title: Development and evaluation of a lightweight root cause analysis method (ARCA method) – Field studies at four software companies

abstract: Abstract in Undetermined ::: Context ::: ::: The key for effective problem prevention is detecting the causes of a problem that has occurred. Root cause analysis (RCA) is a structured investigation of the problem to identify which underlying causes need to be fixed. The RCA method consists of three steps: target problem detection, root cause detection, and corrective action innovation. Its results can help with process improvement. ::: Objective ::: ::: This paper presents a lightweight RCA method, named the ARCA method, and its empirical evaluation. In the ARCA method, the target problem detection is based on a focus group meeting. This is in contrast to prior RCA methods, where the target problem detection is based on problem sampling, requiring heavy startup investments. ::: Method ::: ::: The ARCA method was created with the framework of design science. We evaluated it through field studies at four medium-sized software companies using interviews and query forms to collect feedback from the case attendees. A total of five key representatives of the companies were interviewed, and 30 case participants answered the query forms. The output of the ARCA method was also evaluated by the case attendees, i.e., a total 757 target problem causes and 124 related corrective actions. ::: Results ::: ::: The case attendees considered the ARCA method useful and easy to use, which indicates that it is beneficial for process improvement and problem prevention. In each case, 24–77 target problem root causes were processed and 13–40 corrective actions were developed. The effort of applying the method was 89 man-hours, on average. ::: Conclusion ::: ::: The ARCA method required an acceptable level of effort and resulted in numerous high-quality corrective actions. In contrast to the current company practices, the method is an efficient method to detect new process improvement opportunities and develop new process improvement ideas. Additionally, it is easy to use.


title: THE MULTI INPUT-MULTI OUTPUT STATE SPACE AVERAGE MODEL OF KY BUCK-BOOST CONVERTER INCLUDING ALL OF THE SYSTEM PARAMETERS

abstract: In this paper a complete multi input-multi output state-space average model for the KY buck-boost converter is presented. The introduced model includes the most of the regulator’s parameters and uncertainties. In modeling, the load current is assumed to be unknown, and it is assumed that the inductor, capacitor, diode and regulator active switches are non ideal and have a resistance in conduction condition. Some other non ideal effects look like voltage drop of conduction mode of the diode and active switches are also considered. After presenting the complete model, the KY buck-boost converter Benchmark circuit is simulated in PSpice and its results are compared with our model simulation results in MATLAB SIMULINK. The results show the merit of our model.


title: A community-based approach to identify the most influential nodes in social networks

abstract: One of the important issues concerning the spreading process in social networks is the influence maximization. This is the problem of identifying the set of the most influential nodes in order to begin the spreading process based on an information diffusion model in the social networks. In this study, two new methods considering the community structure of the social networks and influence-based closeness centrality measure of the nodes are presented to maximize the spread of influence on the multiplication threshold, minimum threshold and linear threshold information diffusion models. The main objective of this study is to improve the efficiency with respect to the run time while maintaining the accuracy of the final influence spread. Efficiency improvement is obtained by reducing the number of candidate nodes subject to evaluation in order to find the most influential. Experiments consist of two parts: first, the effectiveness of the proposed influence-based closeness centrality measure is established by comparing it with available centrality measures; second, the evaluations are conducted to compare the two proposed community-based methods with well-known benchmarks in the literature on the real datasets, leading to the results demonstrate the efficiency and effectiveness of these methods in maximizing the influence spread in social networks.


title: CVP: an energy-efficient indirect branch prediction with compiler-guided value pattern

abstract: Indirect branch prediction is becoming increasingly important in modern high-performance processors. However, previous indirect branch predictors either require a significant amount of hardware storage and complexity, or heavily rely on the expensive manual profiling. In this paper, we propose the Compiler-Guided Value Pattern (CVP) prediction, an energy-efficient and accurate indirect branch prediction via compiler-microarchitecture cooperation. The key of CVP prediction is to use the compiler-guided value pattern as the correlated information to hint the dynamic predictor. The value pattern reflects the pattern regularity of the value correlation, and thus significantly improves the prediction accuracy even in the case of deep pipeline stage or long memory latency. CVP prediction relies on the compiler to automatically identify the primary value correlation based on three high-level program substructures: virtual function calls, switch-case statements and function pointer calls. The compiler-identified information is then fed back to the dynamic predictor and is further used to hint the indirect branch prediction at runtime. We show that CVP prediction can be implemented in modern processors with little extra hardware support. Evaluations show that CVP prediction can significantly improve the prediction accuracy by 46% over the traditional BTB-based prediction, leading to the performance improvement of 20%. Compared with the state-of-the-art aggressive ITTAGE and VBBI predictors, CVP prediction can improve the performance by 5.5% and 4.2% respectively.


