title: Assessing the Wearability of Wearable Computers

abstract: In terms of mounting a computer on the body, the computer's weight, size, shape, placement and method of attachment can elicit a number of effects. Inappropriate design may mean that the wearer is unable to perform specific tasks or achieve goals. Excessive stress on the body may result in perceptions of discomfort, which may in turn affect task performance, but ultimately raises issues of health and safety. This paper proposes a methodology for assessing the affects of wearing a computer in terms of physiological energy expenditure, the biomechanical effects due to changes in movement patterns, posture and perceptions of localised pain and discomfort due to musculoskeletal loading, and perceptions of well- being through comfort assessment. From ratings of these effects the paper proposes 5 levels to determine the wearability of a computer.


title: Bi-Modal DRAM Cache: Improving Hit Rate, Hit Latency and Bandwidth

abstract: In this paper, we present Bi-Modal Cache - a flexible stacked DRAM cache organization which simultaneously achieves several objectives: (i) improved cache hit ratio, (ii) moving the tag storage overhead to DRAM, (iii) lower cache hit latency than tags-in-SRAM, and (iv) reduction in off-chip bandwidth wastage. The Bi-Modal Cache addresses the miss rate versus off-chip bandwidth dilemma by organizing the data in a bi-modal fashion - blocks with high spatial locality are organized as large blocks and those with little spatial locality as small blocks. By adaptively selecting the right granularity of storage for individual blocks at run-time, the proposed DRAM cache organization is able to make judicious use of the available DRAM cache capacity as well as reduce the off-chip memory bandwidth consumption. The Bi-Modal Cache improves cache hit latency despite moving the metadata to DRAM by means of a small SRAM based Way Locator. Further by leveraging the tremendous internal bandwidth and capacity that stacked DRAM organizations provide, the Bi-Modal Cache enables efficient concurrent accesses to tags and data to reduce hit time. Through detailed simulations, we demonstrate that the Bi-Modal Cache achieves overall performance improvement (in terms of Average Normalized Turnaround Time (ANTT)) of 10.8%, 13.8% and 14.0% in 4-core, 8-core and 16-core workloads respectively.


title: Opinion control in complex networks

abstract: In many political elections, the electorate appears to be a composite of partisan and independent voters. Given that partisans are not likely to convert to a different party, an important goal for a political party could be to mobilize independent voters toward the party with the help of strong leadership, mass media, partisans, and the effects of peer-to-peer influence. Based on the exact solution of classical voter model dynamics in the presence of perfectly partisan voters (i.e., zealots), we propose a computational method that uses pinning control strategy to maximize the share of a party in a social network of independent voters. The party, corresponding to the controller or zealots, optimizes the nodes to be controlled given the information about the connectivity of independent voters and the set of nodes that the opposing party controls. We show that controlling hubs is generally a good strategy, but the optimized strategy is even better. The superiority of the optimized strategy is particularly eminent when the independent voters are connected as directed (rather than undirected) networks.


title: Electrostatic Separation Methods for Metal Removal from ABS Wastes

abstract: The electrostatic separation techniques have been widely used in the recycling industry. The aim of the present work is to assess the possibility of employing them for the beneficiation of the newest and fastest growing category of wastes: the "end-of-life" information technology equipment. The study was performed on samples extracted from a granular ABS product obtained after disassembling and shredding of IT wastes. The samples, that typically contained less than 0.1% metals in weight, were processed in a laboratory electrostatic separator that enabled the reproduction of the two most important electrode configurations employed in industry. The design of experiments methodology was employed for evaluating the effects of the various electrical and mechanical control variables on the outcome of the separation process. The reported data demonstrate that by appropriate adjustment of the input variables of either a plate-type electrostatic separator or a roll- type corona-electrostatic installation, it is possible to remove all the metallic particles from the ABS product, while keeping at a minimum the quantity of plastics lost in the rejected conductive fraction.


title: Two-Sided Markets and Electronic

abstract: The object of this paper is to discuss on-line intermediation from the perspective of two-sided markets. It builds a simple model of the intermediation activity when trading partners are involved in a commercial relationship and uses it to illustrate some of the results that emerge in the two-sided market literature, as well as to discuss some new aspects. The first part concentrates on a monopoly intermediation service and discusses both efficient pricing and monopoly pricing. The second part discusses the nature of competition between intermediaries, addressing issues as competitive crosssubsidies, multi-homing or tying.


title: A Layered Trust Information Security Architecture

abstract: Information can be considered the most important asset of any modern organization. Securing this information involves preserving confidentially, integrity and availability, the well-known CIA triad. In addition, information security is a risk management job; the task is to manage the inherent risks of information disclosure. Current information security platforms do not deal with the different facets of information technology. This paper presents a layered trust information security architecture (TISA) and its creation was motivated by the need to consider information and security from different points of view in order to protect it. This paper also extends and discusses security information extensions as a way of helping the CIA triad. Furthermore, this paper suggests information representation and treatment elements, operations and support components that can be integrated to show the various risk sources when dealing with both information and security. An overview of how information is represented and treated nowadays in the technological environment is shown, and the reason why it is so difficult to guarantee security in all aspects of the information pathway is discussed.


title: Using Logistic Regression to Trace Multiple Sub-skills in a Dynamic Bayes Net

abstract: A challenge in estimating students’ changing knowledge from sequential observations of their performance arises when each observed step involves multiple subskills. To overcome this mismatch in grain size between modelled skills and observed actions, we use logistic regression over each step’s subskills in a dynamic Bayes net (LR-DBN) to model transition probabilities for the overall knowledge required by the step. Unlike previous methods, LR-DBN can trace knowledge of the individual subskills without assuming they are independent. We evaluate how well it fits children’s oral reading fluency data logged by Project LISTEN’s Reading Tutor, compared to other methods.


title: Adapting Semantic Segmentation Models for Changes in Illumination and Camera Perspective

abstract: Semantic segmentation using deep neural networks has been widely explored to generate high-level contextual information for autonomous vehicles. To acquire a complete $\text{180}^\circ$ semantic understanding of the forward surroundings, we propose to stitch semantic images from multiple cameras with varying orientations. However, previously trained semantic segmentation models showed unacceptable performance after significant changes to the camera orientations and the lighting conditions. To avoid time-consuming hand labeling, we explore and evaluate the use of data augmentation techniques, specifically skew and gamma correction, from a practical real-world standpoint to extend the existing model and provide more robust performance. The experimental results presented have shown significant improvements with varying illumination and camera perspective changes. A comparison of the results from a high-performance network (PSPNet), and a real-time capable network (ENet) is provided.


title: Algorithms in HElib

abstract: HElib is a software library that implements homomorphic encryption (HE), specifically the Brakerski-Gentry-Vaikuntanathan (BGV) scheme, focusing on effective use of the Smart-Vercauteren ciphertext packing techniques and the Gentry-Halevi-Smart optimizations. The underlying cryptosystem serves as the equivalent of a “hardware platform” for HElib, in that it defines a set of operations that can be applied homomorphically, and specifies their cost. This “platform” is a SIMD environment (somewhat similar to Intel SSE and the like), but with unique cost metrics and parameters. In this report we describe some of the algorithms and optimization techniques that are used in HElib for data movement, linear algebra, and other operations over this “platform.”


title: Cross-Modality Binary Code Learning via Fusion Similarity Hashing

abstract: Binary code learning has been emerging topic in large-scale cross-modality retrieval recently. It aims to map features from multiple modalities into a common Hamming space, where the cross-modality similarity can be approximated efficiently via Hamming distance. To this end, most existing works learn binary codes directly from data instances in multiple modalities, which preserve both intra-and inter-modal similarities respectively. Few methods consider to preserve the fusion similarity among multi-modal instances instead, which can explicitly capture their heterogeneous correlation in cross-modality retrieval. In this paper, we propose a hashing scheme, termed Fusion Similarity Hashing (FSH), which explicitly embeds the graph-based fusion similarity across modalities into a common Hamming space. Inspired by the fusion by diffusion, our core idea is to construct an undirected asymmetric graph to model the fusion similarity among different modalities, upon which a graph hashing scheme with alternating optimization is introduced to learn binary codes that embeds such fusion similarity. Quantitative evaluations on three widely used benchmarks, i.e., UCI Handwritten Digit, MIR-Flickr25K and NUS-WIDE, demonstrate that the proposed FSH approach can achieve superior performance over the state-of-the-art methods.


title: Finding Overlapping Communities Using Disjoint Community Detection Algorithms

abstract: Many algorithms have been designed to discover community structure in networks. Most of these detect disjoint communities, while a few can find communities that overlap. We propose a new, two-phase, method of detecting overlapping communities. In the first phase, a network is transformed to a new one by splitting vertices, using the idea of split betweenness; in the second phase, the transformed network is processed by a disjoint community detection algorithm. This approach has the potential to convert any disjoint community detection algorithm into an overlapping community detection algorithm. Our experiments, using several “disjoint” algorithms, demonstrate that the method works, producing solutions, and execution times, that are often better than those produced by specialized “overlapping” algorithms.


