title: Dysregulation of antiviral helicase pathways in systemic lupus erythematosus

abstract: In the autoimmune disease systemic lupus erythematosus (SLE), our normal antiviral defenses are inappropriately activated, resulting in over-activity of the type I interferon (IFN) pathway. This increased activity of the type I IFN pathway is an important primary pathogenic factor in the disease. Emerging evidence has implicated the antiviral helicases in this process. The antiviral helicases normally function as nucleic acid receptors in viral immunity. Genetic variations in antiviral helicase genes have been associated with SLE, supporting the idea that helicase pathways are involved in the primary pathogenesis of SLE. Studies have documented functional consequences of these genetic variations within the type I IFN pathway in human cell lines and SLE patients. In this review, we summarize the function of helicases in the anti-viral immune response, and how this response is dysregulated in SLE patients. In particular, we will focus on known functional genetic polymorphisms in the IFIH1 (MDA5) and mitochondrial antiviral signaling protein genes which have been implicated in human SLE. These data provide fascinating evidence for dysregulation of helicase-mediated innate immunity in SLE, and may support novel therapeutic strategies in the disease.


title: A FAST ITERATIVE METHOD FOR SOLVING THE EIKONAL EQUATION ON TRIANGULATED SURFACES ∗

abstract: This paper presents an efficient, fine-grained parallel algorithm for solving the Eikonal equation on triangular meshes. The Eikonal equation, and the broader class of Hamilton-Jacobi equations to which it belongs, have a wide range of applications from geometric optics and seismology to biological modeling and analysis of geometry and images. The ability to solve such equations accurately and efficiently provides new capabilities for exploring and visualizing parameter spaces and for solving inverse problems that rely on such equations in the forward model. Efficient solvers on state-of-the-art, parallel architectures require new algorithms that are not, in many cases, optimal, but are better suited to synchronous updates of the solution. In previous work [W. K. Jeong and R. T. Whitaker, SIAM J. Sci. Comput., 30 (2008), pp. 2512-2534], the authors proposed the fast iterative method (FIM) to efficiently solve the Eikonal equation on regular grids. In this paper we extend the fast iterative method to solve Eikonal equations efficiently on triangulated domains on the CPU and on parallel architectures, including graphics processors. We propose a new local update scheme that provides solutions of first-order accuracy for both architectures. We also propose a novel triangle-based update scheme and its corresponding data structure for efficient irregular data mapping to parallel single-instruction multiple-data (SIMD) processors. We provide detailed descriptions of the implementations on a single CPU, a multicore CPU with shared memory, and SIMD architectures with comparative results against state-of-the-art Eikonal solvers.


title: Reasoning about causal relationships: : Inferences on causal networks

abstract: Over the last decade, a normative framework for making causal inferences, Bayesian Probabilistic Causal Networks, has come to dominate psychological studies of inference based on causal relationships. The following causal networks—[XiYiZ, X¢YiZ, XiY¢Z]—supply answers for questions like, “Suppose bothXandYoccur, what is the probabilityZoccurs?” or “Suppose you intervene and makeYoccur, what is the probabilityZoccurs?” In this review, we provide a tutorial for how normatively to calculate these inferences. Then, we systematically detail the results of behavioral studies comparing human qualitative and quantitative judgments to the normative calculations for many network structures and for several types of inferences on those networks. Overall, when the normative calculations imply that an inference should increase, judgments usually go up; when calculations imply a decrease, judgments usually go down. However, 2 systematic deviations appear. First, people’s inferences violate the Markov assumption. For example, when inferringZfrom the structureXiYiZ, people think thatXis relevant even whenYcompletely mediates the relationship betweenXandZ. Second, even when people’s inferences are directionally consistent with the normative calculations, they are often not as sensitive to the parameters and the structure of the network as they should be. We conclude with a discussion of productive directions for future research.


title: Mobile Device Usage Characteristics: The Effect of Context and Form Factor on Locked and Unlocked Usage

abstract: Smartphones and tablets are an indispensable part of modern communication and people spend considerable time interacting with their devices every day. While substantial research has been conducted concerning smartphone usage, little is known about how tablets are used. This paper studies mobile device usage characteristics like session length, interaction frequency, and daily usage in locked and unlocked state with respect to location context. Based on logs from 1,585 Android devices (470 years of total usage time), we derive and analyze 23 million usage sessions. We found that devices remain locked for 60% of the interactions and usage at home occurs twice as frequent as at work. With an average of 58 interactions per day, smartphones are used twice as often as tablets, while tablet sessions are 2.5 times longer, resulting in almost equal aggregated daily usage. We conclude that usage session characteristics differ considerably between tablets and smartphones.


title: The force-velocity relationship of the ATP-dependent actin-myosin sliding causing cytoplasmic streaming in algal cells, studied using a centrifuge microscope.

abstract: When uncoated polystyrene beads suspended in Mg-ATP solution were introduced into the internodal cell of an alga Chara corallina, the beads moved along the actin cables with directions and velocities (30-62 microns s-1) similar to those of native cytoplasmic streaming. Bead movement was inhibited both in the absence of ATP and in the presence of CA2+, as with native cytoplasmic streaming. These results indicate that bead movement is caused by cytoplasmic myosin molecules attached to the head surface interacting with actin cables. The steady-state force-velocity relationship of the actin-myosin sliding that produces cytoplasmic streaming was determined by applying constant centrifugal forces to the beads moving on the actin cables. The force-velocity curve in the positive load region was nearly straight, and the implications of this shape are discussed in connection with the kinetic properties of the actin-myosin interaction in cytoplasmic streaming. It is suggested that the time for which a cytoplasmic myosin head is detached from actin in one cycle of actin-myosin interaction is very short. The Ca(2+)-induced actin-myosin linkages, responsible for the Ca(2+)-induced stoppage of cytoplasmic streaming, were shown to be much stronger than the rigor actin-myosin linkages.


title: Big Data Use Case Domains for Telecom Operators

abstract: Telecommunications, Telco in short, companies have a unique advantage compared to other industries is that they control the communication infrastructure which is used frequently by their customers to avail different kind of services. This way they have more data than other industry which enables them to track where their customers are, how they interact, and how they transact business. But leveraging that customer information requires Telco to transform a 19th century business model into one that meets today's demand for real-time business and consumer insight. Telcos have been moving multiple terabytes of data around their networks for years. However, data with properties - volume, variety, and velocity which is collectively termed as Big Data, exposes new opportunities in different ways to get insight from that and at the same time posses challenges to be capable of handling and analyzing this data. This work focuses on finding possible use cases, according to the comments of Telco industry and academic experts, how this multi properties data, Big Data, is currently being utilized and also, could be utilized on Telco industries. Twelve Big Data use case domains for Telco operators including examples, typical required data types and challenges for each such domain are presented.


title: Long-term Tracking in the Wild: A Benchmark

abstract: We introduce the OxUvA dataset and benchmark for evaluating single-object tracking algorithms. Benchmarks have enabled great strides in the field of object tracking by defining standardized evaluations on large sets of diverse videos. However, these works have focused exclusively on sequences that are just tens of seconds in length and in which the target is always visible. Consequently, most researchers have designed methods tailored to this “short-term” scenario, which is poorly representative of practitioners’ needs. Aiming to address this disparity, we compile a long-term, large-scale tracking dataset of sequences with average length greater than two minutes and with frequent target object disappearance. The OxUvA dataset is much larger than the object tracking datasets of recent years: it comprises 366 sequences spanning 14 h of video. We assess the performance of several algorithms, considering both the ability to locate the target and to determine whether it is present or absent. Our goal is to offer the community a large and diverse benchmark to enable the design and evaluation of tracking methods ready to be used “in the wild”. The project website is oxuva.net.


title: Helpfulness of Online Product Reviews as Seen by Consumers: Source and Content Features

abstract: Online product reviews are important determinants of consumers' purchase decision. Although prior research has articulated various benefits of online product reviews, there are few investigations into whether or not they are perceived as helpful by consumers. Product review helpfulness is conceptualized as a second-order formative construct, which is manifested by perceived source credibility, perceived content diagnosticity, and perceived vicarious expression of the product review. In this study, we conduct a laboratory experiment to investigate product review helpfulness as well as its corresponding antecedents from the product review feature perspective (i.e., source- and content-based review features). Findings from the study are threefold. First, the results of the data analysis support the theoretical conceptualization of product review helpfulness as a formative construct. Second, the results support the notion that the source- and content-based review features have direct impact on product review ...


title: Modeling and Analyzing Real-Time Data Streams

abstract: Achieving situation awareness is especially challenging for real-time data stream applications because they i) operate on continuous unbounded streams of data, and ii) have inherent real-time requirements. In this paper we show how formal data stream modeling and analysis can be used to better understand stream behavior, evaluate query costs, and improve application performance. We use MEDAL, a formal specification language based on Petri nets, to model the data stream queries and the Quality-of-Service (QoS) management mechanisms in a data stream system. MEDAL's ability to combine query logic and data admission control in one model allows us to design a single comprehensive model of the system. This model can be used to perform a large set of analyses to help improve the application's performance and QoS.


