Query: The Case for a Visual Discovery Assistant: A Holistic Solution for Accelerating Visual Data Exploration

Relevant passages:
(s|qu)eries: Visual Regular Expressions for Querying and Exploring Event Sequences <=SEP=> Many different domains collect event sequence data and rely on finding and analyzing patterns within it to gain meaningful insights. Current systems that support such queries either provide limited expressiveness, hinder exploratory workflows or present interaction and visualization models which do not scale well to large and multi-faceted data sets. In this paper we present (s|qu)eries (pronounced "Squeries"), a visual query interface for creating queries on sequences (series) of data, based on regular expressions. (s|qu)eries is a touch-based system that exposes the full expressive power of regular expressions in an approachable way and interleaves query specification with result visualizations. Being able to visually investigate the results of different query-parts supports debugging and encourages iterative query-building as well as exploratory work-flows. We validate our design and implementation through a set of informal interviews with data scientists that analyze event sequences on a daily basis.

SnipSuggest: Context-Aware Autocompletion for SQL <=SEP=> In this paper, we present SnipSuggest, a system that provides onthe-go, context-aware assistance in the SQL composition process. SnipSuggest aims to help the increasing population of non-expert database users, who need to perform complex analysis on their large-scale datasets, but have difficulty writing SQL queries. As a user types a query, SnipSuggest recommends possible additions to various clauses in the query using relevant snippets collected from a log of past queries. SnipSuggest’s current capabilities include suggesting tables, views, and table-valued functions in the FROM clause, columns in the SELECT clause, predicates in the WHERE clause, columns in the GROUP BY clause, aggregates, and some support for sub-queries. SnipSuggest adjusts its recommendations according to the context: as the user writes more of the query, it is able to provide more accurate suggestions. We evaluate SnipSuggest over two query logs: one from an undergraduate database class and another from the Sloan Digital Sky Survey database. We show that SnipSuggest is able to recommend useful snippets with up to 93.7% average precision, at interactive speed. We also show that SnipSuggest outperforms naı̈ve approaches, such as recommending popular snippets.

Profiler: integrated statistical analysis and visualization for data quality assessment <=SEP=> Data quality issues such as missing, erroneous, extreme and duplicate values undermine analysis and are time-consuming to find and fix. Automated methods can help identify anomalies, but determining what constitutes an error is context-dependent and so requires human judgment. While visualization tools can facilitate this process, analysts must often manually construct the necessary views, requiring significant expertise. We present Profiler, a visual analysis tool for assessing quality issues in tabular data. Profiler applies data mining methods to automatically flag problematic data and suggests coordinated summary visualizations for assessing the data in context. The system contributes novel methods for integrated statistical and visual analysis, automatic view suggestion, and scalable visual summaries that support real-time interaction with millions of data points. We present Profiler's architecture --- including modular components for custom data types, anomaly detection routines and summary visualizations --- and describe its application to motion picture, natural disaster and water quality data sets.

Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship <=SEP=> Spoken dialogue systems have received increased interest because they are potentially much more natural and powerful methods of communicating with machines than are current graphics-based interfaces. Wired for Speech presents basic research in the psychological and sociological aspects of voice synthesis and recognition. Its major lesson is that people attribute human characteristics to spoken dialogue systems for reasons related to human evolution. But although it contains interesting basic research, the book is mainly aimed at giving technological or marketing advice to those seeking to use voice interfaces when creating commercial applications. The book is oriented around a series of simple experiments designed to show just how pervasive psychological and social influences can be on the opinions and behaviors of people confronted with voice interfaces. Each chapter describes a basic research hypothesis, introduces an experiment to test it, and discusses its implications for designing voice interfaces: gender, personality, accent, ethnicity, emotion, number of distinct voices, use of " I " by the system, voices in concert with faces, mixed synthetic and recorded voices, context, and the effects of errors in human–computer cooperation. Although Wired for Speech is very accessible , especially to the non-scientist, it is written with an unusual bibliography style for an academic book: All references and details are given in a notes section at the end of the book, making up one third of the content. This narrative exposition style will probably not satisfy either type of reader: Scientists will be frustrated at the imprecision in argumen-tation, lack of detail in the book itself, and continually having to refer to the notes. The lack of detail also prevents the book from serving as a reference work. Meanwhile those needing advice when implementing voice interfaces will be puzzled at references to Chomsky, Grice, and Zemlin. Thus the book seems to suffer from not knowing its audience well, which is odd as this is precisely the lesson that the book tries to impart. Another complaint from the scientific point of view is the obsession this particular book has with casting every experimental result as advice for the business and marketing side of voice interfaces, typically concentrating on Web-based e-marketing examples such as buying books on line. Most of the experiments have sample sizes of between 40 and 50, but the authors seem ready to invite multi-billion-dollar businesses to immediately base their deployed systems on these results. Finally, and fundamentally, this …