title: Multi-Hop WBAN Construction for Healthcare IoT Systems

abstract: It is expected that the Internet of Things (IoT) applications for medical services can be one of the most remarkable solution for taking care of aging population which is in the rapid growth. IoT consists of communications and sensors to accomplish purpose. In the diverse kinds of networks, wireless body area network (WBAN) is a highly suitable communication tool for the medical IoT devices. There are many researches about WBAN and sensor network, which are mainly focused on energy efficiency. However, in this paper, we discuss more practical issues for implementation of WBAN to healthcare service. Therefore, we propose a multi-hop WBAN construction scheme that is consists of 4 operations, the clustered topology setup, mobility support, and transmission efficiency enhancement. As an auxiliary benefit, the proposed scheme achieves an energy efficient feature by reducing the number of total control messages. Extensive simulation shows that the proposed scheme remarkably improves the performance of WBAN.


title: A fuzzy logic momentum analysis system for financial brokerage

abstract: The modelling of financial systems continues to hold great interest for not only researchers but also investors and policymakers. Many of the characteristics of these systems, however, cannot be adequately captured by traditional financial modelling approaches. Financial systems are complex, nonlinear, dynamically changing systems in which it is often difficult to identify interdependent variables and their values. Financial brokerage is concerned with executing orders of buying and selling of certain amounts of shares at the best possible price. Many mathematical and algorithmic systems have been developed for this task, however they cannot seem to overcome a standard volume based system. This paper proposes a new framework for high frequency trading using an intelligent fuzzy logic based momentum analysis system. The system was applied to brokerage of financial stocks, and tested against the standard volume based brokerage system. The Fuzzy Logic Momentum Analysis System has proven to outperform the traditional and standard systems that are used in the industry.


title: Learning to Share Latent Tasks for Action Recognition

abstract: Sharing knowledge for multiple related machine learning tasks is an effective strategy to improve the generalization performance. In this paper, we investigate knowledge sharing across categories for action recognition in videos. The motivation is that many action categories are related, where common motion pattern are shared among them (e.g. diving and high jump share the jump motion). We propose a new multi-task learning method to learn latent tasks shared across categories, and reconstruct a classifier for each category from these latent tasks. Compared to previous methods, our approach has two advantages: (1) The learned latent tasks correspond to basic motion patterns instead of full actions, thus enhancing discrimination power of the classifiers. (2) Categories are selected to share information with a sparsity regularizer, avoiding falsely forcing all categories to share knowledge. Experimental results on multiple public data sets show that the proposed approach can effectively transfer knowledge between different action categories to improve the performance of conventional single task learning methods.


title: Integrated 200–240-GHz FMCW Radar Transceiver Module

abstract: We present a 220-GHz homodyne transceiver module intended for frequency modulated continuous wave radar applications. The RF transceiver circuits are fabricated on 3-μm-thick GaAs membranes, and consist of a Schottky diode based transmitter frequency doubler that simultaneously operates as a sub-harmonic down-converting mixer. Two circuits are used in a balanced configuration to improve the noise performance. The output power is > 3 dBm over a 40-GHz bandwidth (BW) centered at 220 GHz, and the receiver function is characterized by a typical mixer conversion loss of 16 dB. We present radar images at 4-m target distance with up to 60-dB dynamic range using a 30-μs chirp time, and near-BW-limited range resolution. The module is intended for applications in high-resolution real-time 3-D radar imaging, and the unit is therefore designed so that it can be assembled into 1-D or 2-D arrays.


title: Design and performance analysis of 5.8 GHz microstrip bandpass filter for WiMAX

abstract: Microwave filters since a long time have been an integral part of the radar and the telecommunication systems. The design of better and more accurate filters is the major requirement for the various possible communication techniques like the LTE (Long Term Evaluation) system and WiMAX systems. This paper aims to design microstrip hairpin and combline bandpass filters for frequency of 5.8 GHz which is designated for WiMAX systems. For microstrip implementation, substrate Rogers TMM10 is used for both designs. The filters have been designed on the ADS software. The final conclusions have been drawn by analysing various performance parameters such as return loss, insertion loss, 3-dB bandwidth etc.


title: Tourists' digital footprint in cities: comparing big data sources

abstract: Abstract There is little knowledge available on the spatial behaviour of urban tourists, and yet tourists generate an enormous quantity of data when they visit cities. These data sources can be used to track their presence through their activities. The aim of this paper is to analyse the digital footprint of urban tourists through Big Data. Unlike other papers that use a single data source, this article examines three sources of data to reflect different tourism activities in cities: Panoramio (sightseeing), Foursquare (consumption), and Twitter (being connected-accommodation). The results show that the data from the three activities are partly spatially redundant and partly complementary, and allow the characterisation of multifunction tourist spaces and spaces specialising in one or various activities. The main conclusion is that it is not sufficient to use one data source to analyse the presence of tourists in cities; several must be used in a complementary manner.


title: Exploiting Depth from Single Monocular Images for Object Detection and Semantic Segmentation

abstract: Augmenting RGB data with measured depth has been shown to improve the performance of a range of tasks in computer vision, including object detection and semantic segmentation. Although depth sensors such as the Microsoft Kinect have facilitated easy acquisition of such depth information, the vast majority of images used in vision tasks do not contain depth information. In this paper, we show that augmenting RGB images with estimated depth can also improve the accuracy of both object detection and semantic segmentation. Specifically, we first exploit the recent success of depth estimation from monocular images and learn a deep depth estimation model. Then, we learn deep depth features from the estimated depth and combine with RGB features for object detection and semantic segmentation. In addition, we propose an RGB-D semantic segmentation method, which applies a multi-task training scheme: semantic label prediction and depth value regression. We test our methods on several data sets and demonstrate that incorporating information from estimated depth improves the performance of object detection and semantic segmentation remarkably.


title: Effective detection of sophisticated online banking fraud on extremely imbalanced data

abstract: Sophisticated online banking fraud reflects the integrative abuse of resources in social, cyber and physical worlds. Its detection is a typical use case of the broad-based Wisdom Web of Things (W2T) methodology. However, there is very limited information available to distinguish dynamic fraud from genuine customer behavior in such an extremely sparse and imbalanced data environment, which makes the instant and effective detection become more and more important and challenging. In this paper, we propose an effective online banking fraud detection framework that synthesizes relevant resources and incorporates several advanced data mining techniques. By building a contrast vector for each transaction based on its customer's historical behavior sequence, we profile the differentiating rate of each current transaction against the customer's behavior preference. A novel algorithm, ContrastMiner, is introduced to efficiently mine contrast patterns and distinguish fraudulent from genuine behavior, followed by an effective pattern selection and risk scoring that combines predictions from different models. Results from experiments on large-scale real online banking data demonstrate that our system can achieve substantially higher accuracy and lower alert volume than the latest benchmarking fraud detection system incorporating domain knowledge and traditional fraud detection methods.


title: LCARS: A Spatial Item Recommender System

abstract: Newly emerging location-based and event-based social network services provide us with a new platform to understand users' preferences based on their activity history. A user can only visit a limited number of venues/events and most of them are within a limited distance range, so the user-item matrix is very sparse, which creates a big challenge to the traditional collaborative filtering-based recommender systems. The problem becomes even more challenging when people travel to a new city where they have no activity information. In this article, we propose LCARS, a location-content-aware recommender system that offers a particular user a set of venues (e.g., restaurants and shopping malls) or events (e.g., concerts and exhibitions) by giving consideration to both personal interest and local preference. This recommender system can facilitate people's travel not only near the area in which they live, but also in a city that is new to them. Specifically, LCARS consists of two components: offline modeling and online recommendation. The offline modeling part, called LCA-LDA, is designed to learn the interest of each individual user and the local preference of each individual city by capturing item cooccurrence patterns and exploiting item contents. The online recommendation part takes a querying user along with a querying city as input, and automatically combines the learned interest of the querying user and the local preference of the querying city to produce the top-k recommendations. To speed up the online process, a scalable query processing technique is developed by extending both the Threshold Algorithm (TA) and TA-approximation algorithm. We evaluate the performance of our recommender system on two real datasets, that is, DoubanEvent and Foursquare, and one large-scale synthetic dataset. The results show the superiority of LCARS in recommending spatial items for users, especially when traveling to new cities, in terms of both effectiveness and efficiency. Besides, the experimental analysis results also demonstrate the excellent interpretability of LCARS.


title: Vehicle distance detection based on monocular vision

abstract: Keeping vehicle safety distance through early warning for road vehicles is very important and it could greatly reduce the occurrence of road traffic accidents. This article introduce a vehicle distance detection method based on monocular vision. Nowadays, due to the progress of deep neural network, object detection technology has reached a higher level, and its detection accuracy (recognition rate, location and object size) has been greatly enhanced. In this paper, the advanced object detection technology is applied to highway vehicle distance detection. Combined with the traditional visual detection research results, we propose a monocular vision ranging method based on convolutional neural network. This method has high accuracy In a certain distance range, and it is significant for the vehicle safety warning.


title: Printed Ridge Gap Waveguide 3-dB Coupler: Analysis and Design Procedure

abstract: Communication systems are witnessing an outstanding revolution that has a clear impact on all aspects of life. The world technology is drifting towards high frequency and data rate solutions to accommodate the future expansion in applications such as 5G communications. The 5G technology will offer advanced features in the mm-Wave frequency band which requires intelligent subsystems such as beam switching. Therefore, the microwave components, especially couplers, still need a significant improvement to follow the rapid variations in future technologies. One of the most recent and promising guiding technologies for mm-Wave applications is the printed ridge gap waveguide (PRGW). In this paper, a design of 3-dB planar quadrature hybrid coupler based on PRGW is presented. The proposed design has superior characteristics such as compactness, low loss, and low dispersion device. The prototype of the proposed coupler is fabricated and tested, where the measured and simulated results show an excellent agreement.


