title: American sign language recognition in game development for deaf children

abstract: CopyCat is an American Sign Language (ASL) game, which uses gesture recognition technology to help young deaf children practice ASL skills. We describe a brief history of the game, an overview of recent user studies, and the results of recent work on the problem of continuous, user-independent sign language recognition in classroom settings. Our database of signing samples was collected from user studies of deaf children playing aWizard of Oz version of the game at the Atlanta Area School for the Deaf (AASD). Our data set is characterized by disfluencies inherent in continuous signing, varied user characteristics including clothing and skin tones, and illumination changes in the classroom. The dataset consisted of 541 phrase samples and 1,959 individual sign samples of five children signing game phrases from a 22 word vocabulary. Our recognition approach uses color histogram adaptation for robust hand segmentation and tracking. The children wear small colored gloves with wireless accelerometers mounted on the back of their wrists. The hand shape information is combined with accelerometer data and used to train hidden Markov models for recognition. We evaluated our approach by using leave-one-out validation; this technique iterates through each child, training on data from four children and testing on the remaining child's data. We achieved average word accuracies per child ranging from 91.75% to 73.73% for the user-independent models.


title: A Novel Contra-Rotating Power Split Transmission System for Wind Power Generation and Its Dual MPPT Control Strategy

abstract: This paper proposes a new brushless contra-rotating power split transmission (CR-PST) system for the direct-drive wind power generation. The core element of this system is a doubly fed dual-rotor flux-modulation permanent magnet machine, which has two contra-rotating rotors connected with two wind blades to absorb the wind energy, and two sets of stator windings to realize torque split between the two shafts. This proposed CR-PST system has compact structure and bidirectional flux-modulation design, as well as increased wind power conversion efficiency. Compared with the conventional wind turbine system, which can only work with single maximum power point tracking (MPPT) control strategy, the proposed CR-PST system can collect increased wind energy with dual MPPT method for the given wind speed. Time-stepping finite-element method is used to analyze the steady and dynamic performance of the proposed system and the control strategy is verified by experiments and simulation results.


title: On the Foundations of Physical Unclonable Functions

abstract: We investigate the foundations of Physical Unclonable Functions from several perspectives. Firstly, we discuss formal and conceptual issues in the various current definitions of PUFs. As we argue, they have the effect that many PUF candidates formally meet no existing definition. Next, we present alternative definitions and a new formalism. It avoids asymptotic concepts like polynomial time, but is based on concrete time bounds and on the concept of a security experiment. The formalism splits the notion of a PUF into two new notions, Strong t-PUFs and Obfuscating t-PUFs. Then, we provide a comparative analysis between the existing definitions and our new notions, by classifying existing PUF implementations with respect to them. In this process, we use several new and unpublished machine learning results. The outcome of this comparative classification is that our definitions seem to match the current PUF landscape well, perhaps better than previous definitions. Finally, we analyze the security and practicality features of Strong and Obfuscating t-PUFs in concrete applications, obtaining further justification for the split into two notions.


title: Stock Selection via Nonlinear Multi-Factor Models

abstract: This paper discusses the use of multilayer feed forward neural networks for predicting a stock's excess return based on its exposure to various technical and fundamental factors. To demonstrate the effectiveness of the approach a hedged portfolio which consists of equally capitalized long and short positions is constructed and its historical returns are benchmarked against T-bill returns and the S&P500 index.


MOLECULAR CHARACTERIZATION OF INFECTIOUS BURSAL DISEASE VIRUS FROM COMMERCIAL POULTRY IN PAKISTAN


title: Dual Wideband Printed Monopole Antenna for WLAN/WiMAX Applications

abstract: A novel printed monopole antenna with dual widebands is presented for simultaneously satisfying wireless local area network (WLAN) and worldwide interoperability for microwave access (WiMAX) applications. The antenna structure consists of a rectangular monopole with a microstrip feedline for excitation and a trapezoid conductor-backed plane for band broadening. The measured 10 dB bandwidth for return loss is from 2.01 to 4.27 GHz and 5.06 to 6.79 GHz, covering all the 2.4/5.2/5.8 GHz WLAN bands and 2.5/3.5/5.5 GHz WiMAX bands


title: 3D Convolutional Networks for Session-based Recommendation with Content Features

abstract: In many real-life recommendation settings, user profiles and past activities are not available. The recommender system should make predictions based on session data, e.g. session clicks and descriptions of clicked items. Conventional recommendation approaches, which rely on past user-item interaction data, cannot deliver accurate results in these situations. In this paper, we describe a method that combines session clicks and content features such as item descriptions and item categories to generate recommendations. To model these data, which are usually of different types and nature, we use 3-dimensional convolutional neural networks with character-level encoding of all input data. While 3D architectures provide a natural way to capture spatio-temporal patterns, character-level networks allow modeling different data types using their raw textual representation, thus reducing feature engineering effort. We applied the proposed method to predict add-to-cart events in e-commerce websites, which is more difficult then predicting next clicks. On two real datasets, our method outperformed several baselines and a state-of-the-art method based on recurrent neural networks.


title: Toward better research on stress and coping.

abstract: In commenting in considerable detail on the four main articles in the special section on stress and coping, the author comes to two main conclusions: First, there is an increasing amount o f high quality research on stress and coping that suggests' the f ield is finally maturing, and this research may help reduce the long-standing gap between research and clinical practice. Second, this research is increasingly using badly needed research designs that have not hitherto been sufficiently emphasized, such as longitudinal or prospect ive designs, f ocused on observations that are day-to-day, microanalytic, and in-depth, and that are compatible with a holistic outlook. The author also addresses the role o f positive emotion in coping, the concept o f defense as it is dealt with nowadays, and the task o f evaluating coping efficacy.


Single Camera Calibration using Partially Visible Calibration Objects Based on Random Dots Marker Tracking Algorithm


title: Learning from feedback: Spacing and the delay-retention effect.

abstract: Most modern research on the effects of feedback during learning has assumed that feedback is an error correction mechanism. Recent studies of feedback-timing effects have suggested that feedback might also strengthen initially correct responses. In an experiment involving cued recall of trivia facts, we directly tested several theories of feedback-timing effects and also examined the effects of restudy and retest trials following immediate and delayed feedback. Results were not consistent with theories assuming that the only function of feedback is to correct initial errors but instead supported a theoretical account assuming that delaying feedback strengthens initially correct responses due to the spacing of encoding opportunities: Delaying feedback increased the probability of correct response perseveration on the final retention test but had minimal effects on error correction or error perseveration probabilities. In a 2nd experiment, the effects of varying the lags between study, test, and feedback trials during learning provided further support for the spacing hypothesis.


title: Brain tumor segmentation: A performance analysis using K-Means, Fuzzy C-Means and Region growing algorithm

abstract: Medical imaging is a technique that is extensively used to create images of human body for medical and research purposes. Magnetic Resonance Imaging (MRI) is a powerful visualization tool that permits to acquire images of internal anatomy of human body in a secure and non-invasive manner. Automatic brain tumor detection from MRI images has become one of the major areas of medical research. The important task in the diagnosis of brain tumor is to determine the exact location, orientation and area of the abnormal tissues. This paper discuss the performance analysis of image segmentation techniques, viz., K-Means Clustering, Fuzzy C-Means Clustering and Region Growing for detection of brain tumor from sample MRI images of brain. The performance evaluation of the above mentioned techniques is done on the basis of error percentage as compared to ground truth. The real time database is taken from Rajiv Gandhi Cancer Institute & Research Centre, Delhi, India (RGCI&RC).


title: Hippocampal Contributions to Control: The Third Way

abstract: Recent experimental studies have focused on the specialization of different neural structures for different types of instrumental behavior. Recent theoretical work has provided normative accounts for why there should be more than one control system, and how the output of different controllers can be integrated. Two particlar controllers have been identified, one associated with a forward model and the prefrontal cortex and a second associated with computationally simpler, habitual, actor-critic methods and part of the striatum. We argue here for the normative appropriateness of an additional, but so far marginalized control system, associated with episodic memory, and involving the hippocampus and medial temporal cortices. We analyze in depth a class of simple environments to show that episodic control should be useful in a range of cases characterized by complexity and inferential noise, and most particularly at the very early stages of learning, long before habitization has set in. We interpret data on the transfer of control from the hippocampus to the striatum in the light of this hypothesis.


title: Real-Time Spherical Panorama Image Stitching Using OpenCL

abstract: This paper presents a webcam-based spherical coordinate conversion system using OpenCL massive parallel computing for panorama video image stitching. With multi-core architecture and its high-bandwidth data transmission rate of memory accesses, modern programmable GPU makes it possible to process multiple video images in parallel for real-time interaction. To get a panorama view of 360 degrees, we use OpenCL to stitch multiple webcam video images into a panorama image and texture mapped it to a spherical object to compose a virtual reality immersive environment. The experimental results show that when we use NVIDIA 9600GT to process eight 640×480 images, OpenCL can achieve ninety times speedups.


title: Fair Energy-Efficient Network Design for Multihop Communications

abstract: We consider the energy-efficient network resource allocation that minimizes a cost function of average user powers for multi-hop wireless networks. A class of fair cost functions is derived to balance the tradeoff between efficiency and fairness in energy-efficient designs. Based on such cost functions, optimal routing, scheduling and power control schemes are developed. Relying on stochastic optimization tools, we further develop stochastic network resource allocation schemes which are capable of dynamically learning the traffic and channel statistics, and converging to the optimal strategy on-the-fly.