Characterizing the usability of interactive applications through query log analysis <=SEP=> People routinely rely on Internet search engines to support their use of interactive systems: they issue queries to learn how to accomplish tasks, troubleshoot problems, and otherwise educate themselves on products. Given this common behavior, we argue that search query logs can usefully augment traditional usability methods by revealing the primary tasks and needs of a product's user population. We term this use of search query logs CUTS - characterizing usability through search. In this paper, we introduce CUTS and describe an automated process for harvesting, ordering, labeling, filtering, and grouping search queries related to a given product. Importantly, this data set can be assembled in minutes, is timely, has a high degree of ecological validity, and is arguably less prone to self-selection bias than data gathered via traditional usability methods. We demonstrate the utility of this approach by applying it to a number of popular software and hardware systems.

Multithreaded Sliding Window Approach to Improve Exact Pattern Matching Algorithms <=SEP=> In this paper an efficient pattern matching approach, based on a multithreading sliding window technique, is proposed to improve the efficiency of the common sequential exact pattern matching algorithms including: (i) Brute Force, (ii) Knuth-Morris-Pratt and (iii) Boyer-Moore. The idea is to divide the text under-search into blocks, each block is allocated one or two threads running concurrently. Reported experimental results indicated that the proposed approach improves the performance of the well-known pattern matching algorithms, in terms of search time, especially when the searched patterns are located at the middle or at the end of the text. Keywords—pattern matching; multithreading; sliding window; Brute Force; Knuth-Morris-Pratt; Boyer-Moore

Techniques of EMG signal analysis: detection, processing, classification and applications <=SEP=> Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.

A particle swarm optimization algorithm for flexible job shop scheduling problem <=SEP=> The classical job shop scheduling problem (JSP) is the most popular machine scheduling model in practice and is well known as NP-hard. The formulation of the JSP is based on the assumption that for each part type or job there is only one process plan that prescribes the sequence of operations and the machine on which each operation has to be performed. Flexible job shop scheduling problem (FJSP) is an extension of the JSP, which allows an operation to be processed by any machine from a given set. Since FJSP requires an additional decision of machine allocation during scheduling, therefore it is much more complex problem than JSP. To solve such NP-hard problems, heuristic approaches are commonly preferred over the traditional mathematical techniques. This paper proposes a particle swarm optimization (PSO) based heuristic for solving the FJSP for minimum makespan time criterion. The performance of the proposed PSO is evaluated by comparing its results with the results obtained using ILOG Solver, a constraint-programming tool. The comparison of the results proves the effectiveness of the proposed PSO for solving FJSP instances.