title: Durability of Three Different Types of Hyaluronic Acid Fillers in Skin: Are There Differences Among Biphasic, Monophasic Monodensified, and Monophasic Polydensified Products?

abstract: Background ::: Hyaluronic acid fillers are used for facial rejuvenation and are classified as non-cross-linked or cross-linked (monophasic mono- or polydensified). ::: ::: ::: Objectives ::: To histologically assess the intradermal durability of three types of fillers (biphasic, monophasic monodensified, and monophasic polydensified), to compare the durability of the products over 6 months, and to evaluate the structural changes after application. ::: ::: ::: Methods ::: In all, 25 volunteers received injections of three different fillers in the dermis of the right lumbar region (in one line), and equal amounts of the fillers were injected into three different sites (in the same column), yielding nine points of application in each patient. Each line was biopsied on days 2, 92, and 184; these skin samples were analyzed histologically, and the presence or absence of these fillers was verified by a dermatopathologist. ::: ::: ::: Results ::: The histological analysis showed that over 182 days, the amount of the injected monophasic polydensified, monophasic monodensified, and biphasic filler products decreased by 62.5%, 25%, and 12.5%, respectively. ::: ::: ::: Conclusions ::: The biphasic and monophasic monodensified fillers presented greater intradermal durability than did the monophasic polydensified filler at 6 months after intradermal injection. ::: ::: ::: Level of Evidence 2


title: Modern Intrusion Detection Systems

abstract: Intrusion detection systems (IDS) help detect unauthorized activities or intrusions that may compromise the confidentiality, integrity or availability of a resource. This paper presents a general overview of IDSs, the way they are classified and the different algorithms used to detect anomalous activities. It also describes the various approaches and the importance of IDSs in information security.


title: A Classification-Based Perspective on GAN Distributions

abstract: A fundamental, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether GANs are actually able to capture the key characteristics of the datasets they are trained on. The current approaches to examining this issue require significant human supervision, such as visual inspection of sampled images, and often offer only fairly limited scalability. In this paper, we propose new techniques that employ a classification-based perspective to evaluate synthetic GAN distributions and their capability to accurately reflect the essential properties of the training data. These techniques require only minimal human supervision and can easily be scaled and adapted to evaluate a variety of state-of-the-art GANs on large, popular datasets. Our analysis indicates that GANs have significant problems in reproducing the more distributional properties of the training dataset. In particular, when seen through the lens of classification, the diversity of GAN data is orders of magnitude less than that of the original data.


title: Machine Learning as an Adversarial Service: Learning Black-Box Adversarial Examples

abstract: Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a misclassification. Until now, black-box attacks against neural networks have relied on transferability of adversarial examples. White-box attacks are used to generate adversarial examples on a substitute model and then transferred to the black-box target model. In this paper, we introduce a direct attack against black-box neural networks, that uses another attacker neural network to learn to craft adversarial examples. We show that our attack is capable of crafting adversarial examples that are indistinguishable from the source input and are misclassified with overwhelming probability - reducing accuracy of the black-box neural network from 99.4% to 0.77% on the MNIST dataset, and from 91.4% to 6.8% on the CIFAR-10 dataset. Our attack can adapt and reduce the effectiveness of proposed defenses against adversarial examples, requires very little training data, and produces adversarial examples that can transfer to different machine learning models such as Random Forest, SVM, and K-Nearest Neighbor. To demonstrate the practicality of our attack, we launch a live attack against a target black-box model hosted online by Amazon: the crafted adversarial examples reduce its accuracy from 91.8% to 61.3%. Additionally, we show attacks proposed in the literature have unique, identifiable distributions. We use this information to train a classifier that is robust against such attacks.


title: W-net: Bridged U-net for 2D Medical Image Segmentation.

abstract: In this paper, we focus on three problems in deep learning based medical image segmentation. Firstly, U-net, as a popular model for medical image segmentation, is difficult to train when convolutional layers increase even though a deeper network usually has a better generalization ability because of more learnable parameters. Secondly, the exponential ReLU (ELU), as an alternative of ReLU, is not much different from ReLU when the network of interest gets deep. Thirdly, the Dice loss, as one of the pervasive loss functions for medical image segmentation, is not effective when the prediction is close to ground truth and will cause oscillation during training. To address the aforementioned three problems, we propose and validate a deeper network that can fit medical image datasets that are usually small in the sample size. Meanwhile, we propose a new loss function to accelerate the learning process and a combination of different activation functions to improve the network performance. Our experimental results suggest that our network is comparable or superior to state-of-the-art methods.


title: Predicting tie strength with social media

abstract: Social media treats all users the same: trusted friend or total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the theme of tie strength. Our work bridges this gap between theory and practice. In this paper, we present a predictive model that maps social media data to tie strength. The model builds on a dataset of over 2,000 social media ties and performs quite well, distinguishing between strong and weak ties with over 85% accuracy. We complement these quantitative findings with interviews that unpack the relationships we could not predict. The paper concludes by illustrating how modeling tie strength can improve social media design elements, including privacy controls, message routing, friend introductions and information prioritization.


title: The thalamic dynamic core theory of conscious experience

abstract: I propose that primary conscious awareness arises from synchronized activity in dendrites of neurons in dorsal thalamic nuclei, mediated particularly by inhibitory interactions with thalamic reticular neurons. In support, I offer four evidential pillars: (1) consciousness is restricted to the results of cortical computations; (2) thalamus is the common locus of action of brain injury in vegetative state and of general anesthetics; (3) the anatomy and physiology of the thalamus imply a central role in consciousness; (4) neural synchronization is a neural correlate of consciousness.


Model Compression with Generative Adversarial Networks


title: Do all birds tweet the same?: characterizing twitter around the world

abstract: Social media services have spread throughout the world in just a few years. They have become not only a new source of information, but also new mechanisms for societies world-wide to organize themselves and communicate. Therefore, social media has a very strong impact in many aspects -- at personal level, in business, and in politics, among many others. In spite of its fast adoption, little is known about social media usage in different countries, and whether patterns of behavior remain the same or not. To provide deep understanding of differences between countries can be useful in many ways, e.g.: to improve the design of social media systems (which features work best for which country?), and influence marketing and political campaigns. Moreover, this type of analysis can provide relevant insight into how societies might differ. In this paper we present a summary of a large-scale analysis of Twitter for an extended period of time. We analyze in detail various aspects of social media for the ten countries we identified as most active. We collected one year's worth of data and report differences and similarities in terms of activity, sentiment, use of languages, and network structure. To the best of our knowledge, this is the first on-line social network study of such characteristics.


title: Top Rank Optimization in Linear Time

abstract: Bipartite ranking aims to learn a real-valued ranking function that orders positive instances before negative instances. Recent efforts of bipartite ranking are focused on optimizing ranking accuracy at the top of the ranked list. Most existing approaches are either to optimize task specific metrics or to extend the rank loss by emphasizing more on the error associated with the top ranked instances, leading to a high computational cost that is super-linear in the number of training instances. We propose a highly efficient approach, titled TopPush, for optimizing accuracy at the top that has computational complexity linear in the number of training instances. We present a novel analysis that bounds the generalization error for the top ranked instances for the proposed approach. Empirical study shows that the proposed approach is highly competitive to the state-of-the-art approaches and is 10-100 times faster.


title: Developing insider attack detection model: A grounded approach

abstract: Insider threats and attacks are a known problem. Within an enterprise it is very difficult to detect and identify insider attacks and abuse against Information Systems. A study was conducted by observing a group of IS security analysts who detect and identify insider attacks. Commonalities and generalizations were made based on the study to create an insider attack detection model. This model will allow other IS security analysts the ability to increase detection of insider attacks and reduce false positives.


title: Neuropsychological and neurophysiological benefits from white noise in children with and without ADHD

abstract: BackgroundOptimal stimulation theory and moderate brain arousal (MBA) model hypothesize that extra-task stimulation (e.g. white noise) could improve cognitive functions of children with attention-deficit/hyperactivity disorder (ADHD). We investigate benefits of white noise on attention and inhibition in children with and without ADHD (7–12 years old), both at behavioral and at neurophysiological levels.MethodsThirty children with and without ADHD performed a visual cued Go/Nogo task in two conditions (white noise or no-noise exposure), in which behavioral and P300 (mean amplitudes) data were analyzed. Spontaneous eye-blink rates were also recorded and participants went through neuropsychological assessment. Two separate analyses were conducted with each child separately assigned into two groups (1) ADHD or typically developing children (TDC), and (2) noise beneficiaries or non-beneficiaries according to the observed performance during the experiment. This latest categorization, based on a new index we called “Noise Benefits Index” (NBI), was proposed to determine a neuropsychological profile positively sensitive to noise.ResultsNoise exposure reduced omission rate in children with ADHD, who were no longer different from TDC. Eye-blink rate was higher in children with ADHD but was not modulated by white noise. NBI indicated a significant relationship between ADHD and noise benefit. Strong correlations were observed between noise benefit and neuropsychological weaknesses in vigilance and inhibition. Participants who benefited from noise had an increased Go P300 in the noise condition.ConclusionThe improvement of children with ADHD with white noise supports both optimal stimulation theory and MBA model. However, eye-blink rate results question the dopaminergic hypothesis in the latter. The NBI evidenced a profile positively sensitive to noise, related with ADHD, and associated with weaker cognitive control.


title: Wide-Coverage Semantic Representations From A CCG Parser

abstract: This paper shows how to construct semantic representations from the derivations produced by a wide-coverage CCG parser. Unlike the dependency structures returned by the parser itself, these can be used directly for semantic interpretation. We demonstrate that well-formed semantic representations can be produced for over 97% of the sentences in unseen WSJ text. We believe this is a major step towards widecoverage semantic interpretation, one of the key objectives of the field of NLP.


