title: Effect of Propranolol on Functional Connectivity in Autism Spectrum Disorder—A Pilot Study

abstract: A decrease in interaction between brain regions is observed in individuals with autism spectrum disorder (ASD), which is believed to be related to restricted neural network access in ASD. Propranolol, a beta-adrenergic antagonist, has revealed benefit during performance of tasks involving flexibility of access to networks, a benefit also seen in ASD. Our goal was to determine the effect of propranolol on functional connectivity in ASD during a verbal decision making task as compared to nadolol, thereby accounting for the potential spurious fMRI effects due to peripheral hemodynamic effects of propranolol. Ten ASD subjects underwent fMRI scans after administration of placebo, propranolol or nadolol, while performing a phonological decision making task. Comparison of functional connectivity between pre-defined ROI-pairs revealed a significant increase with propranolol compared to nadolol, suggesting a potential imaging marker for the cognitive effects of propranolol in ASD.


title: Spatial Deep Learning for Wireless Scheduling

abstract: The optimal scheduling of interfering links in a dense wireless network with full frequency reuse is a challenging task. The traditional method involves first estimating all the interfering channel strengths then optimizing the scheduling based on the model. This model-based method is however resource and computationally intensive, because channel estimation is expensive in dense networks; further, finding even a locally optimal solution of the resulting optimization problem may be computationally complex. This paper shows that by using a deep learning approach, it is possible to bypass channel estimation and to schedule links efficiently based solely on the geographic locations of transmitters and receivers. This is accomplished by using locally optimal schedules generated using a fractional programming method for randomly deployed device-to-device networks as training data, and by using a novel neural network architecture that takes the geographic spatial convolutions of the interfering or interfered neighboring nodes as input over multiple feedback stages to learn the optimum solution. The resulting neural network gives near-optimal performance for sum-rate maximization and is capable of generalizing to larger deployment areas and to deployments of different link densities. Finally, this paper proposes a novel scheduling approach that utilizes the sum-rate optimal scheduling heuristics over judiciously chosen subsets of links to provide fair scheduling across the network.


title: RecSys Challenge 2015 and the YOOCHOOSE Dataset

abstract: The 2015 ACM Recommender Systems Challenge offered the opportunity to work on a large-scale e-commerce dataset from a big retailer in Europe which is accepting recommender system as a service from YOOCHOOSE. Participants tackled the problem of predicting what items a user intends to purchase, if any, given a click sequence performed during an activity session on the e-commerce website. The challenge ran for seven months and was very successful, attracting 850 teams from 49 countries which submitted a total of 5,437 solutions. The winners of the challenge scored approximately 50% of the maximum score, which we considered as an impressive achievement. In this paper we provide a brief overview of the challenge and its results.


title: A new edge feature for head-shoulder detection

abstract: In this work, we introduce a new edge feature to improve the head-shoulder detection performance. Since Head-shoulder detection is much vulnerable to vague contour, our new edge feature is designed to extract and enhance the head-shoulder contour and suppress the other contours. The basic idea is that head-shoulder contour can be predicted by filtering edge image with edge patterns, which are generated from edge fragments through a learning process. This edge feature can significantly enhance the object contour such as human head and shoulder known as En-Contour. To evaluate the performance of the new En-Contour, we combine it with HOG+LBP [1] as HOG+LBP+En-Contour. The HOG+LBP is the state-of-the-art feature in pedestrian detection. Because the human head-shoulder detection is a special case of pedestrian detection, we also use it as our baseline. Our experiments have indicated that this new feature significantly improve the HOG+LBP.


title: Probabilistic Functional Logic Programming

abstract: This paper presents PFLP, a library for probabilistic programming in the functional logic programming language Curry. It demonstrates how the concepts of a functional logic programming language support the implementation of a library for probabilistic programming. In fact, the paradigms of functional logic and probabilistic programming are closely connected. That is, we can apply techniques from one area to the other and vice versa. We will see that an implementation based on the concepts of functional logic programming can have benefits with respect to performance compared to a standard list-based implementation.


title: Real-world acoustic event detection

abstract: Acoustic Event Detection (AED) aims to identify both timestamps and types of events in an audio stream. This becomes very challenging when going beyond restricted highlight events and well controlled recordings. We propose extracting discriminative features for AED using a boosting approach, which outperform classical speech perceptual features, such as Mel-frequency Cepstral Coefficients and log frequency filterbank parameters. We propose leveraging statistical models better fitting the task. First, a tandem connectionist-HMM approach combines the sequence modeling capabilities of the HMM with the high-accuracy context-dependent discriminative capabilities of an artificial neural network trained using the minimum cross entropy criterion. Second, an SVM-GMM-supervector approach uses noise-adaptive kernels better approximating the KL divergence between feature distributions in different audio segments. Experiments on the CLEAR 2007 AED Evaluation set-up demonstrate that the presented features and models lead to over 45% relative performance improvement, and also outperform the best system in the CLEAR AED Evaluation, on detection of twelve general acoustic events in a real seminar environment.


title: Gigantomastia: A problem of local recurrence.

abstract: Summary Gigantomastia by definition means bilateral benign progressive breast enlargement to a degree that requires breast reduction surgery to remove more than 1800 g of tissue on each side. It is seen at puberty or during pregnancy. The etiology for this condition is still not clear, but surgery remains the mainstay of treatment. We present a unique case of Gigantomastia, which was neither related to puberty nor pregnancy and has undergone three operations so far for recurrence.


title: Adaptive Market Making via Online Learning

abstract: We consider the design of strategies for market making in an exchange. A market maker generally seeks to profit from the difference between the buy and sell price of an asset, yet the market maker also takes exposure risk in the event of large price movements. Profit guarantees for market making strategies have typically required certain stochastic assumptions on the price fluctuations of the asset in question; for example, assuming a model in which the price process is mean reverting. We propose a class of "spread-based" market making strategies whose performance can be controlled even under worst-case (adversarial) settings. We prove structural properties of these strategies which allows us to design a master algorithm which obtains low regret relative to the best such strategy in hindsight. We run a set of experiments showing favorable performance on recent real-world stock price data.


title: Learning Radio Resource Management in 5G Networks: Framework, Opportunities and Challenges

abstract: In the fifth generation (5G) of mobile broadband systems, Radio Resources Management (RRM) will reach unprecedented levels of complexity. To cope with the ever more sophisticated RRM functionalities and with the growing variety of scenarios, while carrying out the prompt decisions required in 5G, this manuscript presents a lean 5G RRM architecture that capitalizes on recent advances in the field of machine learning in combination with the large amount of data readily available in the network from measurements and system observations. The architecture relies on a single general-purpose learning framework conceived for RRM directly using the data gathered in the network. The complexity of RRM is shifted to the design of the framework, whilst the RRM algorithms derived from this framework are executed in a computationally efficient distributed manner at the radio access nodes. The potential of this approach is verified in a pair of pertinent scenarios and future directions on applications of machine learning to RRM are discussed.


title: Efficient cultural heritage image restoration with nonuniform illumination enhancement

abstract: Cultural heritage digitization has been of research interest for several decades. For such, the quality of the stored images should be pleasant to see. However, as images captured by digital devices may include undesirable effects, conducting an enhancement on the image is essential. In this context, we present a framework for the purpose of cultural heritage image illumination enhancement. First, a mapping curve based on saturation feedback is created to adjust the contrast. Then illumination is enhanced by applying a modified homomorphic filter in the frequency domain. The technique employs an optimization search process based on the efficient golden section search algorithm to compute the optimal parameters to produce the enhanced image. Finally, a color restoration function is applied to overcome the problem of color violation. The resulted image represents a trade-off among local contrast improvement, detail enhancement, and preserving the naturalness of the image. Experiments are conducted on a collected dataset of cultural heritage images and compared to some of the state-of-the-art image enhancement methods using a set of quantitative assessments criteria. Results have shown that our proposed approach is able to accomplish a wide set of the performance goals.


title: Synthesis of Modulated-Metasurface Antennas With Amplitude, Phase, and Polarization Control

abstract: An effective synthesis procedure for planar antennas realized with nonuniform metasurfaces (MTSs) excited by a point source is presented. This synthesis potentiates previous formulations by introducing a control of the amplitude of the aperture field while improving the polarization and phase performances. The class of MTS antennas we are dealing with is realized by using subwavelength patches of different dimensions printed on a grounded slab, illuminated by a transverse magnetic point source. These antennas are based on the interaction between a cylindrical surface-wave and the periodic modulation of the MTS, which leads to radiation through a leaky-wave (LW) effect. This new design method permits a systematic and simple synthesis of amplitude, phase, and polarization of the aperture field by designing the boundary conditions imposed by the MTS. The polarization control is based on the local value of the MTS anisotropy, the phase is controlled by the shape and periodicity of the modulation, and the amplitude is controlled by the local leakage attenuation parameter of the LW. The synthesis is based on analytical formulas derived by an adiabatic Floquet-wave expansion of currents and fields over the surface, which are simultaneously published in this journal issue. The effectiveness of the procedure is tested through several numerical examples involving realistic structures.


