title: GMM-UBM for text-dependent speaker recognition

abstract: Traditional Text-Dependent Speaker Recognition (TDSR) systems model the user-specific spoken passwords with frame-based features such as mel frequency cepstral coefficient (MFCC) and use Dynamic Time Warping (DTW) or hidden Markov Model (HMM) classifiers to handle the variable length of the feature vector sequence. However, DTW can't deal with cross-channel issue while HMM needs more computational complexity and storage space. In this paper, we introduce text-independent framework GMM-UBM into text-dependent field. It not only solves intersession problem but also a compromise between model accuracy and computational cost. Moreover, a more accurate UBM will get lower EER. A new UBM initialization method, LBG-VQ-EM, will be proposed. Experiments shows that it is better than conventional initialization way like K-means. And we also compare the performance of GMM-UBM and DTW, and two stacked methods of training utterances: frame-based and wave-based. The experimental results showed the performance of GMM-UBM exceeded that of DTW, and that of frame-based outperformed that of wave-based.


title: Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction

abstract: Tracking congestion throughout the network road is a critical component of Intelligent transportation network management systems. Understanding how the traffic flows and short-term prediction of congestion occurrence due to rush-hour or incidents can be beneficial to such systems to effectively manage and direct the traffic to the most appropriate detours. Many of the current traffic flow prediction systems are designed by utilizing a central processing component where the prediction is carried out through aggregation of the information gathered from all measuring stations. However, centralized systems are not scalable and fail provide real-time feedback to the system whereas in a decentralized scheme, each node is responsible to predict its own short-term congestion based on the local current measurements in neighboring nodes. We propose a decentralized deep learning-based method where each node accurately predicts its own congestion state in realtime based on the congestion state of the neighboring stations. Moreover, historical data from the deployment site is not required, which makes the proposed method more suitable for newly installed stations. In order to achieve higher performance, we introduce a regularized euclidean loss function that favors high congestion samples over low congestion samples to avoid the impact of the unbalanced training dataset. A novel dataset for this purpose is designed based on the traffic data obtained from traffic control stations in northern California. Extensive experiments conducted on the designed benchmark reflect a successful congestion prediction.


title: Look away: the anti-saccade task and the voluntary control of eye movement

abstract: The anti-saccade task has emerged as an important task for investigating the flexible control that we have over behaviour. In this task, participants must suppress the reflexive urge to look at a visual target that appears suddenly in the peripheral visual field and must instead look away from the target in the opposite direction. A crucial step involved in performing this task is the top-down inhibition of a reflexive, automatic saccade. Here, we describe recent neurophysiological evidence demonstrating the presence of this inhibitory function in single-cell activity in the frontal eye fields and superior colliculus. Patients diagnosed with various neurological and/or psychiatric disorders that affect the frontal lobes or basal ganglia find it difficult to suppress the automatic pro-saccade, revealing a deficit in top-down inhibition.


title: Everything you know about Dynamic Time Warping is Wrong

abstract: The Dynamic Time Warping (DTW) distance measure is a technique that has long been known in speech recognition community. It allows a non-linear mapping of one signal to another by minimizing the distance between the two. A decade ago, DTW was introduced into Data Mining community as a utility for various tasks for time series problems including classification, clustering, and anomaly detection. The technique has flourished, particularly in the last three years, and has been applied to a variety of problems in various disciplines. In spite of DTW’s great success, there are still several persistent “myths” about it. These myths have caused confusion and led to much wasted research effort. In this work, we will dispel these myths with the most comprehensive set of time series experiments ever conducted.


title: Crowdsourcing based spatial mining of urban emergency events using social media

abstract: With the advances of information communication technologies, it is critical to improve the efficiency and accuracy of emergency management systems through modern data processing techniques. The past decade has witnessed the tremendous technical advances in Sensor Networks, Internet/Web of Things, Cloud Computing, Mobile/Embedded Computing, Spatial/Temporal Data Processing, and Big Data, and these technologies have provided new opportunities and solutions to emergency management. GIS models and simulation capabilities are used to exercise response and recovery plans during non-disaster times. They help the decision-makers understand near real-time possibilities during an event. In this paper, a crowdsourcing based model for mining spatial information of urban emergency events is introduced. Firstly, basic definitions of the proposed method are given. Secondly, positive samples are selected to mine the spatial information of urban emergency events. Thirdly, location and GIS information are extracted from positive samples. At last, the real spatial information is determined based on address and GIS information. At last, a case study on an urban emergency event is given.


title: Activity recognition based on home to home transfer learning

abstract: Activity recognition plays an important role in many areas such as smart environments by offering unprecedented opportunities for assisted living, automation, security and energy efficiency. It's also an essential component for planning and plan recognition in smart environments. One challenge of activity recognition is the need for collecting and annotating huge amounts of data for each new physical setting in order to be able to carry out the conventional activity discovery and recognition algorithms. This extensive initial phase of data collection and annotation results in a prolonged installation process and excessive time investment for each new space. In this paper we propose a new method of transferring learned knowledge of activities to a new physical space in order to leverage the learning process in the new environment. Our method called "Home to Home Transfer Learning" (HHTL) is based on using a semi EM framework and modeling activities using structural, temporal and spatial features. This method allows us to avoid the tedious task of collecting and labeling huge amounts of data in the target space, and allows for a more accelerated and more scalable deployment cycle in the real world. It also allows us to exploit the insights learned in previous spaces. To validate our algorithms, we use the data collected in several smart apartments with different physical layouts.


title: Teleost fish interferons and their role in immunity.

abstract: Interferons (IFNs) are the hallmark of the vertebrate antiviral system. Two of the three IFN families identified in higher vertebrates are now known to be important for antiviral defence in teleost fish. Based on the cysteine patterns, the fish type I IFN family can be divided into two subfamilies, which possibly interact with distinct receptors for signalling. The fish type II IFN family consists of two members, IFN-γ with similar functions to mammalian IFN-γ and a teleost specific IFN-γ related (IFN-γrel) molecule whose functions are not fully elucidated. These two type II IFNs also appear to bind to distinct receptors to exert their functions. It has become clear that fish IFN responses are mediated by the host pattern recognition receptors and an array of transcription factors including the IFN regulatory factors, the Jak/Stat proteins and the suppressor of cytokine signalling (SOCS) molecules.


title: Deep neural network based wake-up-word speech recognition with two-stage detection

abstract: This paper presents a novel far-field voice trigger algorithm utilizing DNN with the objective function of state-level minimum Bayes risk for training, customizing the decoding network to absorb the ambient noise and background speech. We adopt a two-stage classification strategy to integrate the phonetic knowledge and model-based classification into detecting wake-up words. Experimental results of the online test show that it can provide a higher than 90% accuracy and meanwhile false alarms are less than once per nine hours in the noisy home environments where the sound pressure level is about 80dB.


title: Design of an Adaptive Singularity-free Control Moment Gyroscope (ASCMG) actuator for agile and precise attitude control of cubesat

abstract: Design considerations for agile, precise and reliable attitude control of micro-spacecraft using Adaptive Singularity-free Control Moment Gyroscope (ASCMG) actuators are presented here. A complete dynamics model of a spacecraft with an ASCMG is derived using the principles of variational mechanics, relaxing some assumptions made in prior literature on Control Moment Gyroscopes (CMG). The dynamics so obtained shows the complex nonlinear coupling between the internal degrees of freedom associated with an ASCMG and the spacecraft base body's attitude motion. By default, the general ASCMG model is equivalent to that of a Variable Speed Control Moment Gyroscope without symmetrical rotor and gimbal, and can operate as a CMG by spinning the rotor at constant speed. This dynamics model is then extended to include the effects of multiple ASCMGs placed in the spacecraft bus, and sufficient conditions for non-singular ASCMG cluster configurations are obtained to operate the cluster in CMG mode. The adverse effects of the simplifying assumptions that lead to the standard CMG design, and how they lead to CMG singularities, are described. A bare minimum hardware prototype of an ASCMG using low cost COTS components, is shown. A control scheme for agile and precise attitude pointing control of a cubesat using a finite number of ASCMGs in the absence of external torques, is presented. A Geometric Variational Integration scheme is obtained for this multibody spacecraft for numerical and micro-controller implementation.


title: Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system

abstract: This paper provides an analysis of human mobility data in an urban area using the amount of available bikes in the stations of the community bicycle program Bicing in Barcelona. Based on data sampled from the operator's website, it is possible to detect temporal and geographic mobility patterns within the city. These patterns are applied to predict the number of available bikes for any station some minutes/hours ahead. The predictions could be used to improve the bicycle program and the information given to the users via the Bicing website.


title: Heat loss compensation in semi-adiabatic curing test of concrete

abstract: An adiabatic temperature rise maximum, which must be measured through an adiabatic curing test using a specially designed curing chamber, is often imposed for early thermal concrete cracking alleviation. Curing tests conducted on site should be regarded as semi-adiabatic because in practice, for convenience and simplicity, they are not truly adiabatic. The measured temperature rise is lower in a semi-adiabatic curing test due to heat loss than the adiabatic temperature rise by a specimen size-dependent error and the provided heat insulation. A heat loss compensation method using measured temperature spatial and time variation and heat loss compensation for heat loss estimation is proposed to minimize such error. To verify method applicability, ten laboratory-made concrete mixtures were tested. To demonstrate how a semi-adiabatic curing test could become much less affected by imperfect insulation after heat loss compensation, as well as independent of specimen size, two ready mixed concrete batches, each cast into 0.5 and 1.0 m (1.64 and 3.28 ft) cubes, were tested. Adiabatic temperature rise errors were all within ±1.3°C (±2.3°F) with application of the proposed heat loss compensation.