title: Mobile application for guiding tourist activities: tourist assistant - TAIS

abstract: The paper presents category classification of mobile travel applications accessible at the moment for tourists in application stores for most popular mobile operation systems (Android and iOS). The most interesting category is "Travel Guides" that combines "Information Resources" and "Location-Based Services" category. Authors propose application "Tourist assistant - TAIS" that is related to "Travel Guides" category and recommends the tourist attractions around. Information about attractions is extracted from different internet sources.


title: Reconfiguring spatial formation arrangement by robot body orientation

abstract: An information-presenting robot is expected to establish an appropriate spatial relationship with people. Drawing upon sociological studies of spatial relationships involving "F-formation" and "body torque," we examined the effect of a robot rotating its body on the reconfiguration of the F-formation arrangement. The results showed that a robot can change the position of a visitor by rotating its body. We also confirmed that to reconfigure the F-formation arrangement, it is more effective to rotate the whole body of the robot than only its head.


title: What is a Fog Node A Tutorial on Current Concepts towards a Common Definition

abstract: Fog computing has emerged as a promising technology that can bring the cloud applications closer to the physical IoT devices at the network edge. While it is widely known what cloud computing is, and how data centers can build the cloud infrastructure and how applications can make use of this infrastructure, there is no common picture on what fog computing and a fog node, as its main building block, really is. One of the first attempts to define a fog node was made by Cisco, qualifying a fog computing system as a mini-cloud, located at the edge of the network and implemented through a variety of edge devices, interconnected by a variety, mostly wireless, communication technologies. Thus, a fog node would be the infrastructure implementing the said mini-cloud. Other proposals have their own definition of what a fog node is, usually in relation to a specific edge device, a specific use case or an application. In this paper, we first survey the state of the art in technologies for fog computing nodes as building blocks of fog computing, paying special attention to the contributions that analyze the role edge devices play in the fog node definition. We summarize and compare the concepts, lessons learned from their implementation, and show how a conceptual framework is emerging towards a unifying fog node definition. We focus on core functionalities of a fog node as well as in the accompanying opportunities and challenges towards their practical realization in the near future.


title: Weakly-Supervised Cross-Domain Dictionary Learning for Visual Recognition

abstract: We address the visual categorization problem and present a method that utilizes weakly labeled data from other visual domains as the auxiliary source data for enhancing the original learning system. The proposed method aims to expand the intra-class diversity of original training data through the collaboration with the source data. In order to bring the original target domain data and the auxiliary source domain data into the same feature space, we introduce a weakly-supervised cross-domain dictionary learning method, which learns a reconstructive, discriminative and domain-adaptive dictionary pair and the corresponding classifier parameters without using any prior information. Such a method operates at a high level, and it can be applied to different cross-domain applications. To build up the auxiliary domain data, we manually collect images from Web pages, and select human actions of specific categories from a different dataset. The proposed method is evaluated for human action recognition, image classification and event recognition tasks on the UCF YouTube dataset, the Caltech101/256 datasets and the Kodak dataset, respectively, achieving outstanding results.