title: A new rail inspection method based on deep learning using laser cameras

abstract: Rail systems are one of the most important transportation methods used in today's world. The abnormalities that occur on railway tracks due to various causes result in breakdowns and accidents. For this reason, railway tracks must be periodically inspected. This study proposes a new approach for rail inspection. Today, the railway inspection process is generally performed using computer vision. But the oil and dust residues occurring on railway surfaces can be detected as an false-positive by the image processing software can lead to loss of time and additional costs in the railway maintenance process. In this study, a hardware and software architecture are presented to perform railway surface inspection using 3D laser camera and deep learning. The use of 3D laser cameras in railway inspection process provides high accuracy rates in real time. The reading rate of laser cameras to read up to 25.000 profiles per second is another important advantage provided in real time railway inspection. Consequently, a computer vision-based approach in which 3D laser cameras that could allow for contact-free and fast detection of the railway surface and lateral defects such as fracture, scouring and wear with high accuracy are used in the railway inspection process was proposed in the study.


title: A multiparadigm intelligent tutoring system for robotic arm training

abstract: To assist learners during problem-solving activities, an intelligent tutoring system (ITS) has to be equipped with domain knowledge that can support appropriate tutoring services. Providing domain knowledge is usually done by adopting one of the following paradigms: building a cognitive model, specifying constraints, integrating an expert system, and using data mining algorithms to learn domain knowledge. However, for some ill-defined domains, each single paradigm may present some advantages and limitations in terms of the required resources for deploying it, and tutoring support that can be offered. To address this issue, we propose using a multiparadigm approach. In this paper, we explain how we have applied this idea in CanadarmTutor, an ITS for learning to operate the Canadarm2 robotic arm. To support tutoring services in this ill-defined domain, we have developed a multiparadigm model combining: 1) a cognitive model to cover well-defined parts of the task and spatial reasoning, 2) a data mining approach for automatically building a task model from user solutions for ill-defined parts of the task, and 3) a 3D path-planner to cover other parts of the task for which no user data are available. The multiparadigm version of CanadarmTutor allows providing a richer set of tutoring services than what could be offered with previous single paradigm versions of CanadarmTutor.


title: Universal semantic communication I

abstract: Is it possible for two intelligent beings to communicate meaningfully, without any common language or background? This question has interest on its own, but is especially relevant in the context of modern computational infrastructures where an increase in the diversity of computers is making the task of inter-computer interaction increasingly burdensome. Computers spend a substantial amount of time updating their software to increase their knowledge of other computing devices. In turn, for any pair of communicating devices, one has to design software that enables the two to talk to each other. Is it possible instead to let the two computing entities use their intelligence (universality as computers) to learn each others' behavior and attain a common understanding? What is 'common understanding?' We explore this question in this paper. To formalize this problem, we suggest that one should study the 'goal of communication:' why are the two entities interacting with each other, and what do they hope to gain by it? We propose that by considering this question explicitly, one can make progress on the question of universal communication. We start by considering a computational setting for the problem where the goal of one of the interacting players is to gain some computational wisdom from the other player. We show that if the second player is "sufficiently" helpful and powerful, then the first player can gain significant computational power (deciding PSPACE complete languages). Our work highlights some of the definitional issues underlying the task of formalizing universal communication, but also suggests some interesting phenomena and highlights potential tools that may be used for such communication.


title: Detection of Subconscious Face Recognition Using Consumer-Grade Brain-Computer Interfaces

abstract: We test the possibility of tapping the subconscious mind for face recognition using consumer-grade BCIs. To this end, we performed an experiment whereby subjects were presented with photographs of famous persons with the expectation that about 20p of them would be (consciously) recognized; and since the photos are of famous persons, we expected that subjects would have seen before some of the 80p they didn’t (consciously) recognize. Further, we expected that their subconscious would have recognized some of those in the 80p pool that they had seen before. An exit questionnaire and a set of criteria allowed us to label recognitions as conscious, false, no recognitions, or subconscious recognitions. We analyzed a number of event related potentials training and testing a support vector machine. We found that our method is capable of differentiating between no recognitions and subconscious recognitions with promising accuracy levels, suggesting that tapping the subconscious mind for face recognition is feasible.


title: High power density interleaved DC/DC converter using a 3-phase integrated close-coupled inductor set aimed for electric vehicles

abstract: Among eco-friendly electric vehicles, some incorporate DC/DC converters in their electrical drive systems to make full use of their power sources. For increasing the power density of the converters, a 3-phase interleaved converter using an inductor set of inductors and close-coupled inductors is investigated. The inductor set of a 3-phase topology is shown to be smaller in size and lose less power in comparison to that of a 2-phase topology while meeting the required specifications for high powered electric vehicles having a 200∼400 volt battery. The 3-phase converter using the inductor set was designed on the condition of using IGBT with a switching frequency of 15 kHz. It achieved a high power density of 21.4 kW/liter at 120 kW output on a boost ratio of 1.38 with 98.0% efficiency and it achieved an efficiency of more than 97% in a frequent usage zone with low temperature rise.


title: Context based face anti-spoofing

abstract: The face recognition community has finally started paying more attention to the long-neglected problem of spoofing attacks and the number of countermeasures is gradually increasing. Fairly good results have been reported on the publicly available databases but it is reasonable to assume that there exists no superior anti-spoofing technique due to the varying nature of attack scenarios and acquisition conditions. Therefore, we propose to approach the problem of face spoofing as a set of attack-specific subproblems that are solvable with a proper combination of complementary countermeasures. Inspired by how we humans can perform reliable spoofing detection only based on the available scene and context information, this work provides the first investigation in research literature that attempts to detect the presence of spoofing medium in the observed scene. We experiment with two publicly available databases consisting of several fake face attacks of different nature under varying conditions and imaging qualities. The experiments show excellent results beyond the state of the art. More importantly, our cross-database evaluation depicts that the proposed approach has promising generalization capabilities.


title: Supporting self-regulated learning in computer-based learning environments: systematic review of effects of scaffolding in the domain of science education

abstract: Despite the widespread assumption that students require scaffolding support for self-regulated learning (SRL) processes in computer-based learning environments (CBLEs), there is little clarity as to which types of scaffolds are most effective. This study offers a literature review covering the various scaffolds that support SRL processes in the domain of science education. Effective scaffolds are categorized and discussed according to the different areas and phases of SRL. The results reveal that most studies on scaffolding processes focus on cognition, whereas few focus on the non-cognitive areas of SRL. In the field of cognition, prompts appear to be the most effective scaffolds, especially for processes during the control phase. This review also shows that studies have paid little attention to scaffold designs, learner characteristics, or various task characteristics, despite the fact that these variables have been found to have a significant influence. We conclude with the implications of our results on future design and research in the field of SRL using CBLEs. © 2012 Wiley Periodicals, Inc.


Geo-level Bayesian Hierarchical Media Mix Modeling


title: DCM boost PFC converter with high input PF

abstract: Discontinuous current mode (DCM) boost power factor correction (PFC) converter features zero-current turn-on for the switch, no reverse recovery in diode and constant frequency operation, however, the input power factor (PF) is relatively low when the duty cycle is constant in a half line cycle. This paper derives the expressions of the input current and PF of DCM boost PFC converter, and based on which, a variable duty cycle control is proposed so as to improve the PF to nearly unity in the whole input voltage range. A method of fitting the duty cycle is further proposed for simplifying the circuit implementation. Besides a higher PF, the proposed variable duty cycle control achieves a lower output voltage ripple and a higher efficiency over the constant duty cycle control. The experimental results from a 120W universal input prototype are given to verify the effectiveness of the proposed method.


title: Border detection of document images scanned from large books

abstract: Document images usually suffer from some unwanted transformations like skew and warping. When dealing with large books, another problem is also introduced; when we capture a page of a large book using digital camera or scanner, some extra margins appears. The resulting document is often framed by a dark border and noisy text regions from neighboring page. In this paper, we introduce a novel technique for enhancing the document images by automatically detecting the document borders and cutting out noisy area. Our methodology is based on projecting profiles combined with an edge detection process. Experimental results on several document images, mainly historical with a small slope, indicate the effectiveness of the proposed technique.


title: A Dynamic Approach to Information Quality in User-Generated Content

abstract: Information quality (IQ) plays a critical role in ecommerce consumers’ purchase decisions, and product-related user-generated content (PUGC) is an increasingly important source of information. Nevertheless, vendors currently do not take full advantage of PUGC since PUGC is mainly used for evaluation purposes. Integrating PUGC into earlier phases of the purchase-decision process might be promising. However, little is known about which dimensions of IQ consumers evaluate as being most important in different phases of the purchase process. To close this gap, we design a survey study that investigates the perceived relative importance of various IQ dimensions in each of three purchase phases (i.e., screening, filtering, and evaluation). Conceptually, we thus extend the concept of IQ to a dynamic approach. Practically, our findings can inform the design of ecommerce websites that integrate PUGC about how to best support the purchase-decision process with respect to dynamic IQ.