title: Self-inflicted burns: a case series.

abstract: Self-inflicted burns are regularly admitted to burns units worldwide. Most of these patients are referred to psychiatric services and are successfully treated however some return to hospital with recurrent self-inflicted burns. The aim of this study is to explore the characteristics of the recurrent self-inflicted burn patients admitted to the Royal North Shore Hospital during 2004-2011. Burn patients were drawn from a computerized database and recurrent self-inflicted burn patients were identified. Of the total of 1442 burn patients, 40 (2.8%) were identified as self-inflicted burns. Of these patients, 5 (0.4%) were identified to have sustained previous self-inflicted burns and were interviewed by a psychiatrist. Each patient had been diagnosed with a borderline personality disorder and had suffered other forms of deliberate self-harm. Self-inflicted burns were utilized to relieve or help regulate psychological distress, rather than to commit suicide. Most patients had a history of emotional neglect, physical and/or sexual abuse during their early life experience. Following discharge from hospital, the patients described varying levels of psychiatric follow-up, from a post-discharge review at a local community mental health centre to twice-weekly psychotherapy. The patients who engaged in regular psychotherapy described feeling more in control of their emotions and reported having a longer period of abstinence from self-inflicted burn. Although these patients represent a small proportion of all burns, the repeat nature of their injuries led to a significant use of clinical resources. A coordinated and consistent treatment pathway involving surgical and psychiatric services for recurrent self-inflicted burns may assist in the management of these challenging patients.


title: A Graph-Based Framework for Multiparadigmatic Visual Access to Databases

abstract: Describes an approach for multiparadigmatic visual access integration of different interaction paradigms. The user is provided with an adaptive interface augmented by a user model, supporting different visual representations of both data and queries. The visual representations are characterized on the basis of the chosen visual formalisms, namely forms, diagrams and icons. To access different databases, a unified data model called the "graph model" is used as a common underlying formalism to which databases, expressed in the most popular data models, can be mapped. Graph model databases are queried through the adaptive interface. The semantics of the query operations is formally defined in terms of graphical primitives. Such a formal approach permits us to define the concept of an "atomic query", which is the minimal portion of a query that can be transferred from one interaction paradigm to another and processed by the system. Since certain interaction modalities and visual representations are more suitable for certain user classes, the system can suggest to the user the most appropriate interaction modality as well as the visual representation, according to the user model. Some results on user model construction are presented.


title: Cross-Domain Collaborative Filtering over Time

abstract: Collaborative filtering (CF) techniques recommend items to users based on their historical ratings. In real-world scenarios, user interests may drift over time since they are affected by moods, contexts, and pop culture trends. This leads to the fact that a user's historical ratings comprise many aspects of user interests spanning a long time period. However, at a certain time slice, one user's interest may only focus on one or a couple of aspects. Thus, CF techniques based on the entire historical ratings may recommend inappropriate items. In this paper, we consider modeling user-interest drift over time based on the assumption that each user has multiple counterparts over temporal domains and successive counterparts are closely related. We adopt the cross-domain CF framework to share the static group-level rating matrix across temporal domains, and let user-interest distribution over item groups drift slightly between successive temporal domains. The derived method is based on a Bayesian latent factor model which can be inferred using Gibbs sampling. Our experimental results show that our method can achieve state-of-the-art recommendation performance as well as explicitly track and visualize user-interest drift over time.


title: Tracing dynamic features in python programs

abstract: Recent years have seen a number of proposals for adding (retrofitting) static typing to dynamic programming languages, a natural consequence of their growing popularity for non-toy applications across a multitude of domains. These proposals often make assumptions about how programmers write code, and in many cases restrict the way the languages can be used. In the context of Python, this paper describes early results from trace-based collection of run-time data about the use of built-in language features which are inherently hard to type, such as dynamic code generation. The end goal of this work is to facilitate static validation tooling for Python, in particular retrofitting of type systems.


title: Aligning Event Logs and Process Models for Multi-Perspective Conformance Checking: An Approach Based on Integer Linear Programming

abstract: Modern organizations have invested in collections of descriptive and/or normative process models, but these rarely describe the actual processes adequately. Therefore, a variety of techniques for conformance checking have been proposed to pinpoint discrepancies between modeled and observed behavior. However, these techniques typically focus on the control-flow and abstract from data, resources and time. This paper describes an approach that aligns event log and model while taking all perspectives into account (i.e., also data, time and resources). This way it is possible to quantify conformance and analyze differences between model and reality. The approach was implemented using ProM and has been evaluated using both synthetic event logs and a real-life case study.


title: Benchmarking coding standards for digital holography represented on the object plane

abstract: Digital holography is a growing field that owes its success to the provided three-dimensional imaging representation. This is achieved by encoding the wave field transmitted or scattered by an object in the form of an interference pattern with a reference beam. While in conventional imaging systems it is usually impossible to recover the correct focused image from a defocused one, with digital holography the image can be numerically retrieved at any distance from the hologram. Digital holography also allows the reconstruction of multiple objects at different depths. The complex object field at the hologram plane can be separated on real and imaginary, or amplitude and phase components for further compression. It could be inferred that more inter-component redundancies exist in real and imaginary information than in the amplitude and phase information. Also, several compression schemes, like lossless compression, lossy compression, based on subsampling, quantization, and transformation, mainly using wavelets were considered. The benchmark of the main available image coding standard solutions like JPEG, JPEG 2000, and the intra coding modes available on MPEG-2, H264/AVC and HEVC video codecs were also analyzed for digital holographic data compression on the hologram plane. In the current work, the benchmark of the main available image coding standard solutions JPEG, JPEG-XT, JPEG 2000 and the intra mode of HEVC, are performed for digital holographic data represented on the object plane, instead of the hologram plane. This study considers Real-Imaginary and Amplitude-Phase representations. As expected Real, Imaginary and Amplitude information present very similar compression performance and are coded very efficiently with the different standards. However, the phase information requires much higher bitrates (3/4 bpp more) to reach similar quality levels. Moreover, the Amplitude information results in slightly larger bitrates for the same quality level than real or imaginary information. Comparing the different standards, the HEVC intra main coding profile is a very efficient model and outperforms the other standards. Furthermore, JPEG 2000 results in very similar compression performance. A comparison with studies where coding was performed on the hologram plane will reveal the advantages of coding on the object plane. Hence, becomes evident that future representation standards should consider the representation of digital holograms on the object plane instead of the hologram plane.


title: Pricing policy and computational resource provisioning for delay-aware mobile edge computing

abstract: Mobile edge computing is a novel technique to offer cloud-based computation offloading services to mobile users with short delay. However, the Cloud Service Providers (CSPs) of the edge cloud are generally different from the CSPs of remote Internet cloud. Considering the competition between the heterogeneous clouds, we study the optimal provisioning of the computational resource in the edge cloud. We firstly analyze the Nash equilibrium prices of the cloud market when the amount of edge computational resource is given. Based on the pricing policy, we further design an algorithm to optimize the edge computational resource capacity so that the profit of the edge cloud is maximized. We also derive the lower and upper bound of the optimal edge computational resource. The numerical results indicate that the profit of the edge cloud is greatly influenced by the price of the remote cloud when users care more about the price than the delay.


title: Systematic review of pharmacological therapies for the management of ischaemic pain in patients with non-reconstructable critical limb ischaemia

abstract: Background Critical limb ischaemia (CLI) is a severe manifestation of peripheral arterial disease, characterised by chronic ischaemic rest pain, ulcers or gangrene. Management of ischaemic pain is challenging in patients with no options for revascularisation and optimal pharmacological therapies have not been established. Objectives To identify and evaluate the effectiveness of pharmacological therapies to treat ischaemic pain secondary to non-reconstructable CLI. Methods This systematic review was reported in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline. Comprehensive searches of three electronic databases, a PubMed-related articles link search, grey literature search and hand-searches of the bibliographies of relevant papers and textbooks were performed. Studies recruiting adult patients with CLI of any aetiology were eligible for inclusion. Surgical and revascularisation procedures, and all invasive interventions were excluded. Results Of 792 studies, six met full inclusion criteria. These studies researched the use of intravenous lidocaine, intravenous ketamine, oral gabapentin and the combination of transdermal buprenorphine and epidural morphine/ropivacaine infusion. All studies showed an improvement in severity of ischaemic pain in CLI but with varying side effect profiles and quality. The extracted studies showed substantial heterogeneity and therefore a meta-analysis was not performed. Conclusion The pharmacological management of pain secondary to non-reconstructable CLI is a challenging review topic. No recommendations of pharmacological agents can be made following this review but a number of novel approaches to manage pain in this cohort have shown positive results and require further investigation.


title: Transferring GANs: generating images from limited data

abstract: Transferring knowledge of pre-trained networks to new domains by means of fine-tuning is a widely used practice for applications based on discriminative models. To the best of our knowledge this practice has not been studied within the context of generative deep networks. Therefore, we study domain adaptation applied to image generation with generative adversarial networks. We evaluate several aspects of domain adaptation, including the impact of target domain size, the relative distance between source and target domain, and the initialization of conditional GANs. Our results show that using knowledge from pre-trained networks can shorten the convergence time and can significantly improve the quality of the generated images, especially when target data is limited. We show that these conclusions can also be drawn for conditional GANs even when the pre-trained model was trained without conditioning. Our results also suggest that density is more important than diversity and a dataset with one or few densely sampled classes is a better source model than more diverse datasets such as ImageNet or Places.


