Ethical frontiers of ICT and older users: cultural, pragmatic and ethical issues

The reality of an ageing Europe has called attention to the importance of e-inclusion for a growing population of senior citizens. For some, this may mean closing the digital divide by providing access and support to technologies that increase citizen participation; for others, e-inclusion means access to assistive technologies to facilitate and extend their living independently. These initiatives address a social need and provide economic opportunities for European industry. While undoubtedly desirable, and supported by European Union initiatives, several cultural assumptions or issues related to the initiatives could benefit from fuller examination, as could their practical and ethical implications. This paper begins to consider these theoretical and practical concerns. The first part of the paper examines cultural issues and assumptions relevant to adopting e-technologies, and the ethical principles applied to them. These include (1) the persistence of ageism, even in e-inclusion; (2) different approaches to, and implications of independent living; and (3) the values associated with different ethical principles, given their implications for accountability to older users. The paper then discusses practical issues and ethical concerns that have been raised by the use of smart home and monitoring technologies with older persons. Understanding these assumptions and their implications will allow for more informed choices in promoting ethical application of e-solutions for older persons.


Instance Weighting for Domain Adaptation in NLP

Domain adaptation is an important problem in natural language processing (NLP) due to the lack of labeled data in novel domains. In this paper, we study the domain adaptation problem from the instance weighting perspective. We formally analyze and characterize the domain adaptation problem from a distributional view, and show that there are two distinct needs for adaptation, corresponding to the different distributions of instances and classification functions in the source and the target domains. We then propose a general instance weighting framework for domain adaptation. Our empirical results on three NLP tasks show that incorporating and exploiting more information from the target domain through instance weighting is effective.


End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

State-of-the-art sequence labeling systems traditionally require large amounts of taskspecific knowledge in the form of handcrafted features and data pre-processing. In this paper, we introduce a novel neutral network architecture that benefits from both wordand character-level representations automatically, by using combination of bidirectional LSTM, CNN and CRF. Our system is truly end-to-end, requiring no feature engineering or data preprocessing, thus making it applicable to a wide range of sequence labeling tasks. We evaluate our system on two data sets for two sequence labeling tasks — Penn Treebank WSJ corpus for part-of-speech (POS) tagging and CoNLL 2003 corpus for named entity recognition (NER). We obtain state-of-the-art performance on both datasets — 97.55% accuracy for POS tagging and 91.21% F1 for NER.


Improving Named Entity Recognition for Morphologically Rich Languages Using Word Embeddings

In this paper, we addressed the Named Entity Recognition (NER) problem for morphologically rich languages by employing a semi-supervised learning approach based on neural networks. We adopted a fast unsupervised method for learning continuous vector representations of words, and used these representations along with language independent features to develop a NER system. We evaluated our system for the highly inflectional Turkish and Czech languages. We improved the state-of-the-art F-score obtained for Turkish without using gazetteers by 2.26% and for Czech by 1.53%. Unlike the previous state-of-the-art systems developed for these languages, our system does not make use of any language dependent features. Therefore, we believe it can easily be applied to other morphologically rich languages.


Bayesian Landmark Learning for Mobile Robot Localization

To operate successfully in indoor environments, mobile robots must be able to localize themselves. Most current localization algorithms lack flexibility, autonomy, and often optimality, since they rely on a human to determine what aspects of the sensor data to use in localization (e.g., what landmarks to use). This paper describes a learning algorithm, called BaLL, that enables mobile robots to learn what features/landmarks are best suited for localization, and also to train artificial neural networks for extracting them from the sensor data. A rigorous Bayesian analysis of probabilistic localization is presented, which produces a rational argument for evaluating features, for selecting them optimally, and for training the networks that approximate the optimal solution. In a systematic experimental study, BaLL outperforms two other recent approaches to mobile robot localization.


Knowledge Engineering and Knowledge Management

Recently, experts and practitioners in language resources have started recognizing the benefits of the linked data (LD) paradigm for the representation and exploitation of linguistic data on the Web. The adoption of the LD principles is leading to an emerging ecosystem of multilingual open resources that conform to the Linguistic Linked Open Data Cloud, in which datasets of linguistic data are interconnected and represented following common vocabularies, which facilitates linguistic information discovery, integration and access. In order to contribute to this initiative, this paper summarizes several key aspects of the representation of linguistic information as linked data from a practical perspective. The main goal of this document is to provide the basic ideas and tools for migrating language resources (lexicons, corpora, etc.) as LD on the Web and to develop some useful NLP tasks with them (e.g., word sense disambiguation). Such material was the basis of a tutorial imparted at the EKAW’14 conference, which is also reported in the paper.


Hydraulic erosion

This paper presents a generalized solution to modelling hydraulic erosion using ideas from fluid mechanics. The model is based on the Navier–Stokes equations, which provide the dynamics of velocity and pressure. These equations form the basis for the model to balance erosion and deposition that determine changes in the layers between water and erosion material. The eroded material is captured and relocated by water according to a material transport equation. The resulting model is fully 3D and is able to simulate a variety of phenomena including river meanders, low hill sediment wash, natural water springs and receding waterfalls. The simulations show the terrain morphogenesis and can be used for animations as well as for static scene generation. Copyright# 2006 JohnWiley & Sons, Ltd.


Aspect-level opinion mining of online customer reviews

This paper focuses on how to improve aspect-level opinion mining for online customer reviews. We first propose a novel generative topic model, the Joint Aspect/Sentiment (JAS) model, to jointly extract aspects and aspect-dependent sentiment lexicons from online customer reviews. An aspect-dependent sentiment lexicon refers to the aspect-specific opinion words along with their aspect-aware sentiment polarities with respect to a specific aspect. We then apply the extracted aspect-dependent sentiment lexicons to a series of aspect-level opinion mining tasks, including implicit aspect identification, aspect-based extractive opinion summarization, and aspect-level sentiment classification. Experimental results demonstrate the effectiveness of the JAS model in learning aspect- dependent sentiment lexicons and the practical values of the extracted lexicons when applied to these practical tasks.


Cluster validity functions for categorical data: a solution-space perspective

For categorical data, there are three widely-used internal validity functions: the $$k$$ k -modes objective function, the category utility function and the information entropy function, which are defined based on within-cluster information only. Many clustering algorithms have been developed to use them as objective functions and find their optimal solutions. In this paper, we study the generalization, effectiveness and normalization of the three validity functions from a solution-space perspective. First, we present a generalized validity function for categorical data. Based on it, we analyze the generality and difference of the three validity functions in the solution space. Furthermore, we address the problem whether the between-cluster information is ignored when these validity functions are used to evaluate clustering results. To the end, we analyze the upper and lower bounds of the three validity functions for a given data set, which can help us estimate the clustering difficulty on a data set and compare the performance of a clustering algorithm on different data sets.


Soft labeling by Distilling Anatomical knowledge for Improved MS Lesion Segmentation

This paper explores the use of a soft ground-truth mask (“soft mask”) to train a Fully Convolutional Neural Network (FCNN) for segmentation of Multiple Sclerosis (MS) lesions. Detection and segmentation of MS lesions is a complex task largely due to the extreme unbalanced data, with very small number of lesion pixels that can be used for training. Utilizing the anatomical knowledge that the lesion surrounding pixels may also include some lesion level information, we suggest to increase the data set of the lesion class with neighboring pixel data with a reduced confidence weight. A soft mask is constructed by morphological dilation of the binary segmentation mask provided by a given expert, where expert-marked voxels receive label 1 and voxels of the dilated region are assigned a soft label. In the methodology proposed, the FCNN is trained using the soft mask. On the ISBI 2015 challenge dataset, this is shown to provide a better precision-recall tradeoff and to achieve a higher average Dice similarity coefficient. We also show that by using this soft mask scheme we can improve the network segmentation performance when compared to a second independent expert.


Low kickback noise techniques for CMOS latched comparators

The latched comparator is utilized in virtually all analog-to-digital converter architectures. It uses a positive feedback mechanism to regenerate the analog input signal into a full-scale digital level. Such high voltage variations in the regeneration nodes are coupled to the input voltage - kickback noise. This paper reviews existing solutions to minimize the kickback noise and proposes two new ones. HSPICE simulations verify the effectiveness of our techniques.


Characterization and Modeling of Nonfilamentary Ta/TaOx/TiO2/Ti Analog Synaptic Device

A two-terminal analog synaptic device that precisely emulates biological synaptic features is expected to be a critical component for future hardware-based neuromorphic computing. Typical synaptic devices based on filamentary resistive switching face severe limitations on the implementation of concurrent inhibitory and excitatory synapses with low conductance and state fluctuation. For overcoming these limitations, we propose a Ta/TaOx/TiO2/Ti device with superior analog synaptic features. A physical simulation based on the homogeneous (nonfilamentary) barrier modulation induced by oxygen ion migration accurately reproduces various DC and AC evolutions of synaptic states, including the spike-timing-dependent plasticity and paired-pulse facilitation. Furthermore, a physics-based compact model for facilitating circuit-level design is proposed on the basis of the general definition of memristor devices. This comprehensive experimental and theoretical study of the promising electronic synapse can facilitate realizing large-scale neuromorphic systems.