Leave Your Wallet in Your Phone A Consumer ’ s Choice


title: Attention-based Graph Neural Network for Semi-supervised Learning

abstract: Recently popularized graph neural networks achieve the state-of-the-art accuracy on a number of standard benchmark datasets for graph-based semi-supervised learning, improving significantly over existing approaches. These architectures alternate between a propagation layer that aggregates the hidden states of the local neighborhood and a fully-connected layer. Perhaps surprisingly, we show that a linear model, that removes all the intermediate fully-connected layers, is still able to achieve a performance comparable to the state-of-the-art models. This significantly reduces the number of parameters, which is critical for semi-supervised learning where number of labeled examples are small. This in turn allows a room for designing more innovative propagation layers. Based on this insight, we propose a novel graph neural network that removes all the intermediate fully-connected layers, and replaces the propagation layers with attention mechanisms that respect the structure of the graph. The attention mechanism allows us to learn a dynamic and adaptive local summary of the neighborhood to achieve more accurate predictions. In a number of experiments on benchmark citation networks datasets, we demonstrate that our approach outperforms competing methods. By examining the attention weights among neighbors, we show that our model provides some interesting insights on how neighbors influence each other.


title: Corporate Social Responsibility, Customer Satisfaction, and Market Value

abstract: Abstract Although prior research has addressed the influence of corporate social responsibility (CSR) on perceived customer responses, it is not clear whether CSR affects market value of the firm. This study develops and tests a conceptual framework, which predicts that (1) customer satisfaction partially mediates the relationship between CSR and firm market value (i.e., Tobin's q and stock return), (2) corporate abilities (innovativeness capability and product quality) moderate the financial returns to CSR, and (3) these moderated relationships are mediated by customer satisfaction. Based on a large-scale secondary data set, the results show support for this framework. Notably, the authors find that in firms with low innovativeness capability, CSR actually reduces customer satisfaction levels and, through the lowered satisfaction, harms market value. The uncovered mediated and asymmetrically moderated results offer important implications for marketing theory and practice.


title: Thwarting Memory Disclosure with Efficient Hypervisor-enforced Intra-domain Isolation

abstract: Exploiting memory disclosure vulnerabilities like the HeartBleed bug may cause arbitrary reading of a victim's memory, leading to leakage of critical secrets such as crypto keys, personal identity and financial information. While isolating code that manipulates critical secrets into an isolated execution environment is a promising countermeasure, existing approaches are either too coarse-grained to prevent intra-domain attacks, or require excessive intervention from low-level software (e.g., hypervisor or OS), or both. Further, few of them are applicable to large-scale software with millions of lines of code. This paper describes a new approach, namely SeCage, which retrofits commodity hardware virtualization extensions to support efficient isolation of sensitive code manipulating critical secrets from the remaining code. SeCage is designed to work under a strong adversary model where a victim application or even the OS may be controlled by the adversary, while supporting large-scale software with small deployment cost. SeCage combines static and dynamic analysis to decompose monolithic software into several compart- ments, each of which may contain different secrets and their corresponding code. Following the idea of separating control and data plane, SeCage retrofits the VMFUNC mechanism and nested paging in Intel processors to transparently provide different memory views for different compartments, while allowing low-cost and transparent invocation across domains without hypervisor intervention. We have implemented SeCage in KVM on a commodity Intel machine. To demonstrate the effectiveness of SeCage, we deploy it to the Nginx and OpenSSH server with the OpenSSL library as well as CryptoLoop with small efforts. Security evaluation shows that SeCage can prevent the disclosure of private keys from HeartBleed attacks and memory scanning from rootkits. The evaluation shows that SeCage only incurs small performance and space overhead.


Abstract: An Open Source Tracking Testbed and Evaluation Web Site


title: Computing strongly connected components in a linear number of symbolic steps

abstract: We present an algorithm that computes in a linear number of symbolic steps (O(vVv)) the strongly connected components (sccs) of a graph G = 〈V, E〉 represented by an Ordered Binary Decision Diagram (OBDD). This result matches the complexity of the (celebrated) Tarjan's algorithm operating on explicit data structures. To date, the best algorithm for the above problem works in Θ(vVvlogvVv) symbolic steps ([BGS00]).


title: Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey

abstract: This paper provides a state-of-the-art literature review on economic analysis and pricing models for data collection and wireless communication in Internet of Things (IoT). Wireless Sensor Networks (WSNs) are the main component of IoT which collect data from the environment and transmit the data to the sink nodes. For long service time and low maintenance cost, WSNs require adaptive and robust designs to address many issues, e.g., data collection, topology formation, packet forwarding, resource and power optimization, coverage optimization, efficient task allocation, and security. For these issues, sensors have to make optimal decisions from current capabilities and available strategies to achieve desirable goals. This paper reviews numerous applications of the economic and pricing models, known as intelligent rational decision-making methods, to develop adaptive algorithms and protocols for WSNs. Besides, we survey a variety of pricing strategies in providing incentives for phone users in crowdsensing applications to contribute their sensing data. Furthermore, we consider the use of some pricing models in Machine-to-Machine (M2M) communication. Finally, we highlight some important open research issues as well as future research directions of applying economic and pricing models to IoT.


title: Application of single-cell sequencing in human cancer

abstract: Precision medicine is emerging as a cornerstone of future cancer care with the objective of providing targeted therapies based on the molecular phenotype of each individual patient. Traditional bulk-level molecular phenotyping of tumours leads to significant information loss, as the molecular profile represents an average phenotype over large numbers of cells, while cancer is a disease with inherent intra-tumour heterogeneity at the cellular level caused by several factors, including clonal evolution, tissue hierarchies, rare cells and dynamic cell states. Single-cell sequencing provides means to characterize heterogeneity in a large population of cells and opens up opportunity to determine key molecular properties that influence clinical outcomes, including prognosis and probability of treatment response. Single-cell sequencing methods are now reliable enough to be used in many research laboratories, and we are starting to see applications of these technologies for characterization of human primary cancer cells. In this review, we provide an overview of studies that have applied single-cell sequencing to characterize human cancers at the single-cell level, and we discuss some of the current challenges in the field.


title: Challenges in Data Crowdsourcing

abstract: Crowdsourcing refers to solving large problems by involving human workers that solve component sub-problems or tasks. In data crowdsourcing, the problem involves data acquisition, management, and analysis. In this paper, we provide an overview of data crowdsourcing, giving examples of problems that the authors have tackled, and presenting the key design steps involved in implementing a crowdsourced solution. We also discuss some of the open challenges that remain to be solved.


title: Continuous Top-k Monitoring on Document Streams

abstract: The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. In this work, user preferences are indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top- $k$ documents that are most relevant to her keywords. Our objective is to support large numbers of users and high stream rates, while refreshing the top- $k$ results almost instantaneously. Our solution abandons the traditional frequency-ordered indexing approach. Instead, it follows an identifier-ordering paradigm that suits better the nature of the problem. When complemented with a novel, locally adaptive technique, our method offers (i) proven optimality w.r.t. the number of considered queries per stream event, and (ii) an order of magnitude shorter response time (i.e., time to refresh the query results) than the current state-of-the-art.


title: Under-approximation refinement for classical planning

abstract: A general and important problem of search-based planning techniques is the state explosion problem, which is usually tackled with approaches to reduce the branching factor of the planning task. Such approaches often implicitly exploit the observation that the number of available operators is higher than the number of operators that are actually needed to find a plan. In this paper, we propose a simple, but general under-approximation refinement framework for satisficing planning that explicitly exploits this observation. Our approach iteratively searches for plans with operator subsets, which are refined if necessary by adding operators that appear to be needed. Our evaluation shows that even a straight-forward instantiation of this framework yields a competitive planner that often finds plans with small operator sets.


title: A parallel manipulator for mobile manipulating UAVs

abstract: Manipulating objects using arms mounted to unmanned aerial vehicles (UAVs) is attractive because UAVs may access many locations that are otherwise inaccessible to traditional mobile manipulation platforms such as ground vehicles. Most previous efforts seeking to coordinate the combined manipulator-UAV system have focused on using a manipulator to extend the UAV's reach and assume that both the UAV and manipulator can reliably reach commanded goal poses. This work accepts the reality that state of the art UAV positioning precision is not of a high enough quality to reliably perform simple tasks such as grasping objects. A 6 degree of freedom parallel manipulator is used to robustly maintain precise end-effector positions despite host UAV perturbations. A description of a unique parallel manipulator that allows for very little moving mass, and is easily stowed below a quadrotor UAV is presented along with flight test results and an analytical comparison to a serial manipulator.


