title: �-Lactam Antibiotics Targeting PBP1 Selectively Enhance Daptomycin Activity against Methicillin-Resistant Staphylococcus aureus

abstract: The activity of daptomycin (DAP) against methicillin-resistant Staphylococcus aureus (MRSA) is enhanced in the presence of subinhibitory concentrations of antistaphylococcal β-lactam antibiotics by an undefined mechanism. Given the variability in the penicillin-binding protein (PBP)-binding profiles of different β-lactam antibiotics, the purpose of this study was to examine the relative enhancement of DAP activity against MRSA by different β-lactam antibiotics to determine if a specific PBP-binding profile is associated with the ability to enhance the anti-MRSA activity of DAP. We determined that both broad- and narrow-spectrum β-lactam antibiotics known to exhibit PBP1 binding demonstrated potent enhancement of DAP anti-MRSA activity, whereas β-lactam antibiotics with minimal PBP1 binding (cefoxitin, ceftriaxone, cefaclor, and cefotaxime) were less effective. We suspect that PBP1 disruption by β-lactam antibiotics affects pathways of cell division in S. aureus that may be a compensatory response to DAP membrane insertion, resulting in DAP hypersusceptibility.


Race Times for Transgender Athletes


title: Large-Scale Video Classification with Convolutional Neural Networks

abstract: Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Encouraged by these results, we provide an extensive empirical evaluation of CNNs on large-scale video classification using a new dataset of 1 million YouTube videos belonging to 487 classes. We study multiple approaches for extending the connectivity of a CNN in time domain to take advantage of local spatio-temporal information and suggest a multiresolution, foveated architecture as a promising way of speeding up the training. Our best spatio-temporal networks display significant performance improvements compared to strong feature-based baselines (55.3% to 63.9%), but only a surprisingly modest improvement compared to single-frame models (59.3% to 60.9%). We further study the generalization performance of our best model by retraining the top layers on the UCF-101 Action Recognition dataset and observe significant performance improvements compared to the UCF-101 baseline model (63.3% up from 43.9%).


title: The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes

abstract: Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images, thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this paper, we propose to use a virtual world to automatically generate realistic synthetic images with pixel-level annotations. Then, we address the question of how useful such data can be for semantic segmentation – in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show how the inclusion of SYNTHIA in the training stage significantly improves performance on the semantic segmentation task.


title: Cascade R-CNN: Delving Into High Quality Object Detection

abstract: In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU threshold, e.g. 0.5, usually produces noisy detections. However, detection performance tends to degrade with increasing the IoU thresholds. Two main factors are responsible for this: 1) overfitting during training, due to exponentially vanishing positive samples, and 2) inference-time mismatch between the IoUs for which the detector is optimal and those of the input hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, is proposed to address these problems. It consists of a sequence of detectors trained with increasing IoU thresholds, to be sequentially more selective against close false positives. The detectors are trained stage by stage, leveraging the observation that the output of a detector is a good distribution for training the next higher quality detector. The resampling of progressively improved hypotheses guarantees that all detectors have a positive set of examples of equivalent size, reducing the overfitting problem. The same cascade procedure is applied at inference, enabling a closer match between the hypotheses and the detector quality of each stage. A simple implementation of the Cascade R-CNN is shown to surpass all single-model object detectors on the challenging COCO dataset. Experiments also show that the Cascade R-CNN is widely applicable across detector architectures, achieving consistent gains independently of the baseline detector strength. The code will be made available at this https URL.


title: DEAP: A Database for Emotion Analysis ;Using Physiological Signals

abstract: We present a multimodal data set for the analysis of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance, and familiarity. For 22 of the 32 participants, frontal face video was also recorded. A novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection, and an online assessment tool. An extensive analysis of the participants' ratings during the experiment is presented. Correlates between the EEG signal frequencies and the participants' ratings are investigated. Methods and results are presented for single-trial classification of arousal, valence, and like/dislike ratings using the modalities of EEG, peripheral physiological signals, and multimedia content analysis. Finally, decision fusion of the classification results from different modalities is performed. The data set is made publicly available and we encourage other researchers to use it for testing their own affective state estimation methods.


A new collection of studies on Spinoza


title: Analysis and Design of Ultra Thin Electromagnetic Absorbers Comprising Resistively Loaded High Impedance Surfaces

abstract: High-impedance surfaces (HIS) comprising lossy frequency selective surfaces (FSS) are employed to design thin electromagnetic absorbers. The structure, despite its typical resonant behavior, is able to perform a very wideband absorption in a reduced thickness. Losses in the frequency selective surface are introduced by printing the periodic pattern through resistive inks and hence avoiding the typical soldering of a large number of lumped resistors. The effect of the surface resistance of the FSS and dielectric substrate characteristics on the input impedance of the absorber is discussed by means of a circuital model. It is shown that the optimum value of surface resistance is affected both by substrate parameters (thickness and permittivity) and by FSS element shape. The equivalent circuit model is then used to introduce the working principles of the narrowband and the wideband absorbing structure and to derive the best-suited element for wideband absorption.


title: Leveraging Multi-core Computing Architectures in Avionics

abstract: Multi-core computer architectures are on the forefront in consumer electronics and adaptation in safety-critical applications such as avionics could be beneficial due to their potential increased performance. Yet, there are challenges to deploy cutting edge multi-core architectures for safety-critical applications. New computing architectures are more integrated and optimized for average cases. On the other side, safety-critical applications need to be designed for the worst case. For example, the impact of integrating critical applications is not fully understood yet, especially with respect to execution times of critical paths. This paper proposes and argues an approach to quantify the impact of integration of multiple independent applications onto multi-core platforms and evaluates the approach on a specific potential future avionics computing platform. Evaluation results focusing on execution estimates show that multi-core computers may be used for safety-critical applications, but the worst-case execution time (WCET) can be multiple times slower than the same application running on a single core without other cores running interfering applications. The actual factor is very dependent on the application's use of shared resources like memory.


title: Noninvasive Neural Prostheses Using Mobile and Wireless EEG

abstract: Neural prosthetic technologies have helped many patients by restoring vision, hearing, or movement and relieving chronic pain or neurological disorders. While most neural prosthetic systems to date have used invasive or implantable devices for patients with inoperative or malfunc- tioning external body parts or internal organs, a much larger population of Bhealthy( people who suffer episodic or progressive cognitive impairments in daily life can benefit from noninvasive neural prostheses. For example, reduced alertness, lack of attention, or poor decision-making during monotonous, routine tasks can have catastrophic conse- quences. This study proposes a noninvasive mobile prosthetic platform for continuously monitoring high-temporal resolution brain dynamics without requiring application of conductive gels on the scalp. The proposed system features dry micro- electromechanical system electroencephalography sensors, low-power signal acquisition, amplification and digitization, wireless telemetry, online artifact cancellation, and signal processing. Its implications for neural prostheses are examined in two sample studies: 1) cognitive-state monitoring of participants performing realistic driving tasks in the virtual- reality-based dynamic driving simulator and 2) the neural correlates of motion sickness in driving. The experimental results of these studies provide new insights into the under- standing of complex brain functions of participants actively performing ordinary tasks in natural body positions and situations within real operational environments.


title: SMS Encryption using AES Algorithm on Android

abstract: Encryption is of prime importance when confidential data is transmitted over the network. Varied encryption algorithms like AES, DES, RC4 and others are available for the same. The most widely accepted algorithm is AES algorithm. We have developed an application on Android platform which allows the user to encrypt the messages before it is transmitted over the network. We have used the Advanced Encryption Standards algorithm for encryption and decryption of the data. This application can run on any device which works on Android platform. This application provides a secure, fast, and strong encryption of the data. There is a huge amount of confusion and diffusion of the data during encryption which makes it very difficult for an attacker to interpret the encryption pattern and the plain text form of the encrypted data. The messages encrypted by the developed application are also resistant to Brute-Force and pattern attacks. The various uses of this application in real life and its functionality are explained in this paper.


title: Replicable Parallel Branch and Bound Search

abstract: Abstract Combinatorial branch and bound searches are a common technique for solving global optimisation and decision problems. Their performance often depends on good search order heuristics, refined over decades of algorithms research. Parallel search necessarily deviates from the sequential search order, sometimes dramatically and unpredictably, e.g. by distributing work at random. This can disrupt effective search order heuristics and lead to unexpected and highly variable parallel performance. The variability makes it hard to reason about the parallel performance of combinatorial searches. This paper presents a generic parallel branch and bound skeleton, implemented in Haskell, with replicable parallel performance. The skeleton aims to preserve the search order heuristic by distributing work in an ordered fashion, closely following the sequential search order. We demonstrate the generality of the approach by applying the skeleton to 40 instances of three combinatorial problems: Maximum Clique, 0/1 Knapsack and Travelling Salesperson. The overheads of our Haskell skeleton are reasonable: giving slowdown factors of between 1.9 and 6.2 compared with a class-leading, dedicated, and highly optimised C++ Maximum Clique solver. We demonstrate scaling up to 200 cores of a Beowulf cluster, achieving speedups of 100x for several Maximum Clique instances. We demonstrate low variance of parallel performance across all instances of the three combinatorial problems and at all scales up to 200 cores, with median Relative Standard Deviation (RSD) below 2%. Parallel solvers that do not follow the sequential search order exhibit far higher variance, with median RSD exceeding 85% for Knapsack.