title: Plastic specula: can we ease the passage?

abstract: Background: For many years, genitourinary physicians have taught that specula should be used without lubrication other than water, as it was assumed that gel would interfere with the processing of samples, but there seems little evidence to support this. Many clinics are now using plastic specula to avoid reusable instruments, and one of the commonest problems with such specula is increased friction. Methods: We looked at the effect of Aquagel on the culture of different dilutions of Neisseria gonorrhoeae on three standard laboratory media. The effect of Aquagel on the chlamydial strand displacement assay (SDA) test was also assessed by mixing different amounts of Aquagel with the positive and negative control and processing in the usual way. Results: There was found to be no inhibition of culture following emulsification of N gonorrhoeae in Aquagel at any concentration. All the results for the chlamydial SDA test were satisfactory following mixing with Aquagel. Conclusion: We think that the clinician should now be more confident that if a difficult examination requires the use of a lubricant, the test results will not be compromised.


title: An enterprise business intelligence maturity model (EBIMM): Conceptual framework

abstract: Business Intelligence (BI) market trend is hot for the past few years. According to the results from the Gartner Research 2009, Business Intelligence's market is ranked top of ranking in Business and Technology Priorities in 2009. CMM can be applied into various disciplines such as software field, engineering field or IT field. However, there is limited research of CMM that applied in Enterprise Business Intelligence (EBI) domain. This is because BI market is a quite new area. Based on the literature in BI and CMM, a multi-dimensional set of critical factors for characterizing the levels of EBI maturity has been proposed. Specifically, there are three key dimensions of capability influencing the EBI effort: data warehousing, information quality and knowledge process. From a practical standpoint, the proposed model provides useful basis to firms aspiring to elevate their business intelligence endeavour to higher levels of maturity. The research also serves as a foundation for initiating other relevant studies and for assessing EBI maturity.


title: Sliding spotlight SAR processing for TerraSAR-X using a new formulation of the extended chirp scaling algorithm

abstract: This paper describes the sliding spotlight algorithm and the processing strategy to be applied for TerraSAR-X. The steering spotlight geometry is analysed. Analysis of scene size and resolution demonstrates the particularities of this mode. The Doppler frequencies for individual targets and for a whole sliding spotlight scene are analyzed and the result shows the applicabil- ity of azimuth subaperture processing to sliding spotlight data. A description of the Extended Chirp Scaling Algorithm for sliding spotlight is presented.


title: A Time Series Forest for Classification and Feature Extraction

abstract: We propose a tree ensemble method, referred to as time series forest (TSF), for time series classification. TSF employs a combination of the entropy gain and a distance measure, referred to as the Entrance (entropy and distance) gain, for evaluating the splits. Experimental studies show that the Entrance gain criterion improves the accuracy of TSF. TSF randomly samples features at each tree node and has a computational complexity linear in the length of a time series and can be built using parallel computing techniques such as multi-core computing used here. The temporal importance curve is also proposed to capture the important temporal characteristics useful for classification. Experimental studies show that TSF using simple features such as mean, deviation and slope outperforms strong competitors such as one-nearest-neighbor classifiers with dynamic time warping, is computationally efficient, and can provide insights into the temporal characteristics.


title: Contrarian Investment, Extrapolation, and Risk

abstract: For many years, stock market analysts have argued that value strategies outperform the market. These value strategies call for buying stocks that have low prices relative to earnings, dividends, book assets, or other measures of fundamental value. While there is some agreement that value strategies produce higher returns, the interpretation of why they do so is more controversial. This paper provides evidence that value strategies yield higher returns because these strategies exploit the mistakes of the typical investor and not because these strategies are fundamentally riskier.


title: Wideband subarray design for 5G an antenna arrays

abstract: There is a global trend towards migrating to millimeter wave frequencies in the fifth generation of mobile communications (5G) to further enhance the available capacity and data rates. Antenna arrays are usually applied to overcome the innate high path loss at the millimeter wave (mmW) frequency band. Moreover, the use of subarrays decreases design complexity and system cost. The goal of this paper is to present a design of a wideband subarray at the frequency range from 28 GHz to 32 GHz, i.e. the frequency range applied for Local Multipoint Distribution Service (LMDS), to be used in the antenna array design of next generation mobile networks. The proposed subarray consists of four radiating elements of proximity coupled stacked patch antennas. The unit-cell is first designed, fabricated and tested, which confirms the wideband coverage along the required band. The proposed subarray achieves a simulated gain that ranges between 11.1 dB and 12 dB along the frequency band from 28 GHz to 32 GHz. The impedance bandwidth and the 1-dB gain bandwidth are 30% and 21.2%, respectively.


title: End-to-end Language Identification using NetFV and NetVLAD

abstract: In this paper, we apply the NetFV and NetVLAD layers for the end-to-end language identification task. NetFV and NetVLAD layers are the differentiable implementations of the standard Fisher Vector and Vector of Locally Aggregated Descriptors (VLAD) methods, respectively. Both of them can encode a sequence of feature vectors into a fixed dimensional vector which is very important to process those variable-length utterances. We first present the relevances and differences between the classical i-vector and the aforementioned encoding schemes. Then, we construct a flexible end-to-end framework including a convolutional neural network (CNN) architecture and an encoding layer (NetFV or NetVLAD) for the language identification task. Experimental results on the NIST LRE 2007 close-set task show that the proposed system achieves significant EER reductions against the conventional i-vector baseline and the CNN temporal average pooling system, respectively.


title: Learning Sequence Encoders for Temporal Knowledge Graph Completion

abstract: Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entities may only hold for a time interval or a specific point in time. In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations. To incorporate temporal information, we utilize recurrent neural networks to learn time-aware representations of relation types which can be used in conjunction with existing latent factorization methods. The proposed approach is shown to be robust to common challenges in real-world KGs: the sparsity and heterogeneity of temporal expressions. Experiments show the benefits of our approach on four temporal KGs. The data sets are available under a permissive BSD-3 license 1.


title: Sexual Orientation, Controversy, and Science

abstract: Summary Ongoing political controversies around the world exemplify a long-standing and widespread preoccupation with the acceptability of homosexuality. Nonheterosexual people have seen dramatic surges both in their rights and in positive public opinion in many Western countries. In contrast, in much of Africa, the Middle East, the Caribbean, Oceania, and parts of Asia, homosexual behavior remains illegal and severely punishable, with some countries retaining the death penalty for it. Political controversies about sexual orientation have often overlapped with scientific controversies. That is, participants on both sides of the sociopolitical debates have tended to believe that scientific findings—and scientific truths—about sexual orientation matter a great deal in making political decisions. The most contentious scientific issues have concerned the causes of sexual orientation—that is, why are some people heterosexual, others bisexual, and others homosexual? The actual relevance of these issues to social, political, and ethical decisions is often poorly justified, however.


title: Learning to Recommend Descriptive Tags for Questions in Social Forums

abstract: Around 40p of the questions in the emerging social-oriented question answering forums have at most one manually labeled tag, which is caused by incomprehensive question understanding or informal tagging behaviors. The incompleteness of question tags severely hinders all the tag-based manipulations, such as feeds for topic-followers, ontological knowledge organization, and other basic statistics. This article presents a novel scheme that is able to comprehensively learn descriptive tags for each question. Extensive evaluations on a representative real-world dataset demonstrate that our scheme yields significant gains for question annotation, and more importantly, the whole process of our approach is unsupervised and can be extended to handle large-scale data.


title: Design Approach for Multiband Horn Antennas Mixing Multimode and Hybrid Mode Operation

abstract: The design of a quad-band (18, 28, 38, and 48 GHz) feed horn is presented. Corrugations are used for a good polarization purity at the higher frequencies. At the lower frequency, they act as a smooth wall and the horn profile is tuned to achieve a high aperture efficiency.


title: Consensus of Multi-Agent Systems With Unbounded Time-Varying Delays

abstract: In this note, the consensus problem with infinite time-varying delays for linearly coupled static network is investigated. The delay affects only the off-diagonal terms in continuous-time equations. At first, we define an effective consensus ability index. Then, by using the graph theory and a new concept of consensus, we prove that under some mild conditions, the network can realize consensus. An example is given to show the validity of obtained results.


title: Antioxidant Activity, Total Phenolic, Flavonoid and Tannin Contents of Callus and Seeds Extracts of Fenugreek (Trigonella foenum-graecum L.)

abstract: The research aimed to study antioxidant activity, total phenolic, flavonoid and tannin contents of methanolic extracts of callus and seeds and petroleum ether extract of seeds of fenugreek (Traigonella foenum-graecum). Callus was induced from hypocotyls and cotyledons explants in MS medium supplemented with combination of 2 mg/l of different auxin (2, 4-D or NAA) + 0.5 mg/l Kinetin. Free radical scavenging activity was evaluated by DPPH method. The highest scavenging activity was obtained from cotyledons derived callus (91.5±0.16%) followed by hypocotyls derived callus (85.46±0.29%) and methanolic extract of seeds (80.53±0.01%). Petroleum ether extract of seeds demonstrated weak antioxidant activity with inhibition percentage 24.56±0.12%. Significant differences (p hypocotyls derived callus (0.7159mg/g)>cotyledons derived callus (0.4914mg/g)> ascorbic acid (0.1874mg/g). A lower value of IC50 indicates a higher antioxidant activity. Highest phenolic content were observed in callus of cotyledons (412.087 mg/l), compared to callus of hypocotyls (211.1937 mg/l) and methanolic extract of seeds (124.84 mg/l) calculated as mg/l gallic acid equivalent of phenols. Methanolic extract of seeds showed highest level of flavonoid content (424.951 mg/l), compared to callus of cotyledons (217.285 mg/l) and callus of hypocotyls (95.92 mg/l) calculated as mg/l quercetin equivalent of flavonoids. Tannins content were detected only in methanolic extract of seeds (116.259 mg/l calculated as tannic acid equivalent of tannins).