title: Automatic ad format selection via contextual bandits

abstract: Visual design plays an important role in online display advertising: changing the layout of an online ad can increase or decrease its effectiveness, measured in terms of click-through rate (CTR) or total revenue. The decision of which lay- out to use for an ad involves a trade-off: using a layout provides feedback about its effectiveness (exploration), but collecting that feedback requires sacrificing the immediate reward of using a layout we already know is effective (exploitation). To balance exploration with exploitation, we pose automatic layout selection as a contextual bandit problem. There are many bandit algorithms, each generating a policy which must be evaluated. It is impractical to test each policy on live traffic. However, we have found that offline replay (a.k.a. exploration scavenging) can be adapted to provide an accurate estimator for the performance of ad layout policies at Linkedin, using only historical data about the effectiveness of layouts. We describe the development of our offline replayer, and benchmark a number of common bandit algorithms.


title: A Taxi Order Dispatch Model based On Combinatorial Optimization

abstract: Taxi-booking apps have been very popular all over the world as they provide convenience such as fast response time to the users. The key component of a taxi-booking app is the dispatch system which aims to provide optimal matches between drivers and riders. Traditional dispatch systems sequentially dispatch taxis to riders and aim to maximize the driver acceptance rate for each individual order. However, the traditional systems may lead to a low global success rate, which degrades the rider experience when using the app. In this paper, we propose a novel system that attempts to optimally dispatch taxis to serve multiple bookings. The proposed system aims to maximize the global success rate, thus it optimizes the overall travel efficiency, leading to enhanced user experience. To further enhance users' experience, we also propose a method to predict destinations of a user once the taxi-booking APP is started. The proposed method employs the Bayesian framework to model the distribution of a user's destination based on his/her travel histories. We use rigorous A/B tests to compare our new taxi dispatch method with state-of-the-art models using data collected in Beijing. Experimental results show that the proposed method is significantly better than other state-of-the art models in terms of global success rate (increased from 80% to 84%). Moreover, we have also achieved significant improvement on other metrics such as user's waiting-time and pick-up distance. For our destination prediction algorithm, we show that our proposed model is superior to the baseline model by improving the top-3 accuracy from 89% to 93%. The proposed taxi dispatch and destination prediction algorithms are both deployed in our online systems and serve tens of millions of users everyday.


title: The concept of allostasis in biology and biomedicine

abstract: Living organisms have regular patterns and routines that involve obtaining food and carrying out life history stages such as breeding, migrating, molting, and hibernating. The acquisition, utilization, and storage of energy reserves (and other resources) are critical to lifetime reproductive success. There are also responses to predictable changes, e.g., seasonal, and unpredictable challenges, i.e., storms and natural disasters. Social organization in many populations provides advantages through cooperation in providing basic necessities and beneficial social support. But there are disadvantages owing to conflict in social hierarchies and competition for resources. Here we discuss the concept of allostasis, maintaining stability through change, as a fundamental process through which organisms actively adjust to both predictable and unpredictable events. Allostatic load refers to the cumulative cost to the body of allostasis, with allostatic overload being a state in which serious pathophysiology can occur. Using the balance between energy input and expenditure as the basis for applying the concept of allostasis, we propose two types of allostatic overload. Type 1 allostatic overload occurs when energy demand exceeds supply, resulting in activation of the emergency life history stage. This serves to direct the animal away from normal life history stages into a survival mode that decreases allostatic load and regains positive energy balance. The normal life cycle can be resumed when the perturbation passes. Type 2 allostatic overload begins when there is sufficient or even excess energy consumption accompanied by social conflict and other types of social dysfunction. The latter is the case in human society and certain situations affecting animals in captivity. In all cases, secretion of glucocorticosteroids and activity of other mediators of allostasis such as the autonomic nervous system, CNS neurotransmitters, and inflammatory cytokines wax and wane with allostatic load. If allostatic load is chronically high, then pathologies develop. Type 2 allostatic overload does not trigger an escape response, and can only be counteracted through learning and changes in the social structure.


title: A time-based approach to effective recommender systems using implicit feedback

abstract: Recommender systems provide personalized recommendations on products or services to customers. Collaborative filtering is a widely used method of providing recommendations using explicit ratings on items from users. In some e-commerce environments, however, it is difficult to collect explicit feedback data; only implicit feedback is available. In this paper, we present a method of building an effective collaborative filtering-based recommender system for an e-commerce environment without explicit feedback data. Our method constructs pseudo rating data from the implicit feedback data. When building the pseudo rating matrix, we incorporate temporal information such as the user's purchase time and the item's launch time in order to increase recommendation accuracy. Based on this method, we built both user-based and item-based collaborative filtering-based recommender systems for character images (wallpaper) in a mobile e-commerce environment and conducted a variety of experiments. Empirical results show our time-incorporated recommender system is significantly more accurate than a pure collaborative filtering system.


title: VIF Regression: A Fast Regression Algorithm for Large Data

abstract: We propose a fast regression algorithm that can substantially reduce the computational complexity of searching, yet retain good accuracy. It also guarantees to discover correlated features that are collectively predictive, and avoid model over-fitting. Its capability of controlling mFDR (marginal False Discovery Rate) statistically enables the one-pass search of the fast algorithm and guarantees the accuracy of the sparse model chosen by the algorithm without cross validation. Numerical results show that our algorithm is much faster than any other algorithm and is competitively as accurate as the best but slower algorithms.


title: Data Integration: After the Teenage Years

abstract: The field of data integration has expanded significantly over the years, from providing a uniform query and update interface to structured databases within an enterprise to the ability to search, ex- change, and even update, structured or unstructured data that are within or external to the enterprise. This paper describes the evolution in the landscape of data integration since the work on rewriting queries using views in the mid-1990's. In addition, we describe two important challenges for the field going forward. The first challenge is to develop good open-source tools for different components of data integration pipelines. The second challenge is to provide practitioners with viable solutions for the long-standing problem of systematically combining structured and unstructured data.


title: Visual perception of biological motion and a model for its analysis

abstract: This paper reports the first phase of a research program on visual perception of motion patterns characteristic of living organisms in locomotion. Such motion patterns in animals and men are termed here as biological motion. They are characterized by a far higher degree of complexity than the patterns of simple mechanical motions usually studied in our laboratories. In everyday perceptions, the visual information from biological motion and from the corresponding figurative contour patterns (the shape of the body) are intermingled. A method for studying information from the motion pattern per se without interference with the form aspect was devised. In short, the motion of the living body was represented by a few bright spots describing the motions of the main joints. It is found that 10–12 such elements in adequate motion combinations in proximal stimulus evoke a compelling impression of human walking, running, dancing, etc. The kinetic-geometric model for visual vector analysis originally developed in the study of perception of motion combinations of the mechanical type was applied to these biological motion patterns. The validity of this model in the present context was experimentally tested and the results turned out to be highly positive.


title: Spatio-temporal video autoencoder with differentiable memory

abstract: We describe a new spatio-temporal video autoencoder, based on a classic spatial image autoencoder and a novel nested temporal autoencoder. The temporal encoder is represented by a differentiable visual memory composed of convolutional long short-term memory (LSTM) cells that integrate changes over time. Here we target motion changes and use as temporal decoder a robust optical flow prediction module together with an image sampler serving as built-in feedback loop. The architecture is end-to-end differentiable. At each time step, the system receives as input a video frame, predicts the optical flow based on the current observation and the LSTM memory state as a dense transformation map, and applies it to the current frame to generate the next frame. By minimising the reconstruction error between the predicted next frame and the corresponding ground truth next frame, we train the whole system to extract features useful for motion estimation without any supervision effort. We present one direct application of the proposed framework in weakly-supervised semantic segmentation of videos through label propagation using optical flow.


title: Entity Typing Using Distributional Semantics and DBpedia

abstract: Recognising entities in a text and linking them to an external resource is a vital step in creating a structured resource (e.g. a knowledge base) from text. This allows semantic querying over a dataset, for example selecting all politicians or football players. However, traditional named entity recognition systems only distinguish a limited number of entity types (such as Person, Organisation and Location) and entity linking has the limitation that often not all entities found in a text can be linked to a knowledge base. This creates a gap in coverage between what is in the text and what can be annotated with fine grained types.


title: Comparing Automatic Evaluation Measures for Image Description

abstract: Image description is a new natural language generation task, where the aim is to generate a human-like description of an image. The evaluation of computer-generated text is a notoriously difficult problem, however, the quality of image descriptions has typically been measured using unigram BLEU and human judgements. The focus of this paper is to determine the correlation of automatic measures with human judgements for this task. We estimate the correlation of unigram and Smoothed BLEU, TER, ROUGE-SU4, and Meteor against human judgements on two data sets. The main finding is that unigram BLEU has a weak correlation, and Meteor has the strongest correlation with human judgements.


title: CRIMETRACER: Activity space based crime location prediction

abstract: Crime reduction and prevention strategies are vital for policymakers and law enforcement to face inevitable increases in urban crime rates as a side effect of the projected growth of urban population by the year 2030. Studies conclude that crime does not occur uniformly across urban landscapes but concentrates in certain areas. This phenomenon has drawn attention to spatial crime analysis, primarily focusing on crime hotspots, areas with disproportionally higher crime density. In this paper we present C rime T racer , a personalized random walk based approach to spatial crime analysis and crime location prediction outside of hotspots. We propose a probabilistic model of spatial behavior of known offenders within their activity space. Crime Pattern Theory concludes that offenders, rather than venture into unknown territory, frequently commit opportunistic crimes and serial violent crimes by taking advantage of opportunities they encounter in places they are most familiar with as part of their activity space. Our experiments on a large real-world crime dataset show that C rime T racer outperforms all other methods used for location recommendation we evaluate here.