Customer Lifetime Value ( CLV ) Measurement Based on RFM Model

Nowadays companies increasingly derive revenue from the creation and sustenance of long-term relationships with their customers. In such an environment, marketing serves the purpose of maximizing customer lifetime value (CLV) and customer equity, which is the sum of the lifetime values of the company’s customers. A frequently-encountered difficulty for companies wishing to measure customer profitability is that management accounting and reporting systems have tended to reflect product profitability rather than customer profitability. But in spite of these difficulties, Companies looking for methods to know how calculate their customers's CLV. In this paper, we used K-Mean clustering approach to determine customers's CLV and segment them based on recency, frequency and monetary (RFM) measures. We also used Discriminant analysis to approve clustering results. Data required applying this method gathered from one branch of an Iranian private bank which is established newly. Finally, in terms of this segmentation, we proposed customer retention strategies for treating with the bank customers.


Getting users to pay attention to anti-phishing education: evaluation of retention and transfer

Educational materials designed to teach users not to fall for phishing attacks are widely available but are often ignored by users. In this paper, we extend an embedded training methodology using learning science principles in which phishing education is made part of a primary task for users. The goal is to motivate users to pay attention to the training materials. In embedded training, users are sent simulated phishing attacks and trained after they fall for the attacks. Prior studies tested users immediately after training and demonstrated that embedded training improved users' ability to identify phishing emails and websites. In the present study, we tested users to determine how well they retained knowledge gained through embedded training and how well they transferred this knowledge to identify other types of phishing emails. We also compared the effectiveness of the same training materials delivered via embedded training and delivered as regular email messages. In our experiments, we found that: (a) users learn more effectively when the training materials are presented after users fall for the attack (embedded) than when the same training materials are sent by email (non-embedded); (b) users retain and transfer more knowledge after embedded training than after non-embedded training; and (c) users with higher Cognitive Reflection Test (CRT) scores are more likely than users with lower CRT scores to click on the links in the phishing emails from companies with which they have no account.


CO2 bio-mitigation using microalgae

Microalgae are a group of unicellular or simple multicellular photosynthetic microorganisms that can fix CO2 efficiently from different sources, including the atmosphere, industrial exhaust gases, and soluble carbonate salts. Combination of CO2 fixation, biofuel production, and wastewater treatment may provide a very promising alternative to current CO2 mitigation strategies.


A Low-Cost High-Performance Digital Radar Test Bed

This paper describes the design of a dual-channel S-band digital radar test bed. The test bed combines stretch processing with a novel and cost-effective hardware architecture that enables it to achieve an in-band dynamic range of 60 dB over 600 MHz of instantaneous bandwidth. The dual digital receiver channels allow for adaptive digital beamforming which can be used to mitigate a directional source of interference. Experimental test and verification results are presented to demonstrate system performance.


CS principles goes to middle school: learning how to teach "Big Data"

Spurred by evidence that students' future studies are highly influenced during middle school, recent efforts have seen a growing emphasis on introducing computer science to middle school learners. This paper reports on the in-progress development of a new middle school curricular module for Big Data, situated as part of a new CS Principles-based middle school curriculum. Big Data is of widespread societal importance and holds increasing implications for the computer science workforce. It also has appeal as a focus for middle school computer science because of its rich interplay with other important computer science principles. This paper examines three key aspects of a Big Data unit for middle school: its alignment with emerging curricular standards; the perspectives of middle school classroom teachers in mathematics, science, and language arts; and student feedback as explored during a middle school pilot study with a small subset of the planned curriculum. The results indicate that a Big Data unit holds great promise as part of a middle school computer science curriculum.


A low-power capacitor switching scheme with low common-mode voltage variation for successive approximation ADC

In this paper, a new low-energy switching technique with low common-mode voltage variation is proposed for successive-approximation analog-to-digital converters (SA-ADCs). In the proposed scheme, not only the switching energy consumed within the first three comparisons is less than zero, but also other comparisons are made with the low-power monotonic method. Therefore, the switching energy of the capacitive array, including the consumed energy during the sampling phase, is reduced by 90.68% compared with the conventional counterpart. Moreover, since the variation of the input common-mode voltage of the employed comparator is only 0.125Vref, where Vref is the reference voltage of the ADC, the required comparator’s performance can be much more relaxed leading to more power saving. Post-layout simulation results of a 10-bit 1-MS/s SA-ADC in a 0.18-μm CMOS technology show a signal-to-noise-and-distortion ratio (SNDR) of 61 dB, a spurious-free dynamic range (SFDR) of 79.8 dB, and an effective number of 9.84 bits. The ADC consumes 35.3 μW with a 1.8-V supply and achieves a Figure-of-Merit (FoM) of 38.5 fJ/conversion-step.


A comprehensive and conservative approach for the restoration of abrasion and erosion. Part I: concepts and clinical rationale for early intervention using adhesive techniques.

Tooth wear represents a frequent pathology with multifactorial origins. Behavioral changes, unbalanced diet, various medical conditions and medications inducing acid regurgitation or influencing saliva composition and flow rate, trigger tooth erosion. Awake and sleep bruxism, which are widespread nowadays with functional disorders, induce attrition. It has become increasingly important to diagnose early signs of tooth wear so that proper preventive, and if needed, restorative measures are taken. Such disorders have biological, functional, and also esthetic consequences. Following a comprehensive clinical evaluation, treatment objectives, such as a proper occlusal and anatomical scheme as well as a pleasing smile line, are usually set on models with an anterior teeth full-mouth waxup, depending on the severity of tissue loss. Based on the new vertical dimension of occlusion (VDO), combinations of direct and indirect restorations can then help to reestablish anatomy and function. The use of adhesive techniques and resin composites has demonstrated its potential, in particular for the treatment of moderate tooth wear. Part I of this article reviews recent knowledge and clinical concepts dealing with the various forms of early restorative interventions and their potential to restrict ongoing tissue destruction.


Kernel Clustering: Density Biases and Solutions

Kernel methods are popular in clustering due to their generality and discriminating power. However, we show that many kernel clustering criteria have density biases theoretically explaining some practically significant artifacts empirically observed in the past. For example, we provide conditions and formally prove the density mode isolation bias in kernel K-means for a common class of kernels. We call it Breiman’s bias due to its similarity to the histogram mode isolation previously discovered by Breiman in decision tree learning with Gini impurity. We also extend our analysis to other popular kernel clustering methods, e.g., average/normalized cut or dominant sets, where density biases can take different forms. For example, splitting isolated points by cut-based criteria is essentially the sparsest subset bias, which is the opposite of the density mode bias. Our findings suggest that a principled solution for density biases in kernel clustering should directly address data inhomogeneity. We show that density equalization can be implicitly achieved using either locally adaptive weights or locally adaptive kernels. Moreover, density equalization makes many popular kernel clustering objectives equivalent. Our synthetic and real data experiments illustrate density biases and proposed solutions. We anticipate that theoretical understanding of kernel clustering limitations and their principled solutions will be important for a broad spectrum of data analysis applications across the disciplines.


A Framework for Validating Models of Evasion Attacks on Machine Learning, with Application to PDF Malware Detection

Machine learning (ML) techniques are increasingly common in security applications, such as malware and intrusion detection. However, there is increasing evidence that machine learning models are susceptible to evasion attacks, in which an adversary makes small changes to the input (such as malware) in order to cause erroneous predictions (for example, to avoid being detected). Evasion attacks on ML fall into two broad categories: 1) those which generate actual malicious instances and demonstrate both evasion of ML and efficacy of attack (we call these problem space attacks), and 2) attacks which directly manipulate features used by ML, abstracting efficacy of attack into a mathematical cost function (we call these feature space attacks). Central to our inquiry is the following fundamental question: are feature space models of attacks useful proxies for real attacks? In the process of answering this question, we make two major contributions: 1) a general methodology for evaluating validity of mathematical models of ML evasion attacks, and 2) an application of this methodology as a systematic hypothesis-driven evaluation of feature space evasion attacks on ML-based PDF malware detectors. Specific to our case study, we find that a) feature space evasion models are in general not adequate in representing real attacks, b) such models can be significantly improved by identifying conserved features (features that are invariant in real attacks) whenever these exist, and c) ML hardened using the improved feature space models remains robust to alternative attacks, in contrast to ML hardened using a very powerful class of problem space attacks, which does not.