Successfully Managing Impending Skin Necrosis following Hyaluronic Acid Filler Injection, using High-Dose Pulsed Hyaluronidase <=SEP=> Facial fillers are becoming increasingly popular as aesthetic procedures to temporarily reduce the depth of wrinkles or to contour faces. However, even in the hands of very experienced injectors, there is always a small possibility of vascular complications like intra-arterial injection of filler substance. We present a case report of a patient who developed features of vascular obstruction in right infraorbital artery and tell-tale signs of impending skin necrosis, after hyaluronic acid filler injection by an experienced injector. The diagnosis of a vascular complication was made quickly with the help of clinical features like blanching, livedo reticularis, and poor capillary refill. Patient was treated promptly with "high-dose pulsed hyaluronidase protocol" comprising three 1,000-unit pulses of hyaluronidase, administered hourly. There was no further increase in size of the involved area after the first dose of hyaluronidase. All of the involved area, along with 1 cm overlapping in uninvolved skin area, was injected during each injection pulse, using a combination of cannula and needle. Complete reperfusion and good capillary filling were achieved after completion of 3 pulses, and these were taken as the end-point of high-dose pulsed hyaluronidase treatment. Immediate skin changes after filler injections, as well as after hyaluronidase injections and during the 3-week recovery period, were documented with photographs and clinical notes. Involved skin was found to have been fully recovered from this vascular episode, thus indicating that complete recovery of the ischemic skin changes secondary to possible intra-arterial injection could be achieved using high-dose pulsed hyaluronidase protocol.

Combining Neural Networks and Log-linear Models to Improve Relation Extraction <=SEP=> The last decade has witnessed the success of traditional feature-based methods for exploiting the discrete structures such as words or lexical patterns to extract relations from text. Recently, convolutional and recurrent neural networks have been shown to capture effective hidden structures within sentences via continuous representations, thereby significantly advancing the performance of relation extraction. The advantage of convolutional neural networks is their capacity to generalize the consecutive k-grams in the sentences while recurrent neural networks are effective to encode long range sentence context. This paper proposes to combine the traditional feature-based method, the convolutional and recurrent neural networks to simultaneously benefit from their advantages. Our systematic evaluation of different network architectures and combination methods demonstrates the effectiveness of this approach and results in the state-ofthe-art performance on the ACE 2005 and SemEval datasets.

Characterizing and visualizing physical world accessibility at scale using crowdsourcing, computer vision, and machine learning <=SEP=> Poorly maintained sidewalks and street intersections pose considerable accessibility challenges for people with mobility-impairments [13,14]. According to the most recent U.S. Census (2010), roughly 30.6 million adults have physical disabilities that affect their ambulatory activities [22]. Of these, nearly half report using an assistive aid such as a wheelchair (3.6 million) or a cane, crutches, or walker (11.6 million) [22]. Despite comprehensive civil rights legislation for Americans with Disabilities (e.g., [25,26]), many city streets, sidewalks, and businesses in the U.S. remain inaccessible. The problem is not just that street-level accessibility fundamentally affects where and how people travel in cities, but also that there are few, if any, mechanisms to determine accessible areas of a city a priori. Indeed, in a recent report, the National Council on Disability noted that they could not find comprehensive information on the "degree to which sidewalks are accessible" across the US [15]. This lack of information can have a significant negative impact on the independence and mobility of citizens [13,16] For example, in our own initial formative interviews with wheelchair users, we uncovered a prevailing view about navigating to new areas of a city: "I usually don't go where I don't know [about accessible routes]" (Interviewee 3, congenital polyneuropathy). Our overarching research vision is to transform the way in which street-level accessibility information is collected and used to support new types of assistive map-based tools.

Capacitance and Force Computation Due to Direct and Fringing Effects in MEMS/NEMS Arrays <=SEP=> An accurate computation of electrical force is significant in analyzing the performance of microelectromechanical systems and nanoelectromechanical systems. Many analytical and empirical models are available for computing the forces, especially, for a single set of parallel plates. In general, these forces are computed based on the direct electric field between the overlapping areas of the plates and the fringing field effects. Most of the models, which are based on direct electric field effect, consider only the trivial cases of the fringing field effects. In this paper, we propose different models which are obtained from the numerical simulations. It is found to be useful in computing capacitance as well force in simple and complex configurations consisting of an array of beams and electrodes. For the given configurations, the analytical models are compared with the available models and numerical results. While the percentage error of the proposed model is found to be under 6% with respect to the numerical results, the error associated with the analytical model without the fringing field effects is ~50 %. The proposed model can be applied to the devices in which the fringing field effects are dominant.