title: Pure Exploration of Multi-armed Bandit Under Matroid Constraints

abstract: We study the pure exploration problem subject to a matroid constraint (Best-Basis) in a stochastic multi-armed bandit game. In a Best-Basis instance, we are given $n$ stochastic arms with unknown reward distributions, as well as a matroid $\mathcal{M}$ over the arms. Let the weight of an arm be the mean of its reward distribution. Our goal is to identify a basis of $\mathcal{M}$ with the maximum total weight, using as few samples as possible. ::: The problem is a significant generalization of the best arm identification problem and the top-$k$ arm identification problem, which have attracted significant attentions in recent years. We study both the exact and PAC versions of Best-Basis, and provide algorithms with nearly-optimal sample complexities for these versions. Our results generalize and/or improve on several previous results for the top-$k$ arm identification problem and the combinatorial pure exploration problem when the combinatorial constraint is a matroid.


title: Motivations to participate in online communities

abstract: A consistent theoretical and practical challenge in the design of socio-technical systems is that of motivating users to participate in and contribute to them. This study examines the case of Everything2.com users from the theoretical perspectives of Uses and Gratifications and Organizational Commitment to compare individual versus organizational motivations in user participation. We find evidence that users may continue to participate in a site for different reasons than those that led them to the site. Feelings of belonging to a site are important for both anonymous and registered users across different types of uses. Long-term users felt more dissatisfied with the site than anonymous users. Social and cognitive factors seem to be more important than issues of usability in predicting contribution to the site.


title: The Omega on Alpha and Beta

abstract: OK, I confess: I have trouble with alpha/beta ratios, and I want to provoke some discussion. The basic idea behind alpha/beta is a ratio of two different types of cell killing, essentially single hit and multiple hit types of radiation. This is what one gets with x-rays and the linear quadratic formula to explain a cell survival curve. That part is relatively understandable and straightforward. The problem I have is when people propose to use alpha/ betas for treatment of patients. In this issue of the Journal, Tucker et al. (1) quantify what they call an alpha/beta for Grade 2 and higher rectal toxicity from RTOG 94-06. They come up with reasonable numbers based on what we think we know. On the other hand, I question whether the derived numbers truly reflect two differentmechanisms of cell killing from irradiation, which these investigators presume to be the case. It is well understood that, when we treat patients, we try to take advantage of the different characteristics between tumor cells and normal tissues that are to be irradiated. Any kind of patient treatment plan needs to account for both tumor and normal tissue, because both will be exposed, and both are critical for risk–benefit concepts as they apply to the socalled ‘‘therapeutic ratio.’’ (It is a paradox in radiation therapy that what we usually do, in fact, is treat normal tissues to doses that we think are safe on the basis of experience, and accept whatever tumor cytotoxicity that we can achieve with that dose. Such is the impact of primum non nocere). That’s what we suggest by ‘‘tolerance,’’ although virtually no one defines the time factor, which in reality implies as long as the patient lives (whether 10 days or 50 years). It is also understood that tumor cells have one and only one function, and that is to divide, whereas normal tissues not only have to replace depleted cellular compartments by cell division but also have to function in ways that permit organs to function in an integrated fashion. Organs are comprise of many different types of those normal cells. When one tries to assess alpha/beta in tumors, the original end point was quick and in vitro, i.e., a specific


title: Designing Effective Serious Games: Opportunities and Challenges for Research

abstract: Serious Games represent an acknowledged potential for instruction, because they are able to strongly motivate learners. They can also provide immersive environments where advanced users can practice knowledge and skills, also exploiting multimodal interaction. They can combine the effectiveness of computer processing and data storage, with high levels of attractiveness. Our work has investigated the state of the art research on SGs, starting from the cognitive aspects, that are necessary in order to root technological development and applications in sound theoretical foundations. The paper discusses some key aspects about SG design and exploitation: choice of components-off-the-shelf or from-scratch design, tools and methodologies for development or adaptation, intelligent tutoring, virtual coaches and affective learning, living worlds, game mechanics, Human-Computer Interaction. ::: While several SGs have been developed, still the literature stresses a lack of significant, extensive user tests. Further research is necessary to investigate in greater detail the real effectiveness of the various types of SGs. The paper proposes several research questions - that range from requirements elicitation to design and from deployment to use and evaluation - to be answered in order to avoid technology pushing and drive technological research according to the requirements of the end-users and stakeholders. ::: We believe that deepening the analysis about these issues is key to strengthen the foundations of SG research, for which we identify four major directions: definition of metrics and learning progress evaluation tools; methodologies and tools for designing games from various topics and for various users; computing and communication architectures; technologies that can enhance the overall system performance.


title: Type-2 fuzzy ontology-based opinion mining and information extraction: A proposal to automate the hotel reservation system

abstract: The volume of traveling websites is rapidly increasing. This makes relevant information extraction more challenging. Several fuzzy ontology-based systems have been proposed to decrease the manual work of a full-text query search engine and opinion mining. However, most search engines are keyword-based, and available full-text search engine systems are still imperfect at extracting precise information using different types of user queries. In opinion mining, travelers do not declare their hotel opinions entirely but express individual feature opinions in reviews. Hotel reviews have numerous uncertainties, and most featured opinions are based on complex linguistic wording (small, big, very good and very bad). Available ontology-based systems cannot extract blurred information from reviews to provide better solutions. To solve these problems, this paper proposes a new extraction and opinion mining system based on a type-2 fuzzy ontology called T2FOBOMIE. The system reformulates the user's full-text query to extract the user requirement and convert it into the format of a proper classical full-text search engine query. The proposed system retrieves targeted hotel reviews and extracts feature opinions from reviews using a fuzzy domain ontology. The fuzzy domain ontology, user information and hotel information are integrated to form a type-2 fuzzy merged ontology for the retrieving of feature polarity and individual hotel polarity. The Protege OWL-2 (Ontology Web Language) tool is used to develop the type-2 fuzzy ontology. A series of experiments were designed and demonstrated that T2FOBOMIE performance is highly productive for analyzing reviews and accurate opinion mining.


title: Neuroevolution strategies for episodic reinforcement learning

abstract: Because of their convincing performance, there is a growing interest in using evolutionary algorithms for reinforcement learning. We propose learning of neural network policies by the covariance matrix adaptation evolution strategy (CMA-ES), a randomized variable-metric search algorithm for continuous optimization. We argue that this approach, which we refer to as CMA Neuroevolution Strategy (CMA-NeuroES), is ideally suited for reinforcement learning, in particular because it is based on ranking policies (and therefore robust against noise), efficiently detects correlations between parameters, and infers a search direction from scalar reinforcement signals. We evaluate the CMA-NeuroES on five different (Markovian and non-Markovian) variants of the common pole balancing problem. The results are compared to those described in a recent study covering several RL algorithms, and the CMA-NeuroES shows the overall best performance.


Recent Advances in Literature Based Discovery


title: GLOBAL POSTURAL REEDUCATION IN THE TREATMENT OF POSTURAL IMPAIRMENTS

abstract: Global Postural Re-Education (RPG) is an innovative method of posture correction developed by the French physiotherapist Philippe E. Souchard. A RPG session comprises of a series of specific muscle chain stretching positions, which evolves gradually from an intial position with minimum tension, and then applying progressive stretching until a final tension is reached through the end position. This final position depends on each person’s condition, and which motor coordination chain is affected. The RPG method has been widely used in clinical practice, with reported benefits for prevention and rehabilitation of musculoskeletal dysfunctions. Although the method is widely used clinically, the literature is still scarce and provide controversial results.The current study aims to evaluate the efficacy of RPG method in the treatment of postural impairments. Forty six volunteers university students (aged 17 to 25 years old), with postural impairments, were randomly divided into two treatment groups. The RPG group (n=22) performed muscle chain stretching, while the control group (n=24) performed conventional physiotherapy program, including symmetrical exercises for the trunk muscles. After intervention, the RPG group showed a statistically significant correction of postural impairments, correction of compensations, pain and symptoms relief in a long-term, increasing in muscle strength, endurance and balance, which was maintained at a 9-month follow-up. The control group showed no significant change in part of these parameters after intervention or at follow-up. A RPG treatment approach appears more effective than conventionally used physiotherapy program in the treatment of postural impairments.


title: Situational Effects on the Usage Intention of Mobile Games

abstract: As value-added services on mobile devices are developing rapidly, text messaging, multi-media messaging, music, video, games, GPS navigation, RFID, and mobile TV are all accessible from a single device. Mobile games that combine mobile communication with computer games are an emerging industry. The purpose of this research is to explore what situation factors may affect the intention to play mobile game. We propose a research model to fit the nature of mobile games and conducted an online survey to examine the effect of situational factors. The model integrates constructs in TAM and TRA. The findings are as follows. First, Subjective norm affects a user’s intention in using mobile games when a user has no other task. Second, perceived playfulness affects a user’s intention to use mobile games when the user has another task.


title: Evolutional Data Quality: A Theory-Specific View.