Vitamin D: An overview of vitamin D status and intake in Europe

In recent years, there have been reports suggesting a high prevalence of low vitamin D intakes and vitamin D deficiency or inadequate vitamin D status in Europe. Coupled with growing concern about the health risks associated with low vitamin D status, this has resulted in increased interest in the topic of vitamin D from healthcare professionals, the media and the public. Adequate vitamin D status has a key role in skeletal health. Prevention of the well-described vitamin D deficiency disorders of rickets and osteomalacia are clearly important, but there may also be an implication of low vitamin D status in bone loss, muscle weakness and falls and fragility fractures in older people, and these are highly significant public health issues in terms of morbidity, quality of life and costs to health services in Europe. Although there is no agreement on optimal plasma levels of vitamin D, it is apparent that blood 25-hydroxyvitamin D [25(OH)D] levels are often below recommended ranges for the general population and are particularly low in some subgroups of the population, such as those in institutions or who are housebound and non-Western immigrants. Reported estimates of vitamin D status within different European countries show large variation. However, comparison of studies across Europe is limited by their use of different methodologies. The prevalence of vitamin D deficiency [often defined as plasma 25(OH)D <25 nmol/l] may be more common in populations with a higher proportion of at-risk groups, and/or that have low consumption of foods rich in vitamin D (naturally rich or fortified) and low use of vitamin D supplements. The definition of an adequate or optimal vitamin D status is key in determining recommendations for a vitamin D intake that will enable satisfactory status to be maintained all year round, including the winter months. In most European countries, there seems to be a shortfall in achieving current vitamin D recommendations. An exception is Finland, where dietary survey data indicate that recent national policies that include fortification and supplementation, coupled with a high habitual intake of oil-rich fish, have resulted in an increase in vitamin D intakes, but this may not be a suitable strategy for all European populations. The ongoing standardisation of measurements in vitamin D research will facilitate a stronger evidence base on which policies can be determined. These policies may include promotion of dietary recommendations, food fortification, vitamin D supplementation and judicious sun exposure, but should take into account national, cultural and dietary habits. For European nations with supplementation policies, it is important that relevant parties ensure satisfactory uptake of these particularly in the most vulnerable groups of the population.


A Survey of Incentive Mechanisms for Participatory Sensing

Participatory sensing is now becoming more popular and has shown its great potential in various applications. It was originally proposed to recruit ordinary citizens to collect and share massive amounts of sensory data using their portable smart devices. By attracting participants and paying rewards as a return, incentive mechanisms play an important role to guarantee a stable scale of participants and to improve the accuracy/coverage/timeliness of the sensing results. Along this direction, a considerable amount of research activities have been conducted recently, ranging from experimental studies to theoretical solutions and practical applications, aiming at providing more comprehensive incentive procedures and/or protecting benefits of different system stakeholders. To this end, this paper surveys the literature over the period of 2004-2014 from the state of the art of theoretical frameworks, applications and system implementations, and experimental studies of the incentive strategies used in participatory sensing by providing up-to-date research in the literature. We also point out future directions of incentive strategies used in participatory sensing.


Transferring sentiment knowledge between words and tweets

Message-level and word-level polarity classification are two popular tasks in Twitter sentiment analysis. They have been commonly addressed by training supervised models from labelled data. The main limitation of these models is the high cost of data annotation. Transferring existing labels from a related problem domain is one possible solution for this problem. In this paper, we study how to transfer sentiment labels from the word domain to the tweet domain and vice versa by making their corresponding instances compatible. We model instances of these two domains as the aggregation of instances from the other (i.e., tweets are treated as collections of the words they contain and words are treated as collections of the tweets in which they occur) and perform aggregation by averaging the corresponding constituents. We study two different setups for averaging tweet and word vectors: 1) representing tweets by standard NLP features such as unigrams and part-of-speech tags and words by averaging the vectors of the tweets in which they occur, and 2) representing words using skip-gram embeddings and tweets as the average embedding vector of their words. A consequence of our approach is that instances of both domains reside in the same feature space. Thus, a sentiment classifier trained on labelled data from one domain can be used to classify instances from the other one. We evaluate this approach in two transfer learning tasks: 1) sentiment classification of tweets by applying a word-level sentiment classifier, and 2) induction of a polarity lexicon by applying a tweet-level polarity classifier. Our results show that the proposed model can successfully classify words and tweets after transfer.


The Fog computing paradigm: Scenarios and security issues

Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. In this article, we elaborate the motivation and advantages of Fog computing, and analyse its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks. We discuss the state-of-the-art of Fog computing and similar work under the same umbrella. Security and privacy issues are further disclosed according to current Fog computing paradigm. As an example, we study a typical attack, man-in-the-middle attack, for the discussion of security in Fog computing. We investigate the stealthy features of this attack by examining its CPU and memory consumption on Fog device.


IoT Considerations, Requirements, and Architectures for Smart Buildings—Energy Optimization and Next-Generation Building Management Systems

The Internet of Things (IoT) is entering the daily operation of many industries; applications include but are not limited to smart cities, smart grids, smart homes, physical security, e-health, asset management, and logistics. For example, the concept of smart cities is emerging in multiple continents, where enhanced street lighting controls, infrastructure monitoring, public safety and surveillance, physical security, gunshot detection, meter reading, and transportation analysis and optimization systems are being deployed on a city-wide scale. A related and cost-effective user-level IoT application is the support of IoT-enabled smart buildings. Commercial space has substantial requirements in terms of comfort, usability, security, and energy management. IoT-based systems can support these requirements in an organic manner. In particular, power over Ethernet, as part of an IoT-based solution, offers disruptive opportunities in revolutionizing the in-building connectivity of a large swath of devices. However, a number of deployment-limiting issues currently impact the scope of IoT utilization, including lack of comprehensive end-to-end standards, fragmented cybersecurity solutions, and a relative dearth of fully-developed vertical applications. This paper reviews some of the technical opportunities offered and the technical challenges faced by the IoT in the smart building arena.


Cyber security in the Smart Grid: Survey and challenges

The Smart Grid, generally referred to as the next-generation power system, is considered as a revolutionary and evolutionary regime of existing power grids. More importantly, with the integration of advanced computing and communication technologies, the Smart Grid is expected to greatly enhance efficiency and reliability of future power systems with renewable energy resources, as well as distributed intelligence and demand response. Along with the silent features of the Smart Grid, cyber security emerges to be a critical issue because millions of electronic devices are inter-connected via communication networks throughout critical power facilities, which has an immediate impact on reliability of such a widespread infrastructure. In this paper, we present a comprehensive survey of cyber security issues for the Smart Grid. Specifically, we focus on reviewing and discussing security requirements, network vulnerabilities, attack countermeasures, secure communication protocols and architectures in the Smart Grid. We aim to provide a deep understanding of security vulnerabilities and solutions in the Smart Grid and shed light on future research directions for Smart Grid security. 2013 Elsevier B.V. All rights reserved.


EPPA: An Efficient and Privacy-Preserving Aggregation Scheme for Secure Smart Grid Communications

The concept of smart grid has emerged as a convergence of traditional power system engineering and information and communication technology. It is vital to the success of next generation of power grid, which is expected to be featuring reliable, efficient, flexible, clean, friendly, and secure characteristics. In this paper, we propose an efficient and privacy-preserving aggregation scheme, named EPPA, for smart grid communications. EPPA uses a superincreasing sequence to structure multidimensional data and encrypt the structured data by the homomorphic Paillier cryptosystem technique. For data communications from user to smart grid operation center, data aggregation is performed directly on ciphertext at local gateways without decryption, and the aggregation result of the original data can be obtained at the operation center. EPPA also adopts the batch verification technique to reduce authentication cost. Through extensive analysis, we demonstrate that EPPA resists various security threats and preserve user privacy, and has significantly less computation and communication overhead than existing competing approaches.


MigCEP: operator migration for mobility driven distributed complex event processing

A recent trend in communication networks --- sometimes referred to as fog computing --- offers to execute computational tasks close to the access points of the networks. This enables real-time applications, like mobile Complex Event Processing (CEP), to significantly reduce end-to-end latencies and bandwidth usage. Most work studying the placement of operators in such an environment completely disregards the migration costs. However, the mobility of users requires frequent migration of operators, together with possibly large state information, to meet latency restrictions and save bandwidth in the infrastructure.
 This paper presents a placement and migration method for providers of infrastructures that incorporate cloud and fog resources. It ensures application-defined end-to-end latency restrictions and reduces the network utilization by planning the migration ahead of time. Furthermore, we present how the application knowledge of the CEP system can be used to improve current live migration techniques for Virtual Machines to reduce the required bandwidth during the migration. Our evaluations show that we safe up to 49% of the network utilization with perfect knowledge about a users mobility pattern and up to 27% of the network utilization when considering the uncertainty of those patterns.


Beyond PASCAL: A benchmark for 3D object detection in the wild

3D object detection and pose estimation methods have become popular in recent years since they can handle ambiguities in 2D images and also provide a richer description for objects compared to 2D object detectors. However, most of the datasets for 3D recognition are limited to a small amount of images per category or are captured in controlled environments. In this paper, we contribute PASCAL3D+ dataset, which is a novel and challenging dataset for 3D object detection and pose estimation. PASCAL3D+ augments 12 rigid categories of the PASCAL VOC 2012 [4] with 3D annotations. Furthermore, more images are added for each category from ImageNet [3]. PASCAL3D+ images exhibit much more variability compared to the existing 3D datasets, and on average there are more than 3,000 object instances per category. We believe this dataset will provide a rich testbed to study 3D detection and pose estimation and will help to significantly push forward research in this area. We provide the results of variations of DPM [6] on our new dataset for object detection and viewpoint estimation in different scenarios, which can be used as baselines for the community. Our benchmark is available online at http://cvgl.stanford.edu/projects/pascal3d.


Improving retention and graduate recruitment through immersive research experiences for undergraduates

Research experiences for undergraduates are considered an effective means for increasing student retention and encouraging undergraduate students to continue on to graduate school. However, managing a cohort of undergraduate researchers, with varying skill levels, can be daunting for faculty advisors. We have developed a program to engage students in research and outreach in visualization, virtual reality, networked robotics, and interactive games. Our program immerses students into the life of a lab, employing a situated learning approach that includes tiered mentoring and collaboration to enable students at all levels to contribute to research. Students work in research teams comprised of other undergraduates, graduate students and faculty, and participate in professional development and social gatherings within the larger cohort. Results from our first two years indicate this approach is manageable and effective for increasing students' ability and desire to conduct research.