title: A Meta-Framework for Modeling the Human Reading Process in Sentiment Analysis

abstract: This article introduces a sentiment analysis approach that adopts the way humans read, interpret, and extract sentiment from text. Our motivation builds on the assumption that human interpretation should lead to the most accurate assessment of sentiment in text. We call this automated process Human Reading for Sentiment (HRS). Previous research in sentiment analysis has produced many frameworks that can fit one or more of the HRS aspects; however, none of these methods has addressed them all in one approach. HRS provides a meta-framework for developing new sentiment analysis methods or improving existing ones. The proposed framework provides a theoretical lens for zooming in and evaluating aspects of any sentiment analysis method to identify gaps for improvements towards matching the human reading process. Key steps in HRS include the automation of humans low-level and high-level cognitive text processing. This methodology paves the way towards the integration of psychology with computational linguistics and machine learning to employ models of pragmatics and discourse analysis for sentiment analysis. HRS is tested with two state-of-the-art methods; one is based on feature engineering, and the other is based on deep learning. HRS highlighted the gaps in both methods and showed improvements for both.


title: Enabling internet banking adoption: An empirical examination with an augmented technology acceptance model (TAM)

abstract: Purpose ::: ::: ::: ::: ::: The integration of relevant antecedents into TAM would lead to better understanding of the decision factors which act as enablers for the adoption of internet banking. The purpose of the paper is to determine the influence of the antecedents subjective norm, image, banks initiative, internet banking self-efficacy, internet usage efficacy, trust, perceived risk, trialability and government support on the existing constructs of the technology acceptance model (TAM) and to test measurement invariance and the moderating effect of the demographic variables on the relationship between the latent constructs used in this augmented TAM. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: A survey questionnaire was administered on internet banking users and a total of 300 responses were collected. A two-step approach suggested by Hair et al. (2006) and Schumacker and Lomax (2004) was used in this study. The proposed model was assessed using the confirmatory factor analysis approach. The structural model was then tested in order to establish nomological validity. The data based on four demographic dimensions gender, age, income, education were divided into two groups for each of these demographic dimensions. The invariance test was first performed on the measurement model and then on the structural model. The measurement model and structural model were subjected to tests of equivalence of parameters across groups. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: To a large extent the results of the study supports the proposed model and thereby contributes to understand the influence of subjective norm, image, banks initiative, internet banking self-efficacy, internet usage efficacy, trust, perceived risk and government support on internet banking adoption. The predictor variables in the augmented TAM were able to explain 29.9 per cent of the variance in the actual usage of internet banking as compared to the TAM which was able to explain only 26.5 per cent variance in the actual usage of internet banking. A significant difference in the relationship between the different constructs of the model was observed when the model was subjected to multi-group invariance testing. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: ::: The study suffers from the same limitations as most other studies involving TAM. In this study self-reported measures about the usage were taken as the actual usage. The findings of the study can be of use to marketers for target-specific marketing by customizing the marketing campaign focussing on the factors that were found to be strong influencers leading to the usage of internet banking for each target audience. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The main challenge in this study was to develop the conceptual model for the internet banking adoption by extending the TAM and to get a robust theoretical support from the extant literature for the relevant factors along with their relationship to uncover new insights about factors responsible for the internet banking adoption. The augmented model had an improved predictive capability and explanatory utility.


title: Splendid One-Dimensional Nanostructures of Zinc Oxide: A New Nanomaterial Family for

abstract: Zinc oxide is a unique material that exhibits exceptional semiconducting, piezoelectric, and pyroelectric properties. Nanostructures of ZnO are equally as important as carbon nanotubes and silicon nanowires for nanotechnology and have great potential applications in nanoelectronics, optoelectronics, sensors, field emission, light-emitting diodes, photocatalysis, nanogenerators, and nanopiezotronics. Fundamental understanding about the growth of ZnO nanowires is of critical importance for controlling their size, composition, structure, and corresponding physical and chemical properties. The papers by She et al. and Ito et al. in this issue describe the controlled growth and field-emission properties of individual nanostructures, respectively. These studies provide new approaches and insight into the controlled growth and electrical properties of ZnO nanostructures.


title: A Meta-Learning Approach to One-Step Active Learning

abstract: We consider the problem of learning when obtaining the training labels is costly, which is usually tackled in the literature using active-learning techniques. These approaches provide strategies to choose the examples to label before or during training. These strategies are usually based on heuristics or even theoretical measures, but are not learned as they are directly used during training. We design a model which aims at \textit{learning active-learning strategies} using a meta-learning setting. More specifically, we consider a pool-based setting, where the system observes all the examples of the dataset of a problem and has to choose the subset of examples to label in a single shot. Experiments show encouraging results.


title: Multi-Sided Platforms

abstract: We study the economic tradeoffs that drive organizations to position themselves closer to or further away from a multi-sided platform (MSP) business model, relative to three traditional alternatives: vertically integrated firms, resellers or input suppliers. These tradeoffs lead to a comprehensive discussion of the defining features of MSPs. The formal model we develop focuses on the MSP versus vertical integration choice, which we interpret in the context of professional services. A key tradeoff emerges between the need to coordinate decisions that generate spillovers across professionals (best achieved by a vertical integrated firm) and the need to both motivate unobservable effort by professionals and ensure professionals adapt their decisions to their private information (best achieved by a MSP). We show how this baseline tradeoff is impacted by the nature of contracts available to the vertically integrated firm and the MSP, and by the possibility of professionals holding pessimistic expectations when deciding whether or not to join the vertically integrated firm or MSP.


title: Adapting the tf idf vector-space model to domain specific information retrieval

abstract: The default implementation in Lucene, an open-source search engine, is the well-known vector-space model with tf idf weighting. The objective of this paper is to propose and evaluate additional techniques that can be adapted to this search model, in order to meet the particular needs of domainspecific information retrieval (IR). In this paper, we suggest certain specificity measures derived from either information theory or corpus-based linguistics. As an additional feature we suggest accounting for the number of search terms that a query and retrieved documents have in common. To integrate these methods we design and implement four extensions to the classical tf idf model and then evaluate the new IR models by applying them to four different domain-specific collections and comparing them to results found by a probabilistic retrieval model. The results tend to demonstrate that the adapted vector-space models clearly outperform the baseline approach (tf idf) and that performance levels obtained even surpass those found in the Okapi model.


title: TRIZ: the science of systematic innovation

abstract: The author details how the introduction of TRIZ in the last decade has given us a left-brain creativity tool to use for creative problem solving when the right brain tools are not adequate, or not appropriate. TRIZ is the acronym for Theory of Inventive Problem Solving, and TRIZ is the acronym for the same phrase in Russian. The TRIZ methodology is a systematic method for solving problems in all these areas. It can be applied by people with both right-brain and left-brain preferences, and works very well with teams that have both people with both strengths, since the analytical tools, such as conflict resolution, requires left-brain skills, but the development of the analogies between the standard TRIZ solutions and the specific situation of a particular problem require right-brain strengths.


title: (Re)Configuring Bike Station Network via Crowdsourced Information Fusion and Joint Optimization

abstract: Thanks to their great success as a green urban transportation means of first/last-mile connectivity, bike sharing service (BSS) networks has been proliferating all over the globe. Their station (re)placement and dock resizing has thus become an increasingly important problem for bike sharing service providers. In contrast to the use of conventional labor-intensive user surveys, we propose a novel optimization framework called CBikes, (re)configuring the BSS network with crowdsourced station suggestions from online websites. Based on comprehensive real data analyses, we identify and utilize important global trip patterns to (re)configure the BSS network while balancing the local biases of individual feedbacks. Specifically, crowdsourced feedbacks, station usage history, cost and other constraints are fused into a joint optimization of BSS network configuration. We further design a semidefinite programming transformation to solve the bike station (re)placement problem efficiently and effectively. Our evaluation has demonstrated the effectiveness and accuracy of CBikes in (re)placing stations and resizing docks based on 3 large BSS systems (with more than 900 stations) in Chicago, Twin Cities, and Los Angeles, as well as related crowdsourced feedbacks.