abstract: A data evolution life cycle typically consists of four stages of data activities: collection, organization, presentation, and application. During the cycle, data evolve as per the needs and specifications of a theory. In this paper we propose a concept of theory-specific data quality (DQ) that stipulates DQ is defined and measured as the extent to which data meet the needs and specifications of a theory. Depending on when a theory is applied during data evolution, we define DQ respectively at collection, organization, presentation, and application levels. We derive measurement attributes based on a fishbone cause-effect diagram. We compare our models with existing ones and point our future research directions to further validate and apply the proposed measurement models.


title: A GPU-based Approximate SVD Algorithm

abstract: Approximation of matrices using the Singular Value Decomposition (SVD) plays a central role in many science and engineering applications. However, the computation cost of an exact SVD is prohibitively high for very large matrices. In this paper, we describe a GPU-based approximate SVD algorithm for large matrices. Our method is based on the QUIC-SVD introduced by [6], which exploits a tree-based structure to efficiently discover a subset of rows that spans the matrix space. We describe how to map QUIC-SVD onto the GPU, and improve its speed and stability using a blocked Gram-Schmidt orthogonalization method. Using a simple matrix partitioning scheme, we have extended our algorithm to out-of-core computation, suitable for very large matrices that exceed the main memory size. Results show that our GPU algorithm achieves 6˜7 times speedup over an optimized CPU version of QUIC-SVD, which itself is orders of magnitude faster than exact SVD methods.


title: Building a treebank for Vietnamese dependency parsing

abstract: The problem of Vietnamese syntactic parsing, especially constituency parsing, has recently been tackled by several research groups. A common effort of the Vietnamese language processing community has allowed the creation of VietTreebank, a reference parsed corpus containing about 10,000 sentences for the constituency parsing task. In this paper, we present our work to build a reference treebank, based on VietTreebank, for the dependency parsing task, which has not yet been very well studied for Vietnamese. First we define a dependency label set by adapting the dependency schema developed by the NLP group at Stanford university and taking into account the particularities of Vietnamese grammar. Then we propose an algorithm to convert a constituency treebank to a dependency one. The algorithm is tested on a set of 100 sentences of VietTreebank corpus and gives very good results. Finally, we carry out an experiment on Vietnamese dependency parsing using MaltParser tool and the dependency treebank converted from VietTreebank.


title: Designing an Adjustable Wide Range Regulated Current Source

abstract: LLC resonant converter has been used widely as dc-dc converter for achieving constant dc voltage. In this paper, an LLC resonant converter, by adding an inductance to its conventional topology and considering the rectifying stage stray inductances, is proposed for an adjustable wide range regulated current source (20-200 Adc) for using as ion implanter's filament power supply. The additional inductor increases output current adjustment range and efficiency, especially at light loads. Transformer's leakage inductances and rectifying stage stray inductances have been considered. Because of these inductances, the rectifier stage always works in continuous conduction mode, and its conduction angle is forced to be larger than ?, and peak current of the rectifier stage and the output capacitor have been reduced effectively. Switching losses and electromagnetic interference noises have been reduced as well due to zero-voltage switching at the primary and secondary sides of the converter, and zero-current switching at its secondary side. Soft switching is achieved for all power devices under all operating conditions. A developed prototype of the converter has been tested under different load (2.5-12.5 m?) and input voltage conditions (320-370 V dc) with maximum efficiency of 87%. Experimental results confirm high performance of the designed adjustable and regulated current source even under the worst-case conditions.


title: Acoustic Energy Harvesting Using Piezoelectric Generator for Low Frequency Sound Waves Energy Conversion

abstract: The applications of electronic devices with low power consumption, such as wireless sensor network and electronic communication devices, are rapidly increasing. Thus, utilizing environmental energy as an alternative to electrochemical battery, which has a finite lifespan, can be a great advantage to these electronic devices. Harvesting environmental energy, such as solar, thermal, wind flow, water current, and raindrops, has attracted increasing research interest in the field of energy harvesting. In this paper, harvesting sound energy in the form of pressure waves is investigated as an alternative to existing energy harvesting methods. In the experimental work, a piezoelectric generator lead zirconate titanate (PZT-5A) cantilever type is used to extract sound energy from the loudspeaker from various distances and then to convert this energy into electrical energy. A direct piezoelectric effect operating in 31 coupling mode is used. The maximum voltage generated by the piezoelectric generator occurs when its resonant frequency is operating near the frequency of sound. An analytical method with an appropriate equation is used to determine the resonant frequency and is then validated using the experimental result. The result shows that the maximum output voltage of 26.7 mVrms was obtained with the sound intensity of 78.6 dB at resonant frequency of 62 Hz at 1 cm distance in the first mode. In the second mode, the maximum output voltage of 91 mVrms was obtained with the sound intensity of 102.6 dB at resonant frequency of 374 Hz at 1 cm distance which is larger than that of the first mode. However, for both modes, voltage decreases as distance increases. Keyword- sound energy, piezoelectric generator, cantilever, resonant frequency


title: Explanation Generation for a Math Word Problem Solver

abstract: This paper proposes a math operation (e.g., Summation, Addition, Subtraction, Multiplication, Division, etc.) oriented approach to explain how the answers are obtained for math word problems. Based on the reasoning chain given by the inference engine, we search each math operator involved. For each math operator, we generate one sentence. Since explaining math operation does not require complicated syntax, we adopt a specific template to generate the text for each kind of math operator. To the best of our knowledge, this is the first explanation generation that is specifically tailored to solving the math word problem.


title: Mechamagnets: Tactile Mechanisms with Embedded Magnets

abstract: This paper presents Mechamagnets, a technique to rapidly prototype tactile mechanisms for tangible interfaces. We demonstrate how to embed different passive tactile mechanisms in physical systems through a combination of magnets and digitally fabricated parts. We also discuss how DIY materials and 3-axis magnetometers can instrument Mechamagnet interfaces into functional prototypes.


title: Specific Emitter Identification Based on the Natural Measure

abstract: Specific emitter identification (SEI) techniques are often used in civilian and military spectrum-management operations, and they are also applied to support the security and authentication of wireless communication. In this letter, a new SEI method based on the natural measure of the one-dimensional component of the chaotic system is proposed. We find that the natural measures of the one-dimensional components of higher dimensional systems exist and that they are quite diverse for different systems. Based on this principle, the natural measure is used as an RF fingerprint in this letter. The natural measure can solve the problems caused by a small amount of data and a low sample rate. The Kullback–Leibler divergence is used to quantify the difference between the natural measures obtained from diverse emitters and classify them. The data obtained from real application are exploited to test the validity of the proposed method. Experimental results show that the proposed method is not only easy to operate, but also quite effective, even though the amount of data is small and the sample rate is low.


title: The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function

abstract: GeneMANIA (http://www.genemania.org) is a flexible, user-friendly web interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query list, GeneMANIA extends the list with functionally similar genes that it identifies using available genomics and proteomics data. GeneMANIA also reports weights that indicate the predictive value of each selected data set for the query. Six organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Homo sapiens and Saccharomyces cerevisiae) and hundreds of data sets have been collected from GEO, BioGRID, Pathway Commons and I2D, as well as organism-specific functional genomics data sets. Users can select arbitrary subsets of the data sets associated with an organism to perform their analyses and can upload their own data sets to analyze. The GeneMANIA algorithm performs as well or better than other gene function prediction methods on yeast and mouse benchmarks. The high accuracy of the GeneMANIA prediction algorithm, an intuitive user interface and large database make GeneMANIA a useful tool for any biologist.


title: Equilibrium Theory Revisited: Mutual Gaze and Personal Space in Virtual Environments

abstract: During the last half of the twentieth century, psychologists and anthropologists have studied proxemics, or spacing behavior, among people in many contexts. As we enter the twenty-first century, immersive virtual environment technology promises new experimental venues in which researchers can study proxemics. Immersive virtual environments provide realistic and compelling experimental settings without sacrificing experimental control. The experiment reported here tested Argyle and Dean's (1965) equilibrium theory's specification of an inverse relationship between mutual gaze, a nonverbal cue signaling intimacy, and interpersonal distance. Participants were immersed in a three-dimensional virtual room in which a virtual human representation (that is, an embodied agent) stood. Under the guise of a memory task, participants walked towards and around the agent. Distance between the participant and agent was tracked automatically via our immersive virtual environment system. All participants maintained more space around agents than they did around similarly sized and shaped but nonhuman-like objects. Female participants maintained more interpersonal distance between themselves and agents who engaged them in eye contact (that is, mutual gaze behavior) than between themselves and agents who did not engage them in eye contact, whereas male participants did not. Implications are discussed for the study of proxemics via immersive virtual environment technology, as well as the design of virtual environments and virtual humans.