title: Towards Quantifying Vertex Similarity in Networks

abstract: Vertex similarity is a major problem in network science with a wide range of applications. In this work we provide novel perspectives on finding (dis)similar vertices within a network and across two networks with the same number of vertices (graph matching). With respect to the former problem, we propose to optimize a geometric objective which allows us to express each vertex uniquely as a convex combination of a few extreme types of vertices. Our method has the important advantage of supporting efficiently several types of queries such as "which other vertices are most similar to this vertex?" by the use of the appropriate data structures and of mining interesting patterns in the network. With respect to the latter problem (graph matching), we propose the generalized condition number --a quantity widely used in numerical analysis-- $\kappa(L_G,L_H)$ of the Laplacian matrix representations of $G,H$ as a measure of graph similarity, where $G,H$ are the graphs of interest. We show that this objective has a solid theoretical basis and propose a deterministic and a randomized graph alignment algorithm. We evaluate our algorithms on both synthetic and real data. We observe that our proposed methods achieve high-quality results and provide us with significant insights into the network structure.


title: Argumentative texts and clause types

abstract: Argumentative texts have been thoroughly analyzed for their argumentative structure, and recent efforts aim at their automatic classification. This work investigates linguistic properties of argumentative texts and text passages in terms of their semantic clause types. We annotate argumentative texts with Situation Entity (SE) classes, which combine notions from lexical aspect (states, events) with genericity and habituality of clauses. We analyse the correlation of SE classes with argumentative text genres, components of argument structures, and some functions of those components. Our analysis reveals interesting relations between the distribution of SE types and the argumentative text genre, compared to other genres like fiction or report. We also see tendencies in the correlations between argument components (such as premises and conclusions) and SE types, as well as between argumentative functions (such as support and rebuttal) and SE types. The observed tendencies can be deployed for automatic recognition and fine-grained classification of argumentative text passages.


title: Using Spacing to Enhance Diverse Forms of Learning: Review of Recent Research and Implications for Instruction

abstract: Every day, students and instructors are faced with the decision of when to study information. The timing of study, and how it affects memory retention, has been explored for many years in research on human learning. This research has shown that performance on final tests of learning is improved if multiple study sessions are separated—i.e., “spaced” apart—in time rather than massed in immediate succession. In this article, we review research findings of the types of learning that benefit from spaced study, demonstrations of these benefits in educational settings, and recent research on the time intervals during which spaced study should occur in order to maximize memory retention. We conclude with a list of recommendations on how spacing might be incorporated into everyday instruction.


title: Geovisualization and correlation analysis between geotagged Twitter and JMA rainfall data: Case of heavy rain disaster in Hiroshima

abstract: With the development of wireless communication technology along with emergence of location-enabled mobile devices and cyber-physical social sensor networks, nowadays we can collect and store a large amount of geospatial data such as weather phenomena, human mobility, and social networking activities along with time of occurrence. Information or knowledge extraction from this so-called Big Data is a challenge to many geospatial information users due to the nature of data complexity and large data volume. In this article, we discuss handling of Big Data with GIS by utilizing 10-minute intervals of Japan Metrological Agency (JMA) rainfall data synchronized with Twitter messages by specific keywords like “rain” or/and “landslide” to analyse the relationship between environmental phenomena and social responses in heavy rain conditions in the Hiroshima region. The results were analysed and visualized through a geovisualization technique to evaluate the possible use of inputs from social media websites to the government decision-making process, especially for disaster and emergency preparedness in the near future.


title: Business Model Evaluation: Quantifying Walmart's Sources of Advantage

abstract: We develop an analytical framework on the basis of the economics of business performance to provide quantitative insight into the link between a firm's business model choices and their profit consequences. The method is applied to Walmart by building a qualitative representation of its business model and mapping that representation on an analytical model that quantifies the company's sources of advantage over time. The analysis suggests that the effectiveness of a particular business model depends not only on its design (its levers and how they relate to one another) but, most importantly, on its implementation (how the levers are pulled). Copyright © 2014 Strategic Management Society.


Fingerprint Identification in Biometric SecuritySystems


title: Chiron: A Robust Recommendation System

abstract: Recommendation systems have been widely used by commercial service providers for giving suggestions to users based on their previous behaviors. While a large portion of users faithfully express their opinions, some malicious users add noisy ratings in order to change the overall ratings of a specific group of items. The presence of noise can add bias to recommendations, leading to instabilities in estimation and prediction. Although the robustness of different recommendation systems has been extensively studied, designing a robust recommendation system remains a significant challenge as detecting malicious users is computationally expensive. In this work, we propose Chiron, a fast and robust hybrid recommendation system that is not only faster than most state-of-the-art methods, but is also resistant to manipulation by malicious users.


title: Load frequency control for multiple-area power systems

abstract: In an interconnected power system, as a power load demand varies randomly, both area frequency and tie-line power interchange also vary. The objectives of load frequency control (LFC) are to minimize the transient deviations in theses variables (area frequency and tie-line power interchange) and to ensure their steady state errors to be zeros. When dealing with the LFC problem of power systems, unexpected external disturbances, parameter uncertainties and the model uncertainties of the power system pose big challenges for controller design. Active disturbance rejection control (ADRC), as an increasingly popular practical control technique, has the advantages of requiring little information from the plant model and being robust against disturbances and uncertainties. This thesis presents a solution to the LFC problem based on ADRC. The controller is constructed for a three-area power system with different turbine units including non-reheat, reheat and hydraulic units in different areas. The dynamic model of the power system and the controller design based on the model are elaborated in the thesis. Simulation results and frequency-domain analyses proved that ADRC controller is attractive to the LFC problem in its stability and robustness.


title: A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution

abstract: We address the problem of image upscaling in the form of single image super-resolution based on a dictionary of low- and high-resolution exemplars. Two recently proposed methods, Anchored Neighborhood Regression (ANR) and Simple Functions (SF), provide state-of-the-art quality performance. Moreover, ANR is among the fastest known super-resolution methods. ANR learns sparse dictionaries and regressors anchored to the dictionary atoms. SF relies on clusters and corresponding learned functions. We propose A+, an improved variant of ANR, which combines the best qualities of ANR and SF. A+ builds on the features and anchored regressors from ANR but instead of learning the regressors on the dictionary it uses the full training material, similar to SF. We validate our method on standard images and compare with state-of-the-art methods. We obtain improved quality (i.e. 0.2–0.7 dB PSNR better than ANR) and excellent time complexity, rendering A+ the most efficient dictionary-based super-resolution method to date.


title: Transductive Rademacher Complexity and its Applications

abstract: We develop a technique for deriving data-dependent error bounds for transductive learning algorithms based on transductive Rademacher complexity. Our technique is based on a novel general error bound for transduction in terms of transductive Rademacher complexity, together with a novel bounding technique for Rademacher averages for particular algorithms, in terms of their "unlabeled-labeled" representation. This technique is relevant to many advanced graph-based transductive algorithms and we demonstrate its effectiveness by deriving error bounds to three well known algorithms. Finally, we present a new PAC-Bayesian bound for mixtures of transductive algorithms based on our Rademacher bounds.


title: Ontop at Work

abstract: We describe the architecture of the OBDA systemOntop and analyse its performance in a series of experiments. We demonstrate that, for standard ontologies, queries and data stored in relational databases, Ontop is fast, efficient and produces SQL rewritings of high quality.


title: Realistic and Fast Cloud Rendering

abstract: Abstract Clouds are an important aspect of rendering outdoor scenes. This paper describes a cloud system that extends texture splatting on particles to model a dozen cloud types (e.g., strat us, cumulus congest us, cumulonimbus), an improvement over earlier systems that modeled only one type of cumulus. We also achieve fast realtime rendering, even for scenes of dense overcast coverage, which was a limitation for previous systems. We present a new shading model that uses artist-driven controls rather than a programmatic approach to approximate lighting. This is suitable when fine-grained control over the look-and-feel is necessary, and artistic resources are available. We also introduce a way to simulate cloud form ation and dissipation using texture splatted particles.


title: Hardware Trojan: Threats and emerging solutions

abstract: Malicious modification of hardware during design or fabrication has emerged as a major security concern. Such tampering (also referred to as Hardware Trojan) causes an integrated circuit (IC) to have altered functional behavior, potentially with disastrous consequences in safety-critical applications. Conventional design-time verification and post-manufacturing testing cannot be readily extended to detect hardware Trojans due to their stealthy nature, inordinately large number of possible instances and large variety in structure and operating mode. In this paper, we analyze the threat posed by hardware Trojans and the methods of deterring them. We present a Trojan taxonomy, models of Trojan operations and a review of the state-of-the-art Trojan prevention and detection techniques. Next, we discuss the major challenges associated with this security concern and future research needs to address them.


