saturation trend at high electric fields. Next, the concept of the leakage current that can occur in the dielectric material and the corona discharge through air has been introduced. Results show that the voltage level, which corresponds to the beginning of the supply current, matches well with the beginning of the force saturation. With the confirmation of this hypothesis, a working model for electroadhesion actuation is proposed. Based on the experimental results, it is proposed that such a kind of actuator can be driven within a range of optimum high voltage to remain electrically efficient. This practice is recommended for the future design, development and characterization of electroadhesion actuators for robotic applications.


title: An Analytic Approach to People Evaluation in Crowdsourcing Systems

abstract: Worker selection is a significant and challenging issue in crowdsourcing systems. Such selection is usually based on an assessment of the reputation of the individual workers participating in such systems. However, assessing the credibility and adequacy of such calculated reputation is a real challenge. In this paper, we propose an analytic model which leverages the values of the tasks completed, the credibility of the evaluators of the results of the tasks and time of evaluation of the results of these tasks in order to calculate an accurate and credible reputation rank of participating workers and fairness rank for evaluators. The model has been implemented and experimentally validated.


title: On spiking neural P systems

abstract: This work deals with several aspects concerning the formal verification of SN P systems and the computing power of some variants. A methodology based on the information given by the transition diagram associated with an SN P system is presented. The analysis of the diagram cycles codifies invariants formulae which enable us to establish the soundness and completeness of the system with respect to the problem it tries to resolve. We also study the universality of asynchronous and sequential SN P systems and the capability these models have to generate certain classes of languages. Further, by making a slight modification to the standard SN P systems, we introduce a new variant of SN P systems with a special I/O mode, called SN P modules, and study their computing power. It is demonstrated that, as string language acceptors and transducers, SN P modules can simulate several types of computing devices such as finite automata, a-finite transducers, and systolic trellis automata.


title: Benchmarking spliced alignment programs including Spaln2, an extended version of Spaln that incorporates additional species-specific features

abstract: Spliced alignment plays a central role in the precise identification of eukaryotic gene structures. Even though many spliced alignment programs have been developed, recent rapid progress in DNA sequencing technologies demands further improvements in software tools. Benchmarking algorithms under various conditions is an indispensable task for the development of better software; however, there is a dire lack of appropriate datasets usable for benchmarking spliced alignment programs. In this study, we have constructed two types of datasets: simulated sequence datasets and actual cross-species datasets. The datasets are designed to correspond to various real situations, i.e. divergent eukaryotic species, different types of reference sequences, and the wide divergence between query and target sequences. In addition, we have developed an extended version of our program Spaln, which incorporates two additional features to the scoring scheme of the original version, and examined this extended version, Spaln2, together with the original Spaln and other representative aligners based on our benchmark datasets. Although the effects of the modifications are not individually striking, Spaln2 is consistently most accurate and reasonably fast in most practical cases, especially for plants and fungi and for increasingly divergent pairs of target and query sequences.


title: Direct GrayScale Minutiae Detection In Fingerprints

abstract: Most automatic systems for fingerprint comparison are based on minutiae matching. Minutiae are essentially terminations and bifurcations of the ridge lines that constitute a fingerprint pattern. Automatic minutiae detection is an extremely critical process, especially in low-quality fingerprints where noise and contrast deficiency can originate pixel configurations similar to minutiae or hide real minutiae. Several approaches have been proposed in the literature; although rather different from each other, all these methods transform fingerprint images into binary images. In this work we propose an original technique, based on ridge line following, where the minutiae are extracted directly from gray scale images. The results achieved are compared with those obtained through some methods based on image binarization. In spite of a greater conceptual complexity, the method proposed performs better both in terms of efficiency and robustness.


title: Power-aware scheduling of virtual machines in DVFS-enabled clusters

abstract: With the advent of Cloud computing, large-scale virtualized compute and data centers are becoming common in the computing industry. These distributed systems leverage commodity server hardware in mass quantity, similar in theory to many of the fastest Supercomputers in existence today. However these systems can consume a cities worth of power just to run idle, and require equally massive cooling systems to keep the servers within normal operating temperatures. This produces CO 2 emissions and significantly contributes to the growing environmental issue of Global Warming. Green computing, a new trend for high-end computing, attempts to alleviate this problem by delivering both high performance and reduced power consumption, effectively maximizing total system efficiency. This paper focuses on scheduling virtual machines in a compute cluster to reduce power consumption via the technique of Dynamic Voltage Frequency Scaling (DVFS). Specifically, we present the design and implementation of an efficient scheduling algorithm to allocate virtual machines in a DVFS-enabled cluster by dynamically scaling the supplied voltages. The algorithm is studied via simulation and implementation in a multi-core cluster. Test results and performance discussion justify the design and implementation of the scheduling algorithm.


Web Mining in E-Commerce : Pattern Discovery , Issues and Applications


title: Modeling Heterogeneous Network Interference Using Poisson Point Processes

abstract: Cellular systems are becoming more heterogeneous with the introduction of low power nodes including femtocells, relays, and distributed antennas. Unfortunately, the resulting interference environment is also becoming more complicated, making evaluation of different communication strategies challenging in both analysis and simulation. Leveraging recent applications of stochastic geometry to analyze cellular systems, this paper proposes to analyze downlink performance in a fixed-size cell, which is inscribed within a weighted Voronoi cell in a Poisson field of interferers. A nearest out-of-cell interferer, out-of-cell interferers outside a guard region, and cross-tier interference are included in the interference calculations. Bounding the interference power as a function of distance from the cell center, the total interference is characterized through its Laplace transform. An equivalent marked process is proposed for the out-of-cell interference under additional assumptions. To facilitate simplified calculations, the interference distribution is approximated using the Gamma distribution with second order moment matching. The Gamma approximation simplifies calculation of the success probability and average rate, incorporates small-scale and large-scale fading, and works with co-tier and cross-tier interference. Simulations show that the proposed model provides a flexible way to characterize outage probability and rate as a function of the distance to the cell edge.


title: Adversarial Scene Editing: Automatic Object Removal from Weak Supervision

abstract: While great progress has been made recently in automatic image manipulation, it has been limited to object centric images like faces or structured scene datasets. In this work, we take a step towards general scene-level image editing by developing an automatic interaction-free object removal model. Our model learns to find and remove objects from general scene images using image-level labels and unpaired data in a generative adversarial network (GAN) framework. We achieve this with two key contributions: a two-stage editor architecture consisting of a mask generator and image in-painter that co-operate to remove objects, and a novel GAN based prior for the mask generator that allows us to flexibly incorporate knowledge about object shapes. We experimentally show on two datasets that our method effectively removes a wide variety of objects using weak supervision only


title: Beyond trees: common-factor models for 2D human pose recovery

abstract: Tree structured models have been widely used for determining the pose of a human body, from either 2D or 3D data. While such models can effectively represent the kinematic constraints of the skeletal structure, they do not capture additional constraints such as coordination of the limbs. Tree structured models thus miss an important source of information about human body pose, as limb coordination is necessary for balance while standing, walking, or running, as well as being evident in other activities such as dancing and throwing. In this paper, we consider the use of undirected graphical models that augment a tree structure with latent variables in order to account for coordination between limbs. We refer to these as common-factor models, since they are constructed by using factor analysis to identify additional correlations in limb position that are not accounted for by the kinematic tree structure. These common-factor models have an underlying tree structure and thus a variant of the standard Viterbi algorithm for a tree can be applied for efficient estimation. We present some experimental results contrasting common-factor models with tree models, and quantify the improvement in pose estimation for 2D image data.


title: Internal Service: Drivers of (dis)Satisfaction in the Chinese Context

abstract: Internal service encounters facilitate the benefits brought to the firm by external service encounters. However, we lack understanding of internal service encounters in non-Western contexts. This study develops understanding of the internal service encounter in the distinct culture of the Chinese. Data is in the form of critical incidents (n = 526) that coded to 1,373 judgments. Results reveal nine categories that explain internal customers' judgments of internal encounters. The findings reveal the relational orientation, prevalent in Chinese culture, and this forms the basis for exploratory modeling of underlying mechanism of the internal encounter.


title: The Storage vs Repair-Bandwidth Trade-off for Clustered Storage Systems

abstract: We study a generalization of the setting of regenerating codes, motivated by applications to storage systems consisting of clusters of storage nodes. There are $n$ clusters in total, with $m$ nodes per cluster. A data file is coded and stored across the $mn$ nodes, with each node storing $\alpha $ symbols. For availability of data, we require that the file be retrievable by downloading the entire content from any subset of $k$ clusters. Nodes represent entities that can fail. We distinguish between intra-cluster and inter-cluster bandwidth (BW) costs during node repair. Node-repair in a cluster is accomplished by downloading $\beta $ symbols each from any set of $d$ other clusters, dubbed remote helper clusters, and also up to $\alpha $ symbols each from any set of $\ell $ surviving nodes, dubbed local helper nodes, in the host cluster. We first identify the optimal trade-off between storage-overhead and inter-cluster repair-bandwidth under functional repair, and also present optimal exact-repair code constructions for a class of parameters. The new trade-off is strictly better than what is achievable via space-sharing existing coding solutions, whenever $\ell > 0$ . We then obtain sharp lower bounds on the necessary intra-cluster repair BW to achieve optimal trade-off. Under functional repair, random linear network codes (RLNCs) simultaneously optimize usage of both inter- and intra-cluster repair BW; simulation results based on RLNCs suggest optimality of the bounds on intra-cluster repair-bandwidth. Our bounds reveal the interesting fact that, while it is beneficial to increase the number of local helper nodes $\ell $ in order to improve the storage-vs-inter-cluster-repair-BW trade-off, increasing $\ell $ not only increases intra-cluster BW in the host-cluster, but also increases the intra-cluster BW in the remote helper clusters. We also analyze resilience of the clustered storage system against passive eavesdropping by providing file-size bounds and optimal code constructions.