Ground Penetrating Radar Fundamentals


title: Training Artificial Neural Networks by a Hybrid PSO-CS Algorithm

abstract: Presenting a satisfactory and efficient training algorithm for artificial neural networks (ANN) has been a challenging task in the supervised learning area. Particle swarm optimization (PSO) is one of the most widely used algorithms due to its simplicity of implementation and fast convergence speed. On the other hand, Cuckoo Search (CS) algorithm has been proven to have a good ability for finding the global optimum; however, it has a slow convergence rate. In this study, a hybrid algorithm based on PSO and CS is proposed to make use of the advantages of both PSO and CS algorithms. The proposed hybrid algorithm is employed as a new training method for feedforward neural networks (FNNs). To investigate the performance of the proposed algorithm, two benchmark problems are used and the results are compared with those obtained from FNNs trained by original PSO and CS algorithms. The experimental results show that the proposed hybrid algorithm outperforms both PSO and CS in training FNNs.


title: Latent Fingerprint Matching: Fusion of Manually Marked and Derived Minutiae

abstract: Matching unknown latent fingerprints lifted from crime scenes to full (rolled or plain) fingerprints in law enforcement databases is of critical importance for combating crime and fighting terrorism. Compared to good quality full fingerprints acquired using live-scan or inking methods during enrollment, latent fingerprints are often smudgy and blurred, capture only a small finger area, and have large nonlinear distortion. For this reason, features (minutiae and singular points) in latents are typically manually marked by trained latent examiners. However, this introduces an undesired interoperability problem between latent examiners and automatic fingerprint identification systems (AFIS); the features marked by examiners are not always compatible with those automatically extracted by AFIS, resulting in reduced matching accuracy. While the use of automatically extracted minutiae from latents can avoid interoperability problem, such minutiae tend to be very unreliable, because of the poor quality of latents. In this paper, we improve latent to full fingerprint matching accuracy by combining manually marked (ground truth) minutiae with automatically extracted minutiae. Experimental results on a public domain database, NIST SD27, demonstrate the effectiveness of the proposed algorithm.


title: Information Security: End User Behavior and Corporate Culture

abstract: Information is the life blood of all modern organizations yet the news media continue to report stories of critical information loss. The purpose of information security is to protect valuable assets, such as information, hardware, software and people. The majority of information security specialists believe that promoting good end user behavior and constraining bad end user behavior is an important component of an effective Information Security Management System (ISMS). Implementing effective information security involves understanding security-related risk, then developing and implementing appropriate controls. In general the better employees are at applying the controls the more secure the organization will be, because even the best designed technical controls and procedures will be of limited value if the staff involved do not understand why they have been implemented and what they are accomplishing. Achieving the required level of understanding usually requires more than an annual awareness training initiative and represents a major challenge for most organizations. In fact, for many organizations it will involve a cultural change to ensure the integration of information security concepts into the organizational culture.


title: Group Recommender Systems: New Perspectives in the Social Web

abstract: An increasingly important type of recommender systems comprises those that generate suggestions for groups rather than for individuals. In this chapter, we revise state of the art approaches on group formation, modelling and recommendation, and present challenging problems to be included in the group recommender system research agenda in the context of the Social Web.


title: Social Contagion and Information Technology Diffusion: The Adoption of Electronic Medical Records in U.S. Hospitals

abstract: We use a social contagion lens to study the dynamic, temporal process of the diffusion of electronic medical records in the population of U.S. hospitals. Social contagion acknowledges the mutual influence among organizations within an institutional field and implicates information transmission through direct contact and observation as the mechanisms underlying influence transfer. We propose hypotheses predicting a hospital's likelihood of adopting electronic medical records as a function of its susceptibility to the influence of prior adopters, the infectiousness or potency of influence exerted by adopting hospitals, and its social and spatial proximity to prior adopters. Results obtained by fitting a heterogeneous diffusion model to data from a sample drawn from an annual survey, spanning 1975 to 2005, of almost 4,000 U.S. hospitals suggest that diffusion can be accelerated if specific attention is given to increasing social contagion effects. In particular, with respect to susceptibility to influence, greater hospital size and age are positively related to the likelihood of adoption for nonadopters, whereas younger hospitals are associated with greater infectiousness for adopters. A hospital's “celebrity” status also contributes to its infectiousness. We further find strong effects for social proximity and significant regional effects for spatial proximity and hospital size, suggesting that geographical covariates should be included in diffusion studies. Results also reinforce the importance of theorizing about and including interactions in examinations of social contagion.


title: Efficient clustering and quantisation of SIFT features: exploiting characteristics of the SIFT descriptor and interest region detectors under image inversion

abstract: The SIFT keypoint descriptor is a powerful approach to encoding local image description using edge orientation histograms. Through codebook construction via k-means clustering and quantisation of SIFT features we can achieve image retrieval treating images as bags-of-words. Intensity inversion of images results in distinct SIFT features for a single local image patch across the two images. Intensity inversions notwithstanding these two patches are structurally identical. Through careful reordering of the SIFT feature vectors, we can construct the SIFT feature that would have been generated from a non-inverted image patch starting with those extracted from an inverted image patch. Furthermore, through examination of the local feature detection stage, we can estimate whether a given SIFT feature belongs in the space of inverted features, or non-inverted features. Therefore we can consistently separate the space of SIFT features into two distinct subspaces. With this knowledge, we can demonstrate reduced time complexity of codebook construction via clustering by up to a factor of four and also reduce the memory consumption of the clustering algorithms while producing equivalent retrieval results.


title: JackIn: integrating first-person view with out-of-body vision generation for human-human augmentation

abstract: JackIn is a new human-human communication framework for connecting two or more people. With first-person view video streaming from a person (called Body) wearing a transparent head-mounted display and a head-mounted camera, the other person (called Ghost) participates in the shared first-person view. With JackIn, people's activities can be shared and assistance or guidance can be given through other peoples expertise. This can be applied to daily activities such as cooking lessons, shopping navigation, education in craft-work or electrical work, and sharing experiences of sporting and live events. For a better viewing experience with frist-person view, we developed the out-of-body view in which first-person images are integrated to construct a scene around a Body, and a Ghost can virtually control the viewpoint to look around the space surrounding the Body. We also developed a tele-pointing gesture interface. We conducted an experiment to evaluate how effective this framework is and found that Ghosts can understand the spatial situation of the Body.


title: Online Choice of Active Learning Algorithms

abstract: This work is concerned with the question of how to combine online an ensemble of active learners so as to expedite the learning progress in pool-based active learning. We develop an active-learning master algorithm, based on a known competitive algorithm for the multi-armed bandit problem. A major challenge in successfully choosing top performing active learners online is to reliably estimate their progress during the learning session. To this end we propose a simple maximum entropy criterion that provides effective estimates in realistic settings. We study the performance of the proposed master algorithm using an ensemble containing two of the best known active-learning algorithms as well as a new algorithm. The resulting active-learning master algorithm is empirically shown to consistently perform almost as well as and sometimes outperform the best algorithm in the ensemble on a range of classification problems.


title: Profiling ISIS Supporters on Twitter

abstract: Cyber Terrorism is a real threat to the modern society. Many terrorist organizations spread their ideas and recruit new supporters over Online Social Networks. Among all terrorist organizations, ISIS can be considered as the biggest one, which is responsible for inspiring terrorist actions in more than 20 countries. As expected, ISIS uses Twitter for spreading its hatred, and an important issue is how to characterize these supporters in order to understand their motivation. Our work investigates and discusses the way ISIS organizes within Twitter. We base our analyses on two curated datasets. The first dataset, "How ISIS Uses Twitter?" (HIUT), is provided by the Fifth Tribe digital agency. The second dataset, "Syria and ISIS Mentioners?" (SIM), we collected ourselves and curated without participation of experts in the field. We made the SIM dataset publically available, helping new studies in the understanding of ISIS supporters' profiles on Twitter. The main contribution of this work is a characterization of both HIUT and SIM datasets.


title: Learning Whom to Trust with MACE

abstract: Non-expert annotation services like Amazon’s Mechanical Turk (AMT) are cheap and fast ways to evaluate systems and provide categorical annotations for training data. Unfortunately, some annotators choose bad labels in order to maximize their pay. Manual identification is tedious, so we experiment with an item-response model. It learns in an unsupervised fashion to a) identify which annotators are trustworthy and b) predict the correct underlying labels. We match performance of more complex state-of-the-art systems and perform well even under adversarial conditions. We show considerable improvements over standard baselines, both for predicted label accuracy and trustworthiness estimates. The latter can be further improved by introducing a prior on model parameters and using Variational Bayes inference. Additionally, we can achieve even higher accuracy by focusing on the instances our model is most confident in (trading in some recall), and by incorporating annotated control instances. Our system, MACE (Multi-Annotator Competence Estimation), is available for download 1 .


title: Calculation method of three-phase induction machines equipped with combined star-delta windings

abstract: Combined star-delta windings in electrical machines result in a higher fundamental winding factor and cause a smaller spatial harmonic content. This leads to lower I2R losses in the stator and the rotor winding and thus to an increased efficiency. However, compared to an equivalent six-phase winding, additional spatial harmonics are generated due to the different magnetomotive force in the star and delta part of the winding. In this paper, a complete theory and analysis method for the analytical calculation of the efficiency of induction motors equipped with combined star-delta windings is developed. The method takes into account the additional harmonic content due to the different magnetomotive force in the star and delta part. To check the analysis' validity, an experimental test is reported both on a cage induction motor equipped with a combined star-delta winding in the stator and on a reference motor with the same core but with a classical three-phase winding.