Recurrent Neural Networks with Attention for Genre Classification


title: H-store: a high-performance, distributed main memory transaction processing system

abstract: Our previous work has shown that architectural and application shifts have resulted in modern OLTP databases increasingly falling short of optimal performance [10]. In particular, the availability of multiple-cores, the abundance of main memory, the lack of user stalls, and the dominant use of stored procedures are factors that portend a clean-slate redesign of RDBMSs. This previous work showed that such a redesign has the potential to outperform legacy OLTP databases by a significant factor. These results, however, were obtained using a bare-bones prototype that was developed just to demonstrate the potential of such a system. We have since set out to design a more complete execution platform, and to implement some of the ideas presented in the original paper. Our demonstration presented here provides insight on the development of a distributed main memory OLTP database and allows for the further study of the challenges inherent in this operating environment.


Interactive comment on “ Communicating Disaster Risk ? An Evaluation of the Availability and Quality of Flood Maps ” by Daniel Henstra et al


title: ParkNet: drive-by sensing of road-side parking statistics

abstract: Urban street-parking availability statistics are challenging to obtain in real-time but would greatly benefit society by reducing traffic congestion. In this paper we present the design, implementation and evaluation of ParkNet, a mobile system comprising vehicles that collect parking space occupancy information while driving by. Each ParkNet vehicle is equipped with a GPS receiver and a passenger-side-facing ultrasonic range-finder to determine parking spot occupancy. The data is aggregated at a central server, which builds a real-time map of parking availability and could provide this information to clients that query the system in search of parking. Creating a spot-accurate map of parking availability challenges GPS location accuracy limits. To address this need, we have devised an environmental fingerprinting approach to achieve improved location accuracy. Based on 500 miles of road-side parking data collected over 2 months, we found that parking spot counts are 95% accurate and occupancy maps can achieve over 90% accuracy. Finally, we quantify the amount of sensors needed to provide adequate coverage in a city. Using extensive GPS traces from over 500 San Francisco taxicabs, we show that if ParkNet were deployed in city taxicabs, the resulting mobile sensors would provide adequate coverage and be more cost-effective by an estimated factor of roughly 10-15 when compared to a sensor network with a dedicated sensor at every parking space, as is currently being tested in San Francisco.


title: A Robust Approach to Control Robot Manipulators by Fusing Visual and Force Information

abstract: In this paper, a method to combine visual and force information using the information obtained from the movement flow-based visual servoing system is proposed. This method allows not only to achieve a given desired position but also to specify the trajectory that the robot will follow from its initial position to its final one in the 3D space. This paper also extends the visual servoing system in order to increase the robustness when errors in the camera calibration parameters appear. Experiments using an eye-in-hand robotic system demonstrate the correct behaviour when important errors exist in the camera intrinsic parameters. After the description of this strategy, we then describe its application to an insertion task to be performed by the robotic system in which the joint use of visual and force information is required. To combine both sensorial systems, a position-based impedance-control system is implemented, which modifies the trajectory generated by the visual system depending on the robot’s interaction with its setting. This modification is performed without knowledge of the exact camera calibration parameters. Furthermore, the visual-force approach based on impedance control does not require having previous knowledge about the contact geometry.


title: Conversational In-Vehicle Dialog Systems: The past, present, and future

abstract: Automotive technology rapidly advances with increasing connectivity and automation. These advancements aim to assist safe driving and improve user travel experience. Before the realization of a full automation, in-vehicle dialog systems may reduce the driver distraction from many services available through connectivity. Even when a full automation is realized, in-vehicle dialog systems still play a special role in assisting vehicle occupants to perform various tasks. On the other hand, in-vehicle use cases need to address very different user conditions, environments, and industry requirements than other uses. This makes the development of effective and efficient in-vehicle dialog systems challenging; it requires multidisciplinary expertise in automatic speech recognition, spoken language understanding, dialog management (DM), natural language generation, and application management, as well as field system and safety testing. In this article, we review research and development (RaD) activities for in-vehicle dialog systems from both academic and industrial perspectives, examine findings, discuss key challenges, and share our visions for voice-enabled interaction and intelligent assistance for smart vehicles over the next decade.


title: On evaluating stream learning algorithms

abstract: Most streaming decision models evolve continuously over time, run in resource-aware environments, and detect and react to changes in the environment generating data. One important issue, not yet convincingly addressed, is the design of experimental work to evaluate and compare decision models that evolve over time. This paper proposes a general framework for assessing predictive stream learning algorithms. We defend the use of prequential error with forgetting mechanisms to provide reliable error estimators. We prove that, in stationary data and for consistent learning algorithms, the holdout estimator, the prequential error and the prequential error estimated over a sliding window or using fading factors, all converge to the Bayes error. The use of prequential error with forgetting mechanisms reveals to be advantageous in assessing performance and in comparing stream learning algorithms. It is also worthwhile to use the proposed methods for hypothesis testing and for change detection. In a set of experiments in drift scenarios, we evaluate the ability of a standard change detection algorithm to detect change using three prequential error estimators. These experiments point out that the use of forgetting mechanisms (sliding windows or fading factors) are required for fast and efficient change detection. In comparison to sliding windows, fading factors are faster and memoryless, both important requirements for streaming applications. Overall, this paper is a contribution to a discussion on best practice for performance assessment when learning is a continuous process, and the decision models are dynamic and evolve over time.


title: Creating full view panoramic image mosaics and environment maps

abstract: This paper presents a novel approach to creating full view panoramic mosaics from image sequences. Unlike current panoramic stitching methods, which usually require pure horizontal camera panning, our system does not require any controlled motions or constraints on how the images are taken (as long as there is no strong motion parallax). For example, images taken from a hand-held digital camera can be stitched seamlessly into panoramic mosaics. Because we represent our image mosaics using a set of transforms, there are no singularity problems such as those existing at the top and bottom of cylindrical or spherical maps. Our algorithm is fast and robust because it directly recovers 3D rotations instead of general 8 parameter planar perspective transforms. Methods to recover camera focal length are also presented. We also present an algorithm for efficiently extracting environment maps from our image mosaics. By mapping the mosaic onto an artibrary texture-mapped polyhedron surrounding the origin, we can explore the virtual environment using standard 3D graphics viewers and hardware without requiring special-purpose players. CR


title: FLOWGUARD: building robust firewalls for software-defined networks

abstract: Software-Defined Networking (SDN) introduces significant granularity, visibility and flexibility to networking, but at the same time brings forth new security challenges. One of the fundamental challenges is to build robust firewalls for protecting OpenFlow-based networks where network states and traffic are frequently changed. To address this challenge, we introduce FlowGuard, a comprehensive framework, to facilitate not only accurate detection but also effective resolution of firewall policy violations in dynamic OpenFlow-based networks. FlowGuard checks network flow path spaces to detect firewall policy violations when network states are updated. In addition, FlowGuard conducts automatic and real-time violation resolutions with the help of several innovative resolution strategies designed for diverse network update situations. We also implement our framework and demonstrate the efficacy and efficiency of the proposed detection and resolution approaches in FlowGuard through experiments with a real-world network topology.


title: User-level psychological stress detection from social media using deep neural network

abstract: It is of significant importance to detect and manage stress before it turns into severe problems. However, existing stress detection methods usually rely on psychological scales or physiological devices, making the detection complicated and costly. In this paper, we explore to automatically detect individuals' psychological stress via social media. Employing real online micro-blog data, we first investigate the correlations between users' stress and their tweeting content, social engagement and behavior patterns. Then we define two types of stress-related attributes: 1) low-level content attributes from a single tweet, including text, images and social interactions; 2) user-scope statistical attributes through their weekly micro-blog postings, leveraging information of tweeting time, tweeting types and linguistic styles. To combine content attributes with statistical attributes, we further design a convolutional neural network (CNN) with cross autoencoders to generate user-scope content attributes from low-level content attributes. Finally, we propose a deep neural network (DNN) model to incorporate the two types of user-scope attributes to detect users' psychological stress. We test the trained model on four different datasets from major micro-blog platforms including Sina Weibo, Tencent Weibo and Twitter. Experimental results show that the proposed model is effective and efficient on detecting psychological stress from micro-blog data. We believe our model would be useful in developing stress detection tools for mental health agencies and individuals.


title: An Examination of the Determinants of Mobile Shopping Continuance

abstract: While mobile shopping (m-shopping) is convenient in the age of mobile commerce, many people still discontinue their shopping using mobile devices even though they have the experience. Therefore, it is important for the m-shopping providers to understand the determinants of m-shopping continuance. This study extended Expectation-Confirmation Model (ECM) to examine the determinants of m-shopping continuance by incorporating trust. There were 244 effective questionnaires obtained for survey analysis. The results showed that trust can overcome the limitations of ECM on predicating m-shopping continuance and improve the explanatory power of initial ECM.