Role of Food Material Properties and Disintegration Kinetics in Gastric Digestion – A quest for foods for healthy benefits


title: Combating Fraud in Online Social Networks: Characterizing and Detecting Facebook Like Farms

abstract: As businesses increasingly rely on social networking sites to engage with their customers, it is crucial to understand and counter reputation manipulation activities, including fraudulently boosting the number of Facebook page likes using so-called like farms. Thus, social network operators have started to deploy various fraud detection algorithms such as graph clustering methods, however, with limited efficacy. In fact, this paper presents a comprehensive analysis and evaluation of existing graph-based fraud detection algorithms for detecting like farm accounts. Our results show that more sophisticated and stealthy farms can successfully evade detection by spreading likes over longer timespans and by liking many popular pages to mimic normal users. ::: Next, we analyze a wide range of features extracted from users' timeline posts, which we group into two main classes: lexical and interaction-based. We find that like farm accounts tend to more often re-share content, use fewer words and poorer vocabulary, target fewer topics, and generate more (often duplicate) comments and likes compared to normal users. Using these timeline-based features, we experiment with machine learning algorithms to detect like farms accounts, obtaining appreciably high accuracy (as high as 99% precision and 97% recall).


title: Right ventricle-pulmonary circulation dysfunction: a review of energy-based approach

abstract: Patients with repaired or palliated right heart congenital heart disease (CHD) are often left with residual lesions that progress and can result in significant morbidity. However, right ventricular-pulmonary arterial evaluation and the timing of reintvervention is still subjective. Currently, it relies on symptomology, or RV imaging-based metrics from echocardiography or MR derived parameters including right ventricular (RV) ejection fraction (EF), end-systolic pressure (ESP), and end-diastolic volume (EDV). However, the RV is coupled to the pulmonary vasculature, and they are not typically evaluated together. For example, the dysfunctional right ventricular-pulmonary circulation (RV-PC) adversely affects the RV myocardial performance resulting in decreased efficiency. Therefore, comprehensive hemodynamic assessment should incorporate changes in RV-PC and energy efficiency for CHD patients.


title: Trends in consumption of ultra-processed foods and obesity in Sweden between 1960 and 2010

abstract: Objective To investigate how consumption of ultra-processed foods has changed in Sweden in relation to obesity. Design Nationwide ecological analysis of changes in processed foods along with corresponding changes in obesity. Trends in per capita food consumption during 1960–2010 were investigated using data from the Swedish Board of Agriculture. Food items were classified as group 1 (unprocessed/minimally processed), group 2 (processed culinary ingredients) or group 3 (3·1, processed food products; and 3·2, ultra-processed products). Obesity prevalence data were pooled from the peer-reviewed literature, Statistics Sweden and the WHO Global Health Observatory. Setting Nationwide analysis in Sweden, 1960–2010. Subjects Swedish nationals aged 18 years and older. Results During the study period consumption of group 1 foods (minimal processing) decreased by 2 %, while consumption of group 2 foods (processed ingredients) decreased by 34 %. Consumption of group 3·1 foods (processed food products) increased by 116 % and group 3·2 foods (ultra-processed products) increased by 142 %. Among ultra-processed products, there were particularly large increases in soda (315 %; 22 v . 92 litres/capita per annum) and snack foods such as crisps and candies (367 %; 7 v . 34 kg/capita per annum). In parallel to these changes in ultra-processed products, rates of adult obesity increased from 5 % in 1980 to over 11 % in 2010. Conclusions The consumption of ultra-processed products (i.e. foods with low nutritional value but high energy density) has increased dramatically in Sweden since 1960, which mirrors the increased prevalence of obesity. Future research should clarify the potential causal role of ultra-processed products in weight gain and obesity.


title: Calibrating Energy-based Generative Adversarial Networks

abstract: In this paper, we propose to equip Generative Adversarial Networks with the ability to produce direct energy estimates for samples. ::: Specifically, we propose a flexible adversarial training framework, and prove this framework not only ensures the generator converges to the true data distribution, but also enables the discriminator to retain the density information at the global optimal. ::: We derive the analytic form of the induced solution, and analyze the properties. ::: In order to make the proposed framework trainable in practice, we introduce two effective approximation techniques. ::: Empirically, the experiment results closely match our theoretical analysis, verifying the discriminator is able to recover the energy of data distribution.