Self-Supervision for Reinforcement Learning by Parsa Mahmoudieh <=SEP=> Reinforcement learning optimizes policies for expected cumulative reward. Need the supervision be so narrow? Reward is delayed and sparse for many tasks, making it a difficult and impoverished signal for end-to-end optimization. To augment reward, we consider a range of self-supervised tasks that incorporate states, actions, and successors to provide auxiliary losses. These losses offer ubiquitous and instantaneous supervision for representation learning even in the absence of reward. While current results show that learning from reward alone is feasible, pure reinforcement learning methods are constrained by computational and data efficiency issues that can be remedied by auxiliary losses. Self-supervised pretraining and joint optimization improve the data efficiency and policy returns of end-to-end reinforcement learning.

Vertical and horizontal elasticity for dynamic virtual machine reconfiguration <=SEP=> Today, cloud computing applications are rapidly constructed by services belonging to different cloud providers and service owners. This work presents the inter-cloud elasticity framework, which focuses on cloud load balancing based on dynamic virtual machine reconfiguration when variations on load or on user requests volume are observed. We design a dynamic reconfiguration system, called inter-cloud load balancer (ICLB), that allows scaling up or down the virtual resources (thus providing automatized elasticity), by eliminating service downtimes and communication failures. It includes an inter-cloud load balancer for distributing incoming user HTTP traffic across multiple instances of inter-cloud applications and services and we perform dynamic reconfiguration of resources according to the real time requirements. The experimental analysis includes different topologies by showing how real-time traffic variation (using real world workloads) affects resource utilization and by achieving better resource usage in inter-cloud.

Harmony Search Algorithm for Solving Sudoku <=SEP=> Harmony search (HS) algorithm was applied to solving Sudoku puzzle. The HS is an evolutionary algorithm which mimics musicians’ behaviors such as random play, memory-based play, and pitch-adjusted play when they perform improvisation. Sudoku puzzles in this study were formulated as an optimization problem with number-uniqueness penalties. HS could successfully solve the optimization problem after 285 function evaluations, taking 9 seconds. Also, sensitivity analysis of HS parameters was performed to obtain a better idea of algorithm parameter values.

Introduction Advances in Text Comprehension : Model , Process and Development <=SEP=> To a very large extent, children learn in and out of school from written text. Information Communications Technologies (ICT) offers many possibilities to facilitate learning by confronting children with multimodal texts. In order to be able to implement learning environments that optimally facilitate children’s learning, insight is needed into the cognitive processes underlying text comprehension. In this light, the aim of this special issue is to report on new advances in text comprehension research in perspective of educational implications. Starting from recent theoretical frameworks on the cognitive processes underlying text comprehension, the online processing of text will be discussed in adults and in school children. Copyright # 2008 John Wiley & Sons, Ltd.

Food Image Recognition Using Very Deep Convolutional Networks <=SEP=> We evaluated the effectiveness in classifying food images of a deep-learning approach based on the specifications of Google's image recognition architecture Inception. The architecture is a deep convolutional neural network (DCNN) having a depth of 54 layers. In this study, we fine-tuned this architecture for classifying food images from three well-known food image datasets: ETH Food-101, UEC FOOD 100, and UEC FOOD 256. On these datasets we achieved, respectively, 88.28%, 81.45%, and 76.17% as top-1 accuracy and 96.88%, 97.27%, and 92.58% as top-5 accuracy. To the best of our knowledge, these results significantly improve the best published results obtained on the same datasets, while requiring less computation power, since the number of parameters and the computational complexity are much smaller than the competitors?. Because of this, even if it is still rather large, the deep network based on this architecture appears to be at least closer to the requirements for mobile systems.