title: DC fault current control of modular multilevel converter with SiC-based Power Electronics Building Blocks

abstract: The Medium Voltage dc (MVDC) distribution is actively being sought as the eventual structure for the next-generation shipboard electrical power systems and the Modular Multilevel Converter (MMC) has become a preferred choice for MVDC systems. However, the fault response is a big challenge for both the shipboard MVDC system and the MMC. A fault current control is proposed to work with MMCs composed of full-bridge Power Electronics Building Blocks (PEBBs). The converter is able to fast clear the fault current when a dc shortcircuit fault happens and easily restore the service after the fault is isolated. The fault response is verified by both simulation results and experimental results from a three-phase SiC PEBBs based MMC prototype.


title: Stockyard: A discrete event-based stock market exchange simulator

abstract: We describe an agent-based stock market simulator built using an asynchronous discrete event simulation framework. The simulator is unique in that it's driven by real-world financial algorithms and protocols; and it's open source. It utilizes an order book bid and ask matching model, and real-world exchange protocols. Our simulation is based on multiple agents interacting through an exchange agent. This method is distinct from those that utilize historical pricing data. Order book execution supports a more realistic interaction between agents. Pricing in our model arises from the dynamics of matching orders in the order book. Our simulator enables the study of market dynamics, and trading strategies using real-world exchange protocols. We present our design and implementation of a market simulator and discuss our initial results using the message protocols defined by NASDAQ: OUCH and ITCH. Our initial results demonstrate StockYard's capability and efficiency in simulating markets with realistic trade volumes.


title: Microstrip anti-phase Gysel power divider with unequal power division

abstract: A microstrip Gysel power divider with unequal power division and anti-phase feature is presented in this paper. By using transmission line theory, the proposed circuit is analyzed and the design equations are derived. For verification, a 4:1 prototype operating at 3GHz is designed, fabricated and measured. The measured insertion losses at the center frequency are about −1.15 dB and −7.23dB, respectively. The measured return losses are better than −20dB from 2.85 to 3.16GHz. The measured phase difference between two output ports is 180°±6° over the range of 2.8 to 3.2GHz, which show a good agreement with the simulated ones.


title: Using Nvivo for Data Analysis in Qualitative Research

abstract: Qualitative data is characterized by its subjectivity, richness, and comprehensive text-based information. Analyzing qualitative data is often a muddled, vague and time-consuming process. Qualitative data analysis is, the pursuing of the relationship between categories and themes of data seeking to increase the understanding of the phenomenon. Traditionally, researchers utilized colored pens to sort and then cut and categorized these data. The innovations in software technology designed for qualitative data analysis significantly diminish complexity and simplify the difficult task, and consequently make the procedure relatively bearable. NVivo, the qualitative data analysis software developed to manage the 'coding' procedures is considered the best in this regards. This article is devoted to demonstrate the methods in which NVivo can be employed in qualitative data analysis. Qualitative research has become widely accepted across a wide range of education science, including administrative, curriculum, and psychology research. This wide acceptance of qualitative research in education is attributed to large extent to the advantage of this type of research. Unlike the quantitative approach, qualitative inquiry is a method of research that describes phenomena based on the point of view of the informants, discovers multiple realities and develops holistic understanding of the phenomena within a particular context (1). It has been acknowledged that properly employing the qualitative data gleaned from face to face interviews, field observation and document analysis can lead the researcher to gain a deeper understanding of the problem than merely analyzing data on a large scale (2).


title: SimFlex: a fast, accurate, flexible full-system simulation framework for performance evaluation of server architecture

abstract: The new focus on commercial workloads in simulation studies of server systems has caused a drastic increase in the complexity and decrease in the speed of simulation tools. The complexity of a large-scale full-system model makes development of a monolithic simulation tool a prohibitively difficult task. Furthermore, detailed full-system models simulate so slowly that experimental results must be based on simulations of only fractions of a second of execution of the modelled system.This paper presents SIMFLEX, a simulation framework which uses component-based design and rigorous statistical sampling to enable development of complex models and ensure representative measurement results with fast simulation turnaround. The novelty of SIMFLEX lies in its combination of a unique, compile-time approach to component interconnection and a methodology for obtaining accurate results from sampled simulations on a platform capable of evaluating unmodified commercial workloads.


title: SVM based approach for the assessment of elbow flexion with smart textile sensor

abstract: This paper presents a new approach for monitoring the elbow flexion with a smart textile based on the piezo-resistive effect. The sensor is composed of a mix of conductive threads made of stainless steel fibers doped with metal particles and dielectric silk threads. The threads are arranged in a double bridle crochet structure to provide the best compromise between mechanical elasticity and sensor sensitivity. The sensor was integrated to a sweater to measure the joint angle of the elbow. In order to asses the angle in robust and accurate way, we developed a static model for angle recognition based on a machine learning algorithm: a Deep Neural Network. The design process of the static model includes the data collection, the extraction of features and the choice of the best architecture for the Deep Neural Network. This approach allow to reach recognition accuracy between 76.9% and 83.66%. Finally, a comparative study have also been performed in order to compare the performances of our model with different algorithms such as SVM, NB and DT.


title: A miniaturized power combiner for compact design of CMOS phase shifter at K-Band

abstract: This paper proposes a ultra compact Wilkinson power combiner (WPC) incorporating synthetic transmission lines at K-band in CMOS technology. The 50 % improvement on the size reduction can be achieved by increasing the slow-wave factor of synthetic transmission line. The presented Wilkinson power combiner design is analyzed and fabricated by using standard 0.18 µm 1P6M CMOS technology. The prototype has only a chip size of 480 µm × 90 µm, corresponding to 0.0002λ 0 2 at 21.5 GHz. The measured insertion losses and return losses are less and higher than 4 dB and 17.5 dB from 16 GHz to 27 GHz, respectively. Furthermore, the proposed WPC is also integrated into the phase shifter to confirm its feasibility. The prototype of phase shifter shows 15 % size reduction and on-wafer measurements show good linearity of full 360-degree phase shifting from 21 GHz to 27 GHz.


title: Experimental investigation of efficient locomotion of underwater snake robots for lateral undulation and eel-like motion patterns

abstract: AbstractUnderwater snake robots offer many interesting capabilities ::: for underwater operations. The long and slender structure of such robots provide superior capabilities for access through narrow openings and within confined areas. This is interesting for inspection and monitoring operations, for instance within the subsea oil and gas industry and within marine archeology. In addition, underwater snake robots can provide both inspection and intervention capabilities and are thus interesting candidates for the next generation inspection and intervention AUVs. Furthermore, bioinspired locomotion through oscillatory gaits, like lateral undulation and eel-like motion, is interesting from an energy efficiency point of view. Increasing the motion efficiency in terms of the achieved forward speed by improving the method of propulsion is a key issue for underwater robots. Moreover, energy efficiency is one of the main challenges for long-term autonomy of these systems. In this study, we will consider both these two aspects of efficiency. This paper considers the energy efficiency of swimming snake robots by presenting and experimentally investigating fundamental properties of the velocity and the power consumption of an underwater snake robot for both lateral undulation and eel-like motion patterns. In particular, we investigate the relationship between the parameters of the gait patterns, the forward velocity and the energy consumption for different motion patterns. The simulation and experimental results are seen to support the theoretical findings.