title: Quasi-3-D Finite-Element Method for Simulating Cylindrical Induction-Heating Devices

abstract: A cylindrically symmetric problem can be simulated by a two-dimensional (2-D) axisymmetric finite-element model by exploiting the symmetry and thus reducing the original 3-D geometry to a 2-D cross section. This is, however, no longer possible when boundary conditions or excitations that violate the geometrical symmetry are applied. To avoid a computationally expensive 3-D simulation, a quasi-3-D finite-element method is developed. The method uses tensor-product shape functions combining standard finite elements in the cross-sectional plane with harmonic functions to account for the azimuthal variation of the field. The method and its convergence are validated against an analytical model. The method is particular useful for cylindrical induction-heating devices with asymmetric cooling effects, as is demonstrated by the example.


title: Adversarial Training For Sketch Retrieval

abstract: Generative Adversarial Networks (GAN) are able to learn excellent representations for unlabelled data which can be applied to image generation and scene classification. Representations learned by GANs have not yet been applied to retrieval. In this paper, we show that the representations learned by GANs can indeed be used for retrieval. We consider heritage documents that contain unlabelled Merchant Marks, sketch-like symbols that are similar to hieroglyphs. We introduce a novel GAN architecture with design features that make it suitable for sketch retrieval. The performance of this sketch-GAN is compared to a modified version of the original GAN architecture with respect to simple invariance properties. Experiments suggest that sketch-GANs learn representations that are suitable for retrieval and which also have increased stability to rotation, scale and translation compared to the standard GAN architecture.


title: Representing Topics Using Images

abstract: Topics generated automatically, e.g. using LDA, are now widely used in Computational Linguistics. Topics are normally represented as a set of keywords, often the n terms in a topic with the highest marginal probabilities. We introduce an alternative approach in which topics are represented using images. Candidate images for each topic are retrieved from the web by querying a search engine using the top n terms. The most suitable image is selected from this set using a graph-based algorithm which makes use of textual information from the metadata associated with each image and features extracted from the images themselves. We show that the proposed approach significantly outperforms several baselines and can provide images that are useful to represent a topic.


title: Gamification - Design of IT-Based Enhancing Services for Motivational Support and Behavioral Change

abstract: NikeFuel is the fuel of the Nike+ community. A fuel that has made two million users burn more than 68 bn. calories and that proliferates with each kilometer. The athletic performance of Nike+ users is measured via sensors in Nike sports shoes and an Apple iPod or iPhone, documented on the Nike+platform and converted into NikeFuel. In doing so, users may visualize their progress, compare their performance with others, and obtain different status levels that reflect their athletic potential. This approach derives from the domain of game design and is called gamification enriching products, services, and information systems with game-design elements in order to positively influence motivation, productivity, and behavior of users. In the consumer sector, various successful examples for gamication are gaining recognition. Gamication is a persuasive technology that attempts to influence user behavior by activating individual motives via game-design elements. As a consequence, this approach does not deal with designing games that can generally be defined as solving rule-based artificial conflicts or simulations. Thus, gamication needs to be contrasted to related concepts such as serious games and games with a purpose.


title: Knowledge Graph Embedding with Diversity of Structures

abstract: In recent years, different web knowledge graphs, both free and commercial, have been created. Knowledge graphs use relations between entities to describe facts in the world. We engage in embedding a large scale knowledge graph into a continuous vector space. TransE, TransH, TransR and TransD are promising methods proposed in recent years and achieved state-of-the-art predictive performance. In this paper, we discuss that graph structures should be considered in embedding and propose to embed substructures called "one-relation-circle" (ORC) to further improve the performance of the above methods as they are unable to encode ORC substructures. Some complex models are capable of handling ORC structures but sacrifice efficiency in the process. To make a good trade-off between the model capacity and efficiency, we propose a method to decompose ORC substructures by using two vectors to represent the entity as a head or tail entity with the same relation. In this way, we can encode the ORC structure properly when apply it to TransH, TransR and TransD with almost the same model complexity of themselves. We conduct experiments on link prediction with benchmark dataset WordNet. Our experiments show that applying our method improves the results compared with the corresponding original results of TransH, TransR and TransD.


title: A fast image dehazing method that does not introduce color artifacts

abstract: We propose a method for color dehazing with four main characteristics: it does not introduce color artifacts, it does not depend on inverting any physical equation, it is based on models of visual perception, and it is fast, potentially real time. Our method converts the original input image to the HSV color space and works in the saturation and value domains by: (1) reducing the value component via a global constrained histogram flattening; (2) modifying the saturation component in consistency with the previous reduced value; and (3) performing a local contrast enhancement in the value component. Results show that our method competes with the state-of-the-art when dealing with standard hazy images, and outperforms it when dealing with challenging haze cases. Furthermore, our method is able to dehaze a FullHD image on a GPU in 90 ms.


title: A Generalized Cascaded Multilevel Inverter Using Series Connection of Submultilevel Inverters

abstract: Application of multilevel inverters for higher power purposes in industries has become more popular. This is partly because of high-quality output waveform of multilevel inverters in comparison with two-level inverters. In this paper, initially a new topology for submultilevel inverter is proposed and then series connection of the submultilevel inverters is proposed as a generalized multilevel inverter. The proposed multilevel inverter uses reduced number of switching devices. Special attention has been paid to obtain optimal structures regarding different criteria such as number of switches, standing voltage on the switches, number of dc voltage sources, etc. The proposed multilevel inverter has been analyzed in both symmetric and asymmetric conditions. The validity of the proposed multilevel inverter is verified with both computer simulations using PSCAD/EMTDC software and laboratory prototype implementation.


title: A Hybrid Maximum Power Point Tracking Technique for Partially Shaded Photovoltaic Arrays

abstract: This paper introduces a hybrid maximum power point tracking (MPPT) technique for photovoltaic (PV) arrays working under partial shading conditions. This new algorithm can combine a traditional MPPT algorithm, such as perturb and observe, or incremental conductance, with the artificial neural network (ANN) technique. The proposed hybrid MPPT algorithm is based on the ANN and used to predict the global MPP region by estimating its voltage boundaries. Consequently, the conventional MPPT algorithm searches for the MPP in the predicted region. The proposed technique is modeled and simulated using MATLAB/Simulink. The results show the effectiveness of the proposed hybrid MPPT technique to track the global MPP accurately with a rapid response comparing to the ANN; this increases the output power level of the PV array under various shading patterns.


title: Tissue distribution of EDTA encapsulated within liposomes of varying surface properties.

abstract: Liposomes containing ethylenediaminetetraacetic acid (EDTA) were prepared with different surface properties by varying the liposomal lipid constituents. Positively charged liposomes were prepared with a mixture of phosphatidylcholine, cholesterol, and stearylamine. Negatively charged liposomes were prepared with a mixture of phosphatidylcholine, cholesterol, and phosphatidylserine. Neutral liposomes were prepared with phosphatidylcholine alone, dipalmitoyl phosphatidylcholine alone, or with a mixture of phosphatidylcholine and cholesterol. Distribution of 14C-labeled EDTA were determined in mouse tissues from 5 min to 24 h after a single intravenous injection of liposome preparation. Differences in tissue distribution were produced by the different liposomal lipid compositions. Uptake of EDTA by spleen and marrow was highest from negatively charged liposomes. Uptake of EDTA by lungs was highest from positively charged liposomes; lungs and brain retained relatively high levels of EDTA from these liposomes between 1 and 6 h after injection. Liver uptake of EDTA from positively or negatively charged liposomes was similar; the highest EDTA uptake by liver was from the neutral liposomes composed of a mixture of phosphatidylcholine and cholesterol. Liposomes composed of dipalmitoyl phosphatidylcholine produced the lowest liposomal EDTA uptake observed in liver and marrow but modrate uptake by lungs. Tissue uptake and retention of EDTA from all of the liposome preparations were greater than those of non-encapsulated EDTA. The results presented demonstrate that the tissue distribution of a molecule can be modified by encapsulation of that substance into liposomes of different surface properties. Selective delivery of liposome-encapsulated drugs to specific tissues could be effectively used in chemotherapy and membrane biochemistry.


title: Designing parts feeders using dynamic simulation

abstract: Considers the problem of designing traditional (e.g. vibratory bowl) feeders for singulating and orienting industrial parts. The ultimate goal is to prototype new designs using analytically- and geometrically-based methods. The authors have developed a tool for designing industrial parts feeders based on dynamic simulation. The authors' tool allows them to automatically perform multiple feeder design experiments, and to evaluate their outcomes. These results can then be used to compute the probabilities of a Markov model for the feeder. To demonstrate the technique, the authors present preliminary results for the design of two simple feeders. The findings suggest that using dynamic simulation is a promising approach for designing parts feeders.


title: Collective Stance Classification of Posts in Online Debate Forums

abstract: Online debate sites are a large source of informal and opinion-sharing dialogue on current socio-political issues. Inferring users’ stance (PRO or CON) towards discussion topics in domains such as politics or news is an important problem, and is of utility to researchers, government organizations, and companies. Predicting users’ stance supports identification of social and political groups, building of better recommender systems, and personalization of users’ information preferences to their ideological beliefs. In this paper, we develop a novel collective classification approach to stance classification, which makes use of both structural and linguistic features, and which collectively labels the posts’ stance across a network of the users’ posts. We identify both linguistic features of the posts and features that capture the underlying relationships between posts and users. We use probabilistic soft logic (PSL) (Bach et al., 2013) to model post stance by leveraging both these local linguistic features as well as the observed network structure of the posts to reason over the dataset. We evaluate our approach on 4FORUMS (Walker et al., 2012b), a collection of discussions from an online debate site on issues ranging from gun control to gay marriage. We show that our collective classification model is able to easily incorporate rich, relational information and outperforms a local model which uses only linguistic information.