Patterns of HCI Design and HCI Design of Patterns <=SEP=> This chapter introduces the concept of human–computer interaction (HCI) design pattern—also called UI design pattern, interaction design patterns, HCI patterns, user experience pattern and usability engineering pattern. In this book, we mainly use the term HCI Design pattern, but we also use all these terms interchangeably to refer to HCI design pattern. HCI design patterns—have been introduced as a medium to capture and represent users’ experiences as well as to disseminate it as design knowledge. Patterns have the potential of transferring the design expertise of HCI designers to software engineers, who are usually unfamiliar with UI design and usability principles. What is the difference between design patterns and HCI design patterns? Why are they important among HCI designers and SE practitioners? Why design patterns have been considered as a HCI design tool? Why and how HCI design patterns can make a difference? This chapter provides a first answer to these questions that are the key objectives of this book. 1.1 Original Ideas About Design Pattern Among the early attempts to capture and use design knowledge in the format of design patterns, the first major milestone is often attributed to the architect Christopher Alexander, in the late 1970s. In his two books, A Pattern Language (Alexander 1977) and A Timeless Way of Building (Alexander 1979), Alexander, the father of patterns, discusses the avenues to capture and use design knowledge, and presents a large collection of pattern examples to help architects and engineers with the design of buildings, towns, and other urban entities. To illustrate the concept of pattern, Alexander proposes an architectural pattern called Wings of Light (Alexander 1977), where the problem is: Modern buildings are often shaped with no concern for natural light—they depend almost entirely on artificial light. But, buildings which displace natural light as the major source of illumination are not fit places to spend the day. Amongst other information such as design rationale, examples, and links to related patterns, the solution statement to this problem is: 1 The Patterns of HCI Design 2 Arrange each building so that it breaks down into wings which correspond, approximately, to the most important natural social groups within the building. Make each wing long and as narrow as you can—never more than 25 ft wide. According to Alexander, every pattern has three essential elements illustrated in Fig. 1.1, which are: a context, a problem, and a solution. The context describes a recurring set of situations in which the pattern can be applied. The problem refers to a set of forces, i.e., goals and constraints, which occur in the context. Generally, the problem describes when to apply the pattern. The solution refers to a design form or a design rule that can be applied to resolve the forces. It also describes the elements that constitute a pattern, relationships among these elements, as well as responsibilities and collaboration. All of Alexander’s patterns address recurrent problems that designers face by providing a possible solution within a specific context. They follow a similar structure, and the presented information is organized into pattern attributes, such as Problem and Design Rationale. Most noteworthy, the presented solution statement is abstract enough to capture only the invariant properties of good design. The specific pattern implementation is dependent on the design details and the designer’s creativity (Dix et al. 2003). In the example above, there is no mention of specific details such as the corresponding positions of wings to one another, or even the number of wings. These implementation details are left to the designer, allowing different instances of the same pattern solution. In addition, Alexander (1977) recognized that the design and construction of buildings required all stakeholders to make use of a common language for facilitating the implementation of the project from its very beginnings to its completion. If organized properly, patterns could achieve this for all the participants of a design project, acting as a communication tool for design. The pattern concept was not well known until 1987 when patterns appeared again at Object-Oriented Programming, Systems, Languages & Applications (OOPSLA), the object orientation conference in Orlando. There Kent Beck and Ward Cunningham (1987) introduced pattern languages for object-oriented program construction in a seminal paper. Since then many papers and presentations have appeared, authored by renowned software design practitioners such as Grady Booch, Richard Helm, Erich Gamma, and Kent Beck. In 1993, the formation of Hildside Group (1993) by Beck, Cunningham, Coplien, Booch, Johnson and others was the first step forward to forming a design patterns community in the field of software engineering.

Sensor fusion for robot control through deep reinforcement learning <=SEP=> Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the aim for a robot to self-learn useful feature representations from unstructured sensory input leading to the optimal actuation policy. In addition to sensors mounted on the robot, sensors might also be deployed in the environment, although these might need to be accessed via an unreliable wireless connection. In this paper, we demonstrate deep neural network architectures that are able to fuse information generated by multiple sensors and are robust to sensor failures at runtime. We evaluate our method on a search and pick task for a robot both in simulation and the real world.