En Bloc Surgical Dissection for Penile Transplantation for Trans-Men: A Cadaveric Study


CT morphology of COVID-19: Case report and review of literature TT - CT-Morphologie von COVID-19: Fallbeispiel und Literaturubersicht


title: Cicatrizing Conjunctivitis in a Patient Diagnosed With Drug Reaction With Eosinophilia and Systemic Symptoms/Drug-Induced Hypersensitivity Syndrome but With Features of Stevens-Johnson Syndrome.

abstract: PURPOSE ::: Severe cutaneous adverse reactions to drugs (SCARs) such as Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN) and drug reaction with eosinophilia and systemic symptoms/drug-induced hypersensitivity syndrome (DRESS/DIHS) serve as one of the main reasons for inpatient ophthalmic consultation. Although it is well-recognized that SJS/TEN is associated with severe ocular mucosal inflammation and cicatrizing, potentially blinding, sequelae, this association has not been described in relation to other SCARs. We present a patient fulfilling the diagnostic criteria for probable DRESS/DIHS but not for SJS/TEN, yet exhibiting the severe ocular surface involvement characteristic of SJS/TEN. ::: ::: ::: METHODS ::: Case report. ::: ::: ::: RESULTS ::: A 64-year-old man presented with bilateral pseudomembranous conjunctivitis and conjunctival denudation (sloughing) in the setting of a maculopapular rash, fever, liver dysfunction, and hematologic abnormalities 1 month after initiating several medications. A skin biopsy was not consistent with SJS/TEN. The patient was diagnosed with probable DRESS/DIHS and treated with high-dose systemic corticosteroids. The ocular surface inflammation was addressed with intensive topical corticosteroid ointment. The pseudomembranes resolved over a 6-week period, but the patient exhibited residual conjunctival scarring of all palpebral surfaces. ::: ::: ::: CONCLUSIONS ::: The development of severe ocular surface mucosal inflammation and denudation with cicatrizing sequelae in a patient carrying a diagnosis of DRESS/DIHS has diagnostic and therapeutic implications for the ophthalmologist. Careful ophthalmic assessment is indicated in any SCAR patient with ophthalmic symptoms, regardless of formal diagnosis. Furthermore, the early therapeutic interventions recently recommended in SJS/TEN to limit the ophthalmic cicatricial sequelae, such as systemic or topical corticosteroids, may be indicated.


title: Sharp feature detection in point clouds

abstract: This paper presents a new technique for detecting sharp features on point-sampled geometry. Sharp features of different nature and possessing angles varying from obtuse to acute can be identified without any user interaction. The algorithm works directly on the point cloud, no surface reconstruction is needed. Given an unstructured point cloud, our method first computes a Gauss map clustering on local neighborhoods in order to discard all points which are unlikely to belong to a sharp feature. As usual, a global sensitivity parameter is used in this stage. In a second stage, the remaining feature candidates undergo a more precise iterative selection process. Central to our method is the automatic computation of an adaptive sensitivity parameter, increasing significantly the reliability and making the identification more robust in the presence of obtuse and acute angles. The algorithm is fast and does not depend on the sampling resolution, since it is based on a local neighbor graph computation.


Automatic Linkage of Vital Records


title: TM3: flexible transport-layer multi-pipe multiplexing middlebox without head-of-line blocking

abstract: A primary design decision in HTTP/2, the successor of HTTP/1.1, is object multiplexing. While multiplexing improves web performance in many scenarios, it still has several drawbacks due to complex cross-layer interactions. In this paper, we propose a novel multiplexing architecture called TM3 that overcomes many of these limitations. TM3 strategically leverages multiple concurrent multiplexing pipes in a transparent manner, and eliminates various types of head-of-line blocking that can severely impact user experience. TM3 works beyond HTTP over TCP and applies to a wide range of application and transport protocols. Extensive evaluations on LTE and wired networks show that TM3 substantially improves performance e.g., reduces web page load time by an average of 24% compared to SPDY, which is the basis for HTTP/2. For lossy links and concurrent transfers, the improvements are more pronounced: compared to SPDY, TM3 achieves up to 42% of average PLT reduction under losses and up to 90% if concurrent transfers exist.


title: A Glimpse into Babel: An Analysis of Multilinguality in Wikidata

abstract: Multilinguality is an important topic for knowledge bases, especially Wikidata, that was build to serve the multilingual requirements of an international community. Its labels are the way for humans to interact with the data. In this paper, we explore the state of languages in Wikidata as of now, especially in regard to its ontology, and the relationship to Wikipedia. Furthermore, we set the multilinguality of Wikidata in the context of the real world by comparing it to the distribution of native speakers. We find an existing language maldistribution, which is less urgent in the ontology, and promising results for future improvements.


title: Location-based crowdsourcing: extending crowdsourcing to the real world

abstract: The WWW and the mobile phone have become an essential means for sharing implicitly and explicitly generated information and a communication platform for many people. With the increasing ubiquity of location sensing included in mobile devices we investigate the arising opportunities for mobile crowdsourcing making use of the real world context. In this paper we assess how the idea of user-generated content, web-based crowdsourcing, and mobile electronic coordination can be combined to extend crowdsourcing beyond the digital domain and link it to tasks in the real world. To explore our concept we implemented a crowd-sourcing platform that integrates location as a parameter for distributing tasks to workers. In the paper we describe the concept and design of the platform and discuss the results of two user studies. Overall the findings show that integrating tasks in the physical world is useful and feasible. We observed that (1) mobile workers prefer to pull tasks rather than getting them pushed, (2) requests for pictures were the most favored tasks, and (3) users tended to solve tasks mainly in close proximity to their homes. Based on this, we discuss issues that should be considered during designing mobile crowdsourcing applications.


title: A Fast RPC System for Virtual Machines

abstract: Despite the advances in high performance interdomain communications for virtual machines (VM), data intensive applications developed for VMs based on the traditional remote procedure call (RPC) mechanism still suffer from performance degradation due to the inherent inefficiency of data serialization/deserilization operations. This paper presents VMRPC, a lightweight RPC framework specifically designed for VMs that leverages the heap and stack sharing mechanism to circumvent unnecessary data copy and serialization/deserilization. Our evaluation shows that the performance of VMRPC is an order of magnitude better than traditional RPC systems and existing alternative interdomain communication optimization systems. The evaluation on a VMRPC-enhanced networked file system across a varied range of benchmarks further reveals the competitiveness of VMRPC in IO-intensive applications.


title: Virtual Visual Servoing: a framework for real-time augmented reality

abstract: This paper presents a framework to achieve real-time augmented reality applications. We propose a framework based on the visual servoing approach well known in robotics. We consider pose or viewpoint computation as a similar problem to visual servoing. It allows one to take advantage of all the research that has been carried out in this domain in the past. The proposed method features simplicity, accuracy, efficiency, and scalability wrt. to the camera model as well as wrt. the features extracted from the image. We illustrate the efficiency of our approach on augmented reality applications with various real image sequences.


title: Deep Convolutional Matching

abstract: We propose a new matching algorithm, called DeepMatching, to compute dense correspondences between images. DeepMatching relies on a deep, multi-layer, convolutional architecture designed for matching images. The proposed matching algorithm can handle non-rigid deformations and repetitive textures, and can therefore efficiently determine dense correspondences in the presence of significant changes between images. We evaluate the performance of DeepMatching, in comparison with state-of-the-art matching algorithms, on resp. the Mikolajczyk, the MPI-Sintel and the Kitti datasets. The performance measure is"Accuracy@T", that is the percentage of correct pixels for a specified error threshold of T pixels. Deep-Matching outperforms the state-of-the-art algorithms in terms of accuracy@T and shows excellent results in particular for repetitive textures. We also propose a method for estimating optical flow, called DeepFlow, by integrating a DeepMatching term in a variational energy minimization approach. Robustness to large displacements is obtained due to this matching term. DeepFlow obtains competitive performance on public benchmarks for optical flow estimation. In particular, DeepFlow obtains state-of-the-art results on the MPI-Sintel dataset.


title: A large-scale analysis of the security of embedded firmwares

abstract: As embedded systems are more than ever present in our society, their security is becoming an increasingly important issue. However, based on the results of many recent analyses of individual firmware images, embedded systems acquired a reputation of being insecure. Despite these facts, we still lack a global understanding of embedded systems' security as well as the tools and techniques needed to support such general claims. ::: ::: In this paper we present the first public, large-scale analysis of firmware images. In particular, we unpacked 32 thousand firmware images into 1.7 million individual files, which we then statically analyzed. We leverage this large-scale analysis to bring new insights on the security of embedded devices and to underline and detail several important challenges that need to be addressed in future research. We also show the main benefits of looking at many different devices at the same time and of linking our results with other large-scale datasets such as the ZMap's HTTPS survey. ::: ::: In summary, without performing sophisticated static analysis, we discovered a total of 38 previously unknown vulnerabilities in over 693 firmware images. Moreover, by correlating similar files inside apparently unrelated firmware images, we were able to extend some of those vulnerabilities to over 123 different products. We also confirmed that some of these vulnerabilities altogether are affecting at least 140K devices accessible over the Internet. It would not have been possible to achieve these results without an analysis at such wide scale. ::: ::: We believe that this project, which we plan to provide as a firmware unpacking and analysis web service, will help shed some light on the security of embedded devices.


title: Separately Excited Synchronous Motor With Rotary Transformer for Hybrid Vehicle Application

abstract: The cost of rare earth (RE) permanent magnet along with the associated supply volatility have intensified the interests for machine topologies, which eliminate or reduce the RE magnets usage. This paper presents one such design solution, the separately excited synchronous motor (SESM) that eliminates RE magnets, but does not sacrifice the peak torque and power of the motor. The major drawback of such motors is the necessity of brushes to supply the field current. This is especially a challenge for hybrid or electric vehicle applications where the machine is actively cooled with oil inside the transmission. Sealing the brushes from the oil is challenging and would limit the application of such a motor inside a transmission. To overcome this problem, a contactless rotary transformer is designed and implemented for the rotor field excitation. The designed motor is built and tested. The test data show that the designed motor outperforms an equivalent interior permanent magnet (IPM) motor, which is optimized for a hybrid application for both peak torque and power. Better drive system efficiency is measured at high speed compared with the IPM machine, while the latter outperforms (for efficiency) the SESM at low- and medium-speed range.