title: A literature survey on social engineering attacks: Phishing attack

abstract: Phishing is a network type attack where the attacker creates the fake of an existing webpage to fool an online user into elicit personal Information. The prime objective of this review is to do literature survey on social engineering attack: Phishing attack and techniques to detect attack. Phishing is the combination of social engineering and technical methods to convince the user to reveal their personal data. The paper discusses about the Phishing social engineering attack theoretically and their issues in the life of human Beings. Phishing is typically carried out by Email spoofing or instant messaging. It targets the user who has no knowledge about social engineering attacks, and internet security, like persons who do not take care of privacy of their accounts details such as Facebook, Gmail, credit banks accounts and other financial accounts. The paper discusses various types of Phishing attacks such as Tab-napping, spoofing emails, Trojan horse, hacking and how to prevent them. At the same time this paper also provides different techniques to detect these attacks so that they can be easily dealt with in case one of them occurs. The paper gives a thorough analysis of various Phishing attacks along with their advantages and disadvantages.


title: Wireless sensor networks: a survey on recent developments and potential synergies

abstract: Wireless sensor network (WSN) has emerged as one of the most promising technologies for the future. This has been enabled by advances in technology and availability of small, inexpensive, and smart sensors resulting in cost effective and easily deployable WSNs. However, researchers must address a variety of challenges to facilitate the widespread deployment of WSN technology in real-world domains. In this survey, we give an overview of wireless sensor networks and their application domains including the challenges that should be addressed in order to push the technology further. Then we review the recent technologies and testbeds for WSNs. Finally, we identify several open research issues that need to be investigated in future. ::: ::: Our survey is different from existing surveys in that we focus on recent developments in wireless sensor network technologies. We review the leading research projects, standards and technologies, and platforms. Moreover, we highlight a recent phenomenon in WSN research that is to explore synergy between sensor networks and other technologies and explain how this can help sensor networks achieve their full potential. This paper intends to help new researchers entering the domain of WSNs by providing a comprehensive survey on recent developments.


title: Mechanisms of Neuropathic Pain

abstract: Neuropathic pain refers to pain that originates from pathology of the nervous system. Diabetes, infection (herpes zoster), nerve compression, nerve trauma, "channelopathies," and autoimmune disease are examples of diseases that may cause neuropathic pain. The development of both animal models and newer pharmacological strategies has led to an explosion of interest in the underlying mechanisms. Neuropathic pain reflects both peripheral and central sensitization mechanisms. Abnormal signals arise not only from injured axons but also from the intact nociceptors that share the innervation territory of the injured nerve. This review focuses on how both human studies and animal models are helping to elucidate the mechanisms underlying these surprisingly common disorders. The rapid gain in knowledge about abnormal signaling promises breakthroughs in the treatment of these often debilitating disorders.


title: Hough Transform from the Radon Transform

abstract: An appropriate special case of a transform developed by J. Radon in 1917 is shown to have the major properties of the Hough transform which is useful for finding line segments in digital pictures. Such an observation may be useful in further efforts to generalize the Hough transform. Techniques for applying the Radon transform to lines and pixels are developed through examples, and the appropriate generalization to arbitrary curves is discussed.


Ballbot : A Low-Cost , Open-Source , Open-Design Robot for Tennis Ball Retrieval


title: Users in cells: A data traffic analysis

abstract: We present a large-scale cell based measurement analysis of the user behavior in a live operational HSDPA network. The motivations are: first, to understand the statistical properties of users in cells for refining network planning procedures; and second, to provide realistic traffic models for simulations of cellular packet-oriented networks. We provide an analysis of mean cell load over daytime, as well as models for short-term cell load in terms of user activity and throughput. Furthermore, an evaluation of user-sessions with respect to duration and mean throughput is given. Our findings lead to four different models reflecting realistic user-traffic load of cellular networks. To verify the concept we present respective simulations which investigate multiuser-scheduling in an LTE-network. They show that conventional simulation settings can lead to an overestimation of performance.


Syntactic Stylometry: Using Sentence Structure for Authorship Attribution


title: Double-Input Bidirectional DC/DC Converter Using Cell-Voltage Equalizer With Flyback Transformer

abstract: In this paper, a double-input bidirectional dc/dc converter that uses a rechargeable battery and an ultracapacitor (UC) is proposed. This converter is connected to a cell-voltage equalizer between the battery and UC. The cell-voltage equalizer enables cell-voltage equalization and energy transfer between the battery and UC. This converter has six operational modes. These modes are investigated by reduced-power-scale circuit experiment. In addition, the circuit operation under the combination of the six modes is verified using a PSIM simulator in a large power scale.


title: Security analysis of logic obfuscation

abstract: Due to globalization of Integrated Circuit (IC) design flow, rogue elements in the supply chain can pirate ICs, overbuild ICs, and insert hardware trojans. EPIC [1] obfuscates the design by randomly inserting additional gates; only a correct key makes the design to produce correct outputs. We demonstrate that an attacker can decipher the obfuscated nctlist, in a time linear to the number of keys, by sensitizing the key values to the output. We then develop techniques to fix this vulnerability and make obfuscation truly exponential in the number of inserted keys.


title: Digital interactions and brand experience design: a future perspective

abstract: This paper introduces an overview and positioning of the contemporary brand experience in the digital context. With technological advances in games, gamification and emerging technologies, such as Virtual Reality (VR) and Artificial Intelligence (AI), it is possible that brand experiences are getting more pervasive and seamless. In this paper, we review the current theories around multi-sensory brand experience and the role of new technologies in the whole consumer journey, including pre-purchase, purchase and post-purchase stages. After this analysis, we introduce a conceptual framework that promotes a continuous loop of consumer experience and engagement from different and new touch points, which could be augmented by games, gamification and emerging technologies. Based on the framework, we conclude this paper with propositions and recommendations for future research in contemporary brand management, which could help brand managers and designers to deal with technological challenges posed by the contemporary society.


title: Preparation and Application of Standardized Typical Volatile Components Fraction from Turmeric (Curcuma longa L.) by Supercritical Fluid Extraction and Step Molecular Distillation

abstract: A green and reliable method using supercritical fluid extraction (SFE) and molecular distillation (MD) was optimized for the separation and purification of standardized typical volatile components fraction (STVCF) from turmeric to solve the shortage of reference compounds in quality control (QC) of volatile components. A high quality essential oil with 76.0% typical components of turmeric was extracted by SFE. A sequential distillation strategy was performed by MD. The total recovery and purity of prepared STVCF were 97.3% and 90.3%, respectively. Additionally, a strategy, i.e., STVCF-based qualification and quantitative evaluation of major bioactive analytes by multiple calibrated components, was proposed to easily and effectively control the quality of turmeric. Compared with the individual calibration curve method, the STVCF-based quantification method was demonstrated to be credible and was effectively adapted for solving the shortage of reference volatile compounds and improving the QC of typical volatile components in turmeric, especially its functional products.


title: Two-way Communication Channels

abstract: input at terminal 2 and Y2 the corresponding output. Once each second, say, new inputs xi and x2 may be chosen from corresponding input alphabets and put into the channel; outputs yi and Y2 may then be observed. These outputs will be related statistically to the inputs and perhaps historically to previous inputs and outputs if the channel has memory. The problem is to communicate in both directions through the channel as effectively as possible. Particularly, we wish to determine what pairs of signalling rates R1 and R2 for the two directions can be approached with arbitrarily small error probabilities.


title: A typed monadic embedding of aspects

abstract: We describe a novel approach to embed pointcut/advice aspects in a typed functional programming language like Haskell. Aspects are first-class, can be deployed dynamically, and the pointcut language is extensible. Type soundness is guaranteed by exploiting the underlying type system, in particular phantom types and a new anti-unification type class. The use of monads brings type-based reasoning about effects for the first time in the pointcut/advice setting, thereby practically combining Open Modules and EffectiveAdvice, and enables modular extensions of the aspect language.


title: Diabetic retinopathy image database(DRiDB): A new database for diabetic retinopathy screening programs research

abstract: Diabetic retinopathy is one of the leading disabling chronic diseases, and one of the leading causes of preventable blindness in the world. Early diagnosis of diabetic retinopathy enables timely treatment and in order to achieve it a major effort will have to be invested into screening programs and especially into automated screening programs. For automated screening programs to work robustly a representative fundus image database is required. In this paper we give an overview of currently available databases and present a new diabetic retinopathy database. Our database is to our knowledge the first and only database which has diabetic retinopathy pathologies and major fundus structures annotated for every image from the database which makes it perfect for design and evaluation of currently available and new image processing algorithms for early detection of diabetic retinopathy using color fundus images.


title: Towards an effective and unbiased ranking of scientific literature through mutual reinforcement

abstract: It is important to help researchers find valuable scientific papers from a large literature collection containing information of authors, papers and venues. Graph-based algorithms have been proposed to rank papers based on networks formed by citation and co-author relationships. This paper proposes a new graph-based ranking framework MutualRank that integrates mutual reinforcement relationships among networks of papers, researchers and venues to achieve a more synthetic, accurate and fair ranking result than previous graph-based methods. MutualRank leverages the network structure information among papers, authors, and their venues available from a literature collection dataset and sets up a unified mutual reinforcement model that involves both intra- and inter-network information for ranking papers, authors and venues simultaneously. To evaluate, we collect a set of recommended papers from websites of graduate-level computational linguistics courses of 15 top universities as the benchmark and apply different methods to estimate paper importance. The results show that MutualRank greatly outperforms the competitors including Pag-eRank, HITS and CoRank in ranking papers as well as researchers. The experimental results also demonstrate that venues ranked by MutualRank are reasonable.


title: What Does the Retina Know about Natural Scenes?

abstract: By examining the experimental data on the statistical properties of natural scenes together with (retinal) contrast sensitivity data, we arrive at a first principle, theoretical hypothesis for the purpose of retinal processing and its relationship to an animal's environment. We argue that the retinal goal is to transform the visual input as much as possible into a statistically independent basis as the first step in creating a redundancy reduced representation in the cortex, as suggested by Barlow. The extent of this whitening of the input is limited, however, by the need to suppress input noise. Our explicit theoretical solutions for the retinal filters also show a simple dependence on mean stimulus luminance: they predict an approximate Weber law at low spatial frequencies and a De Vries-Rose law at high frequencies. Assuming that the dominant source of noise is quantum, we generate a family of contrast sensitivity curves as a function of mean luminance. This family is compared to psychophysical data.