title: A global controller for flying wing tailsitter vehicles

abstract: We present a global controller for tracking nominal trajectories with a flying wing tailsitter vehicle. The control strategy is based on a first-principles model of the vehicle dynamics that captures all relevant aerodynamic effects, and we apply an onboard parameter learning scheme in order to estimate unknown aerodynamic parameters. A cascaded control architecture is used: Based on position and velocity errors an outer control loop computes a desired attitude keeping the vehicle in coordinated flight, while an inner control loop tracks the desired attitude using a lookup table with precomputed optimal attitude trajectories. The proposed algorithms can be implemented on a typical microcontroller and the performance is demonstrated in various experiments.


title: A targeted web crawling for building malicious javascript collection

abstract: Malicious javascript frequently serves as a starting point of web-based attacks, in particular cross-site scripting. Thus detecting malicious javascript before execution can protect users from attacks such as malware infection, drive-by downloads, and even from participating in denial-of-service attacks as part of botnet sometimes. A large collection of malicious javascript would help with detector development, but by the time crawler arrives at blacklisted domains attackers and malicious scripts are often long gone. We have used classifiers to direct a web crawler better towards more likely locations of malicious scripts, and show how this targeted web crawler performs compared to crawler seed with blacklisted-domains.


title: Compiling Stateful Network Properties for Runtime Verification

abstract: Networks are difficult to configure correctly, and tricky to debug. These problems are accentuated by temporal and stateful behavior. Static verification, while useful, is ineffectual for detecting behavioral deviations induced by hardware faults, security failures, and so on, so dynamic property monitoring is also valuable. Unfortunately, existing monitoring and runtime verification for networks largely focuses on properties about individual packets (such as connectivity) or requires a digest of all network events be sent to a server, incurring enormous cost. ::: We present a network monitoring system that avoids these problems. Because traces of network events correspond well to temporal logic, we use a subset of Metric First-Order Temporal Logic as the query language. These queries are compiled down to execute completely on the network switches. This vastly reduces network load, improves the precision of queries, and decreases detection latency. We show the practical feasibility of our work by extending a widely-used software switch and deploying it on networks. Our work also suggests improvements to network instruction sets to better support temporal monitoring.


title: Morphological characteristics of the interface between resin composite and glass-ionomer cement to thin-walled roots: a microscopic investigation.

abstract: PURPOSE ::: To identify how different treatments of the root dentin surface affect the microscopic appearance of the resin composite/glass-ionomer cement-to-dentin interface. ::: ::: ::: METHODS ::: The root canals of 70 extracted human single-rooted teeth were enlarged to reduce dentin wall thicknesses to 0.5 mm. The roots were randomly divided into seven test groups (n=10) according to the canal irrigant used: no irrigant (control), 5% hydrogen peroxide, 5% sodium hypochlorite, a combination of 5% hydrogen peroxide and sodium hypochlorite, 15% ethylenediaminetetraacetic acid (EDTA), 10% lactic acid, or 20% lactic acid. To simulate thin-walled roots, within each group, crowns were sectioned and the entire surface of each root canal space was enlarged with Profile instrument. Half of treated root canals (n=5) were filled with resin composite (PermaFlo) and the other half were filled with glass-ionomer cement (Fuji II LC). A light-transmitting plastic post (Luminex) was used to create space for a fiber-reinforced post and to ensure polymerization of the restorative material. Specimens were critical-point dried and freeze fractured for scanning electron microscope analysis. Three sites along the root were evaluated (cervical, middle, and apical). ::: ::: ::: RESULTS ::: Scanning electron micrographs showed no differences in the morphology of the resin tags at the cervical, middle or apical levels with any of the irrigants or the restorative materials used. Also, no difference in surface topography was found within individual groups. A resin-dentin interdiffusion zone and resin tags developed after application of resin composite with lactic acid solutions and EDTA but not with the glass-ionomer cement.


title: Computational Approaches for the Understanding of Melody in Carnatic Music

abstract: The classical music traditions of the Indian subcontinent, Hindustani and Carnatic, offer an excellent ground on which to test the limitations of current music information research approaches. At the same time, studies based on these music traditions can shed light on how to solve new and complex music modeling problems. Both traditions have very distinct characteristics, specially compared with western ones: they have developed unique instruments, musical forms, performance practices, social uses and context. In this article, we focus on the Carnatic music tradition of south India, especially on its melodic characteristics. We overview the theoretical aspects that are relevant for music information research and discuss the scarce computational approaches developed so far. We put emphasis on the limitations of the current methodologies and we present open issues that have not yet been addressed and that we believe are important to be worked on.


title: THE LOCAL BINARY PATTERN APPROACH TO TEXTURE ANALYSIS – EXTENSIONS AND APPLICATIONS

abstract: This thesis presents extensions to the local binary pattern (LBP) texture analysis operator. The operator is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local neighborhood. It is made invariant against the rotation of the image domain, and supplemented with a rotation invariant measure of local contrast. The LBP is proposed as a unifying texture model that describes the formation of a texture with micro-textons and their statistical placement rules. The basic LBP is extended to facilitate the analysis of textures with multiple scales by combining neighborhoods with different sizes. The possible instability in sparse sampling is addressed with Gaussian low-pass filtering, which seems to be somewhat helpful. Cellular automata are used as texture features, presumably for the first time ever. With a straightforward inversion algorithm, arbitrarily large binary neighborhoods are encoded with an eight-bit cellular automaton rule, resulting in a very compact multi-scale texture descriptor. The performance of the new operator is shown in an experiment involving textures with multiple spatial scales. An opponent-color version of the LBP is introduced and applied to color textures. Good results are obtained in static illumination conditions. An empirical study with different color and texture measures however shows that color and texture should be treated separately. A number of different applications of the LBP operator are presented, emphasizing real-time issues. A very fast software implementation of the operator is introduced, and different ways of speeding up classification are evaluated. The operator is successfully applied to industrial visual inspection applications and to image retrieval.


title: [Relevance, measurement and assessment of the antioxidative status in farm animals].

abstract: Reactive oxygen species (ROS) are generated in living organisms under physiological and pathological conditions. They have to be neutralized by the antioxidative system which consists of enzymatic and non enzymatic antioxidants. Both, the activity of antioxidative enzymes and the capacity of non enzymatic antioxidants are known as the antioxidative status of the organism. If the balance between prooxidative processes and antioxidative system is disturbed oxidative stress occurs. Oxidative stress is considered to be a major risk factor for the reduction of defence mechanisms and development of diseases. The aim of the present work is to describe various aspects of the antioxidative status in several production animal species. Conclusions for management and therapy are drawn when possible. Furthermore the paper provides an overview of methods for assessment of antioxidative metabolism. Farm animals undergo several periods of severe challenge of the antioxidative system during the production cycle. Especially young animals in the first weeks of their life and animals during the periparturient period are at high risk. High yielding live stock generally have to carry a higher oxidative burden in comparison to animals which are on a medium production level or in extensive systems. Other risk factors are unsuitable or spoiled components in the diet and heat stress. Exogenous antioxidant supply can be optimized by feeding fresh roughage or silage of good quality or if necessary by using additives. Changes of the antioxidative system have been described in association with displaced abomasum (DA), abomasal volvulus (AV) and reproductive problems in swine. Pre- and postoperative supplementation of antioxidants has been used with good results as supportive treatment in therapy of cattle with DA.


title: Location-Based Services with iBeacon Technology

abstract: iBeacons are a new way to interact with hardware. An iBeacon is a Bluetooth Low Energy device that only sends a signal in a specific format. They are like a lighthouse that sends light signals to boats. This paper explains what an iBeacon is, how it works and how it can simplifiy your daily life, what restriction comes with iBeacon and how to improve this restriction., as well as, how to use Location-based Services to track items. E.g., every time you touchdown at an airport and wait for your suitcase at the luggage reclaim, you have no information when your luggage will arrive at the conveyor belt. With an iBeacon inside your suitcase, it is possible to track the luggage and to receive a push notification about it even before you can see it. This is just one possible solution to use them. Ibeacon can create a completely new shopping experience or make your home smarter. This paper demonstrates the luggage tracking use case and evaluates its possibilities and restrictions.


title: Normalized Mutual Information Feature Selection

abstract: A filter method of feature selection based on mutual information, called normalized mutual information feature selection (NMIFS), is presented. NMIFS is an enhancement over Battiti's MIFS, MIFS-U, and mRMR methods. The average normalized mutual information is proposed as a measure of redundancy among features. NMIFS outperformed MIFS, MIFS-U, and mRMR on several artificial and benchmark data sets without requiring a user-defined parameter. In addition, NMIFS is combined with a genetic algorithm to form a hybrid filter/wrapper method called GAMIFS. This includes an initialization procedure and a mutation operator based on NMIFS to speed up the convergence of the genetic algorithm. GAMIFS overcomes the limitations of incremental search algorithms that are unable to find dependencies between groups of features.