title: Hemispherical Confocal Imaging using Turtleback Reflector

abstract: We propose a new imaging method called hemispherical confocal imaging to clearly visualize a particular depth in a 3-D scene. The key optical component is a turtleback reflector which is a specially designed polyhedral mirror. By combining the turtleback reflector with a coaxial pair of a camera and a projector, many virtual cameras and projectors are produced on a hemisphere with uniform density to synthesize a hemispherical aperture. In such an optical device, high frequency illumination can be focused at a particular depth in the scene to visualize only the depth with descattering. Then, the observed views are factorized into masking, attenuation, and texture terms to enhance visualization when obstacles are present. Experiments using a prototype system show that only the particular depth is effectively illuminated and hazes by scattering and attenuation can be recovered even when obstacles exist.


title: The situational factors that affect the software development process: Towards a comprehensive reference framework

abstract: Context: An optimal software development process is regarded as being dependent on the situational characteristics of individual software development settings. Such characteristics include the nature of the application(s) under development, team size, requirements volatility and personnel experience. However, no comprehensive reference framework of the situational factors affecting the software development process is presently available. Objective: The absence of such a comprehensive reference framework of the situational factors affecting the software development process is problematic not just because it inhibits our ability to optimise the software development process, but perhaps more importantly, because it potentially undermines our capacity to ascertain the key constraints and characteristics of a software development setting. Method: To address this deficiency, we have consolidated a substantial body of related research into an initial reference framework of the situational factors affecting the software development process. To support the data consolidation, we have applied rigorous data coding techniques from Grounded Theory and we believe that the resulting framework represents an important contribution to the software engineering field of knowledge. Results: The resulting reference framework of situational factors consists of eight classifications and 44 factors that inform the software process. We believe that the situational factor reference framework presented herein represents a sound initial reference framework for the key situational elements affecting the software process definition. Conclusion: In addition to providing a useful reference listing for the research community and for committees engaged in the development of standards, the reference framework also provides support for practitioners who are challenged with defining and maintaining software development processes. Furthermore, this framework can be used to develop a profile of the situational characteristics of a software development setting, which in turn provides a sound foundation for software development process definition and optimisation.


title: Rethinking user interfaces for feature location

abstract: Locating features in large software systems is a fundamental maintenance task for developers when fixing bugs and extending software. We introduce In Situ Impact Insight (I3), a novel user interface to support feature location. In addition to a list of search results, I3 provides support for developers during browsing and inspecting the retrieved code entities. In situ visualizations augment results and source code with additional information relevant for further exploration. Developers are able to retrieve details on the textual similarity of a source code entity to the search query and to other entities, as well as the information on co-changed entities from a project's history. Execution traces recorded during program runs can be used as filters to further refine the search results. We implemented I3 as an Eclipse plug-in and tested it in a user study involving 18 students and professional developers that were asked to perform three feature location tasks chosen from the issue tracking system of jEdit. The results of our study suggest that I3's user interface is intuitive and unobtrusively supports developers with the required information when and where they need it.


title: Assessing the vulnerability of supply chains using graph theory

abstract: In today's business environment, harsher and more frequent natural and man-made disasters make supply chains more vulnerable. Supply chain disruptions now seem to occur more frequently and with more serious consequences. During and after supply chain disruptions, companies may lose revenue and incur high recovery costs. If supply chain managers were more capable of measuring and managing supply chain vulnerability, they could reduce the number of disruptions and their impact. In this research we developed an approach based on graph theory to quantify and hence mitigate supply chain vulnerability. Quantification of supply chain vulnerability aids managers in assessing the vulnerability of their supply chains (e.g., across and between supply chains, or over time) and in comparing the effectiveness of different risk mitigation strategies.


title: Intrinsic Motivation Inventory: Psychometric Properties in the Context of First Language and Mathematics Learning

abstract: Intrinsic Motivation Inventory (IMI) is a multidimensional measurement grounded on the Self-Determination Theory (SDT) used in assessing the subjective experiences of participants when developing an activity. The aim of this study is to analyze the characteristics of IMI among Portuguese students, testing four organizational models (unidimensional, multidimensional, hierarchical and bi-factor). A total of 3685 students from the 5th to the 12th grades (50.4% boys) participated in the study (M = 13.67, SD = 2.26). Two versions of IMI were used (First Language and Mathematics) with twenty-one items distributed over five subscales: Enjoyment, Perceived Competence, Pressure/Tension, Perceived Choice and Value/Utility. The confirmatory factor analysis corroborated the multidimensionality of intrinsic motivation, and that the bi-factor model presented the best fit indexes. This model showed the existence of one general factor, resulting from the contribution of all individual dimensions and the particularities of most of them. Furthermore, results also highlighted satisfactory reliability scores both through Cronbach's alpha scores and Composite reliability scores. These results indicate that this scale is appropriate to evaluate the underlying constructs of the theoretical model of SDT and allows for the calculation of a global measure of intrinsic motivation, as well as specific measures for their predictors.


DASS: Digital Advertising System Simulation


title: Activity Detection with Latent Sub-event Hierarchy Learning

abstract: In this paper, we introduce a new convolutional layer named the Temporal Gaussian Mixture (TGM) layer and present how it can be used to efficiently capture temporal structure in continuous activity videos. Our layer is designed to allow the model to learn a latent hierarchy of sub-event intervals. Our approach is fully differentiable while relying on a significantly less number of parameters, enabling its end-to-end training with standard backpropagation. We present our convolutional video models with multiple TGM layers for activity detection. Our experiments on multiple datasets including Charades and MultiTHUMOS confirm the benefit of our TGM layers, illustrating that it outperforms other models and temporal convolutions.


title: Point-wise Convolutional Neural Network

abstract: Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently. However, the capability of using point clouds with convolutional neural network has been so far not fully explored. In this technical report, we present a convolutional neural network for semantic segmentation and object recognition with 3D point clouds. At the core of our network is point-wise convolution, a convolution operator that can be applied at each point of a point cloud. Our fully convolutional network design, while being simple to implement, can yield competitive accuracy in both semantic segmentation and object recognition task.


title: A PRAM and NAND flash hybrid architecture for high-performance embedded storage subsystems

abstract: NAND flash-based storage is widely used in embedded systems due to its numerous benefits: low cost, high density, small form factor and so on. However, NAND flash-based storage is still suffering from serious performance degradation for random or small size write access. This degradation mainly comes from the physical constraints of NAND flash: erase-before-program and different unit size of erase and program operations. To overcome these constraints, we propose to use PRAM (Phase-change RAM) which supports advanced features: fast byte access capability and no requirement for erase-before-program. In this paper, we focus on developing a high-performance NAND flash-based storage system by maximally exploiting the advanced feature of PRAM, in terms of performance and wearing out. To do this, we first propose a new hybrid storage architecture which consists of PRAM and NAND flash. Second, we devise two novel software schemes for the proposed hybrid storage architecture; FSMS (File System Metadata Separation) and hFTL (hybrid Flash Translation Layer). Finally, we demonstrate that our hybrid architecture increases the performance up to 290% and doubles the lifespan compared to the existing NAND flash only storage systems.


title: Civic crowdfunding research: Challenges, opportunities, and future agenda

abstract: Civic crowdfunding is a sub-type of crowdfunding through which citizens, in collaboration with government, fund projects providing a community service. Although in the early stages of development, civic crowdfunding is a promising area for both research and application due to its potential impact on citizen engagement, as well as its influence on the success of a wide range of civic projects ranging from physical structures to amenities and local services. However, the field remains under-addressed in academic research and underdeveloped in terms of the number of civic projects posted to crowdfunding platforms. Acknowledging these issues, we outline the history of civic crowdfunding and describe the current landscape, focusing on online crowdfunding platforms established specifically for the funding of civic projects (Citizinvestor, ioby, Neighbor.ly, Spacehive). The challenges and the opportunities of civic crowdfunding are examined, and its distinguishing characteristics are outlined, including a consid...


title: Cilostazol prevents foot ulcers in diabetic patients with peripheral vascular disease

abstract: Diabetic patients are at high risk of foot ulcerations that may lead to limb amputations with important socio-economic impact. Peripheral vascular disease may be frequently associated in diabetes mellitus type II with its main symptom, intermittent claudication. Many studies reported the known efficacy of cilostazol in treating vascular claudication. Metalloproteinase-9 (MMP-9) seems to be a biochemical marker implicated in chronic wounds and in particular in diabetic foot ulcers. Cilostazol appears to have a lowering effect on MMP-9 levels and this may suggest a beneficial effect in order to prevent or retard the onset of foot ulcer in diabetic patients. In our study, two groups of diabetic patients with peripheral vascular disease were divided into two groups according to the presence of claudication in order to receive cilostazol. Group A (31 patients without claudication) were not eligible to receive cilostazol whereas Group B (47 patients with claudication) received cilostazol administration for 24 weeks (100 mg orally twice daily). Median follow up was of 16 months. During the follow up, 4·25% of patients of Group B and 35·48% of patients of Group A (P < 0·01) showed onset of foot ulceration. Although further randomised and controlled studies are required cilostazol seems to show beneficial effects for primary prevention of diabetic foot ulcers.