A highly sensitive method for mapping the 5′ termini of mRNAs


Topic Detection and Tracking


title: Hierarchical Multi-class Iris Classification for Liveness Detection

abstract: In modern society, iris recognition has become increasingly popular. The security risk of iris recognition is increasing rapidly because of the attack by various patterns of fake iris. A German hacker organization called Chaos Computer Club cracked the iris recognition system of Samsung Galaxy S8 recently. In view of these risks, iris liveness detection has shown its significant importance to iris recognition systems. The state-of-the-art algorithms mainly rely on hand-crafted texture features which can only identify fake iris images with single pattern. In this paper, we proposed a Hierarchical Multi-class Iris Classification (HMC) for liveness detection based on CNN. HMC mainly focuses on iris liveness detection of multi-pattern fake iris. The proposed method learns the features of different fake iris patterns by CNN and classifies the genuine or fake iris images by hierarchical multi-class classification. This classification takes various characteristics of different fake iris patterns into account. All kinds of fake iris patterns are divided into two categories by their fake areas. The process is designed as two steps to identify two categories of fake iris images respectively. Experimental results demonstrate an extremely higher accuracy of iris liveness detection than other state-of-the-art algorithms. The proposed HMC remarkably achieves the best results with nearly 100% accuracy on ND-Contact, CASIA-Iris-Interval, CASIA-Iris-Syn and LivDet-Iris-2017-Warsaw datasets. The method also achieves the best results with 100% accuracy on a hybrid dataset which consists of ND-Contact and LivDet-Iris-2017-Warsaw datasets.


title: A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce

abstract: Collaborative filtering is one of the most widely adopted and successful recommendation approaches. Unlike approaches based on intrinsic consumer and product characteristics, CF characterizes consumers and products implicitly by their previous interactions. The simplest example is to recommend the most popular products to all consumers. Researchers are advancing CF technologies in such areas as algorithm design, human- computer interaction design, consumer incentive analysis, and privacy protection.


title: New challenges for feature selection Approximating Mutual Information by Maximum Likelihood Density Ratio Estimation

abstract: Mutual information is useful in various data processing tasks such as feature selection or independent component analysis. In this paper, we propose a new method of approximating mutual information based on maximum likelihood estimation of a density ratio function. Our method, called Maximum Likelihood Mutual Information (MLMI), has several attractive properties, e.g., density estimation is not involved, it is a single-shot procedure, the global optimal solution can be efficiently computed, and cross-validation is available for model selection. Numerical experiments show that MLMI compares favorably with existing methods.


title: Abstract data types and software validation

abstract: A data abstraction can be naturally specified using algebraic axioms. The virtue of these axioms is that they permit a representation-independent formal specification of a data type. An example is given which shows how to employ algebraic axioms at successive levels of implementation. The major thrust of the paper is twofold. First, it is shown how the use of algebraic axiomatizations can simplify the process of proving the correctness of an implementation of an abstract data type. Second, semi-automatic tools are described which can be used both to automate such proofs of correctness and to derive an immediate implementation from the axioms. This implementation allows for limited testing of programs at design time, before a conventional implementation is accomplished.


title: Virtual Reality as Communication Tool: A Sociocognitive Analysis

abstract: Virtual reality (VR) is usually described by the media as a particular collection of technological hardware: a computer capable of 3-D real-time animation, a head-mounted display, and data gloves equipped with one or more position trackers. However, this focus on technology is somewhat disappointing for communication researchers and VR designers. To overcome this limitation, this paper describes VR as a communication tool: a communication medium in the case of multiuser VR and a communication interface in single-user VR. The consequences of this approach for the design and the development of VR systems are presented, together with the methodological and technical implications for the study of interactive communication via computers.


title: An In-field Automatic Wheat Disease Diagnosis System

abstract: Crop diseases are responsible for the major production reduction and economic losses in agricultural industry world- wide. Monitoring for health status of crops is critical to control the spread of diseases and implement effective management. This paper presents an in-field automatic wheat disease diagnosis system based on a weakly super- vised deep learning framework, i.e. deep multiple instance learning, which achieves an integration of identification for wheat diseases and localization for disease areas with only image-level annotation for training images in wild conditions. Furthermore, a new in-field image dataset for wheat disease, Wheat Disease Database 2017 (WDD2017), is collected to verify the effectiveness of our system. Under two different architectures, i.e. VGG-FCN-VD16 and VGG-FCN-S, our system achieves the mean recognition accuracies of 97.95% and 95.12% respectively over 5-fold cross-validation on WDD2017, exceeding the results of 93.27% and 73.00% by two conventional CNN frameworks, i.e. VGG-CNN-VD16 and VGG-CNN-S. Experimental results demonstrate that the proposed system outperforms conventional CNN architectures on recognition accuracy under the same amount of parameters, meanwhile main- taining accurate localization for corresponding disease areas. Moreover, the proposed system has been packed into a real-time mobile app to provide support for agricultural disease diagnosis.


title: Performance improvement of least-recently-used policy in web proxy cache replacement using supervised machine learning

abstract: Web proxy caching is one of the most successful solutions for improving the performance of Web-based systems. In Web proxy caching, Least-Recently-Used (LRU) policy is the most common proxy cache replacement policy, which is widely used in Web proxy cache management. However, LRU are not efficient enough and may suffer from cache pollution with unwanted Web objects. Therefore, in this paper, LRU policy is enhanced using popular supervised machine learning techniques such as a support vector machine (SVM), a naive Bayes classifier (NB) and a decision tree (C4.5). SVM, NB and C4.5 are trained from Web proxy logs files to predict the class of objects that would be re-visited. More significantly, the trained SVM, NB and C4.5 classifiers are intelligently incorporated with the traditional LRU algorithm to present three novel intelligent Web proxy caching approaches, namely SVM-LRU, NB-LRU and C4.5-LRU. In the proposed intelligent LRU approaches, unwanted objects classified by machine learning classifier are placed in the middle of the cache stack used, so these objects are efficiently removed at an early stage to make space for new incoming Web objects. The simulation results demonstrated that the average improvement ratios of hit ratio achieved by SVM-LRU, NB-LRU and C4.5-LRU over LRU increased by 30.15%, 32.60% and 31.05 % respectively, while the average improvement ratios of byte hit ratio increased by 32.43%, 69.56% and 28.41%, respectively.


title: Soft starting control of single-phase induction motor using PWM AC Chopper control technique

abstract: This paper presents a soft starting control scheme of a capacitor start and run type single-phase induction motor(SPIM) using a PWM AC Chopper control technique to control an amplitude of the motor starting current. The actual current of the motor during start up is used as a feedback signal for comparing with a setting value. The error, then, is sent to a PI controller. The output of the PI controller is compared with a carrier to generate a PWM signal. The MATLAB/Simulink is used for simulations. The results of the simulations are the amplitude of the motor starting current in accordance with the setting value which is around 150-200% of the rated motor current, the energy consumption during the start up and the ripples in the electromagnetic torque. It is found that the control scheme proposed in this paper is effective and can be considered as an alternative in a starting method for SPIM.


title: The Role of Conscious Reasoning and Intuition in Moral Judgment Testing Three Principles of Harm

abstract: Is moral judgment accomplished by intuition or conscious reasoning? An answer demands a detailed account of the moral principles in question. We investi- gated three principles that guide moral judgments: (a) Harm caused by action is worse than harm caused by omission,(b)harmintendedasthemeanstoagoalisworse than harm foreseen as the side effect of a goal, and (c) harm involving physical contact with the victim is worse than harm involving no physical contact. Asking whether these principles are invoked to explain moral judgments, we found that subjects generally appealed to the first and third principles in their justifications, but not to the second. This finding has significance for methods and theories of moral psychology: The moral principles used in judgment must be directly compared with those articulated in justification, and doing so shows that some moral principles are available to conscious reasoning whereas others are not.


title: BigIN4: Instant, Interactive Insight Identification for Multi-Dimensional Big Data

abstract: The ability to identify insights from multi-dimensional big data is important for business intelligence. To enable interactive identification of insights, a large number of dimension combinations need to be searched and a series of aggregation queries need to be quickly answered. The existing approaches answer interactive queries on big data through data cubes or approximate query processing. However, these approaches can hardly satisfy the performance or accuracy requirements for ad-hoc queries demanded by interactive exploration. In this paper, we present BigIN4, a system for instant, interactive identification of insights from multi-dimensional big data. BigIN4 gives insight suggestions by enumerating subspaces and answers queries by combining data cube and approximate query processing techniques. If a query cannot be answered by the cubes, BigIN4 decomposes it into several low dimensional queries that can be directly answered by the cubes through an online constructed Bayesian Network and gives an approximate answer within a statistical interval. Unlike the related works, BigIN4 does not require any prior knowledge of queries and does not assume a certain data distribution. Our experiments on ten real-world large-scale datasets show that BigIN4 can successfully identify insights from big data. Furthermore, BigIN4 can provide approximate answers to aggregation queries effectively (with less than 10% error on average) and efficiently (50x faster than sampling-based methods).