title: WHEN SOCIAL NETWORKING TURNS TO SOCIAL OVERLOAD: EXPLAINING THE STRESS, EMOTIONAL EXHAUSTION, AND QUITTING BEHAVIOR FROM SOCIAL NETWORK SITES' USERS

abstract: This research responds to a current phenomenon that individuals experience fatigue, while using social network sites, such as Facebook, which original intend to provide hedonic value to users. To explain this current phenomenon, we propose and evaluate a research model based on the stress-strain-outcome model. Focal point is the stressor social overload, which induces feelings of being emotional exhausted. For that reason, some users of social network sites start to get dissatisfied and report an increasing discontinuous usage intention. In addition, the research article provides evidence for the fact that the effect of stress on the two outcome variables satisfaction and discontinuous usage intention is fully mediated through strain. This is validated with an empirical survey with 523 Facebook users. Several implications for technology adoption research are discussed.


title: Real-time Targeted Influence Maximization for Online Advertisements

abstract: Advertising in social network has become a multi-billion-dollar industry. A main challenge is to identify key influencers who can effectively contribute to the dissemination of information. Although the influence maximization problem, which finds a seed set of k most influential users based on certain propagation models, has been well studied, it is not target-aware and cannot be directly applied to online advertising. In this paper, we propose a new problem, named Keyword-Based Targeted Influence Maximization (KB-TIM), to find a seed set that maximizes the expected influence over users who are relevant to a given advertisement. To solve the problem, we propose a sampling technique based on weighted reverse influence set and achieve an approximation ratio of (1 − 1/e − e). To meet the instant-speed requirement, we propose two disk-based solutions that improve the query processing time by two orders of magnitude over the state-of-the-art solutions, while keeping the theoretical bound. Experiments conducted on two real social networks confirm our theoretical findings as well as the efficiency. Given an advertisement with 5 keywords, it takes only 2 seconds to find the most influential users in a social network with billions of edges.


title: Criminalising the right to hunt: European law perspectives on anti-hunting legislation

abstract: The existence of large hunting/shooting communities across Europe is sufficiently widespread that hunters can be classed as a distinct social group or subculture. Hunters are nevertheless not legally recognized as a distinct protected group even where they are granted considerable recognition within legislative and policy discourse related to their interests. Widespread opposition to anti-hunting legislation across Europe suggests a shared resistance to legislation and public policy detrimental to their ‘sport’ among hunters and those engaged in animal harm linked to traditional fieldsports and hunting interests. ::: ::: Socio-legal discourse, however, suggests that states have not only a right, but sometimes an actual obligation, to introduce laws that serve a utilitarian purpose; even where these marginalize certain interest groups, e.g. hunters This article examines these conflicts through analysis of the UK’s Hunting Act 2004 and challenges to its introduction via European Court of Human Rights (ECtHR) action; particularly arguments that hunting falls within Article 8 (right to a private life) Article 11 (freedom of association/assembly) and Article 14 (freedom from discrimination). The ECtHR’s jurisprudence concludes that hunting is an activity that is only protected by human rights law in specific circumstances. Accordingly, employing a green criminological perspective, this article concludes that European states are entitled to regulate or criminalize hunting where they consider there are legitimate animal protection or moral reasons to do so, even in the face of significant opposition from hunting communities. Thus states may also legitimately criminalize and prosecute illegal hunting activity even where this might be socially constructed as resistance to anti-hunting legislation.


Texture Boundary Classification Using Gabor Filters


title: Effect of Repetition Duration During Resistance Training on Muscle Hypertrophy: A Systematic Review and Meta-Analysis

abstract: BACKGROUND ::: Maximizing the hypertrophic response to resistance training (RT) is thought to be best achieved by proper manipulation of exercise program variables including exercise selection, exercise order, length of rest intervals, intensity of maximal load, and training volume. An often overlooked variable that also may impact muscle growth is repetition duration. Duration amounts to the sum total of the concentric, eccentric, and isometric components of a repetition, and is predicated on the tempo at which the repetition is performed. ::: ::: ::: OBJECTIVE ::: We conducted a systematic review and meta-analysis to determine whether alterations in repetition duration can amplify the hypertrophic response to RT. ::: ::: ::: METHODS ::: Studies were deemed eligible for inclusion if they met the following criteria: (1) were an experimental trial published in an English-language refereed journal; (2) directly compared different training tempos in dynamic exercise using both concentric and eccentric repetitions; (3) measured morphologic changes via biopsy, imaging, and/or densitometry; (4) had a minimum duration of 6 weeks; (5) carried out training to muscle failure, defined as the inability to complete another concentric repetition while maintaining proper form; and (6) used human subjects who did not have a chronic disease or injury. A total of eight studies were identified that investigated repetition duration in accordance with the criteria outlined. ::: ::: ::: RESULTS ::: Results indicate that hypertrophic outcomes are similar when training with repetition durations ranging from 0.5 to 8 s. ::: ::: ::: CONCLUSIONS ::: From a practical standpoint it would seem that a fairly wide range of repetition durations can be employed if the primary goal is to maximize muscle growth. Findings suggest that training at volitionally very slow durations (>10s per repetition) is inferior from a hypertrophy standpoint, although a lack of controlled studies on the topic makes it difficult to draw definitive conclusions.