Construct validity of the Self-Compassion Scale-Short Form among psychotherapy clients <=SEP=> Construct validity of the Self-Compassion ScaleShort Form among psychotherapy clients Jeffrey A. Hayes, Allison J. Lockard, Rebecca A. Janis & Benjamin D. Locke To cite this article: Jeffrey A. Hayes, Allison J. Lockard, Rebecca A. Janis & Benjamin D. Locke (2016): Construct validity of the Self-Compassion Scale-Short Form among psychotherapy clients, Counselling Psychology Quarterly, DOI: 10.1080/09515070.2016.1138397 To link to this article: http://dx.doi.org/10.1080/09515070.2016.1138397

An evolutionary game-theoretic modeling for heterogeneous information diffusion <=SEP=> In this paper, we model and analyze the information diffusion in heterogeneous social networks from an evolutionary game perspective. Users interact with each other according to their individual fitness, which are heterogeneous among different user types. We first study a model where in each social interaction the payoff of a user is independent of the type of the interacted user. In such a case, we derive the information diffusion dynamics of each type of users as well as that of the overall network. The evolutionarily stable states (ESSs) of the dynamics are determined accordingly. Afterwards, we investigate a more general model where in each interaction the payoff of a user depends on the type of the interacted user. We show that the local influence dynamics change much more quickly than the global strategy population dynamics and the former keeps track of the latter throughout the information diffusion process. Based on this observation, the global strategy population dynamics are derived. Finally, simulations are conducted to verify the theoretical results.

Comparative study on power conversion methods for wireless battery charging platform <=SEP=> In this paper, four different power conversion methods (voltage control, duty-cycle control, frequency control and phase-shift control) are compared for wireless power transfer applications by considering the energy transfer efficiency, electromagnetic interference, stability, and implementation complexity. The phase-shift control is found to be the optimal scheme with good efficiency and low electromagnetic interference. Its constant frequency feature is also within the framework of the new international wireless charging standard called ‘Qi’. A high system efficiency of 72% for 5W wireless charging applications has been achieved practically.

A general procedure for the construction of Gorges polygons for multi-phase windings of electrical machines <=SEP=> This paper presents a simple and effective procedure for the determination of the Gorges polygon, suitable for all possible winding configurations in electrical machines. This methodology takes into account the determination of a Winding Distribution Table (WDT), in which all the information about the distribution of the currents along the stator periphery is computed and from which the Görges polygon are easily derived. The proposed method can be applied to both symmetrical and asymmetrical multi-phase windings, including concentrated, fractional, reduced and dead-coil ones. The examples provided in this paper demonstrate the versatility of the proposed method.

Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction <=SEP=> We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles. We create SCIERC, a dataset that includes annotations for all three tasks and develop a unified framework called Scientific Information Extractor (SCIIE) for with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.1

Topic discovery and future trend forecasting for texts <=SEP=> Finding topics from a collection of documents, such as research publications, patents, and technical reports, is helpful for summarizing large scale text collections and the world wide web. It can also help forecast topic trends in the future. This can be beneficial for many applications, such as modeling the evolution of the direction of research and forecasting future trends of the IT industry. In this paper, we propose using association analysis and ensemble forecasting to automatically discover topics from a set of text documents and forecast their evolving trend in a near future. In order to discover meaningful topics, we collect publications from a particular research area, data mining and machine learning, as our data domain. An association analysis process is applied to the collected data to first identify a set of topics, followed by a temporal correlation analysis to help discover correlations between topics, and identify a network of topics and communities. After that, an ensemble forecasting approach is proposed to predict the popularity of research topics in the future. Our experiments and validations on data with 9 years of publication records validate the effectiveness of the proposed design.