title: Mapping the world's photos

abstract: We investigate how to organize a large collection of geotagged photos, working with a dataset of about 35 million images collected from Flickr. Our approach combines content analysis based on text tags and image data with structural analysis based on geospatial data. We use the spatial distribution of where people take photos to define a relational structure between the photos that are taken at popular places. We then study the interplay between this structure and the content, using classification methods for predicting such locations from visual, textual and temporal features of the photos. We find that visual and temporal features improve the ability to estimate the location of a photo, compared to using just textual features. We illustrate using these techniques to organize a large photo collection, while also revealing various interesting properties about popular cities and landmarks at a global scale.


title: Computational Geometry and Computer Graphics

abstract: The interaction between computer graphics and computational geometry is explored through two scenarios. Spatial subdivisions studied from the viewpoint of computational geometry are shown to have found application in computer graphics. Hidden surface removal problems of computer graphics have led to sweep-line and area subdivision algorithms in computational geometry. Two promising research area with practical applications, precise computation and polyhedral decomposition, are examined. >


title: Building personal maps from gps data

abstract: In this article we discuss an assisted cognition information technology system that can learn personal maps customized for each user and infer his daily activities and movements from raw GPS data. The system uses discriminative and generative models for different parts of this task. A discriminative relational Markov network is used to extract significant places and label them; a generative dynamic Bayesian network is used to learn transportation routines, and infer goals and potential user errors at real time. We focus on the basic structures of the models and briefly discuss the inference and learning techniques. Experiments show that our system is able to accurately extract and label places, predict the goals of a person, and recognize situations in which the user makes mistakes, such as taking a wrong bus.


title: An agile implementation of SCRUM

abstract: Is Agile a way to cut corners? To some, the use of an Agile Software Development Methodology has a negative connotation - “Oh, you're just not producing any documentation”. So can a team with no experience in Agile successfully implement and use SCRUM?


title: POPULATION GROWTH AND PREFERENCE CHANGE IN A GENERALIZED SOLOW GROWTH MODEL WITH GENDER TIME DISTRIBUTIONS

abstract: The study builds a model of dynamic interactions between the birth rate, the mortality rate, the population, wealth accumulation, time distribution between work, leisure and children caring, habit formation and preference change. The production technology and markets are built on the Solow growth model. We base our modeling the population dynamics on the Haavelmo population model and the Barro-Becker fertility choice model. This study takes account of habit formation and preference change. Although it is influenced by the Ramsey growth theory with time preference and habit formation, it uses Zhang’s approach to the household with habit formation and preference change. We synthesize different dynamic forces in a compact framework, using the utility function proposed by Zhang. Analytically, we focus on transitional processes as well as economic equilibrium. As the economic system is given by autonomous nonlinear differential equations, it is not easy to analyze its behavior. We simulate the model to demonstrate the existence of an equilibrium point and plot the motion of the dynamic system. We examine the effects of changes in weights given to the habit stock of children, the wife’s wage rate having negative impact on the propensity to have children, the wife weighing less the habit stock of leisure time, the wife’s habit stock of leisure time having negative impact on the husband’s propensity to use leisure time, the wife’s wage rate having negative impact on the husband’s propensity to use leisure time, woman’s human capital being improved, a rise in the total factor productivity, and the mother spending more time on each child fostering.


Pupil Dilation and Eye-tracking


title: Collaborative Filtering on Ordinal User Feedback

abstract: We propose a collaborative filtering (CF) recommendation framework which is based on viewing user feedback on products as ordinal, rather than the more common numerical view. Such an ordinal view frequently provides a more natural reflection of the user intention when providing qualitative ratings, allowing users to have different internal scoring scales. Moreover, we can address scenarios where assigning numerical scores to different types of user feedback would not be easy. The framework can wrap most collaborative filtering algorithms, enabling algorithms previously designed for numerical values to handle ordinal values. We demonstrate our framework by wrapping a leading matrix factorization CF method. A cornerstone of our method is its ability to predict a full probability distribution of the expected item ratings, rather than only a single score for an item. One of the advantages this brings is a novel approach to estimating the confidence level in each individual prediction. Compared to previous approaches to confidence estimation, ours is more principled and empirically superior in its accuracy. We demonstrate the efficacy of the approach on two of the largest publicly available datasets: the Netflix data and the Yahoo! Music data.


title: Synthetic Aperture Sonar: A Review of Current Status

abstract: This is a review paper that surveys past work in, and the recent status of, active synthetic aperture sonar (SAS). It covers the early historical development of SAS with its provenance in synthetic aperture radar (SAR) and flows through into what work has been published in the open literature up to early 2007. The list of references is sufficiently complete to include most past and recent SAS publications in the open refereed literature.


title: Detection of Cyber-Physical Faults and Intrusions from Physical Correlations

abstract: Cyber-physical systems are critical infrastructures that are crucial both to the reliable delivery of resources such as energy, and to the stable functioning of automatic and control architectures. These systems are composed of interdependent physical, control and communications networks described by disparate mathematical models creating scientific challenges that go well beyond the modeling and analysis of the individual networks. A key challenge in cyber-physical defense is a fast online detection and localization of faults and intrusions without prior knowledge of the failure type. We describe a set of techniques for the efficient identification of faults from correlations in physical signals, assuming only a minimal amount of available system information. The performance of our detection method is illustrated on data collected from a large building automation system.


title: Design of a Sliding-Mode-Controlled SEPIC for PV MPPT Applications

abstract: In this paper, the procedure for designing a sliding-mode controller for maximum power point tracking photovoltaic (PV) applications is proposed. It is applied to a single-ended primary inductor converter, thus to a fourth-order topology, but it can be extended to a wide class of converters suitable for PV applications. The reachability and existence conditions give rise to a number of design inequalities that add to the classical steady-state conditions in order to have the desired closed-loop converter's performances. The superiority of the control technique over the classical tracking methods is analytically demonstrated through the procedure proposed in this paper. The analytical results have been validated by means of both simulations and experiments.


title: Symbolic Representation Based on Temporal Order Information for Time Series Classification

abstract: In the last decade symbolic representations approaches have been proposed for knowledge discovery in time series. However, the conventional symbolic methods ignore the temporal order of symbols, so this core feature of time series is lost. In this paper, to treat this problem we present a symbolic representation method to incorporate the temporal information in the symbols. The proposed method was evaluated on a decision tree classification using the Symbolic Aggregate Approximation and Equal Fixed Values Discretization approaches applied to 45 time series datasets that includes artificial and real-world data. The experimental results demonstrate the method effectiveness to improve the classification accuracy and the decision tree size for most datasets while preserving the temporal order information into symbolic representations.


title: Goal setting and task performance: 1969-1980.

abstract: Abstract : A review of both laboratory and field studies on the effect of setting goals when learning or performing a task found that specific, challenging goals led more often to higher performance than easy goals, 'do your best' goals or no goals. This is one of the most robust and replicable findings in the psychological literature, with 90% of the studies showing positive or partially positive results. The main mechanisms by which goals affect performance are by directing attention, mobilizing effort, increasing persistence, and motivating strategy development. Goal setting is most likely to improve task performance when the goals are specific and sufficiently challenging, when the subjects have sufficient ability (and ability differences are controlled), when feedback is provided to show progress in relation to the goal, when rewards such as money are given for goal attainment, when the exerimenter manager is supportive, and when the assigned goals are actually accepted by the individual. No reliable individual differences have emerged in goal setting studies, probably because goals were typically assigned rather than self-set. Need for achievement and self esteem may be the most promising individual difference variables. (Author)


title: A Multidimensional Analysis Approach For Electronic Citizens Participation

abstract: The participation of citizens in governments' decision-making is gaining more and more attention with the emergence and the availability of information and communication technologies (ICTs). However, it is still necessary to seek the most effective means to implement this activity in a way and a time that gives the citizens the opportunity to have a real influence on the decisions being made. This paper proposes a multidimensional analysis framework to better understand citizens' participation in a decision-making perspective. The objective of this framework is to provide a methodological approach for the collection, processing and analysis of information provided by citizens during an electronic citizens participation activity.


title: Low-Cost “ball and Plate” design and implementation for learning control systems

abstract: Undergraduate engineering students require a practical application of theoretical concepts learned in classrooms in order to appropriate a complete management of them. Our aim is to assist students to learn control systems theory in an engineering context, through the design and implementation of a simple and low cost ball and plate plant. Students are able to apply mathematical and computational modelling tools, control systems design, and real-time software-hardware implementation while solving a position regulation problem. The whole project development is presented and may be assumed as a guide for replicate results or as a basis for a new design approach. In both cases, we end up in a tool available to implement and assess control strategies experimentally.