title: Fuzzy model and optimization for airport gate assignment problem

abstract: Gate assignment is an important decision making problem which involves multiple and conflict objectives in airport. In this paper, fuzzy model is proposed to handle two main objectives, minimizing the total walking distance for passengers and maximizing the robustness of assignment. The idle times of flight-to-gate are regarded as fuzzy variables, and whose membership degrees are used to express influence on robustness of assignment. Adjustment function on membership degree is introduced to transfer two objectives into one. Modified genetic algorithm is adopted to optimize the NP-hard problem. Finally, illustrative example is given to evaluate the performance of fuzzy model. Three distribution functions are tested?? and comparison with the method of fixed buffer time is given. Simulation results demonstrate the feasibility and effectiveness of proposed fuzzy method.


title: Microscale technologies for tissue engineering and biology

abstract: Microscale technologies are emerging as powerful tools for tissue engineering and biological studies. In this review, we present an overview of these technologies in various tissue engineering applications, such as for fabricating 3D microfabricated scaffolds, as templates for cell aggregate formation, or for fabricating materials in a spatially regulated manner. In addition, we give examples of the use of microscale technologies for controlling the cellular microenvironment in vitro and for performing high-throughput assays. The use of microfluidics, surface patterning, and patterned cocultures in regulating various aspects of cellular microenvironment is discussed, as well as the application of these technologies in directing cell fate and elucidating the underlying biology. Throughout this review, we will use specific examples where available and will provide trends and future directions in the field.


title: Wise Crowd Content Assessment and Educational Rubrics

abstract: Development of reliable rubrics for educational intervention studies that address reading and writing skills is labor-intensive, and could benefit from an automated approach. We compare a main ideas rubric used in a successful writing intervention study to a highly reliable wise-crowd content assessment method developed to evaluate machine-generated summaries. The ideas in the educational rubric were extracted from a source text that students were asked to summarize. The wise-crowd content assessment model is derived from summaries written by an independent group of proficient students who read the same source text, and followed the same instructions to write their summaries. The resulting content model includes a ranking over the derived content units. All main ideas in the rubric appear prominently in the wise-crowd content model. We present two methods that automate the content assessment. Scores based on the wise-crowd content assessment, both manual and automated, have high correlations with the main ideas rubric. The automated content assessment methods have several advantages over related methods, including high correlations with corresponding manual scores, a need for only half a dozen models instead of hundreds, and interpretable scores that independently assess content quality and coverage.


title: On spatial smoothing for direction-of-arrival estimation of coherent signals

abstract: We present an analysis of a "spatial smoothing" preprocessing scheme, recently suggested by Evans et al., to circumvent problems encountered in direction-of-arrival estimation of fully correlated signals. Simulation results that illustrate the performance of this scheme in conjunction with the eigenstructure technique are described.


title: Recommendation as link prediction: a graph kernel-based machine learning approach

abstract: Recommender systems have demonstrated commercial success in multiple industries. In digital libraries they have the potential to be used as a support tool for traditional information retrieval functions. Among the major recommendation algorithms, the successful collaborative filtering (CF) methods explore the use of user-item interactions to infer user interests. Based on the finding that transitive user-item associations can alleviate the data sparsity problem in CF, multiple heuristic algorithms were designed to take advantage of the user-item interaction networks with both direct and indirect interactions. However, the use of such graph representation was still limited in learning-based algorithms. In this paper, we propose a graph kernel-based recommendation framework. For each user-item pair, we inspect its associative interaction graph (AIG) that contains the users, items, and interactions n steps away from the pair. We design a novel graph kernel to capture the AIG structures and use them to predict possible user-item interactions. The framework demonstrates improved performance on an online bookstore dataset, especially when a large number of suggestions are needed.


title: Question recommendation with constraints for massive open online courses

abstract: Massive Open Online Courses (MOOCs) have experienced a recent boom in interest. Problems students struggle with in the discussion forums, such as difficultly in finding interesting discussion opportunities or attracting helpers to address posted problems, provide new opportunities for recommender systems. In contrast to traditional product recommendation, question recommendation in discussion forums should simultaneously consider constraints on both students and questions. These considerations include (1) Load Balancing - students should not be over-burdened with too many requests; and (2) Expertise Matching - students should not be requested to address problems they are not capable of addressing. In this work, we formulate a novel constrained question recommendation problem to address the above considerations. We design a context-aware matrix factorization model to predict students' preferences over questions, then build a max cost flow model to manage the constraints. Experimental results conducted on three MOOC datasets demonstrate that our method significantly outperforms baseline methods in optimizing overall forum welfare, and in predicting which specific questions students might be interested in.


title: Persuasive system design: state of the art and future directions

abstract: This paper provides an overview of the current state of the art in persuasive systems design. All peer-reviewed full papers published at the first three International Conferences on Persuasive Technology were analyzed employing a literature review framework. Results from this analysis are discussed and directions for future research are suggested. Most research papers so far have been experimental. Five out of six of these papers (84.4%) have addressed behavioral change rather than an attitude change. Tailoring, tunneling, reduction and social comparison have been the most studied methods for persuasion. Quite, surprisingly ethical considerations have remained largely unaddressed in these papers. In general, many of the research papers seem to describe the investigated persuasive systems in a relatively vague manner leaving room for some improvement.


title: How does CRM technology transform into organizational performance? A mediating role of marketing capability☆

abstract: Customer relationship management (CRM) technology has attracted significant attention from researchers and practitioners as a facilitator of organizational performance. Even though companies have made tremendous investments in CRM technology, empirical research offers inconsistent support that CRM technology enhances organizational performance. Given this equivocal effect and the increasing need for the generalization of CRM implementation research outside western context, the authors, using data from Korean companies, address the process concerning how CRM technology translates into business outcomes. The results highlight that marketing capability mediates the association between CRM technology use and performance. Moreover, a customer-centric organizational culture and management system facilitate CRM technology use. This study serves not only to clarify the mechanism between CRM technology use and organizational performance, but also to generalize the CRM results in the Korean context.


title: On the Feasibility of Distributed Beamforming in Wireless Networks

abstract: Energy efficient communication is a fundamental problem in wireless ad-hoc and sensor networks. In this paper, we explore the feasibility of a distributed beamforming approach to this problem, with a cluster of distributed transmitters emulating a centralized antenna array so as to transmit a common message signal coherently to a distant base station. The potential SNR gains from beamforming are well-known. However, realizing these gains requires synchronization of the individual carrier signals in phase and frequency. In this paper we show that a large fraction of the beamforming gains can be realised even with imperfect synchronization corresponding to phase errors with moderately large variance. We present a master-slave architecture where a designated master transmitter coordinates the synchronization of other (slave) transmitters for beamforming. We observe that the transmitters can achieve distributed beamforming with minimal coordination with the base station using channel reciprocity. Thus, inexpensive local coordination with a master transmitter makes the expensive communication with a distant base station receiver more efficient. However, the duplexing constraints of the wireless channel place a fundamental limitation on the achievable accuracy of synchronization. We present a stochastic analysis that demonstrates the robustness of beamforming gains with imperfect synchronization, and demonstrate a tradeoff between synchronization overhead and beamforming gains. We also present simulation results for the phase errors that validate the analysis


title: A review of classification algorithms for EEG-based brain-computer interfaces

abstract: In this paper we review classification algorithms used to design brain–computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.


title: Outcomes of surgery for young children with multivessel pulmonary vein stenosis.

abstract: OBJECTIVE ::: We pursued a multimodality approach to the treatment of patients with pulmonary vein stenosis, incorporating sutureless surgical repair, catheter interventions, and adjunctive chemotherapy. We report our outcomes after surgery. ::: ::: ::: METHODS ::: Between January 2007 and August 2013, 49 patients with multivessel pulmonary vein stenosis underwent operations at our institution. We retrospectively reviewed data from a pulmonary vein stenosis registry and the medical records. ::: ::: ::: RESULTS ::: At the time of the index operation, the median patient age was 6 months (range, 32 days-48 months) and weight was 4.9 kg (range, 2.1-13.4 kg). Fourteen patients (28%) died during the follow-up period (median follow-up was 0.5 years [range, 0.04-4.9 years]). There were 2 deaths (4%) within 30 days. Age at repair <6 months, weight at repair <3 kg, and a preoperative right ventricular systolic pressure < ¾ systemic were found to be associated with mortality. One patient required repeat operation for recurrent stenosis. Thirty-nine patients (80%) underwent postoperative catheterizations. The median number of catheterizations per patient was 2 (range, 0-14). Twenty-nine patients (59%) underwent catheterizations with pulmonary vein intervention. The median number of catheterizations with intervention per patient was 1 (range, 0-14). There were no identifiable associations with need for or number of catheterizations with intervention. Ten patients were listed for lung transplantation: 4 patients were de-listed, 3 patients died waiting, and 3 patients underwent transplant. ::: ::: ::: CONCLUSIONS ::: Using a multimodality approach, we observed acceptable early survival after operation in patients with pulmonary vein stenosis, despite the need for catheter reinterventions. Lung transplantation remains a viable option.