title: Content Analysis: A Flexible Methodology

abstract: Content analysis is a highly fl exible research method that has been widely used in library and information science (LIS) studies with varying research goals and objectives. The research method is applied in qualitative, quantitative, and sometimes mixed modes of research frameworks and employs a wide range of analytical techniques to generate fi ndings and put them into context. This article characterizes content analysis as a systematic, rigorous approach to analyzing documents obtained or generated in the course of research. It briefl y describes the steps involved in content analysis, differentiates between quantitative and qualitative content analysis, and shows that content analysis serves the purposes of both quantitative research and qualitative research. The authors draw on selected LIS studies that have used content analysis to illustrate the concepts addressed in the article. The article also serves as a gateway to methodological books and articles that provide more detail about aspects of content analysis discussed only briefl y in the article.


title: Expectation maximization and posterior constraints

abstract: The expectation maximization (EM) algorithm is a widely used maximum likelihood estimation procedure for statistical models when the values of some of the variables in the model are not observed. Very often, however, our aim is primarily to find a model that assigns values to the latent variables that have intended meaning for our data and maximizing expected likelihood only sometimes accomplishes this. Unfortunately, it is typically difficult to add even simple a-priori information about latent variables in graphical models without making the models overly complex or intractable. In this paper, we present an efficient, principled way to inject rich constraints on the posteriors of latent variables into the EM algorithm. Our method can be used to learn tractable graphical models that satisfy additional, otherwise intractable constraints. Focusing on clustering and the alignment problem for statistical machine translation, we show that simple, intuitive posterior constraints can greatly improve the performance over standard baselines and be competitive with more complex, intractable models.


title: Multimodal Neural Networks: RGB-D for Semantic Segmentation and Object Detection

abstract: This paper presents a novel multi-modal CNN architecture that exploits complementary input cues in addition to sole color information. The joint model implements a mid-level fusion that allows the network to exploit cross-modal interdependencies already on a medium feature-level. The benefit of the presented architecture is shown for the RGB-D image understanding task. So far, state-of-the-art RGB-D CNNs have used network weights trained on color data. In contrast, a superior initialization scheme is proposed to pre-train the depth branch of the multi-modal CNN independently. In an end-to-end training the network parameters are optimized jointly using the challenging Cityscapes dataset. In thorough experiments, the effectiveness of the proposed model is shown. Both, the RGB GoogLeNet and further RGB-D baselines are outperformed with a significant margin on two different task: semantic segmentation and object detection. For the latter, this paper shows how to extract object-level groundtruth from the instance level annotations in Cityscapes in order to train a powerful object detector.


title: Image Autoregressive Interpolation Model Using GPU-Parallel Optimization

abstract: With the growth in the consumer electronics industry, it is vital to develop an algorithm for ultrahigh definition products that is more effective and has lower time complexity. Image interpolation, which is based on an autoregressive model, has achieved significant improvements compared with the traditional algorithm with respect to image reconstruction, including a better peak signal-to-noise ratio (PSNR) and improved subjective visual quality of the reconstructed image. However, the time-consuming computation involved has become a bottleneck in those autoregressive algorithms. Because of the high time cost, image autoregressive-based interpolation algorithms are rarely used in industry for actual production. In this study, in order to meet the requirements of real-time reconstruction, we use diverse compute unified device architecture (CUDA) optimization strategies to make full use of the graphics processing unit (GPU) (NVIDIA Tesla K80), including a shared memory and register and multi-GPU optimization. To be more suitable for the GPU-parallel optimization, we modify the training window to obtain a more concise matrix operation. Experimental results show that, while maintaining a high PSNR and subjective visual quality and taking into account the I/O transfer time, our algorithm achieves a high speedup of 147.3 times for a Lena image and 174.8 times for a 720p video, compared to the original single-threaded C CPU code with -O2 compiling optimization.


title: Drive pattern analyzer using smartphones

abstract: This paper is about analyzing the efficiency of drivers using an android based smartphone. Smartphones have built-in sensors for instance: accelerometer, gyroscope etc. Utilizing these sensors will provide day-end analysis to track the driving behaviour and provide feedback to the drivers if there is any inappropriate or reckless action taken while driving. To summarize this, we propose to build an active and accurate mon-itoring system that gives the response to the driving behaviours of a driver.


title: 3D-printed Ka-band antenna based on stereolithography

abstract: This paper presents a circularly-polarized dual-band antenna for Ka-band satellite communications which operates simultaneously in both civil and military downlink (17.7–21.2 GHz) and uplink (27.5–31 GHz) bands. The antenna is composed of a metallic ridged cavity that is perforated with a crossed-slot. This cavity is excited in sequential rotation through four feeding coaxial cables, thus producing circular polarization (CP). The required feed distribution is obtained by a dedicated beam-forming network (BFN) able to provide phases to generate right/left-handed CP in the downlink/uplink-bands, respectively. The prototype of the antenna cavity is 3D printed basing on stereolithography (SLA), while the BFN is realized in printed circuit technology. Good agreement is obtained between theoretical and measured results, thereby validating the antenna concept and confirming the strong potential of SLA to conceive compact RF components.


title: Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations

abstract: High quality face editing in videos is a growing concern and spreads distrust in video content. However, upon closer examination, many face editing algorithms exhibit artifacts that resemble classical computer vision issues that stem from face tracking and editing. As a consequence, we wonder how difficult it is to expose artificial faces from current generators? To this end, we review current facial editing methods and several characteristic artifacts from their processin pipelines. We also show that relatively simple visual artifacts can be already quite effective in exposing such manipulations, including Deepfakes and Face2Face. Since the methods are based on visual features, they are easily explicable also to non-technical experts. The methods are easy to implement and offer capabilities for rapid adjustment to new manipulation types with little data available. Despite their simplicity, the methods are able to achieve AUC values of up to 0.866.


title: Scalable Algorithms for Large and Dynamic Networks: Reducing Big Data for Small Computations

abstract: In this paper we summarize recent research regarding a novel characterization of large-scale real-life informational networks which can be leveraged to speed computations for network analytics purposes by orders of magnitude. First, using publicly available data, we show that informational networks not only satisfy well-known principles such as the small-world property and variants of the power law degree distribution, but that they also exhibit the geometric property of large-scale negative curvature, also referred to as hyperbolicity. We then provide examples of large-scale physical networks that universally lack this property, thus showing that hyperbolicity is not an ever-present feature of real-life networks in general. We document how hyperbolicity leads to unusually high centrality in informational networks. We then describe an approximation of hyperbolic networks that leverages the observed property of high centrality. We provide evidence that the fidelity of the proposed approximation is not only high for applications such as distance approximation, but that it can speed computation by a factor of 1000X or more. Finally, we discuss two applications of our proposed linear-time distance approximation for informational networks: one for personalized ranking and the other for clustering. These and many more algorithms yet to be developed take full advantage of our proposed tree-approximation of hyperbolic networks and further demonstrate its power and utility.


title: On the development of a novel adaptive prosthetic hand with compliant joints: experimental platform and EMG control

abstract: In this paper, some recent results about the experimental trials we are performing on a functional prosthetic hand characterized by an EMG-control and by a simple and low cost fabrication technology are shown. The innovation described in this paper is the fabrication of a compliant under-actuated prosthetic hand. The structure of the hand (both palm and fingers) is moulded as a soft polymeric single part with compliant joints and embedded tendon driven under-actuated mechanism for providing adaptive grasp. The prosthetic hand has been designed following a biomechatronic approach based on biologically-inspired design solutions. We describe the second release of this prosthesis. The evaluating tests are related to the maximum force exerted during grasping and to the fatigue problems occurring with cables and joint.


title: A model for attribute-based user-role assignment

abstract: The role-based access control (RBAC) model is traditionally used to manually assign users to appropriate roles, based on a specific enterprise policy, thereby authorizing them to use the roles' permissions. In environments where the service-providing enterprise has a huge customer base this task becomes formidable. An appealing solution is to automatically assign users to roles. The central contribution of this paper is to describe a model to dynamically assign users to roles based on a finite set of rules defined by the enterprise. These rules take into consideration the attributes of users and any constraints set forth by the enterprise's security policy. The model also allows dynamic revocation of assigned roles based on conditions specified in the security policy. The model provides a language to express these rules and defines a mechanism to determine seniority among different rules. The paper also shows how to use the model to express mandatory access controls (MAC).


title: Star Ratings versus Sentiment Analysis -- A Comparison of Explicit and Implicit Measures of Opinions

abstract: A typical trade-off in decision making is between the cost of acquiring information and the decline in decision quality caused by insufficient information. Consumers regularly face this trade-off in purchase decisions. Online product/service reviews serve as sources of product/service related information. Meanwhile, modern technology has led to an abundance of such content, which makes it prohibitively costly (if possible at all) to exhaust all available information. Consumers need to decide what subset of available information to use. Star ratings are excellent cues for this decision as they provide a quick indication of the tone of a review. However there are cases where such ratings are not available or detailed enough. Sentiment analysis -text analytic techniques that automatically detect the polarity of text- can help in these situations with more refined analysis. In this study, we compare sentiment analysis results with star ratings in three different domains to explore the promise of this technique.