title: PLDA: Parallel Latent Dirichlet Allocation for Large-scale Applications

abstract: This paper presents PLDA, our parallel implementation of Latent Dirichlet Allocation on MPI and MapReduce. PLDA smooths out storage and computation bottlenecks and provides fault recovery for lengthy distributed computations. We show that PLDA can be applied to large, real-world applications and achieves good scalability. We have released MPI-PLDA to open source at http://code.google.com/p/plda under the Apache License.


title: Performance considerations of network functions virtualization using containers

abstract: The network performance of virtual machines plays a critical role in Network Functions Virtualization (NFV), and several technologies have been developed to address hardware-level virtualization shortcomings. Recent advances in operating system level virtualization and deployment platforms such as Docker have made containers an ideal candidate for high performance application encapsulation and deployment. However, Docker and other solutions typically use lower-performing networking mechanisms. In this paper, we explore the feasibility of using technologies designed to accelerate virtual machine networking with containers, in addition to quantifying the network performance of container-based VNFs compared to the state-of-the-art virtual machine solutions. Our results show that containerized applications can provide lower latency and delay variation, and can take advantage of high performance networking technologies previously only used for hardware virtualization.


title: Wearable jamming mitten for virtual environment haptics

abstract: This paper presents a new mitten incorporating vacuum layer jamming technology to provide haptic feedback to a user. We demonstrate that layer jamming technology can be successfully applied to a mitten, and discuss advantages layer jamming provides as a wearable technology through its low profile form factor. Jamming differs from traditional wearable haptic systems by restricting a user's movement, rather than applying an actuation force on the user's body. Restricting the user's movement is achieved by varying the stiffness of wearable items, such as gloves. We performed a pilot study where the qualitative results showed users found the haptic sensation of the jamming mitten similar to grasping the physical counterpart.


title: Visual Web Information Extraction with Lixto

abstract: We present new techniques for supervised wrapper generation and automated web information extraction, and a system called Lixto implementing these techniques. Our system can generate wrappers which translate relevant pieces of HTML pages into XML. Lixto, of which a working prototype has been implemented, assists the user to semi-automatically create wrapper programs by providing a fully visual and interactive user interface. In this convenient user-interface very expressive extraction programs can be created. Internally, this functionality is reected by the new logicbased declarative language Elog. Users never have to deal with Elog and even familiarity with HTML is not required. Lixto can be used to create an \XML-Companion" for an HTML web page with changing content, containing the continually updated XML translation of the relevant information.


title: Distance measurement system for smart vehicles

abstract: Advanced Driver Assistance Systems (ADAS) refer to the set of smart components such as object detection and tracking systems used to protect drivers and road users. An important task that should be performed by ADAS is the measurement of the distance between a vehicle and a detected object. This helps to anticipate the position of objects in the lane, prevents collisions and improves the safety on the road. For this purpose, Distance Measurement Systems (DMS) including Vision-based techniques, Millimeter wave radars, Infrared ranging, and LIDAR are used in the automotive industry. In this paper, we discuss the design of such systems and their use in smart vehicles. Particular attention is given to vision-based techniques because they are considered among the most accurate systems used to identify targets and to measure the distance from them to a vehicle.


title: Summary on Several Key Techniques in 3D Geological Modeling

abstract: Several key techniques in 3D geological modeling including planar mesh generation, spatial interpolation, and surface intersection are summarized in this paper. Note that these techniques are generic and widely used in various applications but play a key role in 3D geological modeling. There are two essential procedures in 3D geological modeling: the first is the simulation of geological interfaces using geometric surfaces and the second is the building of geological objects by means of various geometric computations such as the intersection of surfaces. Discrete geometric surfaces that represent geological interfaces can be generated by creating planar meshes first and then spatially interpolating; those surfaces intersect and then form volumes that represent three-dimensional geological objects such as rock bodies. In this paper, the most commonly used algorithms of the key techniques in 3D geological modeling are summarized.


title: Expect the Unexpected: Failure to Anticipate Similarities Leads to an Intergroup Forecasting Error

abstract: People often expect interactions with outgroup members to go poorly, but little research examines the accuracy of these expectations, reasons why expectations might be negatively biased, and ways to bring expectations in line with experiences. The authors found that intergroup interactions were more positive than people expected them to be (Pilot Study, Study 1). One reason for this intergroup forecasting error is that people focus on their dissimilarities with outgroup members (Study 1). When the authors focused White participants' attention on the ways they were similar to a Black participant, their intergroup expectations changed to match their positive experiences (Studies 2 & 3). Regardless of focus, Whites expected to have pleasant intragroup interactions, and they were accurate (Study 4).


title: Human-Data Interaction: The Human Face of the Data-Driven Society

abstract: The increasing generation and collection of personal data has created a complex ecosystem, often collaborative but sometimes combative, around companies and individuals engaging in the use of these data. We propose that the interactions between these agents warrants a new topic of study: Human-Data Interaction (HDI). In this paper we discuss how HDI sits at the intersection of various disciplines, including computer science, statistics, sociology, psychology and behavioural economics. We expose the challenges that HDI raises, organised into three core themes of legibility, agency and negotiability, and we present the HDI agenda to open up a dialogue amongst interested parties in the personal and big data ecosystems.


title: The eNTERFACE’05 Audio-Visual Emotion Database

abstract: This paper presents an audio-visual emotion database that can be used as a reference database for testing and evaluating video, audio or joint audio-visual emotion recognition algorithms. Additional uses may include the evaluation of algorithms performing other multimodal signal processing tasks, such as multimodal person identification or audio-visual speech recognition. This paper presents the difficulties involved in the construction of such a multimodal emotion database and the different protocols that have been used to cope with these difficulties. It describes the experimental setup used for the experiments and includes a section related to the segmentation and selection of the video samples, in such a way that the database contains only video sequences carrying the desired affective information. This database is made publicly available for scientific research purposes.


title: Checking email less frequently reduces stress

abstract: Limiting the frequency of checking email throughout the day reduced daily stress.Lower daily stress predicts greater well-being (e.g., higher positive affect).The frequency of checking email did not directly impact other well-being outcomes. Using email is one of the most common online activities in the world today. Yet, very little experimental research has examined the effect of email on well-being. Utilizing a within-subjects design, we investigated how the frequency of checking email affects well-being over a period of two weeks. During one week, 124 adults were randomly assigned to limit checking their email to three times a day; during the other week, participants could check their email an unlimited number of times per day. We found that during the limited email use week, participants experienced significantly lower daily stress than during the unlimited email use week. Lower stress, in turn, predicted higher well-being on a diverse range of well-being outcomes. These findings highlight the benefits of checking email less frequently for reducing psychological stress.


title: Secure communication mechanisms for GSM networks

abstract: With the advance of wireless communications technology, mobile communications has become more convenient than ever. However, because of the openness of wireless communications, the protection of the privacy between communicating parties is becoming a very important issue. We focus on the security of the Global System for Mobile communication (GSM) networks. A secure communication architecture for the GSM network is proposed. In the proposed architecture, we use public-key cryptography for user authentication and stream cipher for message encryption and decryption. An authentication protocol and a key generation method are presented in conjunction with the proposed architecture. Cryptanalysis and operational analysis show that the authentication protocol is secure and efficient. Simulation results indicate that the key generation method can always produce key strings of evenly distributed 0s and 1s and with infinite period.


title: Instantaneous ego-motion estimation using multiple Doppler radars

abstract: The estimation of the ego-vehicle's motion is a key capability for advanced driving assistant systems and mobile robot localization. The following paper presents a robust algorithm using radar sensors to instantly determine the complete 2D motion state of the ego-vehicle (longitudinal, lateral velocity and yaw rate). It evaluates the relative motion between at least two Doppler radar sensors and their received stationary reflections (targets). Based on the distribution of their radial velocities across the azimuth angle, non-stationary targets and clutter are excluded. The ego-motion and its corresponding covariance matrix are estimated. The algorithm does not require any preprocessing steps such as clustering or clutter suppression and does not contain any model assumptions. The sensors can be mounted at any position on the vehicle. A common field of view is not required, avoiding target association in space. As an additional benefit, all targets are instantly labeled as stationary or non-stationary.


title: Enhancing EMV Tokenisation with Dynamic Transaction Tokens

abstract: Europay MasterCard Visa (EMV) Tokenisation specification details how the risk involved in Personal Account Number (PAN) compromise can be prevented by using tokenisation. In this paper, we identify two main potential problem areas that raise concerns about the security of tokenised EMV contactless mobile payments, especially when the same token also called a static token is used to pay for all transactions. We then discuss five associated attack scenarios that would let an adversary compromise payment transactions. It is paramount to address these security concerns to secure tokenised payments, which is the main focus of the paper. We propose a solution that would enhance the security of this process when a smart phone is used to make a tokenised contactless payment. In our design, instead of using a static token in every transaction, a new dynamic token and a token cryptogram is used. The solution is then analysed against security and protocol objectives. Finally the proposed protocol was subjected to mechanical formal analysis using Scyther which did not find any feasible attacks within the bounded state space.