title: High-speed convolution and correlation

abstract: Cooley and Tukey have disclosed a procedure for synthesizing and analyzing Fourier series for discrete periodic complex functions. For functions of period N, where N is a power of 2, computation times are proportional to N log2 N as expressed in Eq. (0).


title: Trends in quality in the analytical laboratory. II. Analytical method validation and quality assurance

abstract: Abstract It is internationally recognized that validation is necessary in analytical laboratories. The use of validated methods is important for an analytical laboratory to show its qualification and competency. In this update on analytical quality, we place validation of analytical methodologies in the broader context of quality assurance (QA). We discuss different approaches to validation, giving attention to the different characteristics of method performance. We deal with the concepts of single-laboratory or in-house validation, inter-laboratory or collaborative study, standardization, internal quality control (IQC), proficiency testing (PT), accreditation and, finally, analytical QA (AQA). This article provides a good, complete, up-to-date collation of relevant information in the fields of analytical method validation and QA. It describes the different aspects of method validation in the framework of QA. It offers insight and direct help to anyone involved in any analytical methodologies, whether they are an academic researcher or in the industrial sector.


title: Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

abstract: Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper we introduce 'deep learning' as a technique to improve the objectivity and efficiency of histopathologic slide analysis. Through two examples, prostate cancer identification in biopsy specimens and breast cancer metastasis detection in sentinel lymph nodes, we show the potential of this new methodology to reduce the workload for pathologists, while at the same time increasing objectivity of diagnoses. We found that all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30-40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention. We conclude that 'deep learning' holds great promise to improve the efficacy of prostate cancer diagnosis and breast cancer staging.


Experimental approaches to the study of personality


title: Probabilistic ontologies for knowledge fusion

abstract: To cope with asymmetric threats in an increasingly network centric environment, todaypsilas command support systems must interoperate with a diverse collection of other systems. As a natural consequence, the focus is changing from data fusion to knowledge fusion. This new reality creates the need for advanced techniques that exploit not only the syntactic structure of knowledge bases, but also the semantic content. Ontologies play a major role in semantically aware systems, providing a means for highly effective knowledge sharing. However, they lack a standardized treatment of uncertainty, a ubiquitous feature of multisource fusion problems. This paper discusses the applicability of Probabilistic Ontologies designed in Probabilistic OWL (PR-OWL) to address this unmet need. The potential of PR-OWL probabilistic ontologies is demonstrated through a case study in the counterterrorism domain. A simple PR-OWL ontology for the case study was implemented using a recently released PR-OWL knowledge representation and reasoning system.


title: Matrix Profile VIII: Domain Agnostic Online Semantic Segmentation at Superhuman Performance Levels

abstract: Unsupervised semantic segmentation in the time series domain is a much-studied problem due to its potential to detect unexpected regularities and regimes in poorly understood data. However, the current techniques have several shortcomings, which have limited the adoption of time series semantic segmentation beyond academic settings for three primary reasons. First, most methods require setting/learning many parameters and thus may have problems generalizing to novel situations. Second, most methods implicitly assume that all the data is segmentable, and have difficulty when that assumption is unwarranted. Finally, most research efforts have been confined to the batch case, but online segmentation is clearly more useful and actionable. To address these issues, we present an algorithm which is domain agnostic, has only one easily determined parameter, and can handle data streaming at a high rate. In this context, we test our algorithm on the largest and most diverse collection of time series datasets ever considered, and demonstrate our algorithm's superiority over current solutions. Furthermore, we are the first to show that semantic segmentation may be possible at superhuman performance levels.


title: De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis

abstract: De novo assembly of RNA-seq data enables researchers to study transcriptomes without the need for a genome sequence; this approach can be usefully applied, for instance, in research on 'non-model organisms' of ecological and evolutionary importance, cancer samples or the microbiome. In this protocol we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-seq data in non-model organisms. We also present Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes. In the procedure, we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from http://trinityrnaseq.sourceforge.net. The run time of this protocol is highly dependent on the size and complexity of data to be analyzed. The example data set analyzed in the procedure detailed herein can be processed in less than 5 h.


title: Accelerometer-based control of an industrial robotic arm

abstract: Most of industrial robots are still programmed using the typical teaching process, through the use of the robot teach pendant. In this paper is proposed an accelerometer-based system to control an industrial robot using two low-cost and small 3-axis wireless accelerometers. These accelerometers are attached to the human arms, capturing its behavior (gestures and postures). An Artificial Neural Network (ANN) trained with a back-propagation algorithm was used to recognize arm gestures and postures, which then will be used as input in the control of the robot. The aim is that the robot starts the movement almost at the same time as the user starts to perform a gesture or posture (low response time). The results show that the system allows the control of an industrial robot in an intuitive way. However, the achieved recognition rate of gestures and postures (92%) should be improved in future, keeping the compromise with the system response time (160 milliseconds). Finally, the results of some tests performed with an industrial robot are presented and discussed.


title: RDF Data Storage and Query Processing Schemes: A Survey

abstract: The Resource Description Framework (RDF) represents a main ingredient and data representation format for Linked Data and the Semantic Web. It supports a generic graph-based data model and data representation format for describing things, including their relationships with other things. As the size of RDF datasets is growing fast, RDF data management systems must be able to cope with growing amounts of data. Even though physically handling RDF data using a relational table is possible, querying a giant triple table becomes very expensive because of the multiple nested joins required for answering graph queries. In addition, the heterogeneity of RDF Data poses entirely new challenges to database systems. This article provides a comprehensive study of the state of the art in handling and querying RDF data. In particular, we focus on data storage techniques, indexing strategies, and query execution mechanisms. Moreover, we provide a classification of existing systems and approaches. We also provide an overview of the various benchmarking efforts in this context and discuss some of the open problems in this domain.


title: Gramatika: A grammar checker for the low-resourced Filipino language

abstract: This research focuses on the implementation of Gramatika, a grammar checker designed for the Filipino language given its available resources and linguistic tools. The checker uses hybrid n-grams generated from n-grams of words, part-of-speech tags, and lemmas of grammatically-correct texts. It covers a variety of error types including those unique in Filipino: wrong word form, and incorrectly merged / unmerged words. The grammar checker performed 64% accuracy on producing the correct suggestions on erroneous phrases and 85% on error-free texts when using Part-of-Speech (POS) tags from a Hybrid POS tagger (HPOST) for Filipino. Recommendations to improve Gramatika is to implement linguistic tools such as constituency parser, incorrect affix detection system, and a spell checker for the Filipino language.


Spatial Econometric Interaction Models


title: Revisiting the Plotting of Mechanical Characteristic of Induction Motor with A Wound Rotor

abstract: In this paper method for determining the parameters of the equivalent circuit of an induction motor with a wound rotor based on the combined data of the no-load and motor operating conditions experiments was discusses.A scheme for measuring the necessary parameters to plotting the motor characteristics was described. A comparison of measured and calculated data was made.


title: Chaotic glowworm swarm optimization algorithm based on Gauss mutation

abstract: Aiming at the shortcoming of solving the optimal value in basic glowworm swarm optimization (GSO) algorithm, the paper proposes a chaotic GSO algorithm based on Gauss mutation (GMCGSO). GMCGSO uses Gauss mutation strategy in the process of glowworm moving, which prevents the algorithm from falling into local optima to some extent, and obtains much higher precision solution by taking use of the ergodicity of chaotic operator. Through the simulation of eight standard test functions we can see that the improved artificial glowworm swarm optimization algorithm has much higher convergence speed, computing precision and the success rate of convergence in comparison with the other algorithms.


title: Searching efficient plans for emergency rescue through simulation: the case of a metro fire

abstract: In this paper we present the way we modelled and simulated a metro system in the case of a tunnel fire, and discuss the ways this simulation may support the search for efficient rescue plans. The metro system was modelled as a complex adaptive system, comprising four interacting and co-evolving subsystems: (i) the fire and the released smoke, (ii) the group of passengers, (iii) the technological system, and (iv) the metro personnel. Based on this model, an agent-based simulation was developed. This simulation provides an appropriate dynamic representation of the designer’s problem space, enabling him (i) to apprehend the critical dependencies and invariants of the system under consideration, (ii) to identify the features that should characterise the designed emergency rescue plans, and (iii) to assess their efficiency. To demonstrate the usefulness of the adopted approach for the design of an efficient emergency rescue plan, the results of two experiments exploring alternative sequences of the metro personnel’s actions under different circumstances are presented.