Silicon carbide Schottky diodes and MOSFETs: Solutions to performance problems

Silicon carbide has long been hailed as the successor to silicon in many power electronics applications. Its superior electrical and thermal properties have delivered devices that operate at higher voltages, higher temperatures and with lower on-resistances than silicon devices. However, SiC Schottky diodes are still the only devices commercially available today. Though SiC Schottkys are now being used with silicon IGBTs in dasiahybridpsila inverter modules, the real advantages will be seen when silicon switching devices can be replaced by SiC. This paper describes the current state of SiC diode and MOSFET technology, discussing possible solutions to making these devices commercially viable.


Remaining trouble spots with computational thinking

Addressing unresolved questions concerning computational thinking.


How do software engineers understand code changes?: an exploratory study in industry

Software evolves with continuous source-code changes. These code changes usually need to be understood by software engineers when performing their daily development and maintenance tasks. However, despite its high importance, such change-understanding practice has not been systematically studied. Such lack of empirical knowledge hinders attempts to evaluate this fundamental practice and improve the corresponding tool support.
 To address this issue, in this paper, we present a large-scale quantitative and qualitative study at Microsoft. The study investigates the role of understanding code changes during software-development process, explores engineers' information needs for understanding changes and their requirements for the corresponding tool support. The study results reinforce our beliefs that understanding code changes is an indispensable task performed by engineers in software-development process. A number of insufficiencies in the current practice also emerge from the study results. For example, it is difficult to acquire important information needs such as a change's completeness, consistency, and especially the risk imposed by it on other software components. In addition, for understanding a composite change, it is valuable to decompose it into sub-changes that are aligned with individual development issues; however, currently such decomposition lacks tool support.


Text Coherence Analysis Based on Deep Neural Network

In this paper, we propose a novel deep coherence model (DCM) using a convolutional neural network architecture to capture the text coherence. The text coherence problem is investigated with a new perspective of learning sentence distributional representation and text coherence modeling simultaneously. In particular, the model captures the interactions between sentences by computing the similarities of their distributional representations. Further, it can be easily trained in an end-to-end fashion. The proposed model is evaluated on a standard Sentence Ordering task. The experimental results demonstrate its effectiveness and promise in coherence assessment showing a significant improvement over the state-of-the-art by a wide margin.


Minimum mean square error detector for multimessage spread spectrum embedding

In this paper, a new minimum mean square error (MMSE) based detector is presented for multi-message detection in both conventional spread spectrum (SS) watermarking and improved spread spectrum (ISS) watermarking. The proposed detector yields an improved detection performance in terms of bit error rate (BER) for both SS and ISS multiple message embedding. Simulation results demonstrate that the proposed MMSE detector outperforms the conventional detector, the matched filter detector, by orders of magnitude.


Dissociation Following Traumatic Stress Etiology and Treatment

We postulate that the cascade ‘‘Freeze-Flight-Fight-Fright-Flag-Faint’’ is a coherent sequence of six fear responses that escalate as a function of defense possibilities and proximity to danger during life-threat. The actual sequence of trauma-related response dispositions acted out in an extremely dangerous situation therefore depends on the appraisal of the threat by the organism in relation to her/his own power to act (e.g., age and gender) as well as the perceived characteristics of threat and perpetrator. These reaction patterns provide optimal adaption for particular stages of imminence. Subsequent to the traumatic threats, portions of the experience may be replayed. The actual individual cascade of defense stages a survivor has gone through during the traumatic event will repeat itself every time the fear network, which has evolved peritraumatically, is activated again (i.e., through internal or external triggers or, e.g., during exposure therapy).When a parasympathetically dominated ‘‘shutdown’’ was the prominent peri-traumatic response during the traumatic incident, comparable dissociative responses may dominate responding to subsequently experienced threat and may also reappear when the traumatic memory is reactivated. Repeated experience of traumatic stress forms a fear network that can become pathologically detached from contextual cues such as time and location of the danger, a condition which manifests itself as posttraumatic stress disorder (PTSD). Intrusions, for example, can therefore be understood as repetitive displays of fragments of the event, which would then, depending on the dominant physiological response during the threat, elicit a corresponding combination of hyperarousal and dissociation. We suggest that trauma treatment must therefore differentiate between patients on two dimensions: those with peritraumatic sympathetic activation versus those who went down the whole defense cascade, which leads to parasympathetic dominance during the trauma and a corresponding replay of physiological and dissociative responding, when reminded. The differential management of dissociative stages (‘‘fright’’ and ‘‘faint’’) has important treatment implications.


Classifying heart sounds using peak location for segmentation and feature construction

In this paper we describe our methodology and results for the Classifying Heart Sounds PASCAL Challenge. We present our algoritm and results for segmentation and classification of S1 and S2 heart sounds.


SORT: A Self-ORganizing Trust Model for Peer-to-Peer Systems

Open nature of peer-to-peer systems exposes them to malicious activity. Building trust relationships among peers can mitigate attacks of malicious peers. This paper presents distributed algorithms that enable a peer to reason about trustworthiness of other peers based on past interactions and recommendations. Peers create their own trust network in their proximity by using local information available and do not try to learn global trust information. Two contexts of trust, service, and recommendation contexts, are defined to measure trustworthiness in providing services and giving recommendations. Interactions and recommendations are evaluated based on importance, recentness, and peer satisfaction parameters. Additionally, recommender's trustworthiness and confidence about a recommendation are considered while evaluating recommendations. Simulation experiments on a file sharing application show that the proposed model can mitigate attacks on 16 different malicious behavior models. In the experiments, good peers were able to form trust relationships in their proximity and isolate malicious peers.


Deep Learning meets Semantic Web: A feasibility study with the Cardiovascular Disease Ontology and PubMed citations

Background: Automatic identification of gene and protein names from biomedical publications can help curators and researchers to keep up with the findings published in the scientific literature. As of today, this is a challenging task related to information retrieval, and in the realm of Big Data Analytics. Objectives: To investigate the feasibility of using word embed-dings (i.e. distributed word representations) from Deep Learning algorithms together with terms from the Cardiovascular Disease Ontology (CVDO) as a step to identifying omics information encoded in the biomedical literature. Methods: Word embeddings were generated using the neural language models CBOW and Skip-gram with an input of more than 14 million PubMed citations (titles and abstracts) corresponding to articles published between 2000 and 2016. Then the abstracts of selected papers from the sysVASC systematic review were manually annotated with gene/protein names. We set up two experiments that used the word embeddings to produce term variants for gene/protein names: the first experiment used the terms manually annotated from the papers; the second experiment enriched/expanded the annotated terms using terms from the human-readable labels of key classes (gene/proteins) from the CVDO ontology. CVDO is formalised in the W3C Web Ontology Language (OWL) and contains 172,121 UniProt Knowledgebase protein classes related to human and 86,792 UniProtKB protein classes related to mouse. The hypothesis is that by enriching the original annotated terms, a better context is provided, and therefore, it is easier to obtain suitable (full and/or partial) term variants for gene/protein names from word embed-dings. Results: From the papers manually annotated, a list of 107 terms (gene/protein names) was acquired. As part of the word embeddings generated from CBOW and Skip-gram, a lexicon with more than 9 million terms was created. Using the cosine similarity metric, a list of the 12 top-ranked terms was generated from word embeddings for query terms present in the generated lexicon. Domain experts evaluated a total of 1968 pairs of terms and classified the retrieved terms as: TV (term variant); PTV (partial term variant); and NTV (non term variant, meaning none of the previous two categories). In experiment I, Skip-gram finds the double amount of (full and/or partial) term variants for gene/protein names as compared with CBOW. Using Skip-gram, the weighted Cohen's Kappa inter-annotator agreement for two domain experts was 0.80 for the first experiment and 0.74 for the second experiment. In the first experiment, suitable (full and/or partial) term variants were found …


A computational approach to motion perception

In this paper it is shown that the computation of the optical flow from a sequence of timevarying images is not, in general, an underconstrained problem. A local algorithm for the computation of the optical flow which uses second order derivatives of the image brightness pattern, and that avoids the aperture problem, is presented. The obtained optical flow is very similar to the true motion field — which is the vector field associated with moving features on the image plane — and can be used to recover 3D motion information. Experimental results on sequences of real images, together with estimates of relevant motion parameters, like time-to-crash for translation and angular velocity for rotation, are presented and discussed. Due to the remarkable accuracy which can be achieved in estimating motion parameters, the proposed method is likely to be very useful in a number of computer vision applications.


A novel booster antenna on flexible substrates for metal proximity NFC applications

In this article a novel booster antenna is proposed in order to increase the read range of a small (1.1×10-4 m2) metal mounted HF (13.56 MHz) RFID. The highly inductive coupling between the reader and booster, and between the RFID and booster is the main propagation method. The communication between the reader and RFID realized through the booster antenna. For this purpose two coils are connected in parallel to each other resonating at the same frequency. The greater coil is coupled to the reader antenna and the smaller coil is coupled to the RFID. The simulation and measurement results show the reliability of the proposed structure.