title: Autosub6000: A Deep Diving Long Range AUV

abstract: With an ultimate range up to 1000 km, a maximum operating depth of 6000 m, and a generous payload capacity, Autosub6000 is well placed to become one of the world’s most capable deep diving Autonomous Underwater Vehicles (AUVs). Recently, Autosub6000 successfully completed its first deep water engineering trials, and in September 2008, fitted with a multibeam sonar, will carry out its first science missions. This paper will describe how we are tackling the design issues that specifically affect a deep diving AUV which must be capable of operating with true autonomy, independently of the mother ship, namely: carrying adequate energy for long endurance and range, coping with varying buoyancy, and maintaining accurate navigation throughout missions lasting up to several days. Results from the recent engineering trails are presented, and future missions and development plans are discussed.


Towards High Performance Video Object Detection for Mobiles 3 2 Revisiting Video Object Detection Baseline


title: Higher order whitening of natural images

abstract: Natural images are approximately scale invariant resulting in long range statistical regularities that typically obey a power law. For example, images have considerable regularity in their second order spatial correlations as measured by the power spectrum. Processing images to remove these expected correlations is known as whitening an image. Because the expected value of the power spectrum has a regular form (a power law) linear processing such as convolution can be used to whiten an image. After whitening an image, higher order regularities that cannot be removed with linear processing still exist in the form of correlations in the magnitude. In this paper it is shown that these correlations also obey a power law and a non-linear method is used to remove them, a process referred to as higher order whitening. The method is invertible demonstrating that while redundancy is removed no information is lost. Experiments are given showing that after higher order whitening the coefficients can be severely quantized yet a good reconstruction is possible despite the nonlinearities.


title: Comparing human perceptions of post-editing effort with post-editing operations

abstract: Post-editing performed by translators is an increasingly common use of machine translated texts. While high quality MT may increase productivity, post-editing poor translations can be a frustrating task which requires more effort than translating from scratch. For this reason, estimating whether machine translations are of sufficient quality to be used for post-editing and finding means to reduce post-editing effort are an important field of study. Post-editing effort consists of different aspects, of which temporal effort, or the time spent on post-editing, is the most visible and involves not only the technical effort needed to perform the editing, but also the cognitive effort required to detect and plan necessary corrections. Cognitive effort is difficult to examine directly, but ways to reduce the cognitive effort in particular may prove valuable in reducing the frustration associated with post-editing work. In this paper, we describe an experiment aimed at studying the relationship between technical post-editing effort and cognitive post-editing effort by comparing cases where the edit distance and a manual score reflecting perceived effort differ. We present results of an error analysis performed on such sentences and discuss the clues they may provide about edits requiring great cognitive effort compared to the technical effort, on one hand, or little cognitive effort, on the other.


title: PID control scheme for twin rotor MIMO system using a real valued genetic algorithm with a predetermined search range

abstract: This paper discusses the proportional-integral-derivative control scheme for a twin rotor multiple input multiple output system (TRMS), which is a nonlinear system with two degrees of freedom and cross couplings. The objectives of the work are to stabilize the TRMS, to attain a particular position and to make it to track a trajectory. Four PID controllers are used for achieving the objectives. The parameters of the PID controllers are tuned using the real valued genetic algorithm (RGA). The initial search range of the RGA is obtained using a control design optimization (CDO) technique. The control scheme is initially implemented for the decoupled system viz., horizontal and vertical 1-degree of freedom (1-DOF) systems, using 1 PID controller for each system, and it is followed by the implementation of the control scheme for the 2-degree of freedom (2-DOF) System.