title: Comparison of Feedback Control Techniques for Torque-Vectoring Control of Fully Electric Vehicles

abstract: Fully electric vehicles (FEVs) with individually controlled powertrains can significantly enhance vehicle response to steering-wheel inputs in both steady-state and transient conditions, thereby improving vehicle handling and, thus, active safety and the fun-to-drive element. This paper presents a comparison between different torque-vectoring control structures for the yaw moment control of FEVs. Two second-order sliding-mode controllers are evaluated against a feedforward controller combined with either a conventional or an adaptive proportional-integral-derivative (PID) controller. Furthermore, the potential performance and robustness benefits arising from the integration of a body sideslip controller with the yaw rate feedback control system are assessed. The results show that all the evaluated controllers are able to significantly change the understeer behavior with respect to the baseline vehicle. The PID-based controllers achieve very good vehicle performance in steady-state and transient conditions, whereas the controllers based on the sliding-mode approach demonstrate a high level of robustness against variations in the vehicle parameters. The integrated sideslip controller effectively maintains the sideslip angle within acceptable limits in the case of an erroneous estimation of the tire-road friction coefficient.


title: Notable design patterns for domain-specific languages

abstract: The realisation of domain-specific languages ( DSL s) diAers in fundamental ways from that of traditional programming languages. We describe eight recurring patterns that we have identified as being used for DSL design and implementation. Existing languages can be extended, restricted, partially used, or become hosts for DSL s. Simple DSL s can be implemented by lexical processing. In addition, DSL s can be used to create front-ends to existing systems or to express complicated data structures. Finally, DSL s can be combined using process pipelines. The patterns described form a pattern language that can be used as a building block for a systematic view of the software development process involving DSL s. ” 2001 Elsevier Science Inc. All rights reserved.


Survey of Graph Clustering Algorithms Using Amazon Reviews


title: Real-time multiple speaker DOA estimation in a circular microphone array based on Matching Pursuit

abstract: We recently proposed an approach inspired by Sparse Component Analysis for real-time localisation of multiple sound sources using a circular microphone array. The method was based on identifying time-frequency zones where only one source is active, reducing the problem to single-source localisation in these zones. A histogram of estimated Directions of Arrival (DOAs) was formed and then processed to obtain improved DOA estimates, assuming that the number of sources was known. In this paper, we extend our previous work by proposing a new method for the final DOA estimations, that outperforms our previous method at lower SNRs and in the case of six simultaneous speakers. In keeping with the spirit of our previous work, the new method is very computationally efficient, facilitating its use in real-time systems.


title: The Heeger-Bergen Pyramid-Based Texture Synthesis Algorithm

abstract: This contribution deals with the Heeger-Bergen pyramid-based texture analysis/synthesis algorithm. It brings a detailed explanation of the original algorithm tested on many characteristic examples. Our analysis reproduces the original results, but also brings a minor improvement concerning non-periodic textures. Inspired by visual perception theories, Heeger and Bergen proposed to characterize a texture by its first-order statistics of both its color and its responses to multiscale and multi-orientation filters, namely the steerable pyramid. The Heeger-Bergen algorithm consists in the following procedure: starting from a white noise image, histogram matchings are performed to the image alternately in the image domain and the steerable pyramid domain, so that the corresponding output histograms match the ones of the input texture. Source Code An on-line demo 1 of the Heeger-Bergen pyramid-based texture synthesis algorithm is available. The demo permits to upload a color image to extract a subimage and to run the texture synthesis algorithm on this subimage. The algorithm available in the demo is a slightly improved version treating non-periodic textures by a “periodic+smooth” decomposition [13]. The algorithm works with color textures and is able to synthesize textures with larger size than the input image. The original version of the Heeger-Bergen algorithm (where the boundaries are handled by mirror symmetrization) is optional in the source code. An ANSI C implementation is available for download here 2 . It is provided with: • An illustrated html documentation; • Source code; This code requires libpng, libfftw3, openmp, and getopt. Compilation and usage instructions are included in the README.txt file of the zip archive. The illustrated HTML documentation can be reproduced from the source code by using


title: Modeling the Correlations of Relations for Knowledge Graph Embedding

abstract: Knowledge graph embedding, which maps the entities and relations into low-dimensional vector spaces, has demonstrated its effectiveness in many tasks such as link prediction and relation extraction. Typical methods include TransE, TransH, and TransR. All these methods map different relations into the vector space separately and the intrinsic correlations of these relations are ignored. It is obvious that there exist some correlations among relations because different relations may connect to a common entity. For example, the triples (Steve Jobs, PlaceOfBrith, California) and (Apple Inc., Location, California) share the same entity California as their tail entity. We analyze the embedded relation matrices learned by TransE/TransH/TransR, and find that the correlations of relations do exist and they are showed as low-rank structure over the embedded relation matrix. It is natural to ask whether we can leverage these correlations to learn better embeddings for the entities and relations in a knowledge graph. In this paper, we propose to learn the embedded relation matrix by decomposing it as a product of two low-dimensional matrices, for characterizing the low-rank structure. The proposed method, called TransCoRe (Translation-Based Method via Modeling the Correlations of Relations), learns the embeddings of entities and relations with translation-based framework. Experimental results based on the benchmark datasets of WordNet and Freebase demonstrate that our method outperforms the typical baselines on link prediction and triple classification tasks.


title: Cardinality change-based early detection of large-scale cyber-attacks

abstract: Cyber-attacks are happening every day, with a variety of behaviors and objects. For example, email spammers may compromise computers to sign-up millions of email accounts for sending spam emails; during worm spreading, each infected host may try to connect to many hosts to further spread the worm, etc. However, many such large-scale and often distributed cyber-attacks share a common characteristic that the activities involved in them result in changes in the cardinality of attack traffic. Examples include: the cardinality of the accounts signed up by a compromised host often increases in spam email delivery scenarios, and the cardinality of the connections made from a host may increase in worm spreading scenarios. In this paper, we focus on changes in the cardinality of the network/attack traffic that may indicate on-going cyber-attacks. We formulate this problem as cardinality-based change point detection in distributed streams of attack traffic, and develop a nonparametric error-bounded scheme for it. Our scheme supports the capability of merging information collected from multiple monitoring points to detect large-scale attacks. Also, our scheme uses small space as well as constant processing time, which makes it applicable for spaceconstrained network or security systems. We have conducted experiments using both real-world traces and synthetic data. Experimental results and theoretical analysis show that our scheme can detect changes in the cardinality within given time and error bounds. We expect the solutions of this work will be deployed as a building block in network and security monitoring systems to detect large distributed cyber attacks.


title: uDecide: A Protégé Plugin for Multiattribute Decision Making

abstract: This paper introduces the Protege plugin uDecide. With the help of uDecide it is possible to solve multi-attribute decision making problems encoded in a straight forward extension of standard Description Logics. The formalism allows to specify background knowledge in terms of an ontology, while each attribute is represented as a weighted class expression. On top of such an approach one can compute the best choice (or the best k-choices) taking background knowledge into account in the appropriate way. We show how to implement the approach on top of existing semantic web technologies and demonstrate its benefits with the help of an interesting use case that illustrates how to convert an existing web resource into an expert system with the help of uDecide.


title: Cognitive outcomes from the Game-Design and Learning (GDL) after-school program

abstract: Abstract The Game-Design and Learning (GDL) initiative engages middle school students in the process of game-design in a variety of in-school, after-school, and summer camp settings. The goal of the GDL initiative is to leverage students' interests in games and design to foster their problem-solving and critical reasoning skills. The present study examines the effectiveness of an after-school version of the GDL program using a quasi-experimental design. Students enrolled in the GDL program were guided in the process of designing games aimed at solving problems. Compared to students in a control group who did not attend the program (n = 24), the children who attended the GDL program (n = 20) showed a significant increase in their problem-solving skills. The results provide empirical support for the hypothesis that participation in the GDL program leads to measurable cognitive changes in children's problem-solving skills. This study bears important implications for educators and theory.


title: Four reference models for transparency requirements in information systems

abstract: Transparency is a key emerging requirement in modern businesses and their information systems. Transparency refers to the information which flows amongst stakeholders for the purpose of informed decision-making and taking the right action. Transparency is generally associated with positive connotations such as trust and accountability. However, it has been shown that it could have adverse effects such as information overload and affecting decisions objectiveness. This calls for systematic approaches for transparency to ensure its cost-effectiveness and avoid such adverse side effects. This is especially true considering that the relatively few works in the literature on transparency requirements have focused mainly on making information available and accessible and have paid little focus on the information receivers’ side and making it meaningful for them. In this paper, we reflect on our previous research on transparency and its multi-faceted constructs and review multi-disciplinary conceptualisation and propose four reference models which are meant to form a holistic conceptual baseline for transparency requirements in information systems. These reference models cover transparency actors, transparency meaningfulness, transparency usefulness, and information quality in transparency. We also discuss the interdependencies amongst these four reference models and their implications for requirements engineers and information system analysts. As a proof of concept, we analyse a mainstream transparency document, the United Kingdom Freedom of Information Act, in the light of our reference models and demonstrate the need to consider transparency more holistically and the need to include the information receiver’s perspective and the inter-relations amongst various properties and constituents of transparency as well. We then highlight areas of improvement informed by our analysis.