Microbial co-operation in the rhizosphere.

Soil microbial populations are immersed in a framework of interactions known to affect plant fitness and soil quality. They are involved in fundamental activities that ensure the stability and productivity of both agricultural systems and natural ecosystems. Strategic and applied research has demonstrated that certain co-operative microbial activities can be exploited, as a low-input biotechnology, to help sustainable, environmentally-friendly, agro-technological practices. Much research is addressed at improving understanding of the diversity, dynamics, and significance of rhizosphere microbial populations and their co-operative activities. An analysis of the co-operative microbial activities known to affect plant development is the general aim of this review. In particular, this article summarizes and discusses significant aspects of this general topic, including (i) the analysis of the key activities carried out by the diverse trophic and functional groups of micro-organisms involved in co-operative rhizosphere interactions; (ii) a critical discussion of the direct microbe-microbe interactions which results in processes benefiting sustainable agro-ecosystem development; and (iii) beneficial microbial interactions involving arbuscular mycorrhiza, the omnipresent fungus-plant beneficial symbiosis. The trends of this thematic area will be outlined, from molecular biology and ecophysiological issues to the biotechnological developments for integrated management, to indicate where research is needed in the future.


Facade Segmentation in the Wild

Urban façade segmentation from automatically acquired imagery, in contrast to traditional image segmentation, poses several unique challenges. 360◦ photospheres captured from vehicles are an effective way to capture a large number of images, but this data presents difficult-to-model warping and stitching artifacts. In addition, each pixel can belong to multiple façade elements, and different facade elements (e.g., window, balcony, sill, etc.) are correlated and vary wildly in their characteristics. In this paper, we propose three network architectures of varying complexity to achieve multilabel semantic segmentation of façade images while exploiting their unique characteristics. Specifically, we propose a MULTIFACSEGNET architecture to assign multiple labels to each pixel, a SEPARABLE architecture as a low-rank formulation that encourages extraction of rectangular elements, and a COMPATIBILITY network that simultaneously seeks segmentation across facade element types allowing the network to ‘see’ intermediate output probabilities of the various façade element classes. Our results on benchmark datasets show significant improvements over existing façade segmentation approaches for the typical façade elements. For example, on one commonly used dataset the accuracy scores for window (the most important architectural element) increases from 0.91 to 0.97 percent compared to the best competing method, and comparable improvements on other element types.


Application of Neural Networks to Stock Market Prediction

All rights reserved. Material in this report may not be reproduced in any form. This material is made available for learning and may not be used in any manner for any profit activities without the permission of the author.


Using Big Data as a window into consumers’ psychology

The rise of ‘Big Data’ had a big impact on marketing research and practice. In this article, we first highlight sources of useful consumer information that are now available at large scale and very little or no cost. We subsequently discuss how this information – with the help of new analytical techniques – can be translated into valuable insights on consumers’ psychological states and traits that can, in turn, be used to inform marketing strategy. Finally, we discuss opportunities and challenges related to the use of Big Data as a window into consumers’ psychology, and provide recommendations for how to implement related technologies in a way that benefits both businesses and consumers.


Maintenance 4.0: Intelligent and Predictive Maintenance System Architecture

In the current manufacturing world, the role of maintenance has been receiving increasingly more attention while companies understand that maintenance, when well performed, can be a strategic factor to achieve the corporate goals. The latest trends of maintenance leans towards the predictive approach, exemplified by the Prognosis and Health Management (PHM) and the Condition-based Maintenance (CBM) techniques. The implementation of such approaches demands a well structured architecture and can be boosted through the use of emergent ICT technologies, namely Internet of Things (IoT), cloud computing, advanced data analytics and augmented reality. Therefore, this paper describes the architecture of an intelligent and predictive maintenance system, aligned with Industry 4.0 principles, that considers advanced and online analysis of the collected data for the earlier detection of the occurrence of possible machine failures, and supports technicians during the maintenance interventions by providing a guided intelligent decision support.


Memory training impacts short-term changes in aging white matter: a longitudinal diffusion tensor imaging study.

A growing body of research indicates benefits of cognitive training in older adults, but the neuronal mechanisms underlying the effect of cognitive intervention remains largely unexplored. Neuroimaging methods are sensitive to subtle changes in brain structure and show potential for enhancing our understanding of both aging- and training-related neuronal plasticity. Specifically, studies using diffusion tensor imaging (DTI) suggest substantial changes in white matter (WM) in aging, but it is not known whether cognitive training might modulate these structural alterations. We used tract-based spatial statistics (TBSS) optimized for longitudinal analysis to delineate the effects of 8 weeks intensive memory training on WM microstructure. 41 participants (mean age 61 years) matched for age, sex and education were randomly assigned to an intervention or control group. All participants underwent MRI-scanning and neuropsychological assessments at the beginning and end of the study. Longitudinal analysis across groups revealed significant increase in frontal mean diffusivity (MD), indicating that DTI is sensitive to WM structural alterations over a 10-week interval. Further, group analysis demonstrated positive effects of training on the short-term changes. Participants in the training group showed a relative increase in fractional anisotropy (FA) compared with controls. Further, a significant relationship between memory improvement and change in FA was found, suggesting a possible functional significance of the reported changes. The training effect on FA seemed to be driven by a relative decrease in radial diffusivity, which might indicate a role for myelin-related processes in WM plasticity.


Anatomy of GPU Memory System for Multi-Application Execution

As GPUs make headway in the computing landscape spanning mobile platforms, supercomputers, cloud and virtual desktop platforms, supporting concurrent execution of multiple applications in GPUs becomes essential for unlocking their full potential. However, unlike CPUs, multi-application execution in GPUs is little explored. In this paper, we study the memory system of GPUs in a concurrently executing multi-application environment. We first present an analytical performance model for many-threaded architectures and show that the common use of misses-per-kilo-instruction (MPKI) as a proxy for performance is not accurate without considering the bandwidth usage of applications. We characterize the memory interference of applications and discuss the limitations of existing memory schedulers in mitigating this interference. We extend the analytical model to multiple applications and identify the key metrics to control various performance metrics. We conduct extensive simulations using an enhanced version of GPGPU-Sim targeted for concurrently executing multiple applications, and show that memory scheduling decisions based on MPKI and bandwidth information are more effective in enhancing throughput compared to the traditional FR-FCFS and the recently proposed RR FR-FCFS policies.


Application-aware prioritization mechanisms for on-chip networks

Network-on-Chips (NoCs) are likely to become a critical shared resource in future many-core processors. The challenge is to develop policies and mechanisms that enable multiple applications to efficiently and fairly share the network, to improve system performance. Existing local packet scheduling policies in the routers fail to fully achieve this goal, because they treat every packet equally, regardless of which application issued the packet.
 This paper proposes prioritization policies and architectural extensions to NoC routers that improve the overall application-level throughput, while ensuring fairness in the network. Our prioritization policies are application-aware, distinguishing applications based on the stall-time criticality of their packets. The idea is to divide processor execution time into phases, rank applications within a phase based on stall-time criticality, and have all routers in the network prioritize packets based on their applications' ranks. Our scheme also includes techniques that ensure starvation freedom and enable the enforcement of system-level application priorities.
 We evaluate the proposed prioritization policies on a 64-core CMP with an 8x8 mesh NoC, using a suite of 35 diverse applications. For a representative set of case studies, our proposed policy increases average system throughput by 25.6% over age-based arbitration and 18.4% over round-robin arbitration. Averaged over 96 randomly-generated multiprogrammed workload mixes, the proposed policy improves system throughput by 9.1% over the best existing prioritization policy, while also reducing application-level unfairness.


Project Adam: Building an Efficient and Scalable Deep Learning Training System

Large deep neural network models have recently demonstrated state-of-the-art accuracy on hard visual recognition tasks. Unfortunately such models are extremely time consuming to train and require large amount of compute cycles. We describe the design and implementation of a distributed system called Adam comprised of commodity server machines to train such models that exhibits world-class performance, scaling and task accuracy on visual recognition tasks. Adam achieves high efficiency and scalability through whole system co-design that optimizes and balances workload computation and communication. We exploit asynchrony throughout the system to improve performance and show that it additionally improves the accuracy of trained models. Adam is significantly more efficient and scalable than was previously thought possible and used 30x fewer machines to train a large 2 billion connection model to 2x higher accuracy in comparable time on the ImageNet 22,000 category image classification task than the system that previously held the record for this benchmark. We also show that task accuracy improves with larger models. Our results provide compelling evidence that a distributed systems-driven approach to deep learning using current training algorithms is worth pursuing.


TunePad: Computational Thinking Through Sound Composition

Computational thinking skills will be important for the next generation of students. However, there is a disparity in the populations joining the field. Integrating computational thinking into artistic fields has shown to increase participation in computer science. In this paper, we present our initial design prototype for TunePad, a sound composition tablet application controlled by a block-based programming environment. TunePad is designed to introduce learners to computational thinking and to prepare them for text-based coding environments. From our preliminary testing, with children ages 7-14, we observed that our design actively engages learners and communicates how the programming blocks control the sounds being played. This testing is a prelude to more formal studies as we continue to improve the design and interface of TunePad. With this work, we intend to engage students in computational thinking who may not have otherwise been exposed, giving the opportunity to more people to enter the computer science field.