title: Classification of basic daily movements using a triaxial accelerometer

abstract: A generic framework for the automated classification of human movements using an accelerometry monitoring system is introduced. The framework was structured around a binary decision tree in which movements were divided into classes and subclasses at different hierarchical levels. General distinctions between movements were applied in the top levels, and successively more detailed subclassifications were made in the lower levels of the tree. The structure was modular and flexible: parts of the tree could be reordered, pruned or extended, without the remainder of the tree being affected. This framework was used to develop a classifier to identify basic movements from the signals obtained from a single, waist-mounted triaxial accelerometer. The movements were first divided into activity and rest. The activities were classified as falls, walking, transition between postural orientations, or other movement. The postural orientations during rest were classified as sitting, standing or lying. In controlled laboratory studies in which 26 normal, healthy subjects carried out a set of basic movements, the sensitivity of every classification exceeded 87%, and the specificity exceeded 94%; the overall accuracy of the system, measured as the number of correct classifications across all levels of the hierarchy, was a sensitivity of 97.7% and a specificity of 98.7% over a data set of 1309 movements.


title: Contextualizing BYOD in SMEs in developing countries

abstract: The advancement in technological development is now altering the conventional order in the diffusion of IT innovation from a top-down approach(organization to employees) to a bottom up approach (employees to organization). This change is more notable in the developed economies and has led to the Bring Your Own Device (BYOD) phenomenon. There have been several studies on corporate adoption of BYOD but few have investigated the phenomenon from a small and medium enterprise (SME) perspective and in developing countries specifically. SMEs, like their large organizations counterparts, can significantly benefit from BYOD given that they are usually resource constrained. This study presents a qualitative review of BYOD literature from the perspective of SMEs with the intention of (1) highlighting contextual issues that SMEs in developing countries need to be aware of prior to BYOD adoption; and (2) calling researchers to pay attention to how BYOD as a technology catalyst can address contextual challenges. Following a literature review analysis approach, the study identifies organizational factors as key to the success of BYOD adoption. For BYOD to become part of an organization there needs to be organizational readiness in terms of technology, governance, business resources, awareness and the availability of human resources. Contextual factors of supporting industry, government readiness and market forces were not given much attention but are perceived as important for adoption.


title: Usability Problem Description and the Evaluator Effect in Usability Testing

abstract: Previous usability evaluation method (UEM) comparison studies have noted an evaluator effect on problem detection in heuristic evaluation, with evaluators differing in problems found and problem severity judgments. There have been few studies of the evaluator effect in usability testing (UT), task-based testing with end-users. UEM comparison studies focus on counting usability problems detected, but we also need to assess the content of usability problem descriptions (UPDs) to more fully measure evaluation effectiveness. The goals of this research were to develop UPD guidelines, explore the evaluator effect in UT, and evaluate the usefulness of the guidelines for grading UPD content. ::: Ten guidelines for writing UPDs were developed by consulting usability practitioners through two questionnaires and a card sort. These guidelines are (briefly): be clear and avoid jargon, describe problem severity, provide backing data, describe problem causes, describe user actions, provide a solution, consider politics and diplomacy, be professional and scientific, describe your methodology, and help the reader sympathize with the user. A fourth study compared usability reports collected from 44 evaluators, both practitioners and graduate students, watching the same 10-minute UT session recording. Three judges measured problem detection for each evaluator and graded the reports for following 6 of the UPD guidelines. ::: There was support for existence of an evaluator effect, even when watching prerecorded sessions, with low to moderate individual thoroughness of problem detection across all/severe problems (22%/34%), reliability of problem detection (37%/50%) and reliability of severity judgments (57% for severe ratings). Practitioners received higher grades averaged across the 6 guidelines than students did, suggesting that the guidelines may be useful for grading reports. The grades for the guidelines were not correlated with thoroughness, suggesting that the guideline grades complement measures of problem detection. ::: A simulation of evaluators working in groups found a 34% increase in severe problems found by adding a second evaluator. The simulation also found that thoroughness of individual evaluators would have been overestimated if the study had included a small number of evaluators. The final recommendations are to use multiple evaluators in UT, and to assess both problem detection and description when measuring evaluation effectiveness.


title: A low-power, wireless, 8-channel EEG monitoring headset

abstract: Micro- and nano-technology has enabled development of smaller and smarter wearable devices for medical and lifestyle related applications. In particular, recent advances in EEG monitoring technologies pave the way for wearable, wireless EEG monitoring devices. Here, a low-power wireless EEG sensor platform that measures 8-channels of EEG, is described. The platform is integrated into a wearable headset for ambulatory monitoring of EEG. While using standard EEG electrodes without conductive gel, a first evaluation shows the wireless headset is comparable to the reference system when looking at alpha wave discrimination. This device combines low-noise, and low-power functionality into an easy-to-use wireless headset, providing a first step towards a fully integrated, fully functional wearable wireless EEG monitoring system.


title: Improving Safety with Information Technology

abstract: Information technology can improve patient safety by preventing errors and facilitating rapid response to adverse events. Computerized prescribing by physicians reduces the rate of medication-related errors. Systems that automatically page clinicians about serious laboratory abnormalities and remote monitoring of patients in intensive care units also appear promising.


title: Discriminant Saliency for Visual Recognition from Cluttered Scenes

abstract: Saliency mechanisms play an important role when visual recognition must be performed in cluttered scenes. We propose a computational definition of saliency that deviates from existing models by equating saliency to discrimination. In particular, the salient attributes of a given visual class are defined as the features that enable best discrimination between that class and all other classes of recognition interest. It is shown that this definition leads to saliency algorithms of low complexity, that are scalable to large recognition problems, and is compatible with existing models of early biological vision. Experimental results demonstrating success in the context of challenging recognition problems are also presented.


title: Comparing Bluetooth HDP and SPP for Mobile Health Devices

abstract: The Bluetooth SIG recently released the Health Device Profile (HDP) in an effort to standardize health device communication using Bluetooth technology. HDP uses the IEEE 11073-20601 Data Exchange Protocol as the transport content. The same traffic can be sent using the popular Serial Port Profile (SPP), but this profile is not configured for health device communication. HDP’s strict configurations and health device-specific requirements give the impression that it would have a reduced performance in comparison to SPP. In this paper we compare HDP with SPP by analyzing multi-rate data transmission in the context of a cardiovascular monitoring application. In particular, we model the expected number of transmissions and packet loss incurred by the two profiles. Our results show that in contrast to popular belief the transmission energy is similar for both profiles, but HDP offers the advantage of plug-and-play interoperability.


title: Learning to Rank with Query-level Semi-supervised Autoencoders

abstract: Learning to rank utilizes machine learning methods to solve ranking problems by constructing ranking models in a supervised way, which needs fixed-length feature vectors of documents as inputs, and outputs the ranking models learned by iteratively reducing the pre-defined ranking loss. The document features are always extracted based on classic textual statistics, and different features contribute differently to ranking performance. Given that well-defined features would contribute more to the retrieval performance, we investigate the usage of autoencoders to enrich the feature representations of documents. Autoencoders, as basic building blocks of deep neural networks, have been successfully used in many text mining tasks for generating effective features. To enrich the feature space for learning to rank, we introduce supervision into the loss functions of autoencoders. Specifically, we first train a linear ranking model on the training data, and then incorporate the learned weights into the reconstruction costs of an autoencoder. Meanwhile, we accumulate the costs of documents for a given query with query-level constraints for producing more useful features. We evaluate the effectiveness of our model on three LETOR datasets, and show that our model can generate effective document features to improve the retrieval performance.


title: Market reaction to a bid-ask spread change: A power-law relaxation dynamics

abstract: We study the relaxation dynamics of the bid-ask spread and of the midprice after a sudden variation of the spread in a double auction financial market. We find that the spread decays as a power law to its normal value. We measure the price reversion dynamics and the permanent impact, i.e., the long-time effect on price, of a generic event altering the spread and we find an approximately linear relation between immediate and permanent impact. We hypothesize that the power-law decay of the spread is a consequence of the strategic limit order placement of liquidity providers. We support this hypothesis by investigating several quantities, such as order placement rates and distribution of prices and times of submitted orders, which affect the decay of the spread.


title: Systems-of-Systems Analysis of Ballistic Missile Defense Architecture Effectiveness through Surrogate Modeling and Simulation

abstract: Traditionally, the analysis of Ballistic Missile Defense System (BMDS) effectiveness has been limited in fidelity due to the inherent complexity of the subject. Indeed, the BMDS battle management process involves monitoring and controlling the actions of many interacting participants (e.g. radar sensors, communications networks and interceptor missiles) in a process whereby a target moves from launch through sensor detection through intercept kill assessment. Because the actions of each participant may evolve independently, the battle management process functions as a true system-of-systems (SoS). Proper SoS analysis requires architecture level engineering, dealing with component functional allocation and inter-component interaction rather than the internal workings of individual participants. Although prior work has been identified that addresses BMD effectiveness at the SoS level, each method sacrifices analysis fidelity of both process elements and individual participants to enable timely decision making. This paper proposes a modeling and simulation (M&S) framework that supports architecture level analysis of the BMDS. The key innovation is the application of neural network surrogate models, which are representations of other high- or medium-fidelity M&S tools, and can be executed rapidly with negligible loss in fidelity. Surrogate models were created of a BMDS analysis tool that included multisensor target tracking and fusion codes. Results will show the benefit of integrating M&S to architecture level analysis. Specific examples include sensitivity of operational level metrics to formation of an integration tracking picture, and the enabling architecture level decision making.


title: Leg stiffness and stride frequency in human running.

abstract: When humans and other mammals run, the body's complex system of muscle, tendon and ligament springs behaves like a single linear spring ('leg spring'). A simple spring-mass model, consisting of a single linear leg spring and a mass equivalent to the animal's mass, has been shown to describe the mechanics of running remarkably well. Force platform measurements from running animals, including humans, have shown that the stiffness of the leg spring remains nearly the same at all speeds and that the spring-mass system is adjusted for higher speeds by increasing the angle swept by the leg spring. The goal of the present study is to determine the relative importance of changes to the leg spring stiffness and the angle swept by the leg spring when humans alter their stride frequency at a given running speed. Human subjects ran on treadmill-mounted force platform at 2.5ms-1 while using a range of stride frequencies from 26% below to 36% above the preferred stride frequency. Force platform measurements revealed that the stiffness of the leg spring increased by 2.3-fold from 7.0 to 16.3 kNm-1 between the lowest and highest stride frequencies. The angle swept by the leg spring decreased at higher stride frequencies, partially offsetting the effect of the increased leg spring stiffness on the mechanical behavior of the spring-mass system. We conclude that the most important adjustment to the body's spring system to accommodate higher stride frequencies is that leg spring becomes stiffer.