title: SIDE: Representation Learning in Signed Directed Networks

abstract: Given a signed directed network, how can we learn node representations which fully encode structural information of the network including sign and direction of edges? Node representation learning or network embedding learns a mapping of each node to a vector. The mapping encodes structural information on network, providing low-dimensional dense node features for general machine learning and data mining frameworks. Since many social networks allow trust (friend) and distrust (enemy) relationships described by signed and directed edges, generalizing network embedding method to learn from sign and direction information in networks is crucial. In addition, social theories are critical tool in signed network analysis. However, none of the existing methods supports all of the desired properties: considering sign, direction, and social theoretical interpretation. In this paper, we propose SIDE, a general network embedding method that represents both sign and direction of edges in the embedding space. SIDE carefully formulates and optimizes likelihood over both direct and indirect signed connections. We provide socio-psychological interpretation for each component of likelihood function. We prove linear scalability of our algorithm and propose additional optimization techniques to reduce the training time and improve accuracy. Through extensive experiments on real-world signed directed networks, we show that SIDE effectively encodes structural information into the learned embedding.


title: EMI common-mode (CM) noise suppression from self-calibration of high-speed SST driver using on-chip process monitoring circuit

abstract: This paper presents a CM noise suppression technique from self-calibration of a 20-Gb/s source-series terminated (SST) driver using an on-chip process monitoring circuit designed and simulated in a 65-nm CMOS process. An unbalanced charging and discharging loop results in an asymmetric rise and fall time of the output signal and is an intrinsic source of the CM noise. For an SST driver, the CM noise effect is especially aggravated under process corner variations due to push-pull NMOS configuration. The on-chip sensor circuit monitors and detects all possible process corners over the entire temperature range. The control circuit operates to self-calibrate the output driver to compensate for the process corner variations and results in a 15% higher symmetric rise and fall time of the output signal for the worst case scenario. The simulation results indicate that the proposed technique reduces the peak CM noise by 7.2x (−86%) without power overhead, as it is a background monitoring and self-calibration scheme.


Structural dynamics experimental activities in ultra-lightweight and inflatable space structures


title: RNA global alignment in the joint sequence–structure space using elastic shape analysis

abstract: The functions of RNAs, like proteins, are determined by their structures, which, in turn, are determined by their sequences. Comparison/alignment of RNA molecules provides an effective means to predict their functions and understand their evolutionary relationships. For RNA sequence alignment, most methods developed for protein and DNA sequence alignment can be directly applied. RNA 3-dimensional structure alignment, on the other hand, tends to be more difficult than protein structure alignment due to the lack of regular secondary structures as observed in proteins. Most of the existing RNA 3D structure alignment methods use only the backbone geometry and ignore the sequence information. Using both the sequence and backbone geometry information in RNA alignment may not only produce more accurate classification, but also deepen our understanding of the sequence–structure–function relationship of RNA molecules. In this study, we developed a new RNA alignment method based on elastic shape analysis (ESA). ESA treats RNA structures as three dimensional curves with sequence information encoded on additional dimensions so that the alignment can be performed in the joint sequence–structure space. The similarity between two RNA molecules is quantified by a formal distance, geodesic distance. Based on ESA, a rigorous mathematical framework can be built for RNA structure comparison. Means and covariances of full structures can be defined and computed, and probability distributions on spaces of such structures can be constructed for a group of RNAs. Our method was further applied to predict functions of RNA molecules and showed superior performance compared with previous methods when tested on benchmark datasets. The programs are available at http://stat.fsu.edu/ ~jinfeng/ESA.html.


title: The Role of Human Movement in the Transmission of Vector-Borne Pathogens

abstract: BACKGROUND ::: Human movement is a key behavioral factor in many vector-borne disease systems because it influences exposure to vectors and thus the transmission of pathogens. Human movement transcends spatial and temporal scales with different influences on disease dynamics. Here we develop a conceptual model to evaluate the importance of variation in exposure due to individual human movements for pathogen transmission, focusing on mosquito-borne dengue virus. ::: ::: ::: METHODOLOGY AND PRINCIPAL FINDINGS ::: We develop a model showing that the relevance of human movement at a particular scale depends on vector behavior. Focusing on the day-biting Aedes aegypti, we illustrate how vector biting behavior combined with fine-scale movements of individual humans engaged in their regular daily routine can influence transmission. Using a simple example, we estimate a transmission rate (R(0)) of 1.3 when exposure is assumed to occur only in the home versus 3.75 when exposure at multiple locations--e.g., market, friend's--due to movement is considered. Movement also influences for which sites and individuals risk is greatest. For the example considered, intriguingly, our model predicts little correspondence between vector abundance in a site and estimated R(0) for that site when movement is considered. This illustrates the importance of human movement for understanding and predicting the dynamics of a disease like dengue. To encourage investigation of human movement and disease, we review methods currently available to study human movement and, based on our experience studying dengue in Peru, discuss several important questions to address when designing a study. ::: ::: ::: CONCLUSIONS/SIGNIFICANCE ::: Human movement is a critical, understudied behavioral component underlying the transmission dynamics of many vector-borne pathogens. Understanding movement will facilitate identification of key individuals and sites in the transmission of pathogens such as dengue, which then may provide targets for surveillance, intervention, and improved disease prevention.


Make Deep Learning Great Again : Character-Level RNN Speech Generation in the Style of Donald Trump


Human Pose Estimation.


title: Control of cable actuated devices using smooth backlash inverse

abstract: Cable conduit actuation provides a simple yet dexterous mode of power transmission for remote actuation. However, they are not preferred because of the nonlinearities arising from friction and cable compliance which lead to backlash type of behavior. Unlike most of the current research in backlash control which generally assumes no knowledge of one of the intermediate states, the controller design in this case can be significantly simplified if output feedback of the system is available. This paper uses a simple feedforward control law for backlash compensation. A novel smooth backlash inverse is proposed, which takes the physical limitations of the actuator in consideration, unlike other designs, and thus makes it more intuitive to use. Implementation of this inverse on physical systems can also improve the system performance over the theoretical exact inverse, as well as other existing smooth inverse designs. Improvement in the performance is shown through experiments on a robot arm of Laprotek surgical system as well as on an experimental setup using polymeric cables for actuation.


title: Predicting Students' Performance Using ID3 And C4.5 Classification Algorithms

abstract: An educational institution needs to have an approximate prior knowledge of enrolled students to predict their performance in future academics. This helps them to identify promising students and also provides them an opportunity to pay attention to and improve those who would probably get lower grades. As a solution, we have developed a system which can predict the performance of students from their previous performances using concepts of data mining techniques under Classification. We have analyzed the data set containing information about students, such as gender, marks scored in the board examinations of classes X and XII, marks and rank in entrance examinations and results in first year of the previous batch of students. By applying the ID3 (Iterative Dichotomiser 3) and C4.5 classification algorithms on this data, we have predicted the general and individual performance of freshly admitted students in future examinations.


title: Hierarchical classification: combining Bayes with SVM

abstract: We study hierarchical classification in the general case when an instance could belong to more than one class node in the underlying taxonomy. Experiments done in previous work showed that a simple hierarchy of Support Vectors Machines (SVM) with a top-down evaluation scheme has a surprisingly good performance on this kind of task. In this paper, we introduce a refined evaluation scheme which turns the hierarchical SVM classifier into an approximator of the Bayes optimal classifier with respect to a simple stochastic model for the labels. Experiments on synthetic datasets, generated according to this stochastic model, show that our refined algorithm outperforms the simple hierarchical SVM. On real-world data, however, the advantage brought by our approach is a bit less clear. We conjecture this is due to a higher noise rate for the training labels in the low levels of the taxonomy.


title: On the Use of Weighted Mean Absolute Error in Recommender Systems

abstract: The classical strategy to evaluate the performance of a Recommender System is to measure the error in rating predictions. But when focusing on a particular dimension in a recommending process it is reasonable to assume that every prediction should not be treated equally, its importance depends on the degree to which the predicted item matches the deemed dimension or feature. In this paper we shall explore the use of weighted Mean Average Error (wMAE) as an alternative to capture and measure their effects on the recommendations. In order to illustrate our approach two different dimensions are considered, one item-dependent and the other that depends on the user preferences.


title: Substrate integrated waveguide cavity backed mushroom antenna with broadband and high gain

abstract: This paper proposes a substrate integrated waveguide (SIW) cavity backed mushroom antenna with broadband and high gain. The proposed antenna with size 40mm∗50mm consists of a 2×2 array mushroom unit cells, microstrip feeding line and a SIW back cavity. The following factors produce broadband: (1) Mushroom antenna is capable of exciting multiple resonances in a N-cell mushroom resonator. (2) The reduced quality factor due to mushroom antenna substrate. The following factors produce high gain: (1) The existence of the mushroom radiating gaps. (2) The SIW back cavity further improves antenna gain. The simulated bandwidth, peak gain are 70% (from 4.9GHz to 8.5GHz) and 9.9dBi, respectively. Compared with reference, the size is reduced by 44%, and the bandwidth is increased by 40%.