12 Intelligent Flight Control of an Autonomous Quadrotor


title: Runtime Prediction for Scale-Out Data Analytics

abstract: Many analytics applications generate mixed workloads, i.e., workloads comprised of analytical tasks with different processing characteristics including data pre-processing, SQL, and iterative machine learning algorithms. Examples of such mixed workloads can be found in web data analysis, social media analysis, and graph analytics, where they are executed repetitively on large input datasets (e.g., "Find the average user time spent on the top 10 most popular web pages on the UK domain web graph."). Scale-out processing engines satisfy the needs of these applications by distributing the data and the processing task efficiently among multiple workers that are first reserved and then used to execute the task in parallel on a cluster of machines. Finding the resource allocation that can complete the workload execution within a given time constraint, and optimizing cluster resource allocations among multiple analytical workloads motivates the need for estimating the runtime of the workload before its actual execution. Predicting runtime of analytical workloads is a challenging problem as runtime depends on a large number of factors that are hard to model a priori execution. These factors can be summarized as workload characteristics (i.e., data statistics and processing costs), the execution configuration (i.e., deployment, resource allocation, and software settings), and the cost model that captures the interplay among all of the above parameters. While conventional cost models proposed in the context of query optimization can assess the relative order among alternative SQL query plans, they are not aimed to estimate absolute runtime. Additionally, conventional models are ill-equipped to estimate the runtime of iterative analytics that are executed repetitively until convergence and that of user defined data pre-processing operators which are not "owned" by the underlying data management system. This thesis demonstrates that runtime for data analytics can be predicted accurately by breaking the analytical tasks into multiple processing phases, collecting key input features during a reference execution on a sample of the dataset, and then using the features to build per-phase cost models. We develop prediction models for three categories of data analytics produced by social media applications: iterative machine learning, data pre-processing, and reporting SQL. The prediction framework for iterative analytics, PREDIcT, addresses the challenging problem of estimating the number of iterations, and per-iteration runtime for a class of iterative machine learning algorithms that are run repetitively until convergence. The hybrid prediction models we develop for data pre-processing tasks and for reporting SQL combine the benefits of analytical modeling with that of machine learning-based models. Through a training methodology and a pruning algorithm we reduce the cost of running training queries to a minimum while maintaining a good level of accuracy for the models.


title: VLSI IMPLEMENTATION OF INTERNET CHECKSUM CALCULATION FOR 10 GIGABIT ETHERNET

abstract: Computer networks are limited in performance by the electronic equipment. Terminals have received little attention, but need to be redesigned in order to be able to manage 10 Gigabit Ethernet. The Internet checksum computation, which is used in TCP and UDP requires specialized processing resources. The TUCFP hardware accelerator calculates the Internet checksum. It processes 32 bits in parallel and is designed for easy integration in the general purpose protocol processor. It handles UDP as well as TCP packets in both IPv4 and IPv6 environments. A synthesized implementation for 0.18 micron technology proves a throughput of over 12 Gigabits/s.


title: ReplayConfusion: Detecting cache-based covert channel attacks using record and replay

abstract: Cache-based covert channel attacks use highly-tuned shared-cache conflict misses to pass information from a trojan to a spy process. Detecting such attacks is very challenging. State of the art detection mechanisms do not consider the general characteristics of such attacks and, instead, focus on specific communication protocols. As a result, they fail to detect attacks using different protocols and, hence, have limited coverage. In this paper, we make the following observation about these attacks: not only are the malicious accesses highly tuned to the mapping of addresses to the caches; they also follow a distinctive cadence as bits are being received. Changing the mapping of addresses to the caches substantially disrupts the conflict miss patterns, but retains the cadence. This is in contrast to benign programs. Based on this observation, we propose a novel, high-coverage approach to detect cache-based covert channel attacks. It is called ReplayConfusion, and is based on Record and deterministic Replay (RnR). After a program's execution is recorded, it is deterministically replayed using a different mapping of addresses to the caches. We then analyze the difference between the cache miss rate timelines of the two runs. If the difference function is both sizable and exhibits a periodic pattern, it indicates that there is an attack. This paper also introduces a new taxonomy of cache-based covert channel attacks, and shows that ReplayConfusion uncovers examples from all the categories. Finally, ReplayConfusion only needs simple hardware.