title: Shoot development in Quercus suber L. somatic embryos

abstract: N6-Benzyladenine (BA; 0.04–4μM) application to germinated Quercus suber somatic embryos considerably increased caulinar apex elongation frequency and maintained active growth in the plantlets, although it did not have a significant effect on the percentage of shoots with normal morphology. The addition of 0.5 μM indoleacetic acid together with the cytokinin did not have any effect. The use of a low concentration (0.04 μM) of BA allowed the appropriate radicle elongation in all germinating somatic embryos, but higher concentrations arrested this elongation.


title: Effects of eCG and FSH on ovarian response, recovery rate and number and quality of oocytes obtained by ovum pick-up in Holstein cows.

abstract: Abstract The goal of the present study was to compare the ovarian response, oocyte yields per animal, and the morphological quality of oocytes collected by ultrasound guided follicular aspiration from Holstein cows treated either with FSH or eCG. Twenty four normal cyclic, German Holstein cows were randomly divided into two groups. Fourteen cows received 3000 IU eCG on day-4 prior to ovum pick-up (OPU) (day 0), 2 days later (day-2), 625 μg cloprostenol was administered. On day-1 GnRH was administered i.m. and 24 h later OPU (day 0) was performed. In ten cows a total dose of 500 IU follicle stimulating hormone (Pluset) was administered intramuscularly in a constant dosage for 4 days with intervals of 12 h, starting on day-5. Luteolysis was induced by application of 625 μg cloprostenol on day-2. On day-1 (24 h after the last FSH treatment) GnRH was administered i.m. and 24 h later OPU (day 0) was performed. Ovarian follicles were visualized on the ultrasound monitor, counted and recorded. All visible antral follicles were punctured. Recovered oocytes were graded morphologically based on the cumulus investment. Average follicle number in ovaries was higher in FSH group than eCG group ( p p p In conclusion, these results suggest that ovarian response, follicle number in ovaries and oocyte quality are affected by the type of gonadotropin and FSH is better alternative than eCG for OPU treatment.


title: Parameter Design of a Two-Current-Loop Controller Used in a Grid-Connected Inverter System With LCL Filter

abstract: LCL filters offer a better choice of attenuating switching frequency harmonics. However, in a grid-connected system, an LCL filter may cause resonance which is a disaster for the system's stability. In order to solve the problem, a two-current-loop control strategy, which includes grid-current outer loop and filter-capacitor-current inner loop, is adopted here. The implementation of this strategy is easy, but the tuning procedure is complex since the outer and inner controllers cannot cooperate well if the parameters of the controllers are not suitable. There is no literature which mentions a method to help give out accurate parameters of the controller. The difficulty of designing the controller is that only two feedbacks cannot provide complete information of a three-order LCL filter. A specific method is proposed in this paper to design the controller. The advantage of this method is to provide a way to maximize the utilization of the two feedbacks to get good system performance through parameter determination. The practicability of the method is tested by using bode diagram, and the antidistortion ability of the system is analyzed. Finally, experimental results verify the availability and correctness of the proposed method.


title: Multimodal detection of head-movement artefacts in EEG.

abstract: Abstract Artefacts arising from head movements have been a considerable obstacle in the deployment of automatic event detection systems in ambulatory EEG. Recently, gyroscopes have been identified as a useful modality for providing complementary information to the head movement artefact detection task. In this work, a comprehensive data fusion analysis is conducted to investigate how EEG and gyroscope signals can be most effectively combined to provide a more accurate detection of head-movement artefacts in the EEG. To this end, several methods of combining these physiological and physical signals at the feature, decision and score fusion levels are examined. Results show that combination at the feature, score and decision levels is successful in improving classifier performance when compared to individual EEG or gyroscope classifiers, thus confirming that EEG and gyroscope signals carry complementary information regarding the detection of head-movement artefacts in the EEG. Feature fusion and the score fusion using the sum-rule provided the greatest improvement in artefact detection. By extending multimodal head-movement artefact detection to the score and decision fusion domains, it is possible to implement multimodal artefact detection in environments where gyroscope signals are intermittently available.


title: Query processing in sensor networks

abstract: Hardware for sensor nodes that combine physical sensors, actuators, embedded processors, and communication components has advanced significantly over the last decade, and made the large-scale deployment of such sensors a reality. Applications range from monitoring applications such as inventory maintenance over health care to military applications. In this paper, we evaluate the design of a query layer for sensor networks. The query layer accepts queries in a declarative language that are then optimized to generate ecient query execution plans with in-network processing which can significantly reduce resource requirements. We examine the main architectural components of such a query layer, concentrating on in-network aggregation, interaction of in-network aggregation with the wireless routing protocol, and distributed query processing. Initial simulation experiments with the ns-2 network simulator show the tradeos of our system.


title: Prediction of laminar vortex shedding over a cylinder using deep learning

abstract: Unsteady laminar vortex shedding over a circular cylinder is predicted using a deep learning technique, a generative adversarial network (GAN), with a particular emphasis on elucidating the potential of learning the solution of the Navier-Stokes equations. Numerical simulations at two different Reynolds numbers with different time-step sizes are conducted to produce training datasets of flow field variables. Unsteady flow fields in the future at a Reynolds number which is not in the training datasets are predicted using a GAN. Predicted flow fields are found to qualitatively and quantitatively agree well with flow fields calculated by numerical simulations. The present study suggests that a deep learning technique can be utilized for prediction of laminar wake flow in lieu of solving the Navier-Stokes equations.


title: Cannabis Roots: A Traditional Therapy with Future Potential for Treating Inflammation and Pain

abstract: Introduction: The roots of the cannabis plant have a long history of medical use stretching back millennia. However, the therapeutic potential of cannabis roots has been largely ignored in modern times. Discussion: In the first century, Pliny the Elder described in Natural Histories that a decoction of the root in water could be used to relieve stiffness in the joints, gout, and related conditions. By the 17th century, various herbalists were recommending cannabis root to treat inflammation, joint pain, gout, and other conditions. There has been a subsequent paucity of research in this area, with only a few studies examining the composition of cannabis root and its medical potential. Active compounds identified and measured in cannabis roots include triterpenoids, friedelin (12.8 mg/kg) and epifriedelanol (21.3 mg/kg); alkaloids, cannabisativine (2.5 mg/kg) and anhydrocannabisativine (0.3 mg/kg); carvone and dihydrocarvone; N-(p-hydroxy-β-phenylethyl)-p-hydroxy-trans-cinnamamide (1.6 mg/kg); various sterols such as sitosterol (1.5%), campesterol (0.78%), and stigmasterol (0.56%); and other minor compounds, including choline. Of note, cannabis roots are not a significant source of Δ9-tetrahydrocannabinol (THC), cannabidiol, or other known phytocannabinoids. Conclusion: The current available data on the pharmacology of cannabis root components provide significant support to the historical and ethnobotanical claims of clinical efficacy. Certainly, this suggests the need for reexamination of whole root preparations on inflammatory and malignant conditions employing modern scientific techniques.


title: DB2OWL: A Tool for Automatic Database-to-Ontology Mapping

abstract: We present a tool, called DB2OWL, to automatically generate ontologies from database schemas. The mapping process starts by detecting particular cases for conceptual elements in the database and accordingly converts database components to the corresponding ontology components. We have implemented a prototype of DB2OWL tool to create OWL ontology from relational database.


title: A Linear Multi-Mode CMOS Power Amplifier With Discrete Resizing and Concurrent Power Combining Structure

abstract: Efficiency degradation effects of power combining transformers with partially disabled inputs are quantitatively analyzed. To improve efficiencies in lower-power modes of a multi-mode class-AB power amplifier (PA), a discrete resizing technique is introduced in combination with a parallel-combining transformer (PCT). The two-stage PA implemented in a 0.18-μm CMOS technology also includes varactor-based tunable matching circuits. The design method involves parallel-combining of two power stages, each of which are divided into three sub-cells to facilitate discrete resizing. The parallel-combining of concurrently resized power cells minimizes undesired power loss through the transformer and helps the PA to utilize the transformer efficiency maximally independent of the number of combining cells. When operating in the high-power mode, the PA exhibits a peak output power of 31 dBm with a PAE of 34.8%. Power back-offs are realized by discretely turning off parallel sub-amplifier cells concurrently, achieving output power levels of 26 dBm and 22.3 dBm with respective PAE of 22.5% and 15%. The EVM has been measured with IEEE 802.11g WLAN and 802.16e WiMAX modulated signals in three operation modes. In the high-power mode, the PA dissipates 590 mA from a 3.3 V supply.


title: One-Shot Learning for Semantic Segmentation

abstract: Low-shot learning methods for image classification support learning from sparse data. We extend these techniques to support dense semantic image segmentation. Specifically, we train a network that, given a small set of annotated images, produces parameters for a Fully Convolutional Network (FCN). We use this FCN to perform dense pixel-level prediction on a test image for the new semantic class. Our architecture shows a 25% relative meanIoU improvement compared to the best baseline methods for one-shot segmentation on unseen classes in the PASCAL VOC 2012 dataset and is at least 3 times faster.