Privacy-Preserving Online Task Assignment in Spatial Crowdsourcing with Untrusted Server

With spatial crowdsourcing (SC), requesters outsource their spatiotemporal tasks (tasks associated with location and time) to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. However, current solutions require the locations of the workers and/or the tasks to be disclosed to untrusted parties (SC server) for effective assignments of tasks to workers. In this paper we propose a framework for assigning tasks to workers in an online manner without compromising the location privacy of workers and tasks. We perturb the locations of both tasks and workers based on geo-indistinguishability and then devise techniques to quantify the probability of reachability between a task and a worker, given their perturbed locations. We investigate both analytical and empirical models for quantifying the worker-task pair reachability and propose task assignment strategies that strike a balance among various metrics such as the number of completed tasks, worker travel distance and system overhead. Extensive experiments on real-world datasets show that our proposed techniques result in minimal disclosure of task locations and no disclosure of worker locations without significantly sacrificing the total number of assigned tasks.


BareDroid: Large-Scale Analysis of Android Apps on Real Devices

To protect Android users, researchers have been analyzing unknown, potentially-malicious applications by using systems based on emulators, such as the Google's Bouncer and Andrubis. Emulators are the go-to choice because of their convenience: they can scale horizontally over multiple hosts, and can be reverted to a known, clean state in a matter of seconds. Emulators, however, are fundamentally different from real devices, and previous research has shown how it is possible to automatically develop heuristics to identify an emulated environment, ranging from simple flag checks and unrealistic sensor input, to fingerprinting the hypervisor's handling of basic blocks of instructions. Aware of this aspect, malware authors are starting to exploit this fundamental weakness to evade current detection systems. Unfortunately, analyzing apps directly on bare metal at scale has been so far unfeasible, because the time to restore a device to a clean snapshot is prohibitive: with the same budget, one can analyze an order of magnitude less apps on a physical device than on an emulator.
 In this paper, we propose BareDroid, a system that makes bare-metal analysis of Android apps feasible by quickly restoring real devices to a clean snapshot. We show how BareDroid is not detected as an emulated analysis environment by emulator-aware malware or by heuristics from prior research, allowing BareDroid to observe more potentially malicious activity generated by apps. Moreover, we provide a cost analysis, which shows that replacing emulators with BareDroid requires a financial investment of less than twice the cost of the servers that would be running the emulators. Finally, we release BareDroid as an open source project, in the hope it can be useful to other researchers to strengthen their analysis systems.


Adtranz: A Mobile Computing System for Maintenance and Collaboration

The paper describes the mobile information and communication aspects of a next generation train maintenance and diagnosis system, discusses the working prototype features, and research results. Wearable/ Mobile computers combined with the wireless technology improve efficiency and accuracy of the maintenance work. This technology enables maintenance personnel at the site to communicate with a remote helpdesk / expertise center through digital data, audio, and image.


Multi-layer FSS for gain improvement of a wide-band stacked printed antenna

In this paper, an attempt has been made to improve the gain of a wide-band antenna using two layers of frequency-selective surfaces. It is seen that using two layers it is possible to achieve flat transmission characteristics over a reasonable bandwidth. However with an actual antenna, the gain improvement has at present been achieved over only a small band. It is expected that with further optimization and possibly increasing the number of layers, wide-band gain enhancement can be achieved.


Power-Management Architecture of the Intel Microarchitecture Code-Named Sandy Bridge

Modern microprocessors are evolving into system-on-a-chip designs with high integration levels, catering to ever-shrinking form factors. Portability without compromising performance is a driving market need. An architectural approach that's adaptive to and cognizant of workload behavior and platform physical constraints is indispensable to meeting these performance and efficiency goals. This article describes power-management innovations introduced on Intel's Sandy Bridge microprocessor.


All-printed diode operating at 1.6 GHz.

Printed electronics are considered for wireless electronic tags and sensors within the future Internet-of-things (IoT) concept. As a consequence of the low charge carrier mobility of present printable organic and inorganic semiconductors, the operational frequency of printed rectifiers is not high enough to enable direct communication and powering between mobile phones and printed e-tags. Here, we report an all-printed diode operating up to 1.6 GHz. The device, based on two stacked layers of Si and NbSi2 particles, is manufactured on a flexible substrate at low temperature and in ambient atmosphere. The high charge carrier mobility of the Si microparticles allows device operation to occur in the charge injection-limited regime. The asymmetry of the oxide layers in the resulting device stack leads to rectification of tunneling current. Printed diodes were combined with antennas and electrochromic displays to form an all-printed e-tag. The harvested signal from a Global System for Mobile Communications mobile phone was used to update the display. Our findings demonstrate a new communication pathway for printed electronics within IoT applications.


AllConcur: Leaderless Concurrent Atomic Broadcast (Extended Version)

Many distributed systems require coordination between the components involved. With the steady growth of such systems, the probability of failures increases, which necessitates scalable fault-tolerant agreement protocols. The most common practical agreement protocol, for such scenarios, is leader-based atomic broadcast. In this work, we propose ALLCONCUR, a distributed system that provides agreement through a leaderless concurrent atomic broadcast algorithm, thus, not suffering from the bottleneck of a central coordinator. In ALLCONCUR, all components exchange messages concurrently through a logical overlay network that employs early termination to minimize the agreement latency. Our implementation of ALLCONCUR supports standard sockets-based TCP as well as high-performance InfiniBand Verbs communications. ALLCONCUR can handle up to 135 million requests per second and achieves 17× higher throughput than today’s standard leader-based protocols, such as Libpaxos. Thus, ALLCONCUR is highly competitive with regard to existing solutions and, due to its decentralized approach, enables hitherto unattainable system designs in a variety of fields.


Short-Term Power Load Forecasting with Deep Belief Network and Copula Models

The complexity and uncertainty in scheduling and operation of the power system are prominently increasing with the penetration of smart grid. An essential task for the effective operation of power systems is the power load forecasting. In this paper, a tandem data-driven method is studied in this research based on deep learning. A deep belief network (DBN) embedded with parametric Copula models is proposed to forecast the hourly load of a power grid. Data collected over a whole year from an urbanized area in Texas, United States is utilized. Forecasting hourly power load in four different seasons in a selected year is examined. Two forecasting scenarios, day-ahead and week-ahead forecasting are conducted using the proposed methods and compared with classical neural networks (NN), support vector regression machine (SVR), extreme learning machine (ELM), and classical deep belief networks (DBN). The accuracy of the forecasted power load is assessed by mean absolute percentage error (MAPE) and root mean square error (RMSE). Computational results confirm the effectiveness of the proposed semi-parametric data-driven method.


Text Classification with Topic-based Word Embedding and Convolutional Neural Networks

Recently, distributed word embeddings trained by neural language models are commonly used for text classification with Convolutional Neural Networks (CNNs). In this paper, we propose a novel neural language model, Topic-based Skip-gram, to learn topic-based word embeddings for biomedical literature indexing with CNNs. Topic-based Skip-gram leverages textual content with topic models, e.g., Latent Dirichlet Allocation (LDA), to capture precise topic-based word relationship and then integrate it into distributed word embedding learning. We then describe two multimodal CNN architectures, which are able to employ different kinds of word embeddings at the same time for text classification. Through extensive experiments conducted on several real-world datasets, we demonstrate that combination of our Topic-based Skip-gram and multimodal CNN architectures outperforms state-of-the-art methods in biomedical literature indexing, clinical note annotation and general textual benchmark dataset classification.


EROS: a fast capability system

EROS is a capability-based operating system for commodity processors which uses a single level storage model. The single level store's persistence is transparent to applications. The performance consequences of support for transparent persistence and capability-based architectures are generally believed to be negative. Surprisingly, the basic operations of EROS (such as IPC) are generally comparable in cost to similar operations in conventional systems. This is demonstrated with a set of microbenchmark measurements of semantically similar operations in Linux.The EROS system achieves its performance by coupling well-chosen abstract objects with caching techniques for those objects. The objects (processes, nodes, and pages) are well-supported by conventional hardware, reducing the overhead of capabilities. Software-managed caching techniques for these objects reduce the cost of persistence. The resulting performance suggests that composing protected subsystems may be less costly than commonly believed.


Simple Method for High-Performance Digit Recognition Based on Sparse Coding

In this brief paper, we propose a method of feature extraction for digit recognition that is inspired by vision research: a sparse-coding strategy and a local maximum operation. We show that our method, despite its simplicity, yields state-of-the-art classification results on a highly competitive digit-recognition benchmark. We first employ the unsupervised Sparsenet algorithm to learn a basis for representing patches of handwritten digit images. We then use this basis to extract local coefficients. In a second step, we apply a local maximum operation to implement local shift invariance. Finally, we train a support vector machine (SVM) on the resulting feature vectors and obtain state-of-the-art classification performance in the digit recognition task defined by the MNIST benchmark. We compare the different classification performances obtained with sparse coding, Gabor wavelets, and principal component analysis (PCA). We conclude that the learning of a sparse representation of local image patches combined with a local maximum operation for feature extraction can significantly improve recognition performance.