Brain Tumor Segmentation using hybrid Genetic Algorithm and Artificial Neural Network Fuzzy Inference System (ANFIS)


title: Improving Chain of Custody in Forensic Investigation of Electronic Digital Systems

abstract: Summary Forensic investigators should acquire and analyze large amount of digital evidence and submit to the court the technical truth about facts in virtual worlds. Since digital evidence is complex, diffuse, volatile and can be accidentally or improperly modified after acquired, the chain of custody must ensure that collected evidence can be accepted as truthful by the court. In this scenario, traditional paper-based chain of custody is inefficient and cannot guarantee that the forensic processes follow legal and technical principles in an electronic society. Computer forensics practitioners use forensic software to acquire copies or images from electronic devices and register associated metadata, like computer hard disk serial number and practitioner name. Usually, chain of custody software and data are insufficient to guarantee to the court the quality of forensic images, or guarantee that only the right person had access to the evidence or even guarantee that copies and analysis only were made by authorized manipulations and in the acceptable addresses. Recent developments in forensic software make possible to collect in multiple locations and analysis in distributed environments. In this work we propose the use of the new network facilities existing in Advanced Forensic Format (AFF), an open and extensible format designed for forensic tolls, to increase the quality of electronic chain of custody.


title: Juniper: A Tree+Table Approach to Multivariate Graph Visualization

abstract: Analyzing large, multivariate graphs is an important problem in many domains, yet such graphs are challenging to visualize. In this paper, we introduce a novel, scalable, tree-table multivariate graph visualization technique, which makes many tasks related to multivariate graph analysis easier to achieve. The core principle we follow is to selectively query for nodes or subgraphs of interest and visualize these subgraphs as a spanning tree of the graph. The tree is laid out linearly, which enables us to juxtapose the nodes with a table visualization where diverse attributes can be shown. We also use this table as an adjacency matrix, so that the resulting technique is a hybrid node-link/adjacency matrix technique. We implement this concept in Juniper and complement it with a set of interaction techniques that enable analysts to dynamically grow, restructure, and aggregate the tree, as well as change the layout or show paths between nodes. We demonstrate the utility of our tool in usage scenarios for different multivariate networks: a bipartite network of scholars, papers, and citation metrics and a multitype network of story characters, places, books, etc.


title: Optimal switching decisions under stochastic volatility with fast mean reversion

abstract: We study infinite-horizon, optimal switching problems for underlying processes that exhibiting “fast” mean-reverting stochastic volatility. We obtain closed-form analytic approximations of the solution for the resulting quasi-variational inequalities, that provide quantitative and qualitative results for the effects of multi-scale variability of the underlying process on the optimal switching rule. The proposed methodology is applicable to a number of operations research problems involving switching flexibility.


title: CS-MUVI: Video compressive sensing for spatial-multiplexing cameras

abstract: Compressive sensing (CS)-based spatial-multiplexing cameras (SMCs) sample a scene through a series of coded projections using a spatial light modulator and a few optical sensor elements. SMC architectures are particularly useful when imaging at wavelengths for which full-frame sensors are too cumbersome or expensive. While existing recovery algorithms for SMCs perform well for static images, they typically fail for time-varying scenes (videos). In this paper, we propose a novel CS multi-scale video (CS-MUVI) sensing and recovery framework for SMCs. Our framework features a co-designed video CS sensing matrix and recovery algorithm that provide an efficiently computable low-resolution video preview. We estimate the scene's optical flow from the video preview and feed it into a convex-optimization algorithm to recover the high-resolution video. We demonstrate the performance and capabilities of the CS-MUVI framework for different scenes.


title: Machine Learning and Portfolio Optimization

abstract: The portfolio optimization model has limited impact in practice because of estimation issues when applied to real data. To address this, we adapt two machine learning methods, regularization and cross-validation, for portfolio optimization. First, we introduce performance-based regularization PBR, where the idea is to constrain the sample variances of the estimated portfolio risk and return, which steers the solution toward one associated with less estimation error in the performance. We consider PBR for both mean-variance and mean-conditional value-at-risk CVaR problems. For the mean-variance problem, PBR introduces a quartic polynomial constraint, for which we make two convex approximations: one based on rank-1 approximation and another based on a convex quadratic approximation. The rank-1 approximation PBR adds a bias to the optimal allocation, and the convex quadratic approximation PBR shrinks the sample covariance matrix. For the mean-CVaR problem, the PBR model is a combinatorial optimization problem, but we prove its convex relaxation, a quadratically constrained quadratic program, is essentially tight. We show that the PBR models can be cast as robust optimization problems with novel uncertainty sets and establish asymptotic optimality of both sample average approximation SAA and PBR solutions and the corresponding efficient frontiers. To calibrate the right-hand sides of the PBR constraints, we develop new, performance-based k-fold cross-validation algorithms. Using these algorithms, we carry out an extensive empirical investigation of PBR against SAA, as well as L1 and L2 regularizations and the equally weighted portfolio. We find that PBR dominates all other benchmarks for two out of three Fama-French data sets. ::: ::: This paper was accepted by Yinyu Ye, optimization.


title: Classifying latent user attributes in twitter

abstract: Social media outlets such as Twitter have become an important forum for peer interaction. Thus the ability to classify latent user attributes, including gender, age, regional origin, and political orientation solely from Twitter user language or similar highly informal content has important applications in advertising, personalization, and recommendation. This paper includes a novel investigation of stacked-SVM-based classification algorithms over a rich set of original features, applied to classifying these four user attributes. It also includes extensive analysis of features and approaches that are effective and not effective in classifying user attributes in Twitter-style informal written genres as distinct from the other primarily spoken genres previously studied in the user-property classification literature. Our models, singly and in ensemble, significantly outperform baseline models in all cases. A detailed analysis of model components and features provides an often entertaining insight into distinctive language-usage variation across gender, age, regional origin and political orientation in modern informal communication.


title: Development of a full bridge GaN HEMT converter for inductive power transfer application

abstract: This paper presents the results of the development and use of a full bridge GaN high electron mobility transistor (HEMT) converter used in an inductive power transfer (IPT) application. Experimental results using a Si MOSFETs converter in an IPT setup has previously been reported. By using GaN HEMTs instead of Si MOSFETs, the converter efficiency can be increased over a broad range of operating points. Component selection regarding the gate isolation and low parasitics are discussed. The efficiency comparison between the Si and GaN converters shows that a GaN converter can outperform the Si in both soft and hard switching operation points if correctly designed.


title: Energy-Efficient Cloud Computing

abstract: Energy efficiency is increasingly important for future information and communication technologies (ICT), because the increased usage of ICT, together with increasing energy costs and the need to reduce green house gas emissions call for energy-efficient technologies that decrease the overall energy consumption of computation, storage and communications. Cloud computing has recently received considerable attention, as a promising approach for delivering ICT services by improving the utilization of data centre resources. In principle, cloud computing can be an inherently energy-efficient technology for ICT provided that its potential for significant energy savings that have so far focused on hardware aspects, can be fully explored with respect to system operation and networking aspects. Thus this paper, in the context of cloud computing, reviews the usage of methods and technologies currently used for energy-efficient operation of computer hardware and network infrastructure. After surveying some of the current best practice and relevant literature in this area, this paper identifies some of the remaining key research challenges that arise when such energy-saving techniques are extended for use in cloud computing environments.


Preventing AIDS: A Guide to Effective Education for the Prevention of HIV Infection


title: Proactive personalized services through fog-cloud computing in large-scale IoT-based healthcare application

abstract: With the development of medical sensors and IoT, personalized service assisted elder and patient living is a critical service in IoT-based healthcare application. However, the scale and complexity of personalized service is increasing because of ubiquitous deployment of various kinds of medical sensors, which cause response time increase and resource waste. Therefore, leveraging the advantage of complex event processing (CEP) in data stream processing, we propose a hierarchical fog-cloud computing CEP architecture for personalized service to accelerate response time and reduce resource waste. Firstly, we introduce the proposed architecture, which includes sensor layer, fog layer and cloud layer. Secondly, we propose a series of optimizations for the architecture, there are a partitioning and clustering approach and a communication and parallel processing policy to optimize the fog and cloud computing. Finally, we implement a prototype system based on the architecture named FogCepCare. Experimental result shows that FogCepCare is superior to the traditional IoT-based healthcare application.


title: Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques

abstract: Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. ANN learns from scratch by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantages of a combination of ANN and FIS are obvious. There are several approaches to integrate ANN and FIS and very often it depends on the application. We broadly classify the integration of ANN and FIS into three categories namely concurrent model, cooperative model and fully fused model. This paper starts with a discussion of the features of each model and generalize the advantages and deficiencies of each model. We further focus the review on the different types of fused neuro-fuzzy systems and citing the advantages and disadvantages of each model.


title: An Efficient Algorithm for Local Distance Metric Learning

abstract: Learning application-specific distance metrics from labeled data is critical for both statistical classification and information retrieval. Most of the earlier work in this area has focused on finding metrics that simultaneously optimize compactness and separability in a global sense. Specifically, such distance metrics attempt to keep all of the data points in each class close together while ensuring that data points from different classes are separated. However, particularly when classes exhibit multimodal data distributions, these goals conflict and thus cannot be simultaneously satisfied. This paper proposes a Local Distance Metric (LDM) that aims to optimize local compactness and local separability. We present an efficient algorithm that employs eigenvector analysis, and bound optimization to learn the LDM from training data in a probabilistic framework. We demonstrate that LDM achieves significant improvements in both classification and retrieval accuracy compared to global distance learning and kernel-based KNN.


title: Postpartum Hemorrhage: Epidemiology, Risk Factors, and Causes

abstract: Postpartum hemorrhage (PPH) is a leading cause of death and morbidity relating to pregnancy. Uterine atony is the leading cause of PPH, and trauma, including iatrogenic trauma, increases the risk for postpartum hemorrhage. Women with PPH in a pregnancy are at increased risk of PPH in a subsequent pr


title: G-SQL: Fast Query Processing via Graph Exploration

abstract: A lot of real-life data are of graph nature. However, it is not until recently that business begins to exploit data's connectedness for business insights. On the other hand, RDBMSs are a mature technology for data management, but they are not for graph processing. Take graph traversal, a common graph operation for example, it heavily relies on a graph primitive that accesses a given node's neighborhood. We need to join tables following foreign keys to access the nodes in the neighborhood if an RDBMS is used to manage graph data. Graph exploration is a fundamental building block of many graph algorithms. But this simple operation is costly due to a large volume of I/O caused by the massive amount of table joins. In this paper, we present G-SQL, our effort toward the integration of a RDBMS and a native in-memory graph processing engine. G-SQL leverages the fast graph exploration capability provided by the graph engine to answer multi-way join queries. Meanwhile, it uses RDBMSs to provide mature data management functionalities, such as reliable data storage and additional data access methods. Specifically, G-SQL is a SQL dialect augmented with graph exploration functionalities and it dispatches query tasks to the in-memory graph engine and its underlying RDMBS. The G-SQL runtime coordinates the two query processors via a unified cost model to ensure the entire query is processed efficiently. Experimental results show that our approach greatly expands capabilities of RDBMs and delivers exceptional performance for SQL-graph hybrid queries.