title: Human Effort and Machine Learnability in Computer Aided Translation

abstract: Analyses of computer aided translation typically focus on either frontend interfaces and human effort, or backend translation and machine learnability of corrections. However, this distinction is artificial in practice since the frontend and backend must work in concert. We present the first holistic, quantitative evaluation of these issues by contrasting two assistive modes: postediting and interactive machine translation (MT). We describe a new translator interface, extensive modifications to a phrasebased MT system, and a novel objective function for re-tuning to human corrections. Evaluation with professional bilingual translators shows that post-edit is faster than interactive at the cost of translation quality for French-English and EnglishGerman. However, re-tuning the MT system to interactive output leads to larger, statistically significant reductions in HTER versus re-tuning to post-edit. Analysis shows that tuning directly to HTER results in fine-grained corrections to subsequent machine output.


title: Web Image Annotation Via Subspace-Sparsity Collaborated Feature Selection

abstract: The number of web images has been explosively growing due to the development of network and storage technology. These images make up a large amount of current multimedia data and are closely related to our daily life. To efficiently browse, retrieve and organize the web images, numerous approaches have been proposed. Since the semantic concepts of the images can be indicated by label information, automatic image annotation becomes one effective technique for image management tasks. Most existing annotation methods use image features that are often noisy and redundant. Hence, feature selection can be exploited for a more precise and compact representation of the images, thus improving the annotation performance. In this paper, we propose a novel feature selection method and apply it to automatic image annotation. There are two appealing properties of our method. First, it can jointly select the most relevant features from all the data points by using a sparsity-based model. Second, it can uncover the shared subspace of original features, which is beneficial for multi-label learning. To solve the objective function of our method, we propose an efficient iterative algorithm. Extensive experiments are performed on large image databases that are collected from the web. The experimental results together with the theoretical analysis have validated the effectiveness of our method for feature selection, thus demonstrating its feasibility of being applied to web image annotation.


title: Consumer Voice Interface Patterns

abstract: Voice User Interfaces (VUI) are a relatively new concept, although voice applications have been around for several years now. The invisible and transient character of sound led to believe that there was no user interface. This misconception rapidly disappeared with the technology advances that triggered the development of novel and rich voice applications for corporate use as well as in cars, phones and everyday appliances. The new and more complex functionality became a whole area known as Voice User Interfaces to address the di erent design forces and complexities behind a voice-based user interface. In the last years we began a pattern language for designing voice applications and documenting the aforementioned design tensions and common practices. In this paper, we continue with this work focusing on more complex issues such as authentication and consumer appliances interfaces.


title: An improved anomaly detection and diagnosis framework for mobile network operators

abstract: The ever increasing complexity of commercial mobile networks drives the need for methods capable of reducing human workload required for network troubleshooting. In order to address this issue, several attempts have been made to develop automatic anomaly detection and diagnosis frameworks for mobile network operators. In this paper, the latest improvements introduced to one of those frameworks are discussed, including more sophisticated profiling and detection capabilities. The new algorithms further reduce the need for human intervention related to the proper configuration of the profiling and anomaly detection apparatus. The main concepts of the new approach are described and illustrated with an explanatory showcase featuring performance data from a live 3 G network.


title: Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging

abstract: Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and the robustness of brain extraction, therefore, are crucial for the accuracy of the entire brain analysis process. The state-of-the-art brain extraction techniques rely heavily on the accuracy of alignment or registration between brain atlases and query brain anatomy, and/or make assumptions about the image geometry, and therefore have limited success when these assumptions do not hold or image registration fails. With the aim of designing an accurate, learning-based, geometry-independent, and registration-free brain extraction tool, in this paper, we present a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2-D patches of different window sizes. We consider two different architectures: 1) a voxelwise approach based on three parallel 2-D convolutional pathways for three different directions (axial, coronal, and sagittal) that implicitly learn 3-D image information without the need for computationally expensive 3-D convolutions and 2) a fully convolutional network based on the U-net architecture. Posterior probability maps generated by the networks are used iteratively as context information along with the original image patches to learn the local shape and connectedness of the brain to extract it from non-brain tissue. The brain extraction results we have obtained from our CNNs are superior to the recently reported results in the literature on two publicly available benchmark data sets, namely, LPBA40 and OASIS, in which we obtained the Dice overlap coefficients of 97.73% and 97.62%, respectively. Significant improvement was achieved via our auto-context algorithm. Furthermore, we evaluated the performance of our algorithm in the challenging problem of extracting arbitrarily oriented fetal brains in reconstructed fetal brain magnetic resonance imaging (MRI) data sets. In this application, our voxelwise auto-context CNN performed much better than the other methods (Dice coefficient: 95.97%), where the other methods performed poorly due to the non-standard orientation and geometry of the fetal brain in MRI. Through training, our method can provide accurate brain extraction in challenging applications. This, in turn, may reduce the problems associated with image registration in segmentation tasks.


title: Deep Neural Network Compression with Single and Multiple Level Quantization

abstract: Network quantization is an effective solution to compress deep neural networks for practical usage. Existing network quantization methods cannot sufficiently exploit the depth information to generate low-bit compressed network. In this paper, we propose two novel network quantization approaches, single-level network quantization (SLQ) for high-bit quantization and multi-level network quantization (MLQ) for extremely low-bit quantization (ternary).We are the first to consider the network quantization from both width and depth level. In the width level, parameters are divided into two parts: one for quantization and the other for re-training to eliminate the quantization loss. SLQ leverages the distribution of the parameters to improve the width level. In the depth level, we introduce incremental layer compensation to quantize layers iteratively which decreases the quantization loss in each iteration. The proposed approaches are validated with extensive experiments based on the state-of-the-art neural networks including AlexNet, VGG-16, GoogleNet and ResNet-18. Both SLQ and MLQ achieve impressive results.


title: Intersection-priority based optimal RSU allocation for VANET

abstract: Roadside Unit (RSU) is an essential unit in a vehicular ad-hoc network (VANET) for collecting and analyzing traffic data given from smart vehicles. Furthermore, RSUs can take part in controlling traffic flow for vehicle's secure driving by broadcasting locally analyzed data, forwarding some important messages, and communicating with other RSUs, and soon. In order to maximize the availability of RSUs in the VANET, RSUs need to be fully distributed over an entire area. Thus, RSUs can make the best use of all traffic data gathered from every intersection. In this paper, we provide intersection-priority based RSU placement methods to find the optimal number and positions of RSUs for the full distribution providing with a maximal connectivity between RSUs while minimizing RSU setup costs. We propose three optimal algorithms: greedy, dynamic and hybrid algorithms. Finally, we provide simulated analyses of our algorithms using real urban roadmaps of JungGu/Jongrogu, YongsanGu, and GangnamGu in Seoul, each of which has a characteristic road style different than each other. We analyze how our algorithms work in such different types of roadways with real traffic data, and find the optimal number and positions of RSUs in these areas.


title: Semantics-aware Content-based Recommender Systems

abstract: Content-based recommender systems (CBRSs) rely on item and user descriptions (content) to build item representations and user profiles that can be effectively exploited to suggest items similar to those a target user already liked in the past. Most content-based recommender systems use textual features to represent items and user profiles, hence they suffer from the classical problems of natural language ambiguity. This chapter presents a comprehensive survey of semantic representations of items and user profiles that attempt to overcome the main problems of the simpler approaches based on keywords. We propose a classification of semantic approaches into top-down and bottom-up. The former rely on the integration of external knowledge sources, such as ontologies, encyclopedic knowledge and data from the Linked Data cloud, while the latter rely on a lightweight semantic representation based on the hypothesis that the meaning of words depends on their use in large corpora of textual documents. The chapter shows how to make recommender systems aware of semantics to realize a new generation of content-based recommenders.


title: Does aversion to the sucker's payoff matter in public goods games?

abstract: A usual explanation to low levels of contribution to public goods is the fear of getting the sucker’s payoff (cooperation by the participant and defection by the other players). In order to disentangle the effect of this fear from other motives, we design a public good game where people have an assurance against getting the sucker’s payoff. We show that contributions to the public good under this ‘protective’ design are significantly higher and interact with expectations on other individuals' contribution to the public good. Some policy implications and extensions are suggested.