title: The Alignment Template Approach To Statistical Machine Translation

abstract: A phrase-based statistical machine translation approach — the alignment template approach — is described. This translation approach allows for general many-to-many relations between words. Thereby, the context of words is taken into account in the translation model, and local changes in word order from source to target language can be learned explicitly. The model is described using a log-linear modeling approach, which is a generalization of the often used source–channel approach. Thereby, the model is easier to extend than classical statistical machine translation systems. We describe in detail the process for learning phrasal translations, the feature functions used, and the search algorithm. The evaluation of this approach is performed on three different tasks. For the German–English speech VERBMOBIL task, we analyze the effect of various system components. On the French–English Canadian HANSARDS task, the alignment template system obtains significantly better results than a single-word-based translation model. In the Chinese–English 2002 National Institute of Standards and Technology (NIST) machine translation evaluation it yields statistically significantly better NIST scores than all competing research and commercial translation systems.


title: A text reading algorithm for natural images

abstract: Reading text in natural images has focused again the attention of many researchers during the last few years due to the increasing availability of cheap image-capturing devices in low-cost products like mobile phones. Therefore, as text can be found on any environment, the applicability of text-reading systems is really extensive. For this purpose, we present in this paper a robust method to read text in natural images. It is composed of two main separated stages. Firstly, text is located in the image using a set of simple and fast-to-compute features highly discriminative between character and non-character objects. They are based on geometric and gradient properties. The second part of the system carries out the recognition of the previously detected text. It uses gradient features to recognize single characters and Dynamic Programming (DP) to correct misspelled words. Experimental results obtained with different challenging datasets show that the proposed system exceeds state-of-the-art performance, both in terms of localization and recognition.


title: Shifted Rayleigh filter: a new algorithm for bearings-only tracking

abstract: A new algorithm, the "shifted Rayleigh filter," is introduced for two- or three-dimensional bearings-only tracking problems. In common with other "moment matching" tracking algorithms such as the extended Kalman filter and its modern refinements, it approximates the prior conditional density of the target state by a normal density; the novel feature is that an exact calculation is then performed to update the conditional density in the light of the new measurement. The paper provides the theoretical justification of the algorithm. It also reports on simulations involving variants on two scenarios, which have been the basis of earlier comparative studies. The first is a "benign" scenario where the measurements are comparatively rich in range-related information; here the shifted Rayleigh filter is competitive with standard algorithms. The second is a more "extreme" scenario, involving multiple sensor platforms, high-dimensional models and noisy measurements; here the performance of the shifted Rayleigh filter matches the performance of a high-order bootstrap particle filter, while reducing the computational overhead by an order of magnitude.


title: Who Wrote This Code? Identifying the Authors of Program Binaries

abstract: Program authorship attribution--identifying a programmer based on stylistic characteristics of code--has practical implications for detecting software theft, digital forensics, and malware analysis. Authorship attribution is challenging in these domains where usually only binary code is available; existing source code-based approaches to attribution have left unclear whether and to what extent programmer style survives the compilation process. Casting authorship attribution as a machine learning problem, we present a novel program representation and techniques that automatically detect the stylistic features of binary code. We apply these techniques to two attribution problems: identifying the precise author of a program, and finding stylistic similarities between programs by unknown authors. Our experiments provide strong evidence that programmer style is preserved in program binaries.


title: Improving Active Learning in Systematic Reviews

abstract: Systematic reviews are essential to summarizing the results of different clinical and social science studies. The first step in a systematic review task is to identify all the studies relevant to the review. The task of identifying relevant studies for a given systematic review is usually performed manually, and as a result, involves substantial amounts of expensive human resource. Lately, there have been some attempts to reduce this manual effort using active learning. In this work, we build upon some such existing techniques, and validate by experimenting on a larger and comprehensive dataset than has been attempted until now. Our experiments provide insights on the use of different feature extraction models for different disciplines. More importantly, we identify that a naive active learning based screening process is biased in favour of selecting similar documents. We aimed to improve the performance of the screening process using a novel active learning algorithm with success. Additionally, we propose a mechanism to choose the best feature extraction method for a given review.


title: Efficient dynamic equations of 3-RPS parallel mechanism through Lagrange method

abstract: By means of an efficient method, the generalized reduced order dynamic equation for 3-RPS parallel mechanism through Lagrange method is derived. Kinematic constraints accompanying the Lagrange method for the constrained set of generalized coordinates, introduces the Lagrange multiplier into dynamical formulation. To omit the Lagrange multipliers the natural orthogonal complement matrix of kinematic constraints' matrix should be found. To reach the natural orthogonal complement matrix, the inverse of a square matrix having order equal to the rank of a kinematic constraints' matrix should be found. For a system having many kinematic constraints like 3-RPS, the rank of the aforementioned matrix will be high. In this research it is shown that for a 3-RPS parallel manipulator, a rearranging matrix derived from the kinematic constraints on constrained coordinates in a special way will simplify the inverse calculation. Instead of inversion of a high order matrix, only inversion of some very low order matrices should be evaluated. Therefore the natural orthogonal complement matrix can be reached without the need for inversion of a high order matrix and Lagrange multipliers can be omitted again very easily.


Adjunctive self-hypnotic relaxation for outpatient medical procedures: A prospective randomized trial with women undergoing large core breast biopsy


Comparative Performance Analysis of Best Performance Round Robin Scheduling Algorithm ( BPRR ) using Dynamic Time Quantum with Priority Based Round Robin ( PBRR ) CPU Scheduling Algorithm in Real Time Systems


title: Sparse iterative closest point

abstract: Rigid registration of two geometric data sets is essential in many applications, including robot navigation, surface reconstruction, and shape matching. Most commonly, variants of the Iterative Closest Point (ICP) algorithm are employed for this task. These methods alternate between closest point computations to establish correspondences between two data sets, and solving for the optimal transformation that brings these correspondences into alignment. A major difficulty for this approach is the sensitivity to outliers and missing data often observed in 3D scans. Most practical implementations of the ICP algorithm address this issue with a number of heuristics to prune or reweight correspondences. However, these heuristics can be unreliable and difficult to tune, which often requires substantial manual assistance. We propose a new formulation of the ICP algorithm that avoids these difficulties by formulating the registration optimization using sparsity inducing norms. Our new algorithm retains the simple structure of the ICP algorithm, while achieving superior registration results when dealing with outliers and incomplete data. The complete source code of our implementation is provided at http://lgg.epfl.ch/sparseicp.


title: Discovering Similar Passages within Large Text Documents

abstract: We present a novel general method for discovering similar passages within large text documents based on adapting and extending the well-known Smith-Waterman dynamic programming local sequence alignment algorithm. We extend that algorithm for large document analysis by defining: (a) a recursive procedure for discovering multiple non-overlapping aligned passages within a given document pair; (b) a matrix splicing method for processing long texts; (c) a chaining method for combining sequence strands; and (d) an inexact similarity measure for determining token matches. We show that an implementation of this method is computationally efficient and produces very high precision with good recall for several types of order-based plagiarism and that it achieves higher overall performance than the best reported methods against the PAN 2013 text alignment test corpus.


title: An Overview of Inter-Vehicular Communication Systems, Protocols and Middleware

abstract: Inter-vehicular communication is an important research area that is rapidly growing due to considerable advances in mobile and wireless communication technologies, as well as the growth of microprocessing capabilities inside today’s cars, and other moving vehicles. A good amount of research has been done to exploit the different services that can be provided to enhance the safety and comfort of the driver. Additional functions provide the car electronics and the passengers with access to the Internet and other core network resources. This paper provides a survey of the latest advances in the area of inter-vehicular communication (IVC) including vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) functions and services. In addition, the paper presents the most important projects and protocols that are involved in IVC systems as well as the different issues and challenges that exist at each layer of the networking model.


title: Mapping between heterogeneous XML and OWL transaction representations in B2B integration

abstract: XML-based standards have been widely used to enable and ease Business-to-Business (B2B) integration. Examples of standards include cXML, CIDX and ebXML. While these XML-based standards are syntactic, contemporary organizations have available new means to structure their internal data representations using semantic descriptions, such as RDF(S) and OWL. This scenario poses an interesting challenge: ''How to reconcile external XML-based standards and internal OWL-based representations in B2B integration scenarios?'' In this paper, we present a conceptual approach, and its implementation, to integrate external syntactic data representations with organizational internal semantic data representations by using the notion of heterogeneous mappings which are established between the two types of representations. The application developed, B2BISS, enables an effective management of mappings. As the number of mappings stored in the repository increases over time, organizations can gradually rely on a semi-automatic to automatic B2B integration.


title: Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems

abstract: Cooperative intelligent transportation systems (cITS) are a promising technology to enhance driving safety and efficiency. Vehicles communicate wirelessly with other vehicles and infrastructure, thereby creating a highly dynamic and heterogeneously managed ad-hoc network. It is these network properties that make it a challenging task to protect integrity of the data and guarantee its correctness. A major component is the problem that traditional security mechanisms like public key infrastructure (PKI)-based asymmetric cryptography only exclude outsider attackers that do not possess key material. However, because attackers can be insiders within the network (i.e., possess valid key material), this approach cannot detect all possible attacks. In this survey, we present misbehavior detection mechanisms that can detect such insider attacks based on attacker behavior and information analysis. In contrast to well-known intrusion detection for classical IT systems, these misbehavior detection mechanisms analyze information semantics to detect attacks, which aligns better with highly application-tailored communication protocols foreseen for cITS. In our survey, we provide an extensive introduction to the cITS ecosystem and discuss shortcomings of PKI-based security. We derive and discuss a classification for misbehavior detection mechanisms, provide an in-depth overview of seminal papers on the topic, and highlight open issues and possible future research trends.