Equilibrium in Competitive Insurance Markets : An Essay on the Economics of Imperfect Information


title: Scaling of GaN HEMTs and Schottky Diodes for Submillimeter-Wave MMIC Applications

abstract: In this paper, we report state-of-the-art high frequency performance of GaN-based high electron mobility transistors (HEMTs) and Schottky diodes achieved through innovative device scaling technologies such as vertically scaled enhancement and depletion mode (E/D mode) AlN/GaN/AlGaN double-heterojunction HEMT epitaxial structures, a low-resistance n+-GaN/2DEG ohmic contact regrown by MBE, a manufacturable 20-nm symmetric and asymmetric self-aligned-gate process, and a lateral metal/2DEG Schottky contact. As a result of proportional scaling of intrinsic and parasitic delays, an ultrahigh fT exceeding 450 GHz (with a simultaneous fmax of 440 GHz) and a fmax close to 600 GHz (with a simultaneous fT of 310 GHz) are obtained in deeply scaled GaN HEMTs while maintaining superior Johnson figure of merit. Because of their extremely low on-resistance and high gain at low drain voltages, the devices exhibited excellent noise performance at low power. 501-stage direct-coupled field-effect transistor logic ring oscillator circuits are successfully fabricated with high yield and high uniformity, demonstrating the feasibility of GaN-based E/D-mode integrated circuits with transistors. Furthermore, self-aligned GaN Schottky diodes with a lateral metal/2DEG Schottky contact and a 2DEG/ n+-GaN ohmic contact exhibited RC-limited cutoff frequencies of up to 2.0 THz.


title: Language within our grasp

abstract: Abstract In monkeys, the rostral part of ventral premotor cortex (area F5) contains neurons that discharge, both when the monkey grasps or manipulates objects and when it observes the experimenter making similar actions. These neurons (mirror neurons) appear to represent a system that matches observed events to similar, internally generated actions, and in this way forms a link between the observer and the actor. Transcranial magnetic stimulation and positron emission tomography (PET) experiments suggest that a mirror system for gesture recognition also exists in humans and includes Broca's area. We propose here that such an observation/execution matching system provides a necessary bridge from `doing' to `communicating', as the link between actor and observer becomes a link between the sender and the receiver of each message.


title: An ultra-low-power FPGA for IoT applications

abstract: The rapid development of the Internet-of-Things requires hardware that is both energy-efficient and flexible, and an ultra-low-power Field-Programmable-Gate-Array (FPGA) is a very promising solution. This paper presents a near/sub-threshold FPGA with low-swing global interconnect, folded switch box (SB), per-path voltage scaling, and power-gating. A fully programmable 512-look-up-table FPGA chip is fabricated in 130nm CMOS. When implementing a 4bit-adder, the measured energy of the proposed FPGA is 15% less than the normalized energy of the state-of-the-art. When implementing fifteen selected low-power applications, the estimated energy of the proposed FPGA is on average 75x lower than Microsemi IGLOO.


title: On the Spectral Bias of Neural Networks

abstract: Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with $100\%$ accuracy. In this work, we present properties of neural networks that complement this aspect of expressivity. By using tools from Fourier analysis, we show that deep ReLU networks are biased towards low frequency functions, meaning that they cannot have local fluctuations without affecting their global behavior. Intuitively, this property is in line with the observation that over-parameterized networks find simple patterns that generalize across data samples. We also investigate how the shape of the data manifold affects expressivity by showing evidence that learning high frequencies gets \emph{easier} with increasing manifold complexity, and present a theoretical understanding of this behavior. Finally, we study the robustness of the frequency components with respect to parameter perturbation, to develop the intuition that the parameters must be finely tuned to express high frequency functions.


title: Learning Distance Metrics for Entity Resolution

abstract: Entity resolution (ER) is to find database records that refer to the same real-world entity. A key component for ER is to choose a proper distance (similarity) function for each database field to quantify the similarity of records. Most existing ER approaches focus on how to define a proper matching rule based on generic or hand-crafted distance metrics. In this paper, we explore two learnable string distance metrics for two kinds of ER problems by employing the principle component analysis and the largest margin nearest neighbor algorithm for training. Experimental results on real data sets show that our approaches can improve entity resolution accuracy over traditional techniques.


title: A Conceptual Model of Multiple Dimensions of Identity.

abstract: A conceptual model of multiple dimensions of identity depicts a core sense of self or one’s personal identity. Intersecting circles surrounding the core identity represent significant identity dimensions (e.g., race, sexual orientation, and religion) and contextual influences (e.g., family background and life experiences). The model evolved from a grounded theory study of a group of 10 women college students ranging in age from 20-24 and of diverse racial-ethnic backgrounds.


title: Electronic model of human brain using verilog

abstract: Computers are man-made machines which work according to the given set of inputs and perform some operation on the input to generate a new set of outputs. They can be programmed to perform huge and complex task yet they lack imagination and ability to understand things. On the hand human brain is a machine which can learn new tasks by process of acquiring knowledge and understanding through thought, experience and the senses. To make computer work or think like human, human brain modeling of machine is necessary. We need to know how human brain works. The basic component of human brain is neuron, which make us think and do smart things. Brains have billions of neurons and they communicate with each other using electrical impulse. These impulses are responsible for making brain think and to have a consciousness. This paper proposes such a model of a part of the brain.


title: Obesity and Obese-related Chronic Low-grade Inflammation in Promotion of Colorectal Cancer Development.

abstract: Colorectal cancer (CRC) is a worldwide health problem, being the third most commonly detected cancer in males and the second in females. Rising CRC incidence trends are mainly regarded as a part of the rapid 'Westernization' of life-style and are associated with calorically excessive high-fat/low-fibre diet, consumption of refined products, lack of physical activity, and obesity. Most recent epidemiological and clinical investigations have consistently evidenced a significant relationship between obesity-driven inflammation in particular steps of colorectal cancer development, including initiation, promotion, progression, and metastasis. Inflammation in obesity occurs by several mechanisms. Roles of imbalanced metabolism (MetS), distinct immune cells, cytokines, and other immune mediators have been suggested in the inflammatory processes. Critical mechanisms are accounted to proinflammatory cytokines (e.g. IL-1, IL-6, IL-8) and tumor necrosis factor-α (TNF-α). These molecules are secreted by macrophages and are considered as major agents in the transition between acute and chronic inflammation and inflammation-related CRC. The second factor promoting the CRC development in obese individuals is altered adipokine concentrations (leptin and adiponectin). The role of leptin and adiponectin in cancer cell proliferation, invasion, and metastasis is attributable to the activation of several signal transduction pathways (JAK/STAT, mitogen-activated protein kinase (MAPK), phosphatidylinositol 3 kinase (PI3K), mTOR, and 5'AMPK signaling pathways) and multiple dysregulation (COX-2 downregulation, mRNA expression).


title: Palliative care and spiritual care: the crucial role of spiritual care in the care of patients with advanced illness

abstract: PURPOSE OF REVIEW ::: Within the hospice and palliative care movement, patients' religion/spirituality (R/S) has been a core component of care incorporated within international and US palliative care guidelines. However, as the discipline of palliative care has been incorporated into the larger biomedical community, the inclusion of spiritual care has become controversial. This review summarizes key empirical research at the intersection of palliative care and R/S in order to assess its validity as a domain of end-of-life care. ::: ::: ::: RECENT FINDINGS ::: Recent research shows that R/S and spiritual care are important components to the care of patients facing advanced illness. Patients - particularly ethnic minorities - rely upon R/S as an important means to interpret and cope with illness. Studies suggest that R/S plays an important role in coping with disease-related symptoms, improves quality of life, and impacts medical decision-making near death. Patients largely desire medical caregivers to take an active role in providing spiritual care, and patients likewise frequently experience multiple spiritual needs arising in the face of life-threatening illness. ::: ::: ::: SUMMARY ::: Despite an empirical evidence for spiritual care as part of palliative care, R/S remains insufficiently addressed by the medical system. Further research is required in order to more clearly identify the roles of healthcare providers and standardize the provision of spiritual care within palliative care.