title: Overcoming Hypoxia-Mediated Tumor Progression: Combinatorial Approaches Targeting pH Regulation, Angiogenesis and Immune Dysfunction

abstract: Hypoxia is an important contributor to the heterogeneity of the microenvironment of solid tumors and is a significant environmental stressor that drives adaptations which are essential for the survival and metastatic capabilities of tumor cells. Critical adaptive mechanisms include altered metabolism, pH regulation, epithelial-mesenchymal transition, angiogenesis, migration/invasion, diminished response to immune cells and resistance to chemotherapy and radiation therapy. In particular, pH regulation by hypoxic tumor cells, through the modulation of cell surface molecules such as extracellular carbonic anhydrases (CAIX and CAXII) and monocarboxylate transporters (MCT-1 and MCT-4) functions to increase cancer cell survival and enhance cell invasion while also contributing to immune evasion. Indeed, CAIX is a vital regulator of hypoxia mediated tumor progression, and targeted inhibition of its function results in reduced tumor growth, metastasis, and cancer stem cell function. However, the integrated contributions of the repertoire of hypoxia-induced effectors of pH regulation for tumor survival and invasion remain to be fully explored and exploited as therapeutic avenues. For example, the clinical use of anti-angiogenic agents has identified a conundrum whereby this treatment increases hypoxia and cancer stem cell components of tumors, and accelerates metastasis. Furthermore, hypoxia results in the infiltration of myeloid-derived suppressor cells (MDSCs), regulatory T cells (Treg) and Tumor Associated Macrophages (TAMs), and also stimulates the expression of PD-L1 on tumor cells, which collectively suppress T-cell mediated tumor cell killing. Therefore, combinatorial targeting of angiogenesis, the immune system and pH regulation in the context of hypoxia may lead to more effective strategies for curbing tumor progression and therapeutic resistance, thereby increasing therapeutic efficacy and leading to more effective strategies for the treatment of patients with aggressive cancer.


title: Dense Semi-rigid Scene Flow Estimation from RGBD Images

abstract: Scene flow is defined as the motion field in 3D space, and can be computed from a single view when using an RGBD sensor. We propose a new scene flow approach that exploits the local and piecewise rigidity of real world scenes. By modeling the motion as a field of twists, our method encourages piecewise smooth solutions of rigid body motions. We give a general formulation to solve for local and global rigid motions by jointly using intensity and depth data. In order to deal efficiently with a moving camera, we model the motion as a rigid component plus a non-rigid residual and propose an alternating solver. The evaluation demonstrates that the proposed method achieves the best results in the most commonly used scene flow benchmark. Through additional experiments we indicate the general applicability of our approach in a variety of different scenarios.


title: Locality Statistics for Anomaly Detection in Time Series of Graphs

abstract: The ability to detect change-points in a dynamic network or a time series of graphs is an increasingly important task in many applications of the emerging discipline of graph signal processing. This paper formulates change-point detection as a hypothesis testing problem in terms of a generative latent position model, focusing on the special case of the Stochastic Block Model time series. We analyze two classes of scan statistics, based on distinct underlying locality statistics presented in the literature. Our main contribution is the derivation of the limiting properties and power characteristics of the competing scan statistics. Performance is compared theoretically, on synthetic data, and empirically, on the Enron email corpus.


title: Ouroboros Genesis: Composable Proof-of-Stake Blockchains with Dynamic Availability

abstract: We present a novel Proof-of-Stake (PoS) protocol, Ouroboros Genesis, that enables parties to safely join (or rejoin) the protocol execution using only the genesis block information. Prior to our work, PoS protocols either required parties to obtain a trusted "checkpoint" block upon joining and, furthermore, to be frequently online or required an accurate estimate of the number of online parties to be hardcoded into the protocol logic. This ability of new parties to "bootstrap from genesis" was a hallmark property of the Bitcoin blockchain and was considered an important advantage of PoW-based blockchains over PoS-based blockchains since it facilitates robust operation in a setting with dynamic availability, i.e., the natural setting---without external trusted objects such as checkpoint blocks---where parties come and go arbitrarily, may join at any moment, or remain offline for prolonged periods of time. We prove the security of Ouroboros Genesis against a fully adaptive adversary controlling less than half of the total stake in a partially synchronous network with unknown message delay and unknown, varying levels of party availability. Our security proof is in the Universally Composable setting assuming the most natural abstraction of a hash function, known as the strict Global Random Oracle (ACM-CCS 2014); this highlights an important advantage of PoS blockchains over their PoW counterparts in terms of composability with respect to the hash function formalisation: rather than a strict GRO, PoW-based protocol security requires a "local" random oracle. Finally, proving the security of our construction against an adaptive adversary requires a novel martingale technique that may be of independent interest in the analysis of blockchain protocols.


title: Adaptive method for multi colored text binarization

abstract: This article presents our recent study on multi colored text binarization. In the output image, we represented foreground content as black and background as white regardless the polarity of foreground and background in original image. Here we applied connected component analysis based approach to group the words or characters within bounding or edge box. The main novelty of this reported work includes the calculation of each edge box based local color threshold value from CIELAB color space. This approach makes the proposed system capable of binarizing multi colored texts where a single character has more than one color. The proposed method has been executed on well-known D1BCO2009 and CMATERdb datasets that contain a large set of images to show the efficiency over other existing methods through qualitative comparison study.


title: Classic Hallucinogens and Mystical Experiences: Phenomenology and Neural Correlates

abstract: This chapter begins with a brief review of descriptions and definitions of mystical-type experiences and the historical connection between classic hallucinogens and mystical experiences. The chapter then explores the empirical literature on experiences with classic hallucinogens in which claims about mystical or religious experiences have been made. A psychometrically validated questionnaire is described for the reliable measurement of mystical-type experiences occasioned by classic hallucinogens. Controlled laboratory studies show that under double-blind conditions that provide significant controls for expectancy bias, psilocybin can occasion complete mystical experiences in the majority of people studied. These effects are dose-dependent, specific to psilocybin compared to placebo or a psychoactive control substance, and have enduring impact on the moods, attitudes, and behaviors of participants as assessed by self-report of participants and ratings by community observers. Other studies suggest that enduring personal meaning in healthy volunteers and therapeutic outcomes in patients, including reduction and cessation of substance abuse behaviors and reduction of anxiety and depression in patients with a life-threatening cancer diagnosis, are related to the occurrence of mystical experiences during drug sessions. The final sections of the chapter draw parallels in human neuroscience research between the neural bases of experiences with classic hallucinogens and the neural bases of meditative practices for which claims of mystical-type experience are sometimes made. From these parallels, a functional neural model of mystical experience is proposed, based on changes in the default mode network of the brain that have been observed after the administration of classic hallucinogens and during meditation practices for which mystical-type claims have been made.


title: The origin of bursts and heavy tails in human dynamics

abstract: What determines the timing of human actions? A big question, but the science of human dynamics is here to tackle it. And its predictions are of practical value: for example, when ISPs decide what bandwidth an institution needs, they use a model of the likely timing and activity level of the individuals. Current models assume that an individual has a well defined probability of engaging in a specific action at a given moment, but evidence that the timing of human actions does not follow this pattern (of Poisson statistics) is emerging. Instead the delay between two consecutive events is best described by a heavy-tailed (power law) distribution. Albert-Laszlo Barabasi proposes an explanation for the prevalence of this behaviour. The ‘bursty’ nature of human dynamics, he finds, is a fundamental consequence of decision making. The dynamics of many social, technological and economic phenomena are driven by individual human actions, turning the quantitative understanding of human behaviour into a central question of modern science. Current models of human dynamics, used from risk assessment to communications, assume that human actions are randomly distributed in time and thus well approximated by Poisson processes1,2,3. In contrast, there is increasing evidence that the timing of many human activities, ranging from communication to entertainment and work patterns, follow non-Poisson statistics, characterized by bursts of rapidly occurring events separated by long periods of inactivity4,5,6,7,8. Here I show that the bursty nature of human behaviour is a consequence of a decision-based queuing process9,10: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, with most tasks being rapidly executed, whereas a few experience very long waiting times. In contrast, random or priority blind execution is well approximated by uniform inter-event statistics. These finding have important implications, ranging from resource management to service allocation, in both communications and retail.


title: Deep Wavelet Prediction for Image Super-Resolution

abstract: Recent advances have seen a surge of deep learning approaches for image super-resolution. Invariably, a network, e.g. a deep convolutional neural network (CNN) or auto-encoder is trained to learn the relationship between low and high-resolution image patches. Recognizing that a wavelet transform provides a "coarse" as well as "detail" separation of image content, we design a deep CNN to predict the "missing details" of wavelet coefficients of the low-resolution images to obtain the Super-Resolution (SR) results, which we name Deep Wavelet Super-Resolution (DWSR). Out network is trained in the wavelet domain with four input and output channels respectively. The input comprises of 4 sub-bands of the low-resolution wavelet coefficients and outputs are residuals (missing details) of 4 sub-bands of high-resolution wavelet coefficients. Wavelet coefficients and wavelet residuals are used as input and outputs of our network to further enhance the sparsity of activation maps. A key benefit of such a design is that it greatly reduces the training burden of learning the network that reconstructs low frequency details. The output prediction is added to the input to form the final SR wavelet coefficients. Then the inverse 2d discrete wavelet transformation is applied to transform the predicted details and generate the SR results. We show that DWSR is computationally simpler and yet produces competitive and often better results than state-of-the-art alternatives.