title: Re-Examining of Moffitt’s Theory of Delinquency through Agent Based Modeling

abstract: Moffitt’s theory of delinquency suggests that at-risk youths can be divided into two groups, the adolescence- limited group and the life-course-persistent group, predetermined at a young age, and social interactions between these two groups become important during the adolescent years. We built an agent-based model based on the microscopic interactions Moffitt described: (i) a maturity gap that dictates (ii) the cost and reward of antisocial behavior, and (iii) agents imitating the antisocial behaviors of others more successful than themselves, to find indeed the two groups emerging in our simulations. Moreover, through an intervention simulation where we moved selected agents from one social network to another, we also found that the social network plays an important role in shaping the life course outcome.


title: Impact of wireless communications technologies on elder people healthcare: Smart home in Australia

abstract: Over the last three decades, there has been a dramatic rise in ageing populations in most countries. Older people are remaining in nursing home care due to the fact that general services and medical support are provided. However, these costly environments often negatively affect older people due to high cost, limited staff and the social impacts they have. A way to overcome these challenges is to place the elderly instead in a smart home environment. The aim of this study is to describe the impact of wireless communications technologies on elderly people in a smart home. This has been compared with conditions in nursing homes. Using smart wireless sensors, wireless communications and ambient intelligent system, it is possible to create systems capable of measuring vital signs of the person in their own home. The findings indicate that wireless technology is the most suitable basis for a communications framework in a smart home to assist older people.


title: High-Resistivity Silicon Dielectric Ribbon Waveguide for Single-Mode Low-Loss Propagation at F/G-Bands

abstract: In this paper, dielectric ribbon waveguides (DRWs) are designed and characterized to work in the $F$ (90–140 GHz) and $G$ (140–220 GHz) frequency bands. High resistivity silicon (HR-silicon) due to its high transparency in these bands, and its low-cost fabrication process, were selected for the fabrication of these waveguides. The guided modes of DRW are studied using Marcatili and multi-line methods. A design procedure for optimal single-mode propagation was introduced. The wave attenuation inside the waveguide is discussed. Our experiments show that HR-silicon DRW has less than 0.087 dB/cm (0.01 ${\hbox{cm}}^{-1}$ ) of loss over the guiding bandwidth. The designed DRWs were realized using microfabrication techniques. The measurements are in very good agreement with theoretical predictions.


title: Passive Infrared (PIR)-Based Indoor Position Tracking for Smart Homes Using Accessibility Maps and A-Star Algorithm

abstract: Indoor occupants’ positions are significant for smart home service systems, which usually consist of robot service(s), appliance control and other intelligent applications. In this paper, an innovative localization method is proposed for tracking humans’ position in indoor environments based on passive infrared (PIR) sensors using an accessibility map and an A-star algorithm, aiming at providing intelligent services. First the accessibility map reflecting the visiting habits of the occupants is established through the integral training with indoor environments and other prior knowledge. Then the PIR sensors, which placement depends on the training results in the accessibility map, get the rough location information. For more precise positioning, the A-start algorithm is used to refine the localization, fused with the accessibility map and the PIR sensor data. Experiments were conducted in a mock apartment testbed. The ground truth data was obtained from an Opti-track system. The results demonstrate that the proposed method is able to track persons in a smart home environment and provide a solution for home robot localization.


title: An effective LED dimming approach

abstract: As light emitting diodes (LEDs) have become increasingly accepted as a light source in many applications, the demand for dimming LEDs has also increased. Typically LEDs are supplied with voltage source. Dimming is accomplished by adjusting the average current in the LEDs through pulse width modulation of a switch in series with the load. This paper proposes a technique that uses a current source as the base supply. The average current is adjusted with a switch in parallel to the load. This circuit is shown to be effective in increasing the dimming range of the LEDs.


title: The relationship between individual characteristics and experienced presence

abstract: People experience different levels of presence (e.g. Krijn et al., 2004; Walshe, Lewis, Kim, O'Sullivan, & Wiederhold, 2003), and different levels of cybersickness (Sharples, Cobb, Moody, & Wilson, 2008) even though they are immersed in the same virtual environment setting. In the current study, we raised the question how differences in individual characteristics might relate to differences in sensed presence for a virtual environment related to public speaking. The individual characteristics included in the experiment were related to visual abilities, personality traits, cognitive styles, and demographic factors. We recruited 88 participants, who were first immersed in a non-stereoscopic neutral environment, and then in a public speaking world: once with stereoscopic rendering and once without stereoscopic rendering, in a counter-balanced order. The results showed that immersive tendency and monocular visual ability were significantly correlated with presence and these correlations were consistent among the three virtual environments. Immersive tendencies and its subscale ''involvement'' were also found to be significantly correlated with cybersickness in all three worlds. Screening people on these variables may help to recognize the users who are more likely to benefit from virtual reality applications and may help to reduce the number of dropouts during virtual reality exposure therapy.


title: Role-based multicast in highly mobile but sparsely connected ad hoc networks

abstract: We present an approach to multicasting messages among highly mobile hosts in ad hoc networks. We suggest a new definition of a role-based multicast that suits the special needs of inter-vehicle communication: Rather than by explicit identification, a multicast group is defined implicitly by location, speed, driving direction and time. As an example, we study a road accident that is reported to nearby vehicles. We focus on sparse deployment of the system which is likely to occur soon after the system is introduced to the market. In this state, the resulting ad hoc network tends to be disconnected. We tailor the proposed algorithm to overcome this problem of network fragmentation. Simulations show us the quality of the proposed protocol by measuring how many vehicles inside a multicast area are informed in time under various conditions.


title: Future Internet: The Internet of Things Architecture, Possible Applications and Key Challenges

abstract: The Internet is continuously changing and evolving. The main communication form of present Internet is human-human. The Internet of Things (IoT) can be considered as the future evaluation of the Internet that realizes machine-to-machine (M2M) learning. Thus, IoT provides connectivity for everyone and everything. The IoT embeds some intelligence in Internet-connected objects to communicate, exchange information, take decisions, invoke actions and provide amazing services. This paper addresses the existing development trends, the generic architecture of IoT, its distinguishing features and possible future applications. This paper also forecast the key challenges associated with the development of IoT. The IoT is getting increasing popularity for academia, industry as well as government that has the potential to bring significant personal, professional and economic benefits.


title: Crowdsourcing for on-street smart parking

abstract: Crowdsourcing has inspired a variety of novel mobile applications. However, identifying common practices across different applications is still challenging. In this paper, we use smart parking as a case study to investigate features of crowdsourcing that may apply to other mobile applications. Based on this we derive principles for efficiently harnessing crowdsourcing. We draw three key guidelines: First, we suggest that that the organizer can play an important role in coordinating participants', a key factor to successful crowdsourcing experience. Second, we suggest that the expected participation rate is a key factor when designing the crowdsourcing system: a system with a lower expected participation rate will place a higher burden in individual participants (e.g., through more complex interfaces that aim to improve the accuracy of the collected data). Finally, we suggest that not only above certain threshold of contributors, a crowdsourcing-based system is resilient to freeriding but, surprisingly, that including freeriders (i.e., actors that do not participate in system effort but share its benefits in terms of coordination) benefits the entire system.


title: GIST: A Model for Design and Management of Content

abstract: Customer-centric Web-based systems, such as e-commerce Web sites, or sites that support customer relationship management (CRM) activities, are themselves information systems, but their design and maintenance need to follow vastly different approaches from the traditional systems lifecycle approach. Based on marketing frameworks that are applicable to the online world, and following design science principles, we develop a model to guide the design and the continuous management of such sites. The model makes extensive use of current technologies for tracking the customers and their behaviors, and combines elements of data mining and statistical analyses. A case study based on a financial services Web site is used to provide a preliminary validation and design evaluation of our approach. The case study showed considerable measured improvement in the effectiveness of the company's Web site. In addition, it also highlighted an important benefit of the our approach: the identification of previously unknown or unexpected segments of visitors. This finding can lead to promising new business opportunities.


title: Combining Lexical and Syntactic Features for Detecting Content-dense Texts in News

abstract: Content-dense news report important factual information about an event in direct, succinct manner. Information seeking applications such as information extraction, question answering and summarization normally assume all text they deal with is content-dense. Here we empirically test this assumption on news articles from the business, U.S. international relations, sports and science journalism domains. Our findings clearly indicate that about half of the news texts in our study are in fact not content-dense and motivate the development of a supervised content-density detector. We heuristically label a large training corpus for the task and train a two-layer classifying model based on lexical and unlexicalized syntactic features. On manually annotated data, we compare the performance of domain-specific classifiers, trained on data only from a given news domain and a general classifier in which data from all four domains is pooled together. Our annotation and prediction experiments demonstrate that the concept of content density varies depending on the domain and that naive annotators provide judgement biased toward the stereotypical domain label. Domain-specific classifiers are more accurate for domains in which content-dense texts are typically fewer. Domain independent classifiers reproduce better naive crowdsourced judgements. Classification prediction is high across all conditions, around 80%.


title: The relationship between passion and the psychological well-being of professional dancers.