title: Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks

abstract: The design of brain-computer interface for the wheelchair for physically disabled people is presented. The design of the proposed system is based on receiving, processing, and classification of the electroencephalographic (EEG) signals and then performing the control of the wheelchair. The number of experimental measurements of brain activity has been done using human control commands of the wheelchair. Based on the mental activity of the user and the control commands of the wheelchair, the design of classification system based on fuzzy neural networks (FNN) is considered. The design of FNN based algorithm is used for brain-actuated control. The training data is used to design the system and then test data is applied to measure the performance of the control system. The control of the wheelchair is performed under real conditions using direction and speed control commands of the wheelchair. The approach used in the paper allows reducing the probability of misclassification and improving the control accuracy of the wheelchair.


title: Auto-grading for parallel programs

abstract: Fundamentals of Parallel Programming (COMP 322) is a required course for all Computer Science majors at Rice University. It introduces students to several basic concepts of parallel programming and parallel algorithms and follows a "pedagogic approach that exposes students to intellectual challenges in parallel software without enmeshing them in jargon and lower-level details of today's parallel systems". The material from COMP 322 has also been used in related courses at other universities including Harvey Mudd College and Brigham Young University. Currently, programming assignments in this class are manually graded by teaching assistants (TAs) for correctness, performance, style, and documentation. Students receive limited feedback as they progress through the assignment because TAs grade their homework only after the submission of a final version. Auto-graders are a common solution to this problem: they allow students to receive feedback as they work on their assignments rather than only after they have completed them. This results in higher quality submissions by allowing students to learn from mistakes as they make them, rather than days or weeks later. It also prevents the development of bad habits or mislearned knowledge by addressing mistakes early. Web-CAT is an advanced, customizable, and comprehensive automated grading system developed at Virginia Tech. It supports many models for program grading, assessment, and feedback generation. This paper describes our work on extending Web-CAT to address the requirements of Rice University's introductory parallel programming course, thereby creating infrastructure that can be used for similar courses at other universities and in online courses. Our system is planned for deployment in Spring 2016, hence this paper focuses on the design and implementation of this system.


Exploiting Depth and Highway Connections in Convolutional Recurrent Deep Neural Networks for Speech Recognition.


title: What makes Web sites credible?: a report on a large quantitative study

abstract: The credibility of web sites is becoming an increasingly important area to understand. To expand knowledge in this domain, we conducted an online study that investigated how different elements of Web sites affect people's perception of credibility. Over 1400 people participated in this study, both from the U.S. and Europe, evaluating 51 different Web site elements. The data showed which elements boost and which elements hurt perceptions of Web credibility. Through analysis we found these elements fell into one of seven factors. In order of impact, the five types of elements that increased credibility perceptions were “real-world feel”, “ease of use”, “expertise”, “trustworthiness”, and “tailoring”. The two types of elements that hurt credibility were “commercial implicationsrand “amateurism”. This large-scale study lays the groundwork for further research into the elements that affect Web credibility. The results also suggest implications for designing credible Web sites.


title: Studying Online Social Networks

abstract: When a computer network connects people or organizations, it is a social network. Yet the study of such computer-supported social networks has not received as much attention as studies of human-computer interaction, online person-to-person interaction, and computer-supported communication within small groups. We argue the usefulness of a social network approach for the study of computer-mediated communication. We review some basic concepts of social network analysis, describe how to collect and analyze social network data, and demonstrate where social network data can be, and have been, used to study computer-mediated communication. Throughout, we show the utility of the social network approach for studying computer-mediated communication, be it in computer-supported cooperative work, in virtual community, or in more diffuse interactions over less bounded systems such as the Internet.


title: Code vectors: understanding programs through embedded abstracted symbolic traces

abstract: With the rise of machine learning, there is a great deal of interest in treating programs as data to be fed to learning algorithms. However, programs do not start off in a form that is immediately amenable to most off-the-shelf learning techniques. Instead, it is necessary to transform the program to a suitable representation before a learning technique can be applied. In this paper, we use abstractions of traces obtained from symbolic execution of a program as a representation for learning word embeddings. We trained a variety of word embeddings under hundreds of parameterizations, and evaluated each learned embedding on a suite of different tasks. In our evaluation, we obtain 93% top-1 accuracy on a benchmark consisting of over 19,000 API-usage analogies extracted from the Linux kernel. In addition, we show that embeddings learned from (mainly) semantic abstractions provide nearly triple the accuracy of those learned from (mainly) syntactic abstractions.


title: Visual Representations for Semantic Target Driven Navigation

abstract: What is a good visual representation for navigation? We study this question in the context of semantic visual navigation, which is the problem of a robot finding its way through a previously unseen environment to a target object, e.g. go to the refrigerator. Instead of acquiring a metric semantic map of an environment and using planning for navigation, our approach learns navigation policies on top of representations that capture spatial layout and semantic contextual cues. We propose to use semantic segmentation and detection masks as observations obtained by state-of-the-art computer vision algorithms and use a deep network to learn the navigation policy. The availability of equitable representations in simulated environments enables joint training using real and simulated data and alleviates the need for domain adaptation or domain randomization commonly used to tackle the sim-to-real transfer of the learned policies. Both the representation and the navigation policy can be readily applied to real non-synthetic environments as demonstrated on the Active Vision Dataset [1]. Our approach successfully gets to the target in 54% of the cases in unexplored environments, compared to 46% for a non-learning based approach, and 28% for a learning-based baseline.


title: Critical Points to Determine Persistence Homology

abstract: Computation of simplicial complexes of a large point cloud often relies on extracting a sample, to reduce the associated computational burden. The sampling of the point cloud should minimally mutilate the features of the underlying object to enable effective "feature extraction" that lies at the center of modern data analysis techniques, e.g., machine learning. The study considers sampling critical points of a Morse function associated with a point cloud, to approximate the Vietoris-Rips complex and to compute persistence homology. The effectiveness of the approach is compared with the farthest point sampling (FPS), in the context of two classification problems. The empirical results suggest that sampling critical points of the Morse function can be more effective than FPS when determining the persistence homology for the cases where the critical points play a decisive role.


title: Middleware for Internet of Things: A Survey

abstract: The Internet of Things (IoT) envisages a future in which digital and physical things or objects (e.g., smartphones, TVs, cars) can be connected by means of suitable information and communication technologies, to enable a range of applications and services. The IoT’s characteristics, including an ultra-large-scale network of things, device and network level heterogeneity, and large numbers of events generated spontaneously by these things, will make development of the diverse applications and services a very challenging task. In general, middleware can ease a development process by integrating heterogeneous computing and communications devices, and supporting interoperability within the diverse applications and services. Recently, there have been a number of proposals for IoT middleware. These proposals mostly addressed wireless sensor networks (WSNs), a key component of IoT, but do not consider RF identification (RFID), machine-to-machine (M2M) communications, and supervisory control and data acquisition (SCADA), other three core elements in the IoT vision. In this paper, we outline a set of requirements for IoT middleware, and present a comprehensive review of the existing middleware solutions against those requirements. In addition, open research issues, challenges, and future research directions are highlighted.


title: Work Engagement and Machiavellianism in the Ethical Leadership Process

abstract: Leaders who express an ethical identity are proposed to affect followers’ attitudes and work behaviors. In two multi-source studies, we first test a model suggesting that work engagement acts as a mediator in the relationships between ethical leadership and employee initiative (a form of organizational citizenship behavior) as well as counterproductive work behavior. Next, we focus on whether ethical leadership always forms an authentic expression of an ethical identity, thus in the second study, we add leader Machiavellianism to the model. For Machiavellian leaders, the publicly expressed identity of ethical leadership is inconsistent with the privately held unethical Machiavellian norms. Literature on surface acting suggests people can at least to some extent pick up on such inauthentic displays, making the effects less strong. We thus argue that the positive effects of ethical leader behavior are likely to be suppressed when leaders are highly Machiavellian. Support for this moderated mediation model was found: The effects of ethical leader behavior on engagement are less strong when ethical leaders are high as opposed to low on Machiavellianism.


title: Performance evaluation of PLL algorithms for single-phase grid-connected systems

abstract: Phase angle, frequency and amplitude of the utility voltage vector are basic information for an increasing number of grid-connected power conditioning equipments, such as PWM rectifiers, uninterruptible power systems (UPS), voltage sag compensators and the emerging distributed generation systems. For these applications, accurate tracking of the utility voltage vector is essential to ensure correct operation of the control system. This paper presents a comparative study of synchronous reference frame PLL algorithms for single-phase systems. Simulation and experimental results, including operation of the PLL structures under distorted utility conditions are presented, to allow a performance evaluation of the PLL algorithms.


title: Toward automated generation of parametric BIMs based on hybrid video and laser scanning data

abstract: Only very few constructed facilities today have a complete record of as-built information. Despite the growing use of Building Information Modelling and the improvement in as-built records, several more years will be required before guidelines that require as-built data modelling will be implemented for the majority of constructed facilities, and this will still not address the stock of existing buildings. A technical solution for scanning buildings and compiling Building Information Models is needed. However, this is a multidisciplinary problem, requiring expertise in scanning, computer vision and videogrammetry, machine learning, and parametric object modelling. This paper outlines the technical approach proposed by a consortium of researchers that has gathered to tackle the ambitious goal of automating as-built modelling as far as possible. The top level framework of the proposed solution is presented, and each process, input and output is explained, along with the steps needed to validate them. Preliminary experiments on the earlier stages (i.e. processes) of the framework proposed are conducted and results are shown; the work toward implementation of the remainder is ongoing.