Exploiting User Demographic Attributes for Solving Cold-Start Problem in Recommender System


title: Acceleration capability in elite sprinters and ground impulse: Push more, brake less?

abstract: Overground sprint studies have shown the importance of net horizontal ground reaction force impulse (IMPH) for acceleration performance, but only investigated one or two steps over the acceleration phase, and not in elite sprinters. The main aim of this study was to distinguish between propulsive (IMPH+) and braking (IMPH-) components of the IMPH and seek whether, for an expected higher IMPH, faster elite sprinters produce greater IMPH+, smaller IMPH-, or both. Nine high-level sprinters (100-m best times range: 9.95-10.60s) performed 7 sprints (2×10 m, 2×15 m, 20 m, 30 m and 40 m) during which ground reaction force was measured by a 6.60 m force platform system. By placing the starting-blocks further from the force plates at each trial, and pooling the data, we could assess the mechanics of an entire "virtual" 40-m acceleration. IMPH and IMPH+ were significantly correlated with 40-m mean speed (r=0.868 and 0.802, respectively; P<0.01), whereas vertical impulse and IMPH- were not. Multiple regression analyses confirmed the significantly higher importance of IMPH+ for sprint acceleration performance. Similar results were obtained when considering these mechanical data averaged over the first half of the sprint, but not over the second half. In conclusion, faster sprinters were those who produced the highest amounts of horizontal net impulse per unit body mass, and those who "pushed more" (higher IMPH+), but not necessarily those who also "braked less" (lower IMPH-) in the horizontal direction.


title: Sugars and dental caries.

abstract: A dynamic relation exists between sugars and oral health. Diet affects the integrity of the teeth; quantity, pH, and composition of the saliva; and plaque pH. Sugars and other fermentable carbohydrates, after being hydrolyzed by salivary amylase, provide substrate for the actions of oral bacteria, which in turn lower plaque and salivary pH. The resultant action is the beginning of tooth demineralization. Consumed sugars are naturally occurring or are added. Many factors in addition to sugars affect the caries process, including the form of food or fluid, the duration of exposure, nutrient composition, sequence of eating, salivary flow, presence of buffers, and oral hygiene. Studies have confirmed the direct relation between intake of dietary sugars and dental caries across the life span. Since the introduction of fluoride, the incidence of caries worldwide has decreased, despite increases in sugars consumption. Other dietary factors (eg, the presence of buffers in dairy products; the use of sugarless chewing gum, particularly gum containing xylitol; and the consumption of sugars as part of meals rather than between meals) may reduce the risk of caries. The primary public health measures for reducing caries risk, from a nutrition perspective, are the consumption of a balanced diet and adherence to dietary guidelines and the dietary reference intakes; from a dental perspective, the primary public health measures are the use of topical fluorides and consumption of fluoridated water.


title: Sketch Recognizers from the End-User's, the Designer's, and the Programmer's Perspective

abstract: Although the accuracy and robustness of sketch recognizers have been steadily improving, there are still many issues involved in using them in applications. This paper details our experiences with using sketch recognizers from three different perspectives—end-users, user interface designers, and programmers—and describes some of the open research problems in each of these areas.


Emotion Regulation Ability 1 Running head : EMOTION REGULATION The Ability to Regulate Emotion is Associated with Greater Well-Being , Income , and Socioeconomic Status


title: A ROS multi-ontology references services: OWL reasoners and application prototyping issues

abstract: The challenge of sharing and communicating information is crucial in complex human-robot interaction (HRI) scenarios. Ontologies and symbolic reasoning are the state-of-the-art approaches for a natural representation of knowledge, especially within the Semantic Web domain. In such a context, scripted paradigms have been adopted to achieve high expressiveness. Nevertheless, since symbolic reasoning is a high complexity problem, optimizing its performance requires a careful design of the knowledge. Specifically, a robot architecture requires the integration of several components implementing different behaviors and generating a series of beliefs. Most of the components are expected to access, manipulate, and reason upon a run-time generated semantic representation of knowledge grounding robot behaviors and perceptions through formal axioms, with soft real-time requirements.


title: Agile Practitioners’ Understanding of Security Requirements: Insights from a Grounded Theory Analysis

abstract: A 2017 systematic review on engineering non-functional requirements in agile projects revealed a number of published proposals for approaching security requirements in agile settings. While these proposals acknowledge the urgent need for methods to systematically engineer security requirements in agile projects, they were designed mostly in academic settings. Very little empirical evaluation of these proposals happened in real-life contexts. In turn, little is known about how agile developers understand security requirements and how they devise their coping strategies regarding these requirements. This position paper presents a qualitative analysis that sought to discover how agile practitioners reason about security requirements, what contextual factors they consider important for shaping the pro-cess of coping with security requirements in agile projects, and what these strategies are. We conclude with some implications for practice and research.


title: Type Classification of Semi-Structured Documents

abstract: Semi-structured documents (e.g. journal art,icles, electronic mail, television programs, mail order catalogs, . ..) a.re often not explicitly typed; the only available t,ype information is the implicit structure. An explicit t,ype, however, is needed in order to a.pply objectoriented technology, like type-specific methods. In this paper, we present a.n experimental vector space cla.ssifier for determining the type of semi-structured documents. Our goal was to design a. high-performa.nce classifier in t,erms of accuracy (recall and precision), speed, and extensibility.


title: An FPGA implementation and comparison of the SHA-256 and Blake-256

abstract: Since the beginning of study of the Secure Hash function (SHA), it has been thoroughly studied by designers with the goal of reducing the area, frequency, throughput and power consumption of the hardware implementation of this cryptosystem. The Secure Hash function algorithm has become the default choice for security services in numerous applications. Following the attacks considerable standard SHA-2, a new version of hash was developed known as SHA3. In this paper, we discussed the study of the SHA-3 hash exposing the protocol chosen for our Blake-256 application. We study the hash function family SHA-256 and Blake-256. Moreover, we conduct a comparative study between the two hash family. The SHA-256 and Blake-256 have been implemented on Xilinx Virtex-5 Virtex-6 Virtex-7 FPGA. Their area, frequency, throughput, efficiency have been compared and it is shown that the blake-256 achieves good performance in terms of area, throughput and efficiency.


title: Analysis of Evolutionary Algorithms on the One-Dimensional Spin Glass with Power-Law Interactions

abstract: This paper provides an in-depth empirical analysis of several hybrid evolutionary algorithms on the one-dimensional spin glass model with power-law interactions. The considered spin glass model provides a mechanism for tuning the effective range of interactions, what makes the problem interesting as an algorithm benchmark. As algorithms, the paper considers the genetic algorithm (GA) with twopoint and uniform crossover, and the hierarchical Bayesian optimization algorithm (hBOA). hBOA is shown to outperform both variants of GA, whereas GA with uniform crossover is shown to perform worst. The differences between the compared algorithms become more significant as the problem size grows and as the range of interactions decreases. Unlike for GA with uniform crossover, for hBOA and GA with twopoint crossover, instances with short-range interactions are shown to be easier. The paper also points out interesting avenues for future research.


title: Understanding Satirical Articles Using Common-Sense

abstract: Automatic satire detection is a subtle text classification task, for machines and at times, even for humans. In this paper we argue that satire detection should be approached using common-sense inferences, rather than traditional word classification methods. We present a highly structured latent variable model capturing the required inferences. The model abstracts over the specific entities appearing in the articles, grouping them into generalized categories thus allowing the model to adapt to previously unseen situations.


title: Understanding crowdfunding work: implications for support tools

abstract: Crowdfunding is changing the way people realize their work by providing a new way to gain support from a distributed audience. This study seeks to understand the work of crowdfunding project creators in order to inform the design of crowdfunding support tools and systems. We conducted interviews with 30 project creators from three popular crowdfunding platforms in order to understand what tasks are involved and what tools people use to accomplish crowdfunding work. Initial results suggest that project creators carry out three main types of work-preparing the campaign material, marketing the project, and following through with project goals-and have adapted general support tools to facilitate doing this work. From our initial findings, we hope to improve and design future crowdfunding support tools and systems.