abstract: The Dualistic Model of Passion defines passion as an intense desire or enthusiasm for a self-defining activity that people love, consider important, and devote significant amounts of time and energy to. The model proposes two distinct types of passion, harmonious (HP) and obsessive (OP). HP occurs when the activity is autonomously internalized into the individual's life and identity, while OP is a result of a controlled internalization of the activity. The aim of this study was to investigate the prevalence and type of passion professional dancers have for dance in relation to their psychological well-being, specifically eating attitudes, self-esteem, and perfectionism. Participants were 92 professional dancers, aged 19 to 35 years (M = 27.03, SD = 3.84), and mostly from the United States, the United Kingdom, and Canada. Results revealed that HP positively predicted self-esteem (SE), while OP positively predicted self-evaluative perfectionism (SEP), conscientious perfectionism (CP), and disordered eating attitudes (EAT-26). Additionally, SEP was found to mediate the relationship between OP and EAT-26, suggesting that OP may lead to SEP, which could in turn motivate disordered eating. Overall, the results of this study have supported and extended previous research suggesting that the two types of passion can have divergent effects on aspects of psychological well-being. Findings indicate that HP should be encouraged and OP discouraged among dancers, for example, via autonomy supportive behaviors of teachers.


title: Understanding the influence and service type of trusted third party on consumers' online trust: evidence from Australian B2C marketplace

abstract: In this study, the trusted third party (TTP) in Australia's B2C marketplace is studied and the factors influencing consumers' trust behaviour are examined from the perspective of consumers' online trust. Based on the literature review and combined with the development status and background of Australia's e-commerce, underpinned by the Theory of Planned Behaviour (TPB) and a conceptual trust model, this paper expatiates the specific factors and influence mechanism of TTP on consumers' trust behaviour. Also this paper explains two different functions of TTP to solve the online trust problem faced by consumers. Meanwhile, this paper summarizes five different types of services provided by TTPs during the establishment of the trust relationship. Finally, the present study selects 100 B2C enterprises by the simple random sampling method and makes a detailed analysis of their TTPs, to verify the services and functions of the proposed TTP in the trust model. This study is of some significance for comprehending the influence mechanism, functions and services of TTPs on consumers' trust behaviour in the realistic Australian B2C environment.


title: A study of wavelets for the solution of electromagnetic integral equations

abstract: The use of wavelet basis functions for the efficient solution of electromagnetic integral equations is studied. It has previously been demonstrated that the use of wavelets for expansion and testing functions produces a sparse moment-method matrix. Here, this effect is examined and analyzed in terms of the radiation/receiving characteristics of the wavelet basis functions. The limitations of wavelets as an efficient solution technique are discussed, and a comparison is made to other fast algorithms. >


title: An Overview of Virtual Private Network (VPN): IP VPN and Optical VPN

abstract: Recently, there has been rapid development and deployment of virtual private network (VPN) services. There are wide varieties of IP-based VPNs and optical VPNs (OVPNs) proposed in the literature and readers could easily get confused with so many different types of VPNs. The purpose of this paper is to present a comprehensive overview of the VPN and discuss the main issues associated with the design of IP VPN and OVPN. We first present a classification of the VPNs including CE-based, network based, customers provisioned, provider provisioned, connection oriented, connectionless oriented, port based, connection based, layer 1 VPN, layer 2 VPN, and layer 3 VPN, and describe different VPN protocols such as IPSec, GRE and MPLS. We then review the recent work on OVPN by different standard bodies, and outline the key requirements for OVPN service providers and customers. Finally, we describe several OVPN architectures appeared in the literature, highlight the future work in OVPN.


title: Technical Market Anomalies: Leveraged ETF Trading with Daily andIntraday Temporal Functionalities

abstract: The main target of this paper is to discuss a short-term trading strategy for ETF instruments (as technical market inefficiencies; "momentum effects") and for this purpose, temporal warning dynamics and triggering trading functionalities (TTF) for the daily time-domain (short-term trading) are introduced. The proposed trading strategy is not a fully documented trading system, because it is derived, as well as it has been back-tested on USA Markets sample data (2000-2016) with an initial formal definition and documentation. According to the back-tested sample data, leveraged ETF short-term trading, with a strategy based on these TTF functionalities, offer great profit opportunities. The current paper contributes to corporate finance literature by examining, analyzing and defining these TTF functionalities. For this purpose, four categories of shareholders are regarded: The long-term investors, the short-term swing traders, the short-term momentary speculators, and the intraday speculators. Paper concludes that, in daily and intraday leveraged ETF trading, the short-term swing traders -if they apply the proposed TTF in their trading plans and strategies- are benefit at the expense of momentary and intraday speculators, while the long-term investors in leveraged ETFs are always the big losers.


title: Clinical Concept Extraction with Contextual Word Embedding

abstract: Automatic extraction of clinical concepts is an essential step for turning the unstructured data within a clinical note into structured and actionable information. In this work, we propose a clinical concept extraction model for automatic annotation of clinical problems, treatments, and tests in clinical notes utilizing domain-specific contextual word embedding. A contextual word embedding model is first trained on a corpus with a mixture of clinical reports and relevant Wikipedia pages in the clinical domain. Next, a bidirectional LSTM-CRF model is trained for clinical concept extraction using the contextual word embedding model. We tested our proposed model on the I2B2 2010 challenge dataset. Our proposed model achieved the best performance among reported baseline models and outperformed the state-of-the-art models by 3.4% in terms of F1-score.


title: Child Education Through Animation: An Experimental Study

abstract: Teachers have tried to teach their students by introducing text books along with verbal instructions in traditional education system. However, teaching and learning methods could be changed for developing Information and Communication Technology. It's time to adapt students with interactive learning system so that they can improve their learning, catching, and memorizing capabilities. It is indispensable to create high quality and realistic leaning environment for students. Visual learning can be easier to understand and deal with their learning. We developed visual learning materials in the form of video for students of primary level using different multimedia application tools. The objective of this paper is to examine the impact of students abilities to acquire new knowledge or skills through visual learning materials and blended leaning that is integration of visual learning materials with teachers instructions. We visited a primary school in Dhaka city for this study and conducted teaching with three different groups of students, (i) teacher taught students by traditional system on same materials and marked level of students ability to adapt by a set of questions, (ii) another group was taught with only visual learning material and assessment was done with 15 questionnaires, (iii) the third group was taught with the video of solar system combined with teachers instructions and assessed with the same questionnaires. This integration of visual materials with verbal instructions is a blended approach of learning. The interactive blended approach greatly promoted students ability of acquisition of knowledge and skills. Students response and perception were very positive towards the blended technique than the other two methods. This interactive blending leaning system may be an appropriate method especially for school children.


title: Overcurrent relay modeling using artificial neural network

abstract: Reducing time operation of protection system is desirable, for the life time of equipment and voltage quality will be much improved. During starting up electrical devices such transformers or electrical motors etc., there must be inrush current. Alternatively, the inrush current and the damaging curves of those are not in the same curve which means that to protect those devices, the minimum time setting of relay protection must be set at least higher than the time delay of inrush current and it can be used the conventional curves of IEC standard. However, it will be much better for the devices and also the power system if nonconventional curve is applied, for it can be regulated to any circumstance of the loads' characteristic curves. Plus, power system protection will be more flexible if this model can be applied in adaptive relay coordination. In this study, Bayesian Regularization Backpropagation Neural Network (BRBPNN) is used in order to model nonconventional curves. BRBPNN is a robust method and is used to model the characteristic curves of overcurrent relay (OCR). It was developed for trend analysis of the input data as load pickup current and tripping time as the target data. It is obvious that the errors between actual data and testing result of simulation and real application of prototype are highly acceptable.


title: ReferItGame: Referring to Objects in Photographs of Natural Scenes

abstract: In this paper we introduce a new game to crowd-source natural language referring expressions. By designing a two player game, we can both collect and verify referring expressions directly within the game. To date, the game has produced a dataset containing 130,525 expressions, referring to 96,654 distinct objects, in 19,894 photographs of natural scenes. This dataset is larger and more varied than previous REG datasets and allows us to study referring expressions in real-world scenes. We provide an in depth analysis of the resulting dataset. Based on our findings, we design a new optimization based model for generating referring expressions and perform experimental evaluations on 3 test sets.


Audio Event Classification using Deep Learning in an End-to-End Approach


title: Fast Policy Learning through Imitation and Reinforcement

abstract: Imitation learning (IL) consists of a set of tools that leverage expert demonstrations to quickly learn policies. However, if the expert is suboptimal, IL can yield policies with inferior performance compared to reinforcement learning (RL). In this paper, we aim to provide an algorithm that combines the best aspects of RL and IL. We accomplish this by formulating several popular RL and IL algorithms in a common mirror descent framework, showing that these algorithms can be viewed as a variation on a single approach. We then propose LOKI, a strategy for policy learning that first performs a small but random number of IL iterations before switching to a policy gradient RL method. We show that if the switching time is properly randomized, LOKI can learn to outperform a suboptimal expert and converge faster than running policy gradient from scratch. Finally, we evaluate the performance of LOKI experimentally in several simulated environments.