title: Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects

abstract: Abstract Although structural magnetic resonance imaging (MRI) has revealed partly non-overlapping brain abnormalities in schizophrenia and bipolar disorder, it is unknown whether structural MRI scans can be used to separate individuals with schizophrenia from those with bipolar disorder. An algorithm capable of discriminating between these two disorders could become a diagnostic aid for psychiatrists. Here, we scanned 66 schizophrenia patients, 66 patients with bipolar disorder and 66 healthy subjects on a 1.5 T MRI scanner. Three support vector machines were trained to separate patients with schizophrenia from healthy subjects, patients with schizophrenia from those with bipolar disorder, and patients with bipolar disorder from healthy subjects, respectively, based on their gray matter density images. The predictive power of the models was tested using cross-validation and in an independent validation set of 46 schizophrenia patients, 47 patients with bipolar disorder and 43 healthy subjects scanned on a 3 T MRI scanner. Schizophrenia patients could be separated from healthy subjects with an average accuracy of 90%. Additionally, schizophrenia patients and patients with bipolar disorder could be distinguished with an average accuracy of 88%.The model delineating bipolar patients from healthy subjects was less accurate, correctly classifying 67% of the healthy subjects and only 53% of the patients with bipolar disorder. In the latter group, lithium and antipsychotics use had no influence on the classification results. Application of the 1.5 T models on the 3 T validation set yielded average classification accuracies of 76% (healthy vs schizophrenia), 66% (bipolar vs schizophrenia) and 61% (healthy vs bipolar). In conclusion, the accurate separation of schizophrenia from bipolar patients on the basis of structural MRI scans, as demonstrated here, could be of added value in the differential diagnosis of these two disorders. The results also suggest that gray matter pathology in schizophrenia and bipolar disorder differs to such an extent that they can be reliably differentiated using machine learning paradigms.


title: Strategyproof peer selection: mechanisms, analyses, and experiments

abstract: We study an important crowdsourcing setting where agents evaluate one another and, based on these evaluations, a subset of agents are selected. This setting is ubiquitous when peer review is used for distributing awards in a team, allocating funding to scientists, and selecting publications for conferences. The fundamental challenge when applying crowd-sourcing in these settings is that agents may misreport their reviews of others to increase their chances of being selected. We propose a new strategyproof (impartial) mechanism called Dollar Partition that satisfies desirable axiomatic properties. We then show, using a detailed experiment with parameter values derived from target real world domains, that our mechanism performs better on average, and in the worst case, than other strategyproof mechanisms in the literature.


title: The internet of energy: a web-enabled smart grid system

abstract: The quest for sustainable energy models is the main factor driving research on smart grid technology. SGs represent the bridging paradigm to enable highly efficient energy production, transport, and consumption along the whole chain, from the source to the user. Although this concept promises to be very fruitful, the research on how to deploy it in the real world has just begun. A discussion on the enabling technologies for SGs and a possible roadmap for the profitable evolution thereof is the focus of this article. After introducing the recent trends that are pushing the SG paradigm, we will discuss various key scenarios for the SG, and briefly introduce some of its key requirements. We will then provide an analysis of how current and future standard solutions in the areas of communications and networking can be engineered into a system that fulfills the needs of the SG vision. We advocate the use of small, cheap, and resource-constrained devices with pervasive computing capabilities as the key component to deploy a ubiquitous energy control system. To this end, the recent efforts carried out by Internet standardization bodies such as the IETF and W3C toward the vision of the Internet of Things (IoT) are especially relevant. The various components of the proposed solution have been successfully showcased in real-world implementations, and relevant actors such as ETSI, ZigBee, and IPSO are already evaluating their potential for future IoT applications, making the Internet-based smart grid vision considered in this article practically achievable in the not too distant future.


Software Reverse Engineering : A Case Study


title: Quality Assessment and Evolution of Open Data Portals

abstract: Despite the enthusiasm caused by the availability of a steadily increasing amount of openly available, structured data, first critical voices appear addressing the emerging issue of low quality in the meta data and data source of Open Data portals which is a serious risk that could disrupt the Open Data project. However, there exist no comprehensive reports about the actual quality of Open Data portals. In this work, we present our efforts to monitor and assess the quality of 82 active Open Data portals, powered by organisations across 35 different countries. We discuss our quality metrics and report comprehensive findings by analysing the data and the evolution of the portals since September 2014. Our results include findings about a steady growth of information, a high heterogeneity across the portals for various aspects and also insights on openness, contact ability and the availability of meta data.


Reducing International Risk through Quantity Contracts


title: Gender Classification Based on Fusion of Different Spatial Scale Features Selected by Mutual Information From Histogram of LBP, Intensity, and Shape

abstract: In this paper, we report our extension of the use of feature selection based on mutual information and feature fusion to improve gender classification of face images. We compare the results of fusing three groups of features, three spatial scales, and four different mutual information measures to select features. We also showed improved results by fusion of LBP features with different radii and spatial scales, and the selection of features using mutual information. As measures of mutual information we use minimum redundancy and maximal relevance (mRMR), normalized mutual information feature selection (NMIFS), conditional mutual information feature selection (CMIFS), and conditional mutual information maximization (CMIM). We tested the results on four databases: FERET and UND, under controlled conditions, the LFW database under unconstrained scenarios, and AR for occlusions. It is shown that selection of features together with fusion of LBP features significantly improved gender classification accuracy compared to previously published results. We also show a significant reduction in processing time because of the feature selection, which makes real-time applications of gender classification feasible.


title: Exploring software cities in virtual reality

abstract: Software visualizations, such as the software city metaphor, are usually displayed on 2D screens and controlled by means of a mouse and thus often do not take advantage of more natural interaction techniques. Virtual reality (VR) approaches aim to improve the user experience. Emerging new technologies, like the Oculus Rift, dramatically enhance the VR experience at an affordable price. Therefore, new technologies have the potential to provide even higher immersion - and thus benefits - than previous VR approaches.


title: Research Article Understanding Social Media Logic

abstract: Over the past decade, social media platforms have penetrated deeply into the mechanics of everyday life, affecting people's informal interactions, as well as institutional structures and professional routines. Far from being neutral platforms for everyone, social media have changed the conditions and rules of social interaction. In this article, we examine the intricate dynamic between social media platforms, mass media, users, and social institutions by calling attention to social media logic—the norms, strategies, mechanisms, and economies—underpinning its dynamics. This logic will be considered in light of what has been identified as mass media logic, which has helped spread the media's powerful discourse outside its institutional boundaries. Theorizing social media logic, we identify four grounding principles—programmability, popularity, connectivity, and datafication—and argue that these principles become increasingly entangled with mass media logic. The logic of social media, rooted in these grounding principles and strategies, is gradually invading all areas of public life. Besides print news and broadcasting, it also affects law and order, social activism, politics, and so forth. Therefore, its sustaining logic and widespread dissemination deserve to be scrutinized in detail in order to better understand its impact in various domains. Concentrating on the tactics and strategies at work in social media logic, we reassess the constellation of power relationships in which social practices unfold, raising questions such as: How does social media logic modify or enhance existing mass media logic? And how is this new media logic exported beyond the boundaries of (social or mass) media proper? The underlying principles, tactics, and strategies may be relatively simple to identify, but it is much harder to map the complex connections between platforms that distribute this logic: users that employ them, technologies that drive them, economic structures that scaffold them, and institutional bodies that incorporate them.


Developing Adaptive Social Robot Tutors for Children.


title: A Survey on the Use of Graphical Passwords in Security

abstract: Beginning around 1996, numerous graphical password schemes have been proposed, motivated by improving password usability and security, two key factors in password scheme evaluation. In this paper, we focus on the security aspects of existing graphical password schemes, which not only gives a simple introduction of attack methods but also intends to provide an in-depth analysis with specific schemes. The paper first categorizes existing graphical password schemes into four kinds according to the authentication style and provides a comprehensive introduction and analysis for each scheme, highlighting security aspects. Then we review the known attack methods, categorize them into two kinds, and summarize the security reported in some user studies of those schemes. Finally, some suggestions are given for future research.


title: Detection and Localization of Robotic Tools in Robot-Assisted Surgery Videos Using Deep Neural Networks for Region Proposal and Detection

abstract: Video understanding of robot-assisted surgery (RAS) videos is an active research area. Modeling the gestures and skill level of surgeons presents an interesting problem. The insights drawn may be applied in effective skill acquisition, objective skill assessment, real-time feedback, and human–robot collaborative surgeries. We propose a solution to the tool detection and localization open problem in RAS video understanding, using a strictly computer vision approach and the recent advances of deep learning. We propose an architecture using multimodal convolutional neural networks for fast detection and localization of tools in RAS videos. To the best of our knowledge, this approach will be the first to incorporate deep neural networks for tool detection and localization in RAS videos. Our architecture applies a region proposal network (RPN) and a multimodal two stream convolutional network for object detection to jointly predict objectness and localization on a fusion of image and temporal motion cues. Our results with an average precision of 91% and a mean computation time of 0.1 s per test frame detection indicate that our study is superior to conventionally used methods for medical imaging while also emphasizing the benefits of using RPN for precision and efficiency. We also introduce a new data set, ATLAS Dione, for RAS video understanding. Our data set provides video data of ten surgeons from Roswell Park Cancer Institute, Buffalo, NY, USA, performing six different surgical tasks on the daVinci Surgical System (dVSS) with annotations of robotic tools per frame.


title: Design and Implementation of Model Predictive Control for a Gyroscopic Inverted Pendulum

abstract: This study proposes the design of an active stabilizing system (ASAS) for a single-track vehicle. Using the gyroscopic effects of two flywheels, this system can generate control torque to stabilize the vehicle in cases where there is centrifugal force of turning. To control the flywheel gimbals to generate stabilizing torque, a model predictive controller (MPC) is applied to control the system. For the controller design and performance evaluations, a model of a gyroscopic inverted pendulum is developed. Control strategies are proposed to stabilize the vehicle in the cases of straight running, circular motion, and path following. The results of the proposed stratgies when controlling the gyroscopic inverted pendulum showed good performance even with physical limitations of the control torques. In order to evaluate the real-time performance and the feasibility of the MPC, a real-time simulator is employed, which includes two embedded STM32F407 boards. The dynamic system and the control algorithms are respectively embedded into two STM32F407 boards for real-time simulation. Implementations of the MPC in this study demonstrate that the proposed controllers are feasible for real-time applications.