title: Incentivizing Blockchain Forks via Whale Transactions

abstract: Bitcoin’s core innovation is its solution to double-spending, called Nakamoto consensus. This provides a probabilistic guarantee that transactions will not be reversed or redirected, presuming that it is improbable for an attacker to obtain a majority of mining power in the network. However, this guarantee can be undermined when miners are assumed to be rational, and hence venal. Accordingly, we present the whale attack, in which a minority attacker increases her chances of double-spending by incentivizing miners to subvert the consensus protocol and to collude via whale transactions, which are bribery transactions carrying anomalously large fees. We analyze the expected cost to carry out the attack with success probability 1, and simulate the attack under realistic system parameters. Our results show that double-spend attacks, conventionally thought to be impractical for minority attackers, can actually be financially feasible and worthwhile under the whale attack. Perhaps more importantly, this work demonstrates that rationality should not underestimated when evaluating the security of cryptocurrencies.


title: Unlocking the learning value of wireless mobile devices

abstract: S.R.I. International Abstract Many researchers see the potential of wireless mobile learning devices to achieve large-scale impact on learning because of portability, low cost, and communications features. This enthusiasm is shared but the lessons drawn from three well-documented uses of connected handheld devices in education lead towards challenges ahead. First, 'wireless, mobile learning' is an imprecise description of what it takes to connect learners and their devices together in a productive manner. Research needs to arrive at a more precise understanding of the attributes of wireless networking that meet acclaimed pedagogical requirements and desires. Second, 'pedagogical applications' are often led down the wrong road by complex views of technology and simplistic views of social practices. Further research is needed that tells the story of rich pedagogical practice arising out of simple wireless and mobile technologies. Third, 'large scale' impact depends on the extent to which a common platform, that meets the requirements of pedagogically rich applications, becomes available. At the moment 'wireless mobile technologies for education' are incredibly diverse and incompatible; to achieve scale, a strong vision will be needed to lead to standardisation, overcoming the tendency to marketplace fragmentation.


Analysis and Review of Encryption and Decryption for Secure Communication


title: Ovariohysterectomy versus ovariectomy for elective sterilization of female dogs and cats: is removal of the uterus necessary?

abstract: both pro-mote elective sterilization of female dogs and cats as in-tegral to reducing euthanasia of unwanted dogs and cats.Sterilization of female dogs and cats can be ac-complished by removing both the ovaries and uterus (ovariohysterectomy) or by removing the ovaries alone (ovariectomy). Ovariohysterectomy has historically been recommended in the United States and Canada and is currently emphasized at schools and colleges of vet-erinary medicine in these countries. To our knowledge, only one of the most frequently used surgical textbooks in the United States and Canada describes ovariectomy,


FOR A GENERAL STRAIN THEORY OF CRIME AND


title: Neurophysiological differences in the adolescent brain following a single night of restricted sleep – A 7T fMRI study

abstract: Abstract Sleep deprivation in youth has garnered international attention in recent years, as correlational studies have demonstrated significant relationships between lack of sleep and detrimental behavioral and academic outcomes. However, no study to date has systematically examined the neurophysiological consequences of a single night of sleep restriction (i.e., 4 h) in adolescents using ultra-high field functional neuroimaging. Much of what we know regarding the neural consequences of sleep deprivation has come from the adult literature, and among those studies, the majority use region of interest (ROI) approaches, thus disregarding the dynamic mechanisms that may subserve the behavioral effects of sleep restriction. Leveraging a crossover within-subjects design, we demonstrate that pivotal brain regions involved in the default mode and limbic regulatory centers have disrupted functioning following a night of restricted sleep compared to a night of “normal sleep”. Specifically, a normal night (i.e., 8 h) of sleep led to increased global and local efficiency of bilateral amygdala, and less efficiency in the posterior cingulate, as measured by graph theory, compared to a night of sleep restriction. Furthermore, aberrant functional connectivity patterns were identified in key fronto-limbic circuitry, suggesting a potential pathophysiological mechanism underlying the widespread effects of sleep deprivation in youth.


title: Depth-Preserving Warping for Stereo Image Retargeting

abstract: The popularity of stereo images and various display devices poses the need of stereo image retargeting techniques. Existing warping-based retargeting methods can well preserve the shape of salient objects in a retargeted stereo image pair. Nevertheless, these methods often incur depth distortion, since they attempt to preserve depth by maintaining the disparity of a set of sparse correspondences, rather than directly controlling the warping. In this paper, by considering how to directly control the warping functions, we propose a warping-based stereo image retargeting approach that can simultaneously preserve the shape of salient objects and the depth of 3D scenes. We first characterize the depth distortion in terms of warping functions to investigate the impact of a warping function on depth distortion. Based on the depth distortion model, we then exploit binocular visual characteristics of stereo images to derive region-based depth-preserving constraints which directly control the warping functions so as to faithfully preserve the depth of 3D scenes. Third, with the region-based depth-preserving constraints, we present a novel warping-based stereo image retargeting framework. Since the depth-preserving constraints are derived regardless of shape preservation, we relax the depth-preserving constraints to fulfill a tradeoff between shape preservation and depth preservation. Finally, we propose a quad-based implementation of the proposed framework. The results demonstrate the efficacy of our method in both depth and shape preservation for stereo image retargeting.


title: Electric Vehicle Battery-Ultracapacitor Energy System Optimization

abstract: In order to improve the performances of the electric vehicle power supply, a Battery/Ultracapacitors Hybrid Energy Storage System (HESS) has been proposed. We have examined the HESS parameters for an EV configuration propelled by two in-wheel connected directly to the vehicle frontal wheels and a single EM coupled to a differential transmission system that drives the rear wheels. This paper presents a new energy management strategy for Battery/Ultracapacitor HESS to both parameters: minimize the sizing of the HESS and maximize the EV drive range. To reach this purpose, it is adopted a genetic algorithm (GA) technique. Finally, the Pareto frontier including optimal HESS parameter groups has been obtained, which indicates that it is possible to reduce the HESS mass without harming the EV drive range.


title: Dolphin: Ultrasonic-Based Gesture Recognition on Smartphone Platform

abstract: User experience of smart mobile devices can be improved in numerous scenarios with the assist of in-air gesture recognition. Most existing methods proposed by industry and academia are based on special sensors. On the contrary, a special sensor-independent in-air gesture recognition method named Dolphin is proposed in this paper which can be applied to off-the-shelf smart devices directly. The only sensors Dolphin needs are the loudspeaker and microphone embedded in the device. Dolphin emits a continuous 21 KHz tone by the loudspeaker and receive the gesture-reflecting ultrasonic wave by the microphone. The gesture performed is encoded into the reflected ultrasonic in the form of Doppler shift. By combining manual recognition and machine leaning methods, Dolphin extracts features from Doppler shift and recognizes a rich set of pre-defined gestures with high accuracy in real time. Parameter selection strategy and gesture recognition under several scenarios are discussed and evaluated in detail. Dolphin can be adapted to multiple devices and users by training using machine learning methods.


title: Efficient Inorganic–Organic Hybrid Perovskite Solar Cells Based on Pyrene Arylamine Derivatives as Hole-Transporting Materials

abstract: A set of three N,N-di-p-methoxyphenylamine-substituted pyrene derivatives have successfully been synthesized and characterized by (1)H/(13)C NMR spectroscopy, mass spectrometry, and elemental analysis. The optical and electronic structures of the pyrene derivatives were adjusted by controlling the ratio of N,N-di-p-methoxyphenylamine to pyrene, and investigated by UV/vis spectroscopy and cyclic voltammetry. The pyrene derivatives were employed as hole-transporting materials (HTMs) in fabricating mesoporous TiO2/CH3NH3PbI3/HTMs/Au solar cells. The pyrene-based derivative Py-C exhibited a short-circuit current density of 20.2 mA/cm(2), an open-circuit voltage (Voc) of 0.886 V, and a fill factor of 69.4% under an illumination of 1 sun (100 mW/cm(2)), resulting in an overall power conversion efficiency of 12.4%. The performance is comparable to that of the well-studied spiro-OMeTAD, even though the Voc is slightly lower. Thus, this newly synthesized pyrene derivative holds promise as a HTM for highly efficient perovskite-based solar cells.


title: End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables

abstract: We present a convolutional-recurrent neural network architecture with long short-term memory for real-time processing and classification of digital sensor data. The network implicitly performs typical signal processing tasks such as filtering and peak detection, and learns time-resolved embeddings of the input signal. We use a prototype multi-sensor wearable device to collect over 180h of photoplethysmography (PPG) data sampled at 20Hz, of which 36h are during atrial fibrillation (AFib). We use end-to-end learning to achieve state-of-the-art results in detecting AFib from raw PPG data. For classification labels output every 0.8s, we demonstrate an area under ROC curve of 0.9999, with false positive and false negative rates both below $2\times 10^{-3}$. This constitutes a significant improvement on previous results utilising domain-specific feature engineering, such as heart rate extraction, and brings large-scale atrial fibrillation screenings within imminent reach.


title: AC-DC Multi-port Domestic Distribution Interface

abstract: The rapid development of PV systems and energy storage techniques have created opportunities for further evolvements on the utility side. On the other hand, the number of conversion stages required to feed inherent and converter controlled dc loads from the ac grid have compromised the efficient operation of the power grid. The proposed multi-port distribution interface combines dc and ac power resources and dc and ac loads with minimum number of power electronic conversion stages thus increases the energy efficiency. This paper presents the development of the converters and the controller functions of the proposed interface which is to be implemented in a domestic scale. Along with the individual simulations the paper includes several case studies where different behaviors of the overall system are discussed.