title: Efeito agudo da ingestão de concentrado de uva sobre os biomarcadores do estresse oxidativo em triatletas

abstract: Abstract – The aim of this crossover study was to evaluate the effect of a grape concentrate (test drink [TD]) on oxidative stress markers (thiobarbituric acid reactive substances [TBARS], catalase [CAT], superoxide dismutase [SOD], and glutathione [GSH]). Six triathletes had their physical fitness, body fat composition (%BF) and food intake evaluated. Afterwards, the athletes received two doses of 300 mL of the TD (45.8g of polyphenols/kg) or a placebo drink (PL), at breakfast and after a training session (100 km of cycling, 6 km of running and 1.5 km of swimming). Blood samples (5 ml) were collected after an overnight fasting, immediately after exercise, and one hour after exercise. The triathletes presented the following characteristics (mean and standard-deviation): 43.8±10.2 years old, VO 2 max 45±5.15 mL/kg/min, %BF 13.6±4.2 %, training 270.8±87.1 km/week, 3.1±1.88 hours/training/day. There was a significant increase in SOD from the 1 st to the 2 nd (p=0.027) and 3 rd (p=0.02) blood tests, in response to exercise, regardless of the drink consumed. One hour after exercise, the increase in glutathione values was greater when the PL was consumed (27.5%) in relation to the TD intake (1.8%). In both tests, exercise increased TBARS values; however, when PL was consumed, subjects’ values were higher (PL=2.5±1.1 nmol/ml vs. BT=1.77±1.3 nmol/ml). When PL was consumed, mean CAT values (BT=34.2±6.9 U/mgHb vs. PL=24.6±12.5 U/mgHb) reduced from the 1


title: Computation Reuse in DNNs by Exploiting Input Similarity

abstract: In recent years, Deep Neural Networks (DNNs) have achieved tremendous success for diverse problems such as classification and decision making. Efficient support for DNNs on CPUs, GPUs and accelerators has become a prolific area of research, resulting in a plethora of techniques for energy-efficient DNN inference. However, previous proposals focus on a single execution of a DNN. Popular applications, such as speech recognition or video classification, require multiple back-to-back executions of a DNN to process a sequence of inputs (e.g., audio frames, images). In this paper, we show that consecutive inputs exhibit a high degree of similarity, causing the inputs/outputs of the different layers to be extremely similar for successive frames of speech or images of a video. Based on this observation, we propose a technique to reuse some results of the previous execution, instead of computing the entire DNN. Computations related to inputs with negligible changes can be avoided with minor impact on accuracy, saving a large percentage of computations and memory accesses. We propose an implementation of our reuse-based inference scheme on top of a state-of-the-art DNN accelerator. Results show that, on average, more than 60% of the inputs of any neural network layer tested exhibit negligible changes with respect to the previous execution. Avoiding the memory accesses and computations for these inputs results in 63% energy savings on average.


title: The adoption of electronic tax filing systems: an empirical study

abstract: Abstract This paper discusses the factors affecting the adoption of electronic tax-filing systems. Using the technology acceptance model (TAM) as a theoretical framework, this study introduces “perceived credibility” as a new factor that reflects the user's intrinsic belief in the electronic tax-filing systems, and examines the effect of computer self-efficacy on the intention to use an electronic tax-filing system. Based on a sample of 260 users from a telephone interview, the results strongly support the extended TAM in predicting the intention of users to adopt electronic tax-filing systems. The results also demonstrate the significant effect that computer self-efficacy has on behavioral intention through perceived ease of use, perceived usefulness, and perceived credibility. Based on the findings of this study, implications for electronic tax filing in particular and for e-government services in general are discussed. Finally, this paper concludes by discussing limitations that could be addressed in future studies.


title: Forensics vs. anti-forensics: A decision and game theoretic framework

abstract: In order to combat the spread of digital forgeries, researchers have developed a variety of forensic techniques to verify the authenticity of digital multimedia files. Though many of these techniques can reliably detect traditional forgeries, recent research has shown that they can easily be fooled by anti-forensic operations designed to hide evidence of forgery. In response, new forensic techniques have been developed to detect the use of anti-forensics. In light of this, there is now a need to develop a theoretical understanding of the interactions between a forger using anti-forensics and a forensic investigator. In this paper, we propose techniques to evaluate the performance of anti-forensic algorithms along with a game theoretic framework for analyzing the interplay between forensics and anti-forensics. Furthermore, we propose a new automatic video frame deletion detection technique along with a technique to detect the use of video anti-forensics. We evaluate these techniques using our proposed analytical framework.


title: Imaging with array detectors using chopping and other forms of differential detection

abstract: To detect faint signals in the presence of a high background, differential techniques are often used. In IR astronomy this has led to the so-called chopping and nodding techniques. The introduction of array detectors especially for imaging in the thermal IR region of the astronomical spectrum requires an adaptations of these techniques which also takes into account the pixel nonuniformity of the array detector. I describe one possible imaging algorithm and the associated requirements for the chopping mechanisms of an 8-m telescope.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Survey on Frequent Pattern Mining

abstract: Frequent itemsets play an essential role in many data mining tasks that try to find interesting patterns from databases, such as association rules, correlations, sequences, episodes, classifiers, clusters and many more of which the mining of association rules is one of the most popular problems. The original motivation for searching association rules came from the need to analyze so called supermarket transaction data, that is, to examine customer behavior in terms of the purchased products. Association rules describe how often items are purchased together. For example, an association rule “beer ⇒ chips (80%)” states that four out of five customers that bought beer also bought chips. Such rules can be useful for decisions concerning product pricing, promotions, store layout and many others. Since their introduction in 1993 by Argawal et al. [3], the frequent itemset and association rule mining problems have received a great deal of attention. Within the past decade, hundreds of research papers have been published presenting new algorithms or improvements on existing algorithms to solve these mining problems more efficiently. In this chapter, we explain the basic frequent itemset and association rule mining problems. We describe the main techniques used to solve these problems and give a comprehensive survey of the most influential algorithms that were proposed during the last decade.


title: Reading between the pixels: Photographic steganography for camera display messaging

abstract: We exploit human color metamers to send light-modulated messages less visible to the human eye, but recoverable by cameras. These messages are a key component to camera-display messaging, such as handheld smartphones capturing information from electronic signage. Each color pixel in the display image is modified by a particular color gradient vector. The challenge is to find the color gradient that maximizes camera response, while minimizing human response. The mismatch in human spectral and camera sensitivity curves creates an opportunity for hidden messaging. Our approach does not require knowledge of these sensitivity curves, instead we employ a data-driven method. We learn an ellipsoidal partitioning of the six-dimensional space of colors and color gradients. This partitioning creates metamer sets defined by the base color at the display pixel and the color gradient direction for message encoding. We sample from the resulting metamer sets to find color steps for each base color to embed a binary message into an arbitrary image with reduced visible artifacts. Unlike previous methods that rely on visually obtrusive intensity modulation, we embed with color so that the message is more hidden. Ordinary displays and cameras are used without the need for expensive LEDs or high speed devices. The primary contribution of this work is a framework to map the pixels in an arbitrary image to a metamer pair for steganographic photo messaging.


title: Reasoning within Fuzzy Description Logics

abstract: Description Logics (DLs) are suitable, well-known, logics for managing structured knowledge. They allow reasoning about individuals and well defined concepts, i.e. set of individuals with common properties. The experience in using DLs in applications has shown that in many cases we would like to extend their capabilities. In particular, their use in the context of Multimedia Information Retrieval (MIR) leads to the convincement that such DLs should allow the treatment of the inherent imprecision in multimedia object content representation and retrieval. ::: ::: In this paper we will present a fuzzy extension of ALC, combining Zadeh's fuzzy logic with a classical DL. In particular, concepts becomes fuzzy and, thus, reasoning about imprecise concepts is supported. We will define its syntax, its semantics, describe its properties and present a constraint propagation calculus for reasoning in it.


title: Flower Categorization using Deep Convolutional Neural Networks

abstract: We have developed a deep learning network for classification of different flowers. For this, we have used Visual Geometry Group's 102 category flower dataset having 8189 images of 102 different flowers from University of Oxford. The method is basically divided into two parts; Image segmentation and classification. We have compared the performance of two different Convolutional Neural Network architectures GoogLeNet and AlexNet for classification purpose. By keeping the hyper parameters same for both architectures, we have found that the top 1 and top 5 accuracies of GoogLeNet are 47.15% and 69.17% respectively whereas the top 1 and top 5 accuracies of AlexNet are 43.39% and 68.68% respectively. These results are extremely good when compared to random classification accuracy of 0.98%. This method for classification of flowers can be implemented in real time applications and can be used to help botanists for their research as well as camping enthusiasts.


title: Phase change memory as synapse for ultra-dense neuromorphic systems: Application to complex visual pattern extraction

abstract: We demonstrate a unique energy efficient methodology to use Phase Change Memory (PCM) as synapse in ultra-dense large scale neuromorphic systems. PCM devices with different chalcogenide materials were characterized to demonstrate synaptic behavior. Multi-physical simulations were used to interpret the results. We propose special circuit architecture (“the 2-PCM synapse”), read, write, and reset programming schemes suitable for the use of PCM in neural networks. A versatile behavioral model of PCM which can be used for simulating large scale neural systems is introduced. First demonstration of complex visual pattern extraction from real world data using PCM synapses in a 2-layer spiking neural network (SNN) is shown. System power analysis for different scaled PCM technologies is also provided.


title: Modeling Indirect Influence on Twitter

abstract: Social influence in social networks has been extensively researched. Most studies have focused on direct influence, while another interesting question can be raised as whether indirect influence exists between two users who're not directly connected in the network and what affects such influence. In addition, the theory of complex contagion tells us that more spreaders will enhance the indirect influence between two users. The authors' observation of intensity of indirect influence, propagated by n parallel spreaders and quantified by retweeting probability in two Twitter social networks, shows that complex contagion is validated globally but is violated locally. In other words, the retweeting probability increases non-monotonically with some local drops. A quantum cognition based probabilistic model is proposed to account for these local drops.