title: Blockchain Meets IoT: An Architecture for Scalable Access Management in IoT

abstract: The Internet of Things (IoT) is stepping out of its infancy into full maturity and establishing itself as a part of the future Internet. One of the technical challenges of having billions of devices deployed worldwide is the ability to manage them. Although access management technologies exist in IoT, they are based on centralized models which introduce a new variety of technical limitations to manage them globally. In this paper, we propose a new architecture for arbitrating roles and permissions in IoT. The new architecture is a fully distributed access control system for IoT based on blockchain technology. The architecture is backed by a proof of concept implementation and evaluated in realistic IoT scenarios. The results show that the blockchain technology could be used as access management technology in specific scalable IoT scenarios.


title: VLSI friendly fast CU/PU mode decision for HEVC intra encoding: Leveraging convolution neural network

abstract: To alleviate the computational intensity of Intra encoding for High Efficiency Video Coding (HEVC), we introduce the convolution neural network to reduce the number of the promising CU/PU candidate modes to carry out the exhaustive RDO processing. The practical merits include: Firstly, the proposed algorithm reduces the maximum computational complexity at the grain of 64 × 64 coding tree unit(CTU), which makes it efficient to ameliorate the complexity of the real-time hardwired encoder implementation. Secondly, because the CU/PU mode decision is made based on the analysis of source block textures, our algorithm does not depend on intermediate results of encoding. That is, the proposed algorithm will not deteriorate the processing schedule of CTU encoding. Experimental results show that, when our algorithm is integrated with HM12.0, the 61.1% Intra encoding time was saved, whereas the averaging BDBR augment is merely 3.39%.


title: Adaptive Cruise Control: A Behavioral Assessment of Following Traffic Participants Due to Energy Efficient Driving Strategies

abstract: Improvement of vehicle safety and driving comfort have been the main objectives of driver assistance systems. Because of the general need to reduce the fuel consumption, the scope of driver assistance systems has been enlarged. Today, Adaptive Cruise Control (ACC) systems are developed which realize a more energy efficient driving style. However, energy optimal driving may lead to a trade-off between energy efficiency, vehicle safety and driving comfort. For example long coasting phases might irritate following drivers and provoke them to approach below the safety distance or overtake the ego-vehicle. This raises the question, how far energy optimized ACC affects following traffic and how the trade-off can be optimized to reach a higher acceptance. Within this work, a system to assess the driving behavior of following vehicles is developed and validated. The system is used to study the behavior of following traffic participants due to energy efficient ACC driving strategies. The results show that the median of the time gap between the following vehicle and ego-vehicle is significant lowered when driving with energy efficient ACC in comparison to driving manually. Whereas, the time to collision shows no significant difference between ACC and manually driven. At last, different concepts are presented which try to find an optimum between efficiency, safety and driving comfort.


title: Factors Impacting Customers' Initial Trust in E-Businesses: An Empirical Study

abstract: Developing customers' initial trust in e-businesses is critical for many online vendors, especially for startup companies. As an effort to understand initial trust in the business-to-customer e-commerce context, this study uses previous studies and logical reasoning to define initial trust and examines its predictors. Empirical results explaining the factors impacting customers' initial trust in e-businesses are also presented. The results indicate that website quality, among others, has the most significant impact on customers' initial trust in e-businesses.


title: Active Learning in Recommender Systems

abstract: Recommender Systems (RSs) are often assumed to present items to users for one reason – to recommend items a user will likely be interested in. Of course RSs do recommend, but this assumption is biased, with no help of the title, towards the “recommending” the system will do. There is another reason for presenting an item to the user: to learn more about his/her preferences, or his/her likes and dislikes. This is where Active Learning (AL) comes in. Augmenting RSs with AL helps the user become more self-aware of their own likes/dislikes while at the same time providing new information to the system that it can analyze for subsequent recommendations. In essence, applying AL to RSs allows for personalization of the recommending process, a concept that makes sense as recommending is inherently geared towards personalization. This is accomplished by letting the system actively influence which items the user is exposed to (e.g. the items displayed to the user during sign-up orduring regular use), and letting the user explore his/her interests freely.


title: Compact CPW-fed circular slot antenna for ultra-wideband applications

abstract: A compact circular slot antenna for ultra-wideband applications is proposed in this letter. The antenna consists of a circular patch and an open annulus, and is fed by coplanar waveguide. The total dimension of the antenna is 30 mmtimes33 mmtimes1.57 mm. The proposed antenna is simulated, fabricated and tested. Results show that a maximum relative impedance bandwidth of more than 130% for |S11| < -10 dB can be achieved, covering frequency range of 2.9-13.73 GHz, which meets the demand of UWB applications. The effects of the structure parameters on bandwidth are investigated.


title: Signal Space CoSaMP for Sparse Recovery With Redundant Dictionaries

abstract: Compressive sensing (CS) has recently emerged as a powerful framework for acquiring sparse signals. The bulk of the CS literature has focused on the case where the acquired signal has a sparse or compressible representation in an orthonormal basis. In practice, however, there are many signals that cannot be sparsely represented or approximated using an orthonormal basis, but that do have sparse representations in a redundant dictionary. Standard results in CS can sometimes be extended to handle this case provided that the dictionary is sufficiently incoherent or well conditioned, but these approaches fail to address the case of a truly redundant or overcomplete dictionary. In this paper, we describe a variant of the iterative recovery algorithm CoSaMP for this more challenging setting. We utilize the \mbi D-RIP, a condition on the sensing matrix analogous to the well-known restricted isometry property. In contrast to prior work, the method and analysis are “signal-focused”; that is, they are oriented around recovering the signal rather than its dictionary coefficients. Under the assumption that we have a near-optimal scheme for projecting vectors in signal space onto the model family of candidate sparse signals, we provide provable recovery guarantees. Developing a practical algorithm that can provably compute the required near-optimal projections remains a significant open problem, but we include simulation results using various heuristics that empirically exhibit superior performance to traditional recovery algorithms.


title: Information needs in bug reports: improving cooperation between developers and users

abstract: For many software projects, bug tracking systems play a central role in supporting collaboration between the developers and the users of the software. To better understand this collaboration and how tool support can be improved, we have quantitatively and qualitatively analysed the questions asked in a sample of 600 bug reports from the MOZILLA and ECLIPSE projects. We categorised the questions and analysed response rates and times by category and project. Our results show that the role of users goes beyond simply reporting bugs: their active and ongoing participation is important for making progress on the bugs they report. Based on the results, we suggest four ways in which bug tracking systems can be improved.


title: BLE device indoor localization based on RSS fingerprinting mapped by propagation modes

abstract: Nowadays, Bluetooth Low Energy (BLE) technology has a great attention in the field of wireless localization techniques, especially in the case of indoor scenarios. Study presented in this paper explores BLE localization performance in indoor scenarios based on a received signal strength (RSS). Firstly, we present a Ray-Launching based application to emulate BLE radio frequency (RF) signal propagation in the office environment. Secondly, an appropriate measurement workplace and its setup is proposed to measure RSS values. Obtained results are used to create an RSS-fingerprinting map. Furthermore, accuracy of position determination is calculated and evaluated. Results show advantage of BLE technology for indoor localization purposes (without calibration measurement). However, its performance highly depends on the number of considered BLE nodes and on applied evaluation method (e.g. number of considered sectors).


title: Workload-driven modulation of mixed-reality robot-human communication

abstract: In this work we explore how Augmented Reality annotations can be used as a form of Mixed Reality gesture, how neurophysiological measurements can inform the decision as to whether or not to use such gestures, and whether and how to adapt language when using such gestures. In this paper, we propose a preliminary investigation of how decisions regarding robot-to-human communication modality in mixed reality environments might be made on the basis of humans' perceptual and cognitive states. Specifically, we propose to use brain data acquired with high-density functional near-infrared spectroscopy (fNIRS) to measure the neural correlates of cognitive and emotional states with particular relevance to adaptive human-robot interaction (HRI). In this paper we describe several states of interest that fNIRS is well suited to measure and that have direct implications to HRI adaptations and we leverage a framework developed in our prior work to explore how different neurophysiological measures could inform the selection of different communication strategies. We then describe results from a feasibility experiment where multilabel Convolutional Long Short Term Memory Networks were trained to classify the target mental states of 10 participants and we discuss a research agenda for adaptive human-robot teams based on our findings.