title: Cloud technologies for flexible 5G radio access networks

abstract: The evolution toward 5G mobile networks will be characterized by an increasing number of wireless devices, increasing device and service complexity, and the requirement to access mobile services ubiquitously. Two key enablers will allow the realization of the vision of 5G: very dense deployments and centralized processing. This article discusses the challenges and requirements in the design of 5G mobile networks based on these two key enablers. It discusses how cloud technologies and flexible functionality assignment in radio access networks enable network densification and centralized operation of the radio access network over heterogeneous backhaul networks. The article describes the fundamental concepts, shows how to evolve the 3GPP LTE a


title: Machine learning methods for predicting failures in hard drives: A multiple-instance application

abstract: We compare machine learning methods applied to a difficult real-world problem: predicting computer hard-drive failure using attributes monitored internally by individual drives. The problem is one of detecting rare events in a time series of noisy and nonparametrically-distributed data. We develop a new algorithm based on the multiple-instance learning framework and the naive Bayesian classifier (mi-NB) which is specifically designed for the low false-alarm case, and is shown to have promising performance. Other methods compared are support vector machines (SVMs), unsupervised clustering, and non-parametric statistical tests (rank-sum and reverse arrangements). The failure-prediction performance of the SVM, rank-sum and mi-NB algorithm is considerably better than the threshold method currently implemented in drives, while maintaining low false alarm rates. Our results suggest that nonparametric statistical tests should be considered for learning problems involving detecting rare events in time series data. An appendix details the calculation of rank-sum significance probabilities in the case of discrete, tied observations, and we give new recommendations about when the exact calculation should be used instead of the commonly-used normal approximation. These normal approximations may be particularly inaccurate for rare event problems like hard drive failures.


title: A survey on non-invasive blood glucose monitoring using NIR

abstract: Diabetes is considered as one of the major contributors of precipitate infirmity and death in non-contagious diseases. Prevailing method for determination of blood glucose concentration is using a self-monitoring glucose meter. This process involves pricking the finger and extracting the blood from the forearm and doing the chemical analysis with the help of disposable test strips. The ache, throe and difficulty caused by this technique have lead to the development of a noninvasive method of measurement based on the survey conducted. This technique makes use of a near infrared sensor for transmission and reception of rays from fingertip. Near-infrared (NIR) is sent through the fingertip, before and after occluding the blood flow. By analyzing the variation in received signal intensity obtained after reflection in both the cases, glucose present in blood can be predicted and wirelessly transmitted to a remote PC.


title: Discrete 2-Tensor Fields on Triangulations

abstract: Geometry processing has made ample use of discrete representations of tangent vector fields and antisymmetric tensors i.e., forms on triangulations. Symmetric 2-tensors, while crucial in the definition of inner products and elliptic operators, have received only limited attention. They are often discretized by first defining a coordinate system per vertex, edge or face, then storing their components in this frame field. In this paper, we introduce a representation of arbitrary 2-tensor fields on triangle meshes. We leverage a coordinate-free decomposition of continuous 2-tensors in the plane to construct a finite-dimensional encoding of tensor fields through scalar values on oriented simplices of a manifold triangulation. We also provide closed-form expressions of pairing, inner product, and trace for this discrete representation of tensor fields, and formulate a discrete covariant derivative and a discrete Lie bracket. Our approach extends discrete/finite-element exterior calculus, recovers familiar operators such as the weighted Laplacian operator, and defines discrete notions of divergence-free, curl-free, and traceless tensors-thus offering a numerical framework for discrete tensor calculus on triangulations. We finally demonstrate the robustness and accuracy of our operators on analytical examples, before applying them to the computation of anisotropic geodesic distances on discrete surfaces.


State-of-the-art in heterogeneous computing


title: Towards End-To-End Speech Recognition with Recurrent Neural Networks

abstract: This paper presents a speech recognition system that directly transcribes audio data with text, without requiring an intermediate phonetic representation. The system is based on a combination of the deep bidirectional LSTM recurrent neural network architecture and the Connectionist Temporal Classification objective function. A modification to the objective function is introduced that trains the network to minimise the expectation of an arbitrary transcription loss function. This allows a direct optimisation of the word error rate, even in the absence of a lexicon or language model. The system achieves a word error rate of 27.3% on the Wall Street Journal corpus with no prior linguistic information, 21.9% with only a lexicon of allowed words, and 8.2% with a trigram language model. Combining the network with a baseline system further reduces the error rate to 6.7%.


title: Semantic Image Segmentation with Contextual Hierarchical Models

abstract: Semantic segmentation is the problem of assigning an object label to each pixel. It unifies the image segmentation and object recognition problems. The importance of using contextual information in semantic segmentation frameworks has been widely realized in the field. We propose a contextual framework, called contextual hierarchical model (CHM) , which learns contextual information in a hierarchical framework for semantic segmentation. At each level of the hierarchy, a classifier is trained based on downsampled input images and outputs of previous levels. Our model then incorporates the resulting multi-resolution contextual information into a classifier to segment the input image at original resolution. This training strategy allows for optimization of a joint posterior probability at multiple resolutions through the hierarchy. Contextual hierarchical model is purely based on the input image patches and does not make use of any fragments or shape examples. Hence, it is applicable to a variety of problems such as object segmentation and edge detection. We demonstrate that CHM performs at par with state-of-the-art on Stanford background and Weizmann horse datasets. It also outperforms state-of-the-art edge detection methods on NYU depth dataset and achieves state-of-the-art on Berkeley segmentation dataset (BSDS 500).


title: Unsupervised PCFG Induction for Grounded Language Learning with Highly Ambiguous Supervision

abstract: "Grounded" language learning employs training data in the form of sentences paired with relevant but ambiguous perceptual contexts. Borschinger et al. (2011) introduced an approach to grounded language learning based on unsupervised PCFG induction. Their approach works well when each sentence potentially refers to one of a small set of possible meanings, such as in the sportscasting task. However, it does not scale to problems with a large set of potential meanings for each sentence, such as the navigation instruction following task studied by Chen and Mooney (2011). This paper presents an enhancement of the PCFG approach that scales to such problems with highly-ambiguous supervision. Experimental results on the navigation task demonstrates the effectiveness of our approach.


title: Brushless DC motor control without position and speed sensors

abstract: The control strategy using a DSP-TMS320C25 is developed on the basis of the instantaneous voltage equation of the brushless DC motor. Without a position sensor, the controller has no information about rotor position: therefore, the controller determines the applied voltage according to the hypothetical rotor position, which is not necessarily coincident with the actual rotor position. Since the voltage supplied through the inverter is the actual applied voltage, it can be measured. Under the ideal condition that the hypothetical rotor position is coincident with the actual rotor position, the ideal applied voltage can be calculated by using the instantaneous voltage equation of the motor and the detected current. The difference between the actual and the ideal voltages is proportional to the angular difference of the hypothetical and actual rotor positions. Therefore, self-synchronization is possible by reducing the angular difference to zero. Once the rotor position is estimated, the motor can be operated without position and speed sensors. >


title: An information security knowledge sharing model in organizations

abstract: Knowledge sharing plays an important role in the domain of information security, due to its positive effect on employees' information security awareness. It is acknowledged that security awareness is the most important factor that mitigates the risk of information security breaches in organizations. In this research, a model has been presented that shows how information security knowledge sharing (ISKS) forms and decreases the risk of information security incidents. The Motivation Theory and Theory of Planned Behavior besides Triandis model were applied as the theoretical backbone of the conceptual framework. The results of the data analysis showed that earning a reputation, and gaining promotion as an extrinsic motivation and curiosity satisfaction as an intrinsic motivation have positive effects on employees' attitude toward ISKS. However, self-worth satisfaction does not influence ISKS attitude. In addition, the findings revealed that attitude, perceived behavioral control, and subjective norms have positive effects on ISKS intention and ISKS intention affects ISKS behavior. The outcomes also showed that organizational support influences ISKS behavior more than trust. The results of this research should be of interest to academics and practitioners in the domain of information security. Information security knowledge sharing mitigates information security breaches.Intrinsic motivations influence information security knowledge sharing in firms.Extrinsic motivations influence information security knowledge sharing in firms.Organizational support facilitates information security knowledge sharing in firms.


title: Distillation Techniques for Pseudo-rehearsal Based Incremental Learning

abstract: The ability to learn from incrementally arriving data is essential for any life-long learning system. However, standard deep neural networks forget the knowledge about the old tasks, a phenomenon called catastrophic forgetting, when trained on incrementally arriving data. We discuss the biases in current Generative Adversarial Networks (GAN) based approaches that learn the classifier by knowledge distillation from previously trained classifiers. These biases cause the trained classifier to perform poorly. We propose an approach to remove these biases by distilling knowledge from the classifier of AC-GAN. Experiments on MNIST and CIFAR10 show that this method is comparable to current state of the art rehearsal based approaches. The code for this paper is available at this https URL


title: All-soft material system for strong soft actuators

abstract: In this paper, we present an improvement to a recently developed thermally responsive silicone-based actuator. The actuator is made of silicone with dispersed solvent droplets and expands when the droplets undergo a liquidgas phase transition, which is achieved at low voltages using an embedded Joule heater. In previous work, the embedded heater was a nickel-chromium spiral-shaped Joule heating wire. In the present work, we replace the wire with a silicone-based conductive composite to create a fully soft actuator. We characterize the thermal response of the conductive composite for Joule heating the actuator and the blocked force of the actuator when implemented as a McKibben-like muscle. From this, we show that the conductive composite performs as well as the original wire heater, with improved material compatibility. Finally, we demonstrate a 20 g silicone actuator embedded with the conductive composite lifting its 4.3 kg DC power source.