Standard cell library and automated design flow for circuits on flexible substrates

A-Si:H TFTs are traditionally used in backplane arrays for active matrix displays and occasionally in row or column drive electronics with current efforts focusing on flexible displays and drivers. This paper extends flexible electronics to complex digital circuitry by designing a standard cell library for a-Si:H TFTs on flexible stainless steel and plastic substrates. The standard cell library enables layout automation with a standard cell place and route tool, significantly speeding the layout of a-Si:H digital circuits on the backplane to enhance display functionality. Since only n-channel transistors are available, the gates are designed with a bootstrap pull-up network to ensure good output voltage swings. The library developed consists of 7 gates: 5 combinational gates (Inverter, NAND2, NOR2, NOR3, and MUX2) and 2 sequential gates (latch and ‘D’ flip-flop). Test structures have been fabricated to experimentally characterize the delay vs. fan-out of the standard cells. Automatic extraction of electrical interconnections from layout, enabling layout versus schematic (LVS) has also incorporated into the existing tool suite for bottom gate a-Si:H TFTs. A 3bit counter was designed, fabricated and tested to demonstrate the standard cell library is described.


Challenges Facing the Industrial Implementation of Fog Computing

Recently, the industries converge to the integration of the industry 4.0 paradigm to keep responding to the variable market demands. This integration is realized by the adoption of several components of the industry 4.0 such as IoT, Big Data and Cloud Computing, etc. Several difficulties concerning the integration of data management were encountered during first level of Industry 4.0 integration because of the unexpected quantity of data generated by IoT devices. The Fog computing can be considered as a new component of Industry 4.0 to resolve this kind of problem. However its implementation in the industrial field faces several challenges from different natures. This paper explains the role of Fog Computing solution to enhance the Cloud layer (distribution, low latency, real-time,. . . ) and studies its ability to be implemented in manufacturing systems. The Fog Manufacturing is introduced as the new industrial Fog vision. The challenges preventing the Fog Manufacturing implementation are studied and the links between each other are justified. A future use case is described to carry out the solutions given to satisfy the Fog Manufacturing challenges.


An empirical study of dormant bugs

Over the past decade, several research efforts have studied the quality of software systems by looking at post-release bugs. However, these studies do not account for bugs that remain dormant (i.e., introduced in a version of the software system, but are not found until much later) for years and across many versions. Such dormant bugs skew our under- standing of the software quality. In this paper we study dormant bugs against non-dormant bugs using data from 20 different open-source Apache foundation software systems. We find that 33% of the bugs introduced in a version are not reported till much later (i.e., they are reported in future versions as dormant bugs). Moreover, we find that 18.9% of the reported bugs in a version are not even introduced in that version (i.e., they are dormant bugs from prior versions). In short, the use of reported bugs to judge the quality of a specific version might be misleading. Exploring the fix process for dormant bugs, we find that they are fixed faster (median fix time of 5 days) than non- dormant bugs (median fix time of 8 days), and are fixed by more experienced developers (median commit counts of developers who fix dormant bug is 169% higher). Our results highlight that dormant bugs are different from non-dormant bugs in many perspectives and that future research in software quality should carefully study and consider dormant bugs.


Chronic ulcerative stomatitis: a distinct clinical entity?

Chronic ulcerative stomatitis (CUS) is a mucocutaneous disorder which is characterised by persistent oral mucosal ulceration. The clinical appearance is often reminiscent of oral lichen planus (OLP) leading to erroneous diagnoses. The immune mediated inhibition of the CUS protein (CUSP) is implicated in the pathogenesis of CUS. CUSP acts as an anti-apoptotic protein and when its action is prevented it may result in significant epithelial injury. The objective or this article is to present the first documented case of CUS in South Africa, with relevant reference to current international literature. CUS should be considered in patients previously diagnosed with OLP but who are unresponsive to glucocorticosteroid therapy. The condition can be successfully managed using hydroxychloroquine.


Small and fast moving object detection and tracking in sports video sequences

We propose an algorithm for detection and tracking of small and fast moving objects, like a ping pong ball or a cricket ball, in sports video sequences. For detection, the proposed method uses only motion as a cue; moreover it does not use any texture information. Our method is able to detect the object with very low contrast and negligible texture content. Along with detection, we also propose a tracking algorithm using the multiple filter bank approach. Thus we provide a complete solution. The tracking algorithm is able to track maneuvering as well as non-maneuvering movements of the object without using any a priori information about the target dynamics


Social entrepreneurship research : A source of explanation , prediction , and delight

Social entrepreneurship, as a practice and a field for scholarly investigation, provides a unique opportunity to challenge, question, and rethink concepts and assumptions from different fields of management and business research. This article puts forward a view of social entrepreneurship as a process that catalyzes social change and addresses important social needs in a way that is not dominated by direct financial benefits for the entrepreneurs. Social entrepreneurship is seen as differing from other forms of entrepreneurship in the relatively higher priority given to promoting social value and development versus capturing economic value. To stimulate future research the authors introduce the concept of embeddedness as a nexus between theoretical perspectives for the study of social entrepreneurship. # 2005 Elsevier Inc. All rights reserved.


Ultrasound measurements of axillary recess capsule thickness in unilateral frozen shoulder: study of correlation with MRI measurements

The aims of this study were to compare the ultrasound thickness of the affected axillary recess capsule (ARC) with that of the unaffected ARC in patients with frozen shoulder (FS), to analyze whether the ultrasound measurements of the ARC thickness correlate with those obtained using MRI, and to assess whether the ultrasound thickness of the ARC correlates with the patterns of range of motion limitation. Forty-four patients with clinically diagnosed unilateral FS and MRI evaluation performed ultrasound measurement of ARC. The ultrasound measurement of the ARC thickness was performed with the patients in a supine position with their shoulder abducted by 40°. The ARC thickness was also measured by MRI on oblique coronal images by another physician blinded to the ultrasound measurements. With both ultrasound and MRI, ARC thickness was determined at the widest portion of the capsule. The ultrasound thickness of ARC was significantly higher in the affected shoulder (4.4 ± 1.1 mm) than in the unaffected one (2.2 ± 0.5 mm) (p < 0.001). The ultrasound thickness of the ARC in the affected shoulder correlated with that measured by MRI (8.9 ± 1.9 mm) (p < 0.001, r = 0.83). The ARC thickness, whether measured by ultrasound or MRI, was not significantly related to the limitation of movement in specific directions. Ultrasound can demonstrate the difference in ARC thickness between affected and unaffected shoulders in patients with unilateral FS. The ARC thickness measured by ultrasound correlates with that measured by MRI.


A scaling normalization method for differential expression analysis of RNA-seq data

The fine detail provided by sequencing-based transcriptome surveys suggests that RNA-seq is likely to become the platform of choice for interrogating steady state RNA. In order to discover biologically important changes in expression, we show that normalization continues to be an essential step in the analysis. We outline a simple and effective method for performing normalization and show dramatically improved results for inferring differential expression in simulated and publicly available data sets.


Image-based Human Character Modeling and Reconstruction for Virtual Reality Exposure Therapy

We design a modeling platform to construct virtual environments for Virtual Reality Exposure Therapy. In this platform, the scene is built by image-based reconstruction followed by manual refinement (when needed). The main technical contribution of this paper is on enhanced human character modeling, which is done through reconstructing 3D face geometry, body shape and posture from provided 2D photos.


Detecting People in Artwork with CNNs

CNNs have massively improved performance in object detection in photographs. However research into object detection in artwork remains limited. We show state-of-the-art performance on a challenging dataset, People-Art, which contains people from photos, cartoons and 41 different artwork movements. We achieve this high performance by fine-tuning a CNN for this task, thus also demonstrating that training CNNs on photos results in overfitting for photos: only the first three or four layers transfer from photos to artwork. Although the CNN’s performance is the highest yet, it remains less than 60% AP, suggesting further work is needed for the cross-depiction problem.


Introduction to Space-Time Wireless Communications

published by the press syndicate of the university of cambridge A catalog record for this book is available from the British Library ISBN 0 521 82615 2 hardback Contents List of figures page xiv List of tables xxii Preface xxiii List of abbreviations xxvi List of symbols xxix vii viii Contents 2.6 Polarization and field diverse channels 27 2.7 Antenna array topology 28 2.8 Degenerate channels 29 2.9 Reciprocity and its implications 31 3 ST channel and signal models


A quasi-orthogonal space-time block code

It has been shown that a complex orthogonal design that provides full diversity and full transmission rate for a space–time block code is not possible for more than two antennas. Previous attempts have been concentrated in generalizing orthogonal designs which provide space–time block codes with full diversity and a high transmission rate. In this work, we design rate one codes which are quasi-orthogonal and provide partial diversity. The decoder of the proposed codes works with pairs of transmitted symbols instead of single symbols.