Interventions to Break and Create Consumer Habits Bas Verplanken and


title: BUCCAL MORPHINE—A NEW ROUTE FOR ANALGESIA?

abstract: Abstract The analgesic effects of buccal and intramuscular morphine were compared in a prospective, double-blind, double-dummy study in forty patients who experienced pain after elective orthopaedic operations. Each patient simultaneously received a buccal tablet and an intramuscular injection, only one of which contained morphine sulphate (13.3 mg); the patients were randomly allocated to two equal groups so twenty patients received each active preparation. The two preparations produced a similar degree of postoperative analgesia, assessed by the mean reduction in pain score and the pain relief score. Peak plasma morphine concentrations were slightly lower after buccal than after intramuscular administration but they declined more slowly; consequently, the drug's bioavailability was 40-50% greater after buccal than after intramuscular administration. The adverse effects of buccal morphine were generally less than those of intramuscular morphine.


title: Medial Meniscus Resection Increases and Medial Meniscus Repair Preserves Anterior Knee Laxity: A Cohort Study of 4497 Patients With Primary Anterior Cruciate Ligament Reconstruction

abstract: Background:There are still controversies regarding the effects on knee laxity of additional meniscus resection or repair in the setting of anterior cruciate ligament reconstruction (ACLR).Hypothesis/Purpose:The purpose was to determine the effects on knee laxity of resection or repair of medial meniscus (MM) or lateral meniscus (LM) injuries in the ACLR knee. The hypothesis was that patients with an additional meniscus resection would have significantly increased postoperative knee laxity versus that of an isolated ACLR, whereas patients with meniscus repair would have laxity comparable to that of an isolated ACLR.Study Design:Cohort study; Level of evidence, 3.Methods:The KT-1000 arthrometer, with an anterior tibial load of 134 N, was used to evaluate knee laxity preoperatively and at 6-month postoperative follow-up for a total of 4497 patients with primary hamstring tendon ACLR. Patients with isolated ACLR or ACLR with additional MM resection, MM repair, LM resection, LM repair, or MM plus LM resection ...


title: Characterizing Netflix bandwidth consumption

abstract: The widespread deployment and adoption of the Dynamic Adaptive Streaming over HTTP (DASH) standard is making Internet video-on-demand a `standard' Internet application similar in impact as email and web browsing. While video streaming has been widely deployed and studied for decades, DASH-based streaming is very different as it involves adaptation both by the application and by TCP. The dynamics and implications of multiple levels of end-to-end congestion control are not well understood. The contribution of the research presented in this paper is twofold: first, we characterize the bandwidth consumption of a widely deployed DASH application (i.e., Netflix); second, we provide insight in how different implementations and different access networks can impact bandwidth consumption. Our results suggest that Netflix adaptation defaults to underlying TCP mechanisms during periods of heavy, sustained network congestion. However, the application algorithm is clearly intertwined with the underlying TCP mechanisms during periods of volatile network conditions. In one network scenario, we observed that a backlogged TCP flow achieved a throughput of 6 Mbps while a Netflix session (under similar path conditions) consumed less than 3 Mbps of bandwidth.


title: Measuring consumer satisfaction in internet banking: a core framework

abstract: What service-quality attributes must Internet banks offer to induce consumers to switch to online transactions and keep using them?


title: Recent Research in Cooperative Control of Multivehicle Systems

abstract: This paper presents a survey of recent research in cooperative control of multivehicle systems, using a common mathematical framework to allow different methods to be described in a unified way. The survey has three primary parts: an overview of current applications of cooperative control, a summary of some of the key technical approaches that have been explored, and a description of some possible future directions for research. Specific technical areas that are discussed include formation control, cooperative tasking, spatiotemporal planning, and consensus.


title: A study of Monte Carlo localization on robot operating system

abstract: Localization is one of the problems that often appears in the world of robotics. Monte Carlo Localization (MCL) are the one of the popular algorithms in localization because easy to implement on issues Global Localization. This algorithm using particles to represent the robot position. MCL can simulated by Robot Operating System (ROS) using robot type is Pioneer3-dx. In this paper we will discuss about this algorithm on ROS, by analyzing the influence of the number particle that are used for localization of the actual robot position.


title: Towards automatic generation of vulnerability-based signatures

abstract: In this paper we explore the problem of creating vulnerability signatures. A vulnerability signature matches all exploits of a given vulnerability, even polymorphic or metamorphic variants. Our work departs from previous approaches by focusing on the semantics of the program and vulnerability exercised by a sample exploit instead of the semantics or syntax of the exploit itself. We show the semantics of a vulnerability define a language which contains all and only those inputs that exploit the vulnerability. A vulnerability signature is a representation (e.g., a regular expression) of the vulnerability language. Unlike exploit-based signatures whose error rate can only be empirically measured for known test cases, the quality of a vulnerability signature can be formally quantified for all possible inputs. We provide a formal definition of a vulnerability signature and investigate the computational complexity of creating and matching vulnerability signatures. We also systematically explore the design space of vulnerability signatures. We identify three central issues in vulnerability-signature creation: how a vulnerability signature represents the set of inputs that may exercise a vulnerability, the vulnerability coverage (i.e., number of vulnerable program paths) that is subject to our analysis during signature creation, and how a vulnerability signature is then created for a given representation and coverage. We propose new data-flow analysis and novel adoption of existing techniques such as constraint solving for automatically generating vulnerability signatures. We have built a prototype system to test our techniques. Our experiments show that we can automatically generate a vulnerability signature using a single exploit which is of much higher quality than previous exploit-based signatures. In addition, our techniques have several other security applications, and thus may be of independent interest.


title: Treatment of internet addiction: A meta-analysis

abstract: Abstract Internet addiction (IA) has become a widespread and problematic phenomenon. Little is known about the efficacy of treatment approaches for IA. Therefore, our objective was to perform an effect size analysis of psychological and pharmacological interventions for IA. We conducted a literature search using PsycINFO, PSYNDEX, MEDLINE, EMBASE, PQDT OPEN, WorldCat, Cochrane Clinical Trials Library, and manual searches. Our meta-analysis was based on 16 studies, covered a total of 670 participants, and used a random effects model. Special emphasis was given to the inclusion of studies from “non-western” countries. Effect size estimates suggest that psychological and pharmacological interventions were highly effective for improving IA ( g = 1.61), time spent online ( g = 0.94), depression ( g = 0.90) and anxiety ( g = 1.25) from pre- to post-treatment in the overall sample. Moderator analyses revealed that studies including individual treatments, a higher number of female participants, older patients, or a North-American sample had larger effect sizes for some outcome variables. Most effect sizes were high, robust, unrelated to study quality or design, and maintained over follow-up. Due to a small number of included studies and methodological limitations the results of this meta-analysis should be regarded as preliminary.


title: Introducing SECURESTREAMS: Scalable Middleware for Reactive and Secure Data Stream Processing

abstract: We introduce SECURESTREAMS, a middleware framework for secure stream processing. Its design builds on Intel's Secure Guard Extensions (SGX) to guarantee the privacy and the integrity of the data being processed. Our initial experimental results of SECURESTREAMS are promising: the framework is easy to use, and delivers high throughput, enabling developers to implement complex processing pipelines in a few lines of scripting code.


title: UHF Receiver Front-End: Implementation and Analog Baseband Design Considerations

abstract: An integrated ultrahigh-frequency (UHF) receiver is presented. A systematic analysis to quantify the interdependence of baseband filter and analog-to-digital converter (ADC) dynamic range in broadband receivers is presented. This analysis shows that: (1) low-order Butterworth filters are favorable when undesired power is dominated by far out blockers and (2) high-order inverse Chebyshev filters can reduce the resolution of a subsequent ADC by up to two additional bits in the presence of adjacent analog narrowband blockers. Based on the analysis, a cascaded, programmable, hybrid active-RC and switched-capacitor (SC) baseband filter is proposed. An all-digital nonoverlap clock tuning system to minimize the variation of available settling time window in SC circuits is also proposed. The receiver integrates the proposed filter with an RF variable gain amplifier (RFVGA) and a passive mixer. This receiver achieves a measured noise figure of 7.9 dB, an IIP3 of -8 dBm at maximum gain and +2 dBm at 9-dB RF attenuation. The chip consumes 120 mW (RFVGA, mixer and I-channel baseband) from 1.8-V analog/2.5-V digital dual supply and occupies 2.14 mm2 in IBM 0.18-μm RF CMOS technology.


title: Power transmission and workload balancing policies in eHealth mobile cloud computing scenarios

abstract: Abstract The Internet of Things (IoT) holds big promises for healthcare, especially in proactive personal eHealth. Prediction of symptomatic crises in chronic diseases in the IoT scenario leads to the deployment of ambulatory monitoring systems. These systems place a major concern in the amount of data to be processed and the intelligent management of the energy consumption. The huge amount of data generated for these systems require high computing capabilities only available in Data Centers. This paper presents a real case of prediction in the eHealth scenario, devoted to neurological disorders. The presented case study focuses on the migraine headache, a disease that affects around 15% of the European population. This paper extrapolates results from real data and simulations in a study where migraine patients are monitored using an unobtrusive Wireless Body Sensor Network. Low-power techniques are applied in monitorization nodes. Techniques such us: on-node signal processing and radio policies to make node’s autonomy longer and save energy, have been applied. Workload balancing policies are carried out in the coordinator nodes and Data Centers to reduce the computational burden in these facilities and minimize its energy consumption. Our results draw average savings of € 288 million in this eHealth scenario applied only to 2% of European migraine sufferers; in addition to savings of € 1272 million due to the benefits of the migraine prediction.


title: Visual Question Answering: A Tutorial

abstract: The task of visual question answering (VQA) is receiving increasing interest from researchers in both the computer vision and natural language processing fields. Tremendous advances have been seen in the field of computer vision due to the success of deep learning, in particular on low- and midlevel tasks, such as image segmentation or object recognition. These advances have fueled researchers' confidence for tackling more complex tasks that combine vision with language and high-level reasoning. VQA is a prime example of this trend. This article presents the ongoing work in the field and the current approaches to VQA based on deep learning. VQA constitutes a test for deep visual understanding and a benchmark for general artificial intelligence (AI). While the field of VQA has seen recent successes, it remains a largely unsolved task.