title: Conflict-based search for optimal multi-agent pathfinding

abstract: In the multi-agent pathfinding problem (MAPF) we are given a set of agents each with respective start and goal positions. The task is to find paths for all agents while avoiding collisions. Most previous work on solving this problem optimally has treated the individual agents as a single 'joint agent' and then applied single-agent search variants of the A* algorithm.In this paper we present the Conflict Based Search (CBS) a new optimal multi-agent pathfinding algorithm. CBS is a two-level algorithm that does not convert the problem into the single 'joint agent' model. At the high level, a search is performed on a Conflict Tree (CT) which is a tree based on conflicts between individual agents. Each node in the CT represents a set of constraints on the motion of the agents. At the low level, fast single-agent searches are performed to satisfy the constraints imposed by the high level CT node. In many cases this two-level formulation enables CBS to examine fewer states than A* while still maintaining optimality. We analyze CBS and show its benefits and drawbacks.Additionally we present the Meta-Agent CBS (MA-CBS) algorithm. MA-CBS is a generalization of CBS. Unlike basic CBS, MA-CBS is not restricted to single-agent searches at the low level. Instead, MA-CBS allows agents to be merged into small groups of joint agents. This mitigates some of the drawbacks of basic CBS and further improves performance. In fact, MA-CBS is a framework that can be built on top of any optimal and complete MAPF solver in order to enhance its performance. Experimental results on various problems show a speedup of up to an order of magnitude over previous approaches.


title: Performance of a low-power wide-area network based on LoRa technology: Doppler robustness, scalability, and coverage

abstract: The article provides an analysis and reports experimental validation of the various performance metrics of the LoRa low-power wide-area network technology. The LoRa modulation is based on chirp spread spectrum, which enables use of low-quality oscillators in the end device, and to make the synchronization faster and more reliable. Moreover, LoRa technology provides over 150 dB link budget, providing good coverage. Therefore, LoRa seems to be quite a promising option for implementing communication in many diverse Internet of Things applications. In this article, we first briefly overview the specifics of the LoRa technology and analyze the scalability of the LoRa wide-area network. Then, we introduce setups of the performance measurements. The results show that using the transmit power of 14 dBm and the highest spreading factor of 12, more than 60% of the packets are received from the distance of 30 km on water. With the same configuration, we measured the performance of LoRa communication in mobile scenarios. The presented results reveal that at around 40 km/h, the communication performance gets worse, because duration of the LoRa-modulated symbol exceeds coherence time. However, it is expected that communication link is more reliable when lower spreading factors are used.


title: Status and trends of mobile-health applications for iOS devices: A developer's perspective

abstract: Modern smart mobile devices offer media-rich and context-aware features that are highly useful for electronic-health (e-health) applications. It is therefore not surprising that these devices have gained acceptance as target devices for e-health applications, turning them into m-health (mobile-health) apps. In particular, many e-health application developers have chosen Apple's iOS mobile devices such as iPad, iPhone, or iPod Touch as the target device to provide more convenient and richer user experience, as evidenced by the rapidly increasing number of m-health apps in Apple's App Store. In this paper, the top two hundred of such apps from the App Store were examined from a developer's perspective to provide a focused overview of the status and trends of iOS m-health apps and an analysis of related technology, architecture, and user interface design issues. The top 200 apps were classified into different groups according to their purposes, functions, and user satisfaction. It was shown that although the biggest group of apps was medical information reference apps that were delivered from or related to medical articles, websites, or journals, mobile users disproportionally favored tracking tools. It was clear that m-health apps still had plenty of room to grow to take full advantage of unique mobile platform features and truly fulfill their potential. In particular, introduction of two- or three-dimensional visualization and context-awareness could further enhance m-health app's usability and utility. This paper aims to serve as a reference point and guide for developers and practitioners interested in using iOS as a platform for m-health applications, particular from the technical point of view.


title: Condition monitoring of wind turbines: Techniques and methods

abstract: Wind Turbines (WT