title: A 0.02-to-6GHz SDR balun-LNA using a triple-stage inverter-based amplifier

abstract: This paper describes a software-defined-radio (SDR) balun low-noise amplifier (LNA) with no explicit bias circuit, inductor or ac-coupling network. It relies on a triple-stage inverter-based amplifier with resistive feedback to maximize the bandwidth and realize single-to-differential conversion. RC degeneration applied at the last gain stage enhances both linearity and output gain-phase balancing. Optimized in 65-nm CMOS the balun-LNA covers the 0.02-to-6-GHz band with S 11 >−11 dB, voltage gain of 21.2-dB and noise figure below 3.2-dB. The in-band IIP2 (IIP3) is +36 to +51 dBm (−5.6 to −5.3 dBm). The power consumption is 7.9 mW at 1.2 V.


title: 'Smart' Cane for the Visually Impaired: Design and Controlled Field Testing of an Affordable Obstacle Detection System

abstract: Visually challenged persons face great difficulty in independent mobility and use the white cane as a mobility aid to detect close-by obstacles on the ground. However, the cane has two major limitations: 1. It can only detect obstacles up to knee-level. Hence, the user cannot detect raised obstacles like elevated bars and frequently collides with them. 2. The cane can only detect obstacles within 1m from the user. Also, obstacles like moving vehicles cannot be detected until dangerously close to the person. Almost 90% of the blind persons live in developing countries, with a majority below poverty line. Current devices available internationally are unaffordable. In this work the authors present the design and usability features of a low-cost knee-above obstacle detection system and report results from controlled field experiments.


title: Automatic Power Line Inspection Using UAV Images

abstract: Power line inspection ensures the safe operation of a power transmission grid. Using unmanned aerial vehicle (UAV) images of power line corridors is an effective way to carry out these vital inspections. In this paper, we propose an automatic inspection method for power lines using UAV images. This method, known as the power line automatic measurement method based on epipolar constraints (PLAMEC), acquires the spatial position of the power lines. Then, the semi patch matching based on epipolar constraints (SPMEC) dense matching method is applied to automatically extract dense point clouds within the power line corridor. Obstacles can then be automatically detected by calculating the spatial distance between a power line and the point cloud representing the ground. Experimental results show that the PLAMEC automatically measures power lines effectively with a measurement accuracy consistent with that of manual stereo measurements. The height root mean square (RMS) error of the point cloud was 0.233 m, and the RMS error of the power line was 0.205 m. In addition, we verified the detected obstacles in the field and measured the distance between the canopy and power line using a laser range finder. The results show that the difference of these two distances was within ±0.5 m.


title: Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components

abstract: Brain–computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5% and 94.4%.


title: Web Server Protection against Application Layer DDoS Attacks Using Machine Learning and Traffic Authentication

abstract: Application layer Distributed Denial of Service (DDoS) attacks are among the deadliest kinds of attacks that have significant impact on destination servers and networks due to their ability to be launched with minimal computational resources to cause an effect of high magnitude. Commercial and government Web servers have become the primary target of these kinds of attacks, with the recent mitigation efforts struggling to deaden the problem efficiently. Most application layer DDoS attacks can successfully mimic legitimate traffic without being detected by Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS). IDSs and IPSs can also mistake a normal and legitimate activity for a malicious one, producing a False Positive (FP) that affects Web users if it is ignored or dropped. False positives in a large and complex network topology can potentially be dangerous as they may cause IDS/IPS to block the user's benign traffic. Our focus and contributions in this paper are first, to mitigate the undetected malicious traffic mimicking legitimate traffic and developing a special anti-DDoS module for general and specific DDoS tools attacks by using a trained classifier in a random tree machine-learning algorithm. We use labeled datasets to generate rules to incorporate and fine-tune existing IDS/IPS such as Snort. Secondly, we further assist IDS/IPS by processing traffic that is classified as malicious by the IDS/IPS in order to identify FPs and route them to their intended destinations. To achieve this, our approach uses active authentication of traffic source of both legitimate and malicious traffic at the Bait and Decoy server respectively before destined to the Web server.


title: Comparison of the quality of sleep and intensity of headache between school-age children with migraine, tension headache and healthy children

abstract: Objective Headache and sleep problems are commonly reported in children, and both can adversely impact the child’s life. This study aimed to compare the sleep quality and intensity of headache between school-age children with migraine and tension headache and healthy children. Materials and Methods In this cross-sectional study, sample was 198 children 6-12 years old in three groups. Migraine and tension headache groups from the outpatient clinic of Imam Khomeini of Hamadan and healthy group from elementary schools were randomly selected (66 children in each group) Data were collected using demographic questionnaire, Child Sleep Habits Questionnaire (CSHQ), Numeric Scale of Pain Intensity and Wong- Baker Faces Pain Rating Scale. The data were analyzed using SPSS 16 software by descriptive statistic and multivariate ANOVA, one way ANOVA, chi-square, kruskal-wallis and linear regression tests. Results The majority of subjects in migraine group (57.6%), tension headache (60.6%) and healthy children (45.5%) were female. Approximately, 45.5% of children with migraine and 37.9% of them with tension headache had experienced severe headache. Only a significant relationship was seen between mean scores of headache intensity and sleep quality in migraine group (p< 0.05). There was a significant difference in mean scores of sleep quality among three groups (P<0.001). Conclusion The children with migraine experienced more unsuitable sleep in duration of severe headache. Highlighting the co-morbidity between intensity of headache and sleep problem of children with migraine and tension headache is important to improve treatment strategies and to know the impact of headache on their normal life.


title: A novel methodology for mapping objective video quality metrics to the subjective MOS scale

abstract: With the rapid growth in video-based services, and as users are becoming increasingly quality-aware, the reliable estimation of video quality has become extremely important. While a multitude of objective Video Quality Assessment (VQA) metrics with various performance and complexity have been proposed, the nonlinearity of video quality and the lack of clear interpretations of the metrics make difficult to understand how the objective metric values reflect the video quality as perceived subjectively in terms of Mean Opinion Scores (MOS). This paper proposes and evaluates a methodology for mapping objective VQA metric values to subjective MOS scores based on publicly available VQA databases. Three different databases were used for comparing the performance of various objective metrics and evaluating the proposed methodology.


title: Noise Makes Sense in Neuronal Computing

abstract: The united efforts of assemblies of neurons in the brain9s primary visual cortex translate incoming visual signals into action potentials. These action potentials encode, for example, the contrast and orientation of different parts of the image. Some neurons are sensitive to one particular orientation, other are sensitive to other orientations, but all neurons respond equally well to the image contrast. In a Perspective, ::: Volgushev and Eysel ::: explain the finding ( ::: Anderson ::: et al.) that neurons are able to maintain this sensitivity to the orientation of a stimulus regardless of the contrast by adding noise to the membrane potential, such that action potentials can also be generated in response to weak signals at low contrast.


title: Keeping requirements and test cases consistent: Towards an ontology-based approach

abstract: Due to increasing functionality and use of software within mechatronic systems, their complexity increases. Quality and correctness of these systems' functionality must be ensured making an approach for requirements and test case management necessary. To provide a first step towards solving these challenges, this paper presents a modeling approach that enables the early integration between requirements and test cases. Moreover, a first study on using semantic technologies for ensuring consistency is presented. By that, the foundations for supporting test engineers in specifying and testing the system are formed.


title: 99.3% Efficiency of three-phase inverter for motor drive using GaN-based Gate Injection Transistors

abstract: In this paper, we present a successful operation of Gallium Nitride(GaN)-based three-phase inverter with high efficiency of 99.3% for driving motor at 900W under the carrier frequency of 6kHz. This efficiency well exceeds the value by IGBT (Insulated Gate Bipolar Transistor). This demonstrates that GaN has a great potential for power switching application competing with SiC. Fully reduced on-state resistance in a new normally-off GaN transistor called Gate Injection Transistor (GIT) greatly helps to increase the efficiency. In addition, use of the bidirectional operation of the lateral and compact GITs with synchronous gate driving, the inverter is operated free from fly-wheel diodes which have been connected in parallel with IGBTs in a conventional inverter system.


title: Crop recommendation system for precision agriculture

abstract: Data mining is the practice of examining and deriving purposeful information from the data. Data mining finds its application in various fields like finance, retail, medicine, agriculture etc. Data mining in agriculture is used for analyzing the various biotic and abiotic factors. Agriculture in India plays a predominant role in economy and employment. The common problem existing among the Indian farmers are they don't choose the right crop based on their soil requirements. Due to this they face a serious setback in productivity. This problem of the farmers has been addressed through precision agriculture. Precision agriculture is a modern farming technique that uses research data of soil characteristics, soil types, crop yield data collection and suggests the farmers the right crop based on their site-specific parameters. This reduces the wrong choice on a crop and increase in productivity. In this paper, this problem is solved by proposing a recommendation system through an ensemble model with majority voting technique using Random tree, CHAID, K-Nearest Neighbor and Naive Bayes as learners to recommend a crop for the site specific parameters with high accuracy and efficiency.