title: Design and application of intelligent dynamic crawler for web data mining

abstract: Web data acquisition is the foundation of Web data mining. Web crawler is an important tool for Web data acquisition, but the frequent updates of Web data structures, data sources and distribution channels, resulted in high costs of crawler program development and maintenance. In order to solve this problem, this paper designed and implemented an intelligent dynamic crawler, which stored the data extraction rules of XPath in database, loaded the rules dynamically according to the target, and used TF-IDF method to calculate the relevance. The Web crawling rules can be automatically acquired, which made the crawler intelligent and dynamic, improved the adaptability of the crawler for the complex web environment, and reduced the maintenance and update cost. Finally, this paper applies the intelligent dynamic crawler to the threat awareness of public vulnerabilities, with the method of data collection and analysis of the vulnerability community and the network node search engine. The experiment used the prototype system on three vulnerability communities to collect and analyze the data. The results showed that the intelligent dynamic crawler can realize the high-efficient and flexible data collection function, and laid the foundation for Web data mining.


title: © 2012 INFORMS On the Development of a Soccer Player Performance Rating System for the English Premier League

abstract: The EA Sports Player Performance Index is a rating system for soccer players used in the top two tiers of soccer in England—the Premier League and the Championship. Its development was a collaboration among professional soccer leagues, a news media association, and academia. In this paper, we describe the index and its construction. The novelty of the index lies in its attempts to rate all players using a single score, regardless of their playing specialty, based on player contributions to winning performances. As one might expect, players from leading teams lead the index, although surprises happen.


title: Adolescents who experienced sexual abuse: fears, needs and impediments to disclosure

abstract: Abstract Objective: Understanding the impediments that prevented sexually abused adolescents from disclosure to their family or to professionals, and analyzing the responses they received when they did disclose. Methods: In depth anonymous interviews were conducted in Italy through a toll-free telephone line with 36 young people who experienced sexual abuse in adolescence. A qualitative analysis was carried out of the adolescents’ feelings, fears and needs, and of the help received, if any. Results: The main impediments to disclose to a family member were fear of not being believed, shame, and fear of causing trouble to the family. The main impediments for not seeking services were ignorance of the existence/functioning of protective agencies, wish to keep the secret, lack of awareness of being abused, mistrust of adults and professionals, and fear of the consequences of disclosure. When they did disclose to professionals, the teens received very limited support. Conclusion: Adolescents need to receive proper information about the risk of being sexually abused and about the help they can receive from their social network and protective agencies. There is a crucial need for appropriate training of professionals.


title: REVIEW Assessing the Accuracy of Prediction Algorithms for Classification: An Overview

abstract: We provide a unified overview of methods that currently are widely used to assess the accuracy of prediction algorithms, from raw percentages, quadratic error measures and other distances, and correlation coefficients, and to information theoretic measures such as relative entropy and mutual information. We briefly discuss the advantages and disadvantages of each approach. For classification tasks, we derive new learning algorithms for the design of prediction systems by directly optimising the correlation coefficient. We observe and prove several results relating sensitivity and specificity of optimal systems. While the principles are general, we illustrate the applicability on specific problems such as protein secondary structure and signal peptide prediction.


title: Dengue fever prediction using K-means clustering algorithm

abstract: Dengue fever is a virus infection which is transmitted to humans by mosquitoes that living in tropical and subtropical climates and carries the virus. The dengue viruses occur in 4 serotypes (DENV-1 to DENV-4). A dengue disease ranges from mild febrile disease to severe hemorrhagic fever. Predicting the relationship between the dengue serotypes will surely help the biotechnologists and bioinformaticians to move one step forward to discover antibiotic for dengue. This paper has been focused four stages namely preprocessing, attribute selection, clustering and predicting the dengue fever. R 3.3.2 Tool is used for preprocessing the household of dengue dataset. D win's method has been applied to generate filled dataset by substituting all missing values for nominal and numeric attributes with mode and mean value. Dengue virus can be predicted by applying different data mining techniques. The main goal of research work is to predict the people who are affected by dengue depending upon categorization of age group using K-means clustering algorithm has been implemented.


title: Dynamic Conversion Behavior at E-Commerce Sites

abstract: This paper develops a model of conversion behavior (i.e., converting store visits into purchases) that predicts each customer's probability of purchasing based on an observed history of visits and purchases. We offer an individual-level probability model that allows for different forms of customer heterogeneity in a very flexible manner. Specifically, we decompose an individual's conversion behavior into two components: one for accumulating visit effects and another for purchasing threshold effects. Each component is allowed to vary across households as well as over time.Visit effects capture the notion that store visits can play different roles in the purchasing process. For example, some visits are motivated by planned purchases, while others are associated with hedonic browsing (akin to window shopping); our model is able to accommodate these (and several other) types of visit-purchase relationships in a logical, parsimonious manner. Thepurchasing threshold captures the psychological resistance to online purchasing that may grow or shrink as a customer gains more experience with the purchasing process at a given website. We test different versions of the model that vary in the complexity of these two key components and also compare our general framework with popular alternatives such as logistic regression. We find that the proposed model offers excellent statistical properties, including its performance in a holdout validation sample, and also provides useful managerial diagnostics about the patterns underlying online buyer behavior.


title: Demystifying Arm TrustZone: A Comprehensive Survey

abstract: The world is undergoing an unprecedented technological transformation, evolving into a state where ubiquitous Internet-enabled “things” will be able to generate and share large amounts of security- and privacy-sensitive data. To cope with the security threats that are thus foreseeable, system designers can find in Arm TrustZone hardware technology a most valuable resource. TrustZone is a System-on-Chip and CPU system-wide security solution, available on today’s Arm application processors and present in the new generation Arm microcontrollers, which are expected to dominate the market of smart “things.” Although this technology has remained relatively underground since its inception in 2004, over the past years, numerous initiatives have significantly advanced the state of the art involving Arm TrustZone. Motivated by this revival of interest, this paper presents an in-depth study of TrustZone technology. We provide a comprehensive survey of relevant work from academia and industry, presenting existing systems into two main areas, namely, Trusted Execution Environments and hardware-assisted virtualization. Furthermore, we analyze the most relevant weaknesses of existing systems and propose new research directions within the realm of tiniest devices and the Internet of Things, which we believe to have potential to yield high-impact contributions in the future.


title: Ring Resonator Modulators in Silicon for Interchip Photonic Links

abstract: Silicon photonics is the most promising pathway to achieve >10 Tb/s off-chip I/O bandwidth required by next-generation high-performance computing and switching systems. Ring resonator modulators offer the advantages of small footprint, low power, high efficiency, low loss, high speed, and CMOS compatibility for silicon photonic links. This paper presents an in-depth discussion of practical microring modulators in silicon, covering their performance metrics, design tradeoffs, optimization, p-n junction geometries, complex ring configurations, and tuning solutions. Various demonstrated Si ring modulators are reviewed and potential future developments are briefly discussed.


title: Robotic co-manipulation with 6 DoF admittance control: Application to patient positioning in proton-therapy

abstract: The paper focuses on controlling by hand a robot carrying a patient lying down on a “couch top” located at the extremity of the end-effector. The Orion prototype, is a six Degree-of-Freedom (DoF) robotic system. The control of the robot is achieved through a home-made co-manipulated haptic device located under the couch top and operated by a paramedical assistant. This device enables the operator to move the couch top, on which the patient lies down, and within an identified operational safety space. The 6 DoF admittance control algorithm is implemented on this new robotic platform. An experimental validation has been carried out to validate the co-manipulation control of the robot. The adaptable admittance parameters have been tuned on several experiments in order to observe the mass-spring-damper equivalent behaviour and demonstrate the validity of the implemented admittance algorithm.


title: Derive: a tool that automatically reverse-engineers instruction encodings

abstract: Many binary tools, such as disassemblers, dynamic code generation systems, and executable code rewriters, need to understand how machine instructions are encoded. Unfortunately, specifying such encodings is tedious and error-prone. Users must typically specify thousands of details of instruction layout, such as opcode and field locations values, legal operands, and jump offset encodings. We have built a tool called DERIVE that extracts these details from existing software: the system assembler. Users need only provide the assembly syntax for the instructions for which they want encodings. DERIVE automatically reverse-engineers instruction encoding knowledge from the assembler by feeding it permutations of instructions and doing equation solving on the output. ::: DERIVE is robust and general. It derives instruction encodings for SPARC, MIPS, Alpha, PowerPC, ARM, and x86. In the last case, it handles variable-sized instructions, large instructions, instruction encodings determined by operand size, and other CISC features. DERIVE is also remarkably simple: it is a factor of 6 smaller than equivalent, more traditional systems. Finally, its declarative specifications eliminate the mis-specification errors that plague previous approaches, such as illegal registers used as operands or incorrect field offsets and sizes. This paper discusses our current DERIVE prototype, explains how it computes instruction encodings, and also discusses the more general implications of the ability to extract functionality from installed software.