Fake News in Social Networks <=SEP=> We model the spread of news as a social learning game on a network. Agents can either endorse or oppose a claim made in a piece of news, which itself may be either true or false. Agents base their decision on a private signal and their neighbors’ past actions. Given these inputs, agents follow strategies derived via multi-agent deep reinforcement learning and receive utility from acting in accordance with the veracity of claims. Our framework yields strategies with agent utility close to a theoretical, Bayes optimal benchmark, while remaining flexible to model re-specification. Optimized strategies allow agents to correctly identify most false claims, when all agents receive unbiased private signals. However, an adversary’s attempt to spread fake news by targeting a subset of agents with a biased private signal can be successful. Even more so when the adversary has information about agents’ network position or private signal. When agents are aware of the presence of an adversary they re-optimize their strategies in the training stage and the adversary’s attack is less effective. Hence, exposing agents to the possibility of fake news can be an effective way to curtail the spread of fake news in social networks. Our results also highlight that information about the users’ private beliefs and their social network structure can be extremely valuable to adversaries and should be well protected.

Comparative Study on Vision Based Rice Seed Varieties Identification <=SEP=> This paper presents a system for automated classification of rice variety for rice seed production using computer vision and image processing techniques. Rice seeds of different varieties are visually very similar in color, shape and texture that make the classification of rice seed varieties at high accuracy challenging. We investigated various feature extraction techniques for efficient rice seed image representation. We analyzed the performance of powerful classifiers on the extracted features for finding the robust one. Images of six different rice seed varieties in northern Vietnam were acquired and analyzed. Our experiments have demonstrated that the average accuracy of our classification system can reach 90.54% using Random Forest method with a simple feature extraction technique. This result can be used for developing a computer-aided machine vision system for automated assessment of rice seeds purity.

Anatomical Variants in Prostate Artery Embolization: A Pictorial Essay <=SEP=> Prostate artery embolization (PAE) has emerged as a new treatment option for patients with symptomatic benign prostatic hyperplasia. The main challenges related to this procedure are navigating arteries with atherosclerosis and anatomical variations, and the potential risk of non-target embolization to pelvic structures due to the presence of collateral shunts and reflux of microparticles. Knowledge of classical vascular anatomy and the most common variations is essential for safe embolization, good clinical practice, and optimal outcomes. The aim of this pictorial essay is to illustrate the pelvic vascular anatomy relevant to PAE in order to provide a practical guide that includes the most common anatomical variants as well as to discuss the technical details related to each.

Lock-free linked lists and skip lists <=SEP=> Lock-free shared data structures implement distributed objects without the use of mutual exclusion, thus providing robustness and reliability. We present a new lock-free implementation of singly-linked lists. We prove that the worst-case amortized cost of the operations on our linked lists is linear in the length of the list plus the contention, which is better than in previous lock-free implementations of this data structure. Our implementation uses backlinks that are set when a node is deleted so that concurrent operations visiting the deleted node can recover. To avoid performance problems that would arise from traversing long chains of backlink pointers, we introduce flag bits, which indicate that a deletion of the next node is underway. We then give a lock-free implementation of a skip list dictionary data structure that uses the new linked list algorithms to implement individual levels. Our algorithms use the single-word C&S synchronization primitive.

A study into the believability of animated characters in the context of bullying intervention <=SEP=> The VICTEC (Virtual ICT with Empathic Characters) project provides an opportunity to explore the use of animated characters in a virtual environment for educational issues such as bullying behaviour. Our research aim was to evaluate whether an early prototype of the VICTEC demonstrator could provide useful information about character and story believability, physical aspects of the characters and story comprehensibility. Results from an evaluation with 76 participants revealed high levels of bullying story believability and character conversation was rated as convincing and interesting. In contrast, character movement was poorly rated. Overall the results imply that poor physical aspects of characters do not have detrimental effects on story believability and interest levels with the demonstrator. It is concluded that even at this early design phase, the demonstrator provides a suitable means to explore how virtual environments in terms of character and storyline believability may assist in the development of systems to deal with the educational issue of bullying.