Space-Time block codes from orthogonal designs

We introduce space–time block coding , a new paradigm for communication over Rayleigh fading channels using multiple transmit antennas. Data is encoded using a space–time block code and the encoded data is split inton streams which are simultaneously transmitted usingn transmit antennas. The received signal at each receive antenna is a linear superposition of the n transmitted signals perturbed by noise. Maximumlikelihood decoding is achieved in a simple way through decoupling of the signals transmitted from different antennas rather than joint detection. This uses the orthogonal structure of the space–time block code and gives a maximum-likelihood decoding algorithm which is based only on linear processing at the receiver. Space–time block codes are designed to achieve the maximum diversity order for a given number of transmit and receive antennas subject to the constraint of having a simple decoding algorithm. The classical mathematical framework of orthogonal designs is applied to construct space–time block codes. It is shown that space–time block codes constructed in this way only exist for few sporadic values ofn. Subsequently, a generalization of orthogonal designs is shown to provide space–time block codes for both real and complex constellations for any number of transmit antennas. These codes achieve the maximum possible transmission rate for any number of transmit antennas using any arbitrary real constellation such as PAM. For an arbitrary complex constellation such as PSK and QAM, space–time block codes are designed that achieve1=2 of the maximum possible transmission rate for any number of transmit antennas. For the specific cases of two, three, and four transmit antennas, space–time block codes are designed that achieve, respectively, all, 3=4, and 3=4 of maximum possible transmission rate using arbitrary complex constellations. The best tradeoff between the decoding delay and the number of transmit antennas is also computed and it is shown that many of the codes presented here are optimal in this sense as well.


Space-Time Codes for High Data Rate Wireless Communications

Space-time codes (STC) are a class of signaling techniques, offering coding and diversity gains along with improved spectral efficiency. These codes exploit both the spatial and the temporal diversity of the wireless link by combining the design of the error correction code, modulation scheme and array processing. STC are well suited for improving the downlink performance, which is the bottleneck in asymmetric applications such as downstream Internet. Three original contributions to the area of STC are presented in this dissertation. First, the development of analytic tools that determine the fundamental limits on the performance of STC in a variety of channel conditions. For trellis-type STC, transfer function based techniques are applied to derive performance bounds over Rayleigh, Rician and correlated fading environments. For block-type STC, an analytic framework that supports various complex orthogonal designs with arbitrary signal cardinalities and array configurations is developed. In the second part of the dissertation, the Virginia Tech Space-Time Advanced Radio (VT-STAR) is designed, introducing a multi-antenna hardware laboratory test bed, which facilitates characterization of the multiple-input multiple-output (MIMO) channel and validation of various space-time approaches. In the third part of the dissertation, two novel space-time architectures paired with iterative processing principles are proposed. The first scheme extends the suitability of STC to outdoor wireless communications by employing iterative equalization/decoding for time dispersive channels and the second scheme employs iterative interference cancellation/decoding to solve the error propagation problem of Bell-Labs Layered Space-Time Architecture (BLAST). Results show that remarkable energy and spectral efficiencies are achievable by combining concepts drawn from space-time coding, multiuser detection, array processing and iterative decoding.


Haplogroup Prediction from Y-STR Values Using an Allele-Frequency Approach

A new approach to predicting the Y-chromosome haplogroup from a set of Y-STR marker values is presented and compared to other approaches. The method has been implemented in an Excel-based program, where an arbitrary number of STR markers may be input and a “goodness of fit” score for 10 haplogroups (E3a, E3b, G, I1a, I1b, I1c, J2, N3, R1a, and R1b) is returned. This method has been applied to 101 R1b haplotypes and 50 I1a haplotypes (all having 37 STR markers available), and the distribution of results is presented. In the case of I1a, the results are compared with the predictions of another method.


A design of miniaturization LPDA antenna with ultra wideband

This paper designed a Log-periodic miniaturization antenna working in 8GHz to 18GHz. It satisfies the characteristic of high gain, good radiation pattern, and a large proportion of the size reduction which other antenna don't possess. The lengthwise is 80mm, it reduces more than 50% of the transverse dimension with the original specification. A “mountain” terminal loaded design is used to shrink size of antenna under the premise of guaranteeing directivity of antenna. By optimizing backward feeding structure and antenna array to improve antenna performance. The final result is superior to other same type antenna distinctly.


Learning styles and performance in the introductory programming sequence

This paper reports on the implication of different preferred learning styles on students' performance in the introductory programming sequence and on work in progress on how to accommodate these different styles.Students were given a learning styles preference test and then their preferred learning styles were compared to their performance on the exam and the practical programming part of the introductory programming module. There were significant differences in performance between groups of students.This result could lead one to two possible conclusions. One might be that some students' learning styles are more suited to learning programming than others.An alternative explanation is that our current methods of teaching advantage students with certain learning preference styles. We are at present in the process of testing this second assumption by providing students with a wider range of learning materials. We will then see if student performance is improved by using our current results as a baseline for comparison


Human facial expressions as adaptations: Evolutionary questions in facial expression research.

The importance of the face in social interaction and social intelligence is widely recognized in anthropology. Yet the adaptive functions of human facial expression remain largely unknown. An evolutionary model of human facial expression as behavioral adaptation can be constructed, given the current knowledge of the phenotypic variation, ecological contexts, and fitness consequences of facial behavior. Studies of facial expression are available, but results are not typically framed in an evolutionary perspective. This review identifies the relevant physical phenomena of facial expression and integrates the study of this behavior with the anthropological study of communication and sociality in general. Anthropological issues with relevance to the evolutionary study of facial expression include: facial expressions as coordinated, stereotyped behavioral phenotypes, the unique contexts and functions of different facial expressions, the relationship of facial expression to speech, the value of facial expressions as signals, and the relationship of facial expression to social intelligence in humans and in nonhuman primates. Human smiling is used as an example of adaptation, and testable hypotheses concerning the human smile, as well as other expressions, are proposed.


A Comparison Between Single-Phase Quasi-  $Z$-Source and Quasi-Switched Boost Inverters

The properties of a single-phase quasi Z-source inverter (qZSI) and a single-phase quasi-switched boost inverter (qSBI), both of which are single-stage buck-boost inverters, are investigated and compared. For the same operating conditions, qSBI has the following advantages over qZSI: 1) Three capacitors are saved; 2) the current rating on both of its switches and diodes is lower; 3) its boost factor is higher with an equivalent parasitic effect; and 4) its efficiency is higher. However, qSBI has one more active switch and one more diode than Z-source/ qZSIs. In addition, the capacitor voltage stress of qSBI is higher than that of qZSI. The dc and ac component circuit analysis, impedance design with low-frequency and high-frequency ripples, component stresses, and power loss calculation are presented. A prototype based on a TMS320F28335 DSP is built in order to compare the operating principle of qSBI and qZSI.


Panel 1 : Terms used in qualitative

A broad base of medical and scientific knowledge is needed if medicine is to maintain its identity as a discipline founded on scientific knowledge. However, interpretive action must also be included in medical knowledge. In my first article, 1 I investigated the nature of clinical knowledge in medicine, exposed some of the shortcomings of quantitative research methods, and briefly introduced qualitative methods as an approach for improved understanding. Here, I shall discuss how scientific quality can be maintained when qualitative research methods are applied. I present some overall standards, describe specific challenges met when the medical researcher uses qualitative research methods, and subsequently propose guidelines for qualitative inquiry in medical research. I do not intend to provide comprehensive guidance for the inexperienced qualitative researcher, who must be prepared to acquire basic skills of qualitative research from the relevant literature. Some of the specific terms that I use are presented in panel 1. Standards Qualitative research methods involve the systematic collection, organisation, and interpretation of textual material derived from talk or observation. It is used in the exploration of meanings of social phenomena as experienced by individuals themselves, in their natural context. Qualitative research is still regarded with scepticism by the medical community, accused of its subjective nature and the absence of facts. Although the adequacy of guidelines has been vigorously debated within this cross-disciplinary field, 6,7 scientific standards, criteria, and checklists do exist. 3,8–11 However, as Chapple and Rogers 12 point out, medical researchers often encounter difficulties when they try to apply guidelines designed by social scientists, which deal with issues important in their own discipline, but which are not necessarily generically valid as scientific standards. Hamberg and colleagues, 13 for example, claim that the established criteria for scientific rigour in quantitative research cannot be applied to qualitative studies. Referring to Lincoln and Guba, 2 they suggest alternative criteria: credibility, dependability, confirmability, and trans-ferability. They admit that these criteria correspond with traditional ones in some ways, comparing credibility with internal validity, confirmability with objectivity, and transferability with generalisability. Mays and Pope, 7 however, maintain that qualitative research can be assessed with reference to the same broad criteria as quantitative research, albeit used in a different way. Referring to Hammersley, 14 they suggest that validity and relevance are essential. Neither of these criteria are straightforward to assess though, and each requires judgments to be made. To improve validity, Mays and Pope …


A Neural Schema Architecture for Autonomous Robots

As autonomous robots become more complex in their behavior, more sophisticated software architectures are required to support the ever more sophisticated robotics software. These software architectures must support complex behaviors involving adaptation and learning, implemented, in particular, by neural networks. We present in this paper a neural based schema [2] software architecture for the development and execution of autonomous robots in both simulated and real worlds. This architecture has been developed in the context of adaptive robotic agents, ecological robots [6], cooperating and competing with each other in adapting to their environment. The architecture is the result of integrating a number of development and execution systems: NSL, a neural simulation language; ASL, an abstract schema language; and MissionLab, a schema-based mission-oriented simulation and robot system. This work contributes to modeling in Brain Theory (BT) and Cogniti ve Psychology, with applications in Distributed Artificial Intell igence (DAI), Autonomous Agents and Robotics. Areas: Robotics, Agent-oriented programming, Neural Nets