Automatic detection of abusive South African tweets using a semi-supervised learning approach


title: A Softwarization Architecture for UAVs and WSNs as Part of the Cloud Environment

abstract: The development of Unmanned Aerial Vehicles (UAV) and wireless sensor network (WSN) applications depends on the availability of resources in UAVs and sensor nodes. In traditional applications, UAVs obtain the required data from specific sensors. However, this tightly coupled architecture restricts the usage of the infrastructure for specific applications. This paper proposes a softwarization architecture for UAVs and WSNs. In addition, the higher layers are proposed to be part of the cloud to benefit from the various cloud opportunities. The proposed architecture is based on the network softwarization concept and the Software Defined Networks (SDN) concept along with the Network Functional Virtualization (NFV) technologies. These concepts are associated with decoupling the hardware infrastructure from the control layer that virtualizes the infrastructure resources for the higher layers. The architecture is illustrated with an agricultural example for a cooperative system of a WSN and UAVs. We implement a prototype system that consists of a sensing node, UAV, WSN controller, UAV controller, and orchestration layer to provide a proof of concept for the proposed architecture.


title: Mechanism Design via Differential Privacy

abstract: We study the role that privacy-preserving algorithms, which prevent the leakage of specific information about participants, can play in the design of mechanisms for strategic agents, which must encourage players to honestly report information. Specifically, we show that the recent notion of differential privacv, in addition to its own intrinsic virtue, can ensure that participants have limited effect on the outcome of the mechanism, and as a consequence have limited incentive to lie. More precisely, mechanisms with differential privacy are approximate dominant strategy under arbitrary player utility functions, are automatically resilient to coalitions, and easily allow repeatability. We study several special cases of the unlimited supply auction problem, providing new results for digital goods auctions, attribute auctions, and auctions with arbitrary structural constraints on the prices. As an important prelude to developing a privacy-preserving auction mechanism, we introduce and study a generalization of previous privacy work that accommodates the high sensitivity of the auction setting, where a single participant may dramatically alter the optimal fixed price, and a slight change in the offered price may take the revenue from optimal to zero.


A Comparison of Lasso-type Algorithms on Distributed Parallel Machine Learning Platforms


title: Reconfigurable Four-Parasitic-Elements Patch Antenna for High-Gain Beam Switching Application

abstract: A reconfigurable beamforming of a four-parasitic-elements patch antenna (FPPA) for WiMAX application is presented. The proposed FPPA is successfully capable to steer the radiation pattern in azimuth planes (0 °, 45 °, 135 °, 225 °, and 315 ° angles) and in elevation plane (0 °, 13 °, 15 °, 10 °, and 12 °). This is realized in the unique form of four parasitic elements encircling the center main radiator. The activation of the parasitic required a shorting pin to the ground that indicates on state condition, and vice versa. It is discovered in CST simulation software that the specified location of the pins are really significant to ensure the parasitic performs either as a reflector or director. Moreover, each of the shorting pins is linked to the RF p-i-n diode BAR5002v switch. Also, the FPPA is fabricated on a 130-mm square Taconic substrate. The proposed antenna design has a maximum gain of 8.2 dBi at all desired angles with a half-power beamwidth of 58 °.


title: Emotion recognition using a hierarchical binary decision tree approach

abstract: Automated emotion state tracking is a crucial element in the computational study of human communication behaviors. It is important to design robust and reliable emotion recognition systems that are suitable for real-world applications both to enhance analytical abilities to support human decision making and to design human-machine interfaces that facilitate efficient communication. We introduce a hierarchical computational structure to recognize emotions. The proposed structure maps an input speech utterance into one of the multiple emotion classes through subsequent layers of binary classifications. The key idea is that the levels in the tree are designed to solve the easiest classification tasks first, allowing us to mitigate error propagation. We evaluated the classification framework on two different emotional databases using acoustic features, the AIBO database and the USC IEMOCAP database. In the case of the AIBO database, we obtain a balanced recall on each of the individual emotion classes using this hierarchical structure. The performance measure of the average unweighted recall on the evaluation data set improves by 3.37% absolute (8.82% relative) over a Support Vector Machine baseline model. In the USC IEMOCAP database, we obtain an absolute improvement of 7.44% (14.58%) over a baseline Support Vector Machine modeling. The results demonstrate that the presented hierarchical approach is effective for classifying emotional utterances in multiple database contexts.


ATTACKS ON SSL A COMPREHENSIVE STUDY OF BEAST , CRIME , TIME , BREACH , LUCKY 13 & RC 4 BIASES


title: Deep Reinforcement Learning for Multimedia Traffic Control in Software Defined Networking

abstract: Software Defined Networking (SDN) is a promising paradigm to provide centralized traffic control. Multimedia traffic control based on SDN is crucial but challenging for Quality of Experience (QoE) optimization. It is very difficult to model and control multimedia traffic because solutions mainly depend on an understanding of the network environment, which is complicated and dynamic. Inspired by the recent advances in artificial intelligence (AI) technologies, we study the adaptive multimedia traffic control mechanism leveraging Deep Reinforcement Learning (DRL). This paradigm combines deep learning with reinforcement learning, which learns solely from rewards by trial-and-error. Results demonstrate that the proposed mechanism is able to control multimedia traffic directly from experience without referring to a mathematical model.


title: Beyond Digital Incunabula : Modeling the Next Generation of Digital Libraries

abstract: This paper describes several incunabular assumptions that impose upon early digital libraries the limitations drawn from print, and argues for a design strategy aimed at providing customization and personalization services that go beyond the limiting models of print distribution, based on services and experiments developed for the Greco-Roman collections in the Perseus Digital Library. Three features fundamentally characterize a successful digital library design: finer granularity of collection objects, automated processes, and decentralized community contributions.


title: Model-based and actual independence for fairness-aware classification

abstract: The goal of fairness-aware classification is to categorize data while taking into account potential issues of fairness, discrimination, neutrality, and/or independence. For example, when applying data mining technologies to university admissions, admission criteria must be non-discriminatory and fair with regard to sensitive features, such as gender or race. In this context, such fairness can be formalized as statistical independence between classification results and sensitive features. The main purpose of this paper is to analyze this formal fairness in order to achieve better trade-offs between fairness and prediction accuracy, which is important for applying fairness-aware classifiers in practical use. We focus on a fairness-aware classifier, Calders and Verwer’s two-naive-Bayes (CV2NB) method, which has been shown to be superior to other classifiers in terms of fairness. We hypothesize that this superiority is due to the difference in types of independence. That is, because CV2NB achieves actual independence, rather than satisfying model-based independence like the other classifiers, it can account for model bias and a deterministic decision rule. We empirically validate this hypothesis by modifying two fairness-aware classifiers, a prejudice remover method and a reject option-based classification (ROC) method, so as to satisfy actual independence. The fairness of these two modified methods was drastically improved, showing the importance of maintaining actual independence, rather than model-based independence. We additionally extend an approach adopted in the ROC method so as to make it applicable to classifiers other than those with generative models, such as SVMs.


title: Accessibility in context: understanding the truly mobile experience of smartphone users with motor impairments

abstract: Lab-based studies on touchscreen use by people with motor impairments have identified both positive and negative impacts on accessibility. Little work, however, has moved beyond the lab to investigate the truly mobile experiences of users with motor impairments. We conducted two studies to investigate how smartphones are being used on a daily basis, what activities they enable, and what contextual challenges users are encountering. The first study was a small online survey with 16 respondents. The second study was much more in depth, including an initial interview, two weeks of diary entries, and a 3-hour contextual session that included neighborhood activities. Four expert smartphone users participated in the second study and we used a case study approach for analysis. Our findings highlight the ways in which smartphones are enabling everyday activities for people with motor impairments, particularly in overcoming physical accessibility challenges in the real world and supporting writing and reading. We also identified important situational impairments, such as the inability to retrieve the phone while in transit, and confirmed many lab-based findings in the real-world setting. We present design implications and directions for future work.


title: A Rule-Based Change Impact Analysis Approach in Software Architecture for Requirements Changes

abstract: Software systems usually operate in a dynamic context where their requirements change continuously and new requirements emerge frequently. A single requirement hardly exists in isolation: it is related to other requirements and to the software development artifacts that implement it. When a requirements change is introduced, the requirements engineer may have to manually analyze all requirements and architectural elements for a single change. This may result in neglecting the actual impact of a change. We aim at improving change impact analysis in software architecture for requirements changes by using formal semantics of requirements relations, requirements changes and traces between Requirements & Architecture. In our previous work we presented a technique for change impact analysis in requirements. The technique uses the formal semantics of requirements relations and changes. Its output is a set of candidate requirements for the impact with proposed changes and a propagation path in the requirements model. In this paper we present a complementary technique which propagates requirements changes to software architecture and finds out which architectural elements are impacted by these changes. The formalization of requirements relations, changes and traces between R&A is used to determine candidate architectural elements for the impact of requirements changes in the architecture. The tool support is an extension of our Tool for Requirements Inferencing and Consistency Checking (TRIC). Our approach helps in the elimination of some false positive impacts in change propagation. We illustrate our approach in an industrial example which shows that the formal semantics of requirements relations, changes and traces enables the identification of candidate architectural elements with the reduction of some false positive impacts.


title: Routing security in wireless ad hoc networks

abstract: A mobile ad hoc network consists of a collection of wireless mobile nodes that are capable of communicating with each other without the use of a network infrastructure or any centralized administration. MANET is an emerging research area with practical applications. However, wireless MANET is particularly vulnerable due to its fundamental characteristics, such as open medium, dynamic topology, distributed cooperation, and constrained capability. Routing plays an important role in the security of the entire network. In general, routing security in wireless MANETs appears to be a problem that is not trivial to solve. In this article we study the routing security issues of MANETs, and analyze in detail one type of attack-the "black hole" problem-that can easily be employed against the MANETs. We also propose a solution for the black hole problem for ad hoc on-demand distance vector routing protocol.


title: Paraphrasing With Bilingual Parallel Corpora

abstract: Previous work has used monolingual parallel corpora to extract and generate paraphrases. We show that this task can be done using bilingual parallel corpora, a much more commonly available resource. Using alignment techniques from phrase-based statistical machine translation, we show how paraphrases in one language can be identified using a phrase in another language as a pivot. We define a paraphrase probability that allows paraphrases extracted from a bilingual parallel corpus to be ranked using translation probabilities, and show how it can be refined to take contextual information into account. We evaluate our paraphrase extraction and ranking methods using a set of manual word alignments, and contrast the quality with paraphrases extracted from automatic alignments.


title: Highly Efficient Leaky-Wave Antenna Array for 28-GHz Millimeter-Wave Terminals

abstract: A highly efficient leaky-wave antenna (LWA) array is presented in this paper. The proposed LWA array covers 28-GHz millimeter-wave frequency band and can be useful for 5G terminals. The antenna has been designed to be integrated to a substrate of dimensions 120 mm × 70 mm and occupies a small area of 24.5 mm × 24.5 mm. A dielectric load is used to increase the antenna efficiency. The efficiency of the array varies between 97\% and 98\% within the 28-GHz millimeter-wave band. It has a wide operating bandwidth of 6 GHz (26 - 32 GHz) and a peak gain of 9.1 dBi at 28 GHz. Within the 28-GHz millimeter-wave band the proposed LWA array has stable boresight radiation in the x-z and y-z planes.


title: Genome re-sequencing reveals the history of apple and supports a two-stage model for fruit enlargement

abstract: Human selection has reshaped crop genomes. Here we report an apple genome variation map generated through genome sequencing of 117 diverse accessions. A comprehensive model of apple speciation and domestication along the Silk Road is proposed based on evidence from diverse genomic analyses. Cultivated apples likely originate from Malus sieversii in Kazakhstan, followed by intensive introgressions from M. sylvestris. M. sieversii in Xinjiang of China turns out to be an “ancient” isolated ecotype not directly contributing to apple domestication. We have identified selective sweeps underlying quantitative trait loci/genes of important fruit quality traits including fruit texture and flavor, and provide evidences supporting a model of apple fruit size evolution comprising two major events with one occurring prior to domestication and the other during domestication. This study outlines the genetic basis of apple domestication and evolution, and provides valuable information for facilitating marker-assisted breeding and apple improvement.Apple is one of the most important fruit crops. Here, the authors perform deep genome resequencing of 117 diverse accessions and reveal comprehensive models of apple origin, speciation, domestication, and fruit size evolution as well as candidate genes associated with important agronomic traits.


title: P.: Greatly expanded tropical warm pool and weakened Hadley Circulation in the early Pliocene

abstract: The Pliocene warm interval has been difficult to explain. We reconstructed the latitudinal distribution of sea surface temperature around 4 million years ago, during the early Pliocene. Our reconstruction shows that the meridional temperature gradient between the equator and subtropics was greatly reduced, implying a vast poleward expansion of the ocean tropical warm pool. Corroborating evidence indicates that the Pacific temperature contrast between the equator and 32 degrees N has evolved from approximately 2 degrees C 4 million years ago to approximately 8 degrees C today. The meridional warm pool expansion evidently had enormous impacts on the Pliocene climate, including a slowdown of the atmospheric Hadley circulation and El Niño-like conditions in the equatorial region. Ultimately, sustaining a climate state with weak tropical sea surface temperature gradients may require additional mechanisms of ocean heat uptake (such as enhanced ocean vertical mixing).


title: Limited Feedback Hybrid Precoding for Multi-User Millimeter Wave Systems

abstract: Antenna arrays will be an important ingredient in millimeter wave (mmWave) cellular systems. A natural application of antenna arrays is simultaneous transmission to multiple users. Unfortunately, the hardware constraints in mmWave systems make it difficult to apply conventional lower frequency multiuser MIMO precoding techniques at mmWave. This paper develops low complexity hybrid analog/digital precoding for downlink multiuser mmWave systems. Hybrid precoding involves a combination of analog and digital processing that is inspired by the power consumption of complete radio frequency and mixed signal hardware. The proposed algorithm configures hybrid precoders at the transmitter and analog combiners at multiple receivers with a small training and feedback overhead. The performance of the proposed algorithm is analyzed in the large dimensional regime and in single path channels. When the analog and digital precoding vectors are selected from quantized codebooks, the rate loss due to the joint quantization is characterized and insights are given into the performance of hybrid beamforming compared with analog-only beamforming solutions. Analytical and simulation results show that the proposed techniques offer higher sum rates compared with analog-only beamforming solutions, and approach the performance of the unconstrained digital beamforming with relatively small codebooks.


A myoelectrically controlled powered elbow.


title: Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement.

abstract: Clinical electroencephalography (EEG) records vast amounts of human complex data yet is still reviewed primarily by human readers. Deep belief nets (DBNs) are a relatively new type of multi-layer neural network commonly tested on two-dimensional image data but are rarely applied to times-series data such as EEG. We apply DBNs in a semi-supervised paradigm to model EEG waveforms for classification and anomaly detection. DBN performance was comparable to standard classifiers on our EEG dataset, and classification time was found to be 1.7?103.7 times faster than the other high-performing classifiers. We demonstrate how the unsupervised step of DBN learning produces an autoencoder that can naturally be used in anomaly measurement. We compare the use of raw, unprocessed data?a rarity in automated physiological waveform analysis?with hand-chosen features and find that raw data produce comparable classification and better anomaly measurement performance. These results indicate that DBNs and raw data inputs may be more effective for online automated EEG waveform recognition than other common techniques.


title: Working memory and binding in sentence recall

abstract: A series of experiments explored whether chunking in short-term memory for verbal materials depends on attentionally limited executive processes. Secondary tasks were used to disrupt components of working memory and chunking was indexed by the sentence superiority effect, whereby immediate recall is better for sentences than word lists. To facilitate comparisons and maximise demands on working memory, materials were constrained by re-sampling a small set of words. Experiment 1 confirmed a reliable sentence superiority effect with constrained materials. Experiment 2 showed that secondary tasks of concurrent articulation and visual choice reaction impaired recall, but did not remove or reduce the sentence superiority effect. This was also the case with visual and verbal n-back concurrent tasks (Experiment 3), and with concurrent backward counting (Experiment 4). Backward counting did however interact with mode of presenting the memory materials, suggesting that our failure to find interactions between concurrent task and materials was not attributable to our methodology. We conclude that executive processes are not crucial for the sentence chunking advantage and we discuss implications for the episodic buffer and other theoretical accounts of working memory and chunking.


title: A Survey of Attacks on Ethereum Smart Contracts SoK

abstract: Smart contracts are computer programs that can be correctly executed by a network of mutually distrusting nodes, without the need of an external trusted authority. Since smart contracts handle and transfer assets of considerable value, besides their correct execution it is also crucial that their implementation is secure against attacks which aim at stealing or tampering the assets. We study this problem in Ethereum, the most well-known and used framework for smart contracts so far. We analyse the security vulnerabilities of Ethereum smart contracts, providing a taxonomy of common programming pitfalls which may lead to vulnerabilities. We show a series of attacks which exploit these vulnerabilities, allowing an adversary to steal money or cause other damage.


title: New Perspectives in the Model of Stress Response

abstract: The reactions of human organism to changes of internal or external environment termed as stress response have been at the center of interest during recent decades. Several theories were designed to describe the regulatory mechanisms which maintain the stability of vital physiological functions under conditions of threat or other environmental challenges. However, most of the models of stress reactivity were focused on specific aspects of the regulatory outcomes - physiological (e.g. neuroendocrine), psychological or behavioral regulation. Recently, a novel complex theory based on evolutionary and developmental biology has been introduced. The Adaptive Calibration Model of stress response employs a broad range of the findings from previous theories of stress and analyzes the responsivity to stress with respect to interindividual differences as a consequence of conditional adaptation - the ability to modify developmental trajectory to match the conditions of the social and physical environment. This review summarizes the contributions of the most important models in the field of stress response and emphasizes the importance of complex analysis of the psycho-physiological mechanisms. Moreover, it outlines the implications for nonpharmacological treatment of stress-related disorders with the application of biofeedback training as a promising tool based on voluntary modification of neurophysiological functions.


title: Overview of research on the educational use of video games

abstract: This paper overviews research on the educational use of video games by examining the viability of the different learning theories in the field, namely behaviorism, cognitivism, constructionism and the socio-cultural approach. In addition, five key tensions that emerge from the current research are examined: 1) Learning vs. playing, 2) freedom vs. control, 3) drill-and-practice games vs. microworlds, 4) transmission vs. construction, 5) teacher intervention vs. no teacher intervention.


A Recommendation Engine based on Social Metrics


title: Beyond storage APIs: provable semantics for storage stacks

abstract: Applications are deployed upon deep, diverse storage stacks that are constructed on-demand. Although many storage stacks share a common API to allow portability, application behavior differs in subtle ways depending upon unspecified properties of the underlying storage stack. Currently, there is no way to test whether an application will behave correctly on a given storage stack: corruption or data loss could occur at any point in the application lifetime. ::: ::: We argue that we require an expressive language for specifying the complex storage guarantees required by different applications. The same language can be used to write a high-level specification capturing the design of different storage-stack layers. Given the required guarantees, and the storage-stack specifications, we can prove that stacks constructed dynamically (by composing different storage-stack layers) provide the guarantees required by the application.


title: A Framework and Algorithm for Energy Efficient Container Consolidation in Cloud Data Centers

abstract: One of the major challenges that cloud providers face is minimizing power consumption of their data centers. To this point, majority of current research focuses on energy efficient management of resources in the Infrastructure as a Service model and through virtual machine consolidation. However, containers are increasingly gaining popularity and going to be major deployment model in cloud environment and specifically in Platform as a Service. This paper focuses on improving the energy efficiency of servers for this new deployment model by proposing a framework that consolidates containers on virtual machines. We first formally present the container consolidation problem and then we compare a number of algorithms and evaluate their performance against metrics such as energy consumption, Service Level Agreement violations, average container migrations rate, and average number of created virtual machines. Our proposed framework and algorithms can be utilized in a private cloud to minimize energy consumption, or alternatively in a public cloud to minimize the total number of hours the virtual machines leased.


Phase transition in the nonlinearσmodel in a (2+ε)-dimensional continuum


title: Motion history image: its variants and applications

abstract: The motion history image (MHI) approach is a view-based temporal template method which is simple but robust in representing movements and is widely employed by various research groups for action recognition, motion analysis and other related applications. In this paper, we provide an overview of MHI-based human motion recognition techniques and applications. Since the inception of the MHI template for motion representation, various approaches have been adopted to improve this basic MHI technique. We present all important variants of the MHI method. This paper points some areas for further research based on the MHI method and its variants.


title: GaN X-band 43% internally-matched FET with 60W output power

abstract: An X-band high efficiency GaN internally-matched FET has been developed. Asymmetric matching circuit layout is employed to avoid decrease of efficiency caused by impedance mismatch and unbalance power dividing in matching circuits. The designed asymmetric input and output matching circuits have achieved equal dividing characteristics. A power added efficiency (PAE) of 43.4% and an output power of 47.8 dBm (60.3 W) have been achieved with the developed GaN internally-matched FET.


title: Antioxidant and antiproliferative activities of sweet potato (Ipomoea batatas (L.) Lam 'Tainong 57') constituents

abstract: The aim of this study is to examine possible antioxidant and antiproliferative activities of the different extracts from sweet potato (Ipomoea batatas (L.) Lam 'Tainong 57') organs. DPPH staining, total phenolic com- pounds and flavonoid content, DPPH radical, reducing power method, FTC method, and cell proliferation were all employed. In the DPPH staining, ethanol extract of vein had the highest radical-scavenging activity when it was diluted to 6.25 mg dry matter/mL. Among all the extracts, the highest amount of total phenolic and flavonoid com- pounds was found in the ethanol extract of vein. In the DPPH colorimetric method, it was found that ethanol extract of leaf had the highest radical-scavenging activity, followed by water extract of vein. In the reducing power activity assay, it was found that the water extract of leaf had the highest reducing power activity, followed by ethanol extract of vein. Like phenolic compounds, the highest FTC activity was found in the ethanol extract of vein. The antipro- liferative activities aof sweet potato were studied in vitro using human lymphoma NB4 cells, and the following re- sults were found: water extract of vein had the highest antiproliferative activity with an EC 50 of 449.6 ± 27.73 µg/mL, followed by water extract of storage root, water extract of leaf, ethanol extract of storage root, and ethanol extract of leaf. Although the ethanol extract of vein showed strong antioxidant activity, it had no antiproliferative activity under the experimental conditions tested.


title: Adaptation to tempo changes in sensorimotor synchronization: effects of intention, attention, and awareness.

abstract: Adaptation to tempo changes in sensorimotor synchronization is hypothesized to rest on two processes, one (phase correction) being largely automatic and the other (period correction) requiring conscious awareness and attention. In this study, participants tapped their finger in synchrony with auditory sequences containing a tempo change and continued tapping after sequence termination. Their intention to adapt or not to adapt to the tempo change was manipulated through instructions, their attentional resources were varied by introducing a concurrent secondary task (mental arithmetic), and their awareness of the tempo changes was assessed through perceptual judgements. As predicted, period correction was found to be strongly dependent on all three variables, whereas phase correction depended only on intention.


title: Mobile robot path planning using artificial bee colony and evolutionary programming

abstract: Graphical abstractDisplay Omitted HighlightsWe solve the path planning problem using the combination of two evolutionary methods.First, an artificial bee colony (ABC) finds a feasible path in the free space.Second, evolutionary programming (EP) optimizes the path length and smoothness.The proposed approach was compared to a probabilistic roadmap (PRM) method.The ABC-EP approach outperforms the PRM approach on problems of varying complexity. In this paper, an evolutionary approach to solve the mobile robot path planning problem is proposed. The proposed approach combines the artificial bee colony algorithm as a local search procedure and the evolutionary programming algorithm to refine the feasible path found by a set of local procedures. The proposed method is compared to a classical probabilistic roadmap method (PRM) with respect to their planning performances on a set of benchmark problems and it exhibits a better performance. Criteria used to measure planning effectiveness include the path length, the smoothness of planned paths, the computation time and the success rate in planning. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed method are also shown.


abstract: UNIVERSITY OF C A L I F O R N I A A T B E R K E L E Y Department of Economics Berkeley, California 94720-3880 Working Paper No. 97-250 First Impressions Matter: A Model of Confirmatory Bias Matthew Rabin University of California, Berkeley and Joel Schrag Emory University January 1997 Key words: confirmatory bias, overconfidence, bounded rationality JEL Classification: A12, B49, D83 We thank Jimmy Chan, Erik Eyster, Bruce Hsu, Clara Wang, and especially Steven Blatt for research assistance. We thank Steven Blatt, George Loewenstein, and seminar participants at Berkeley and Camegie-Mellon for helpful comments. For financial support, Rabin thanks the Alfred P. Sloan and Russell Sage Foundations and Schrag thanks the University Research Committee of Emory University.


title: Transformational leadership in nursing: a concept analysis.

abstract: AIM ::: To analyse the concept of transformational leadership in the nursing context. ::: ::: ::: BACKGROUND ::: Tasked with improving patient outcomes while decreasing the cost of care provision, nurses need strategies for implementing reform in health care and one promising strategy is transformational leadership. Exploration and greater understanding of transformational leadership and the potential it holds is integral to performance improvement and patient safety. ::: ::: ::: DESIGN ::: Concept analysis using Walker and Avant's (2005) concept analysis method. ::: ::: ::: DATA SOURCES ::: PubMed, CINAHL and PsychINFO. ::: ::: ::: METHODS ::: This report draws on extant literature on transformational leadership, management, and nursing to effectively analyze the concept of transformational leadership in the nursing context. ::: ::: ::: IMPLICATIONS FOR NURSING ::: This report proposes a new operational definition for transformational leadership and identifies model cases and defining attributes that are specific to the nursing context. The influence of transformational leadership on organizational culture and patient outcomes is evident. Of particular interest is the finding that transformational leadership can be defined as a set of teachable competencies. However, the mechanism by which transformational leadership influences patient outcomes remains unclear. ::: ::: ::: CONCLUSION ::: Transformational leadership in nursing has been associated with high-performing teams and improved patient care, but rarely has it been considered as a set of competencies that can be taught. Also, further research is warranted to strengthen empirical referents; this can be done by improving the operational definition, reducing ambiguity in key constructs and exploring the specific mechanisms by which transformational leadership influences healthcare outcomes to validate subscale measures.


title: A Robust and Scalable Algorithm for the Steiner Problem in Graphs

abstract: We present an effective heuristic for the Steiner Problem in Graphs. Its main elements are a multistart algorithm coupled with aggressive combination of elite solutions, both leveraging recently-proposed fast local searches. We also propose a fast implementation of a well-known dual ascent algorithm that not only makes our heuristics more robust (by quickly dealing with easier cases), but can also be used as a building block of an exact (branch-and-bound) algorithm that is quite effective for some inputs. On all graph classes we consider, our heuristic is competitive with (and sometimes more effective than) any previous approach with similar running times. It is also scalable: with long runs, we could improve or match the best published results for most open instances in the literature.


title: STUDENT MOTIVATION IN PHYSICAL EDUCATION - THE EVIDENCE IN A NUTSHELL

abstract: Student motivation plays an important role in the teaching and learning process in general educational settings as well as in physical education (PE). This review should provide a brief and concise overview of the empirical evidence that is available regarding student motivation in PE. The review will organize research findings on student motivation in PE according to its relation to physical activity, motivational factors, barriers to motivation, motivational profiles, and interventions.


The UAV@LGL BW Project – A NMCA Case Study


A Neural Architecture for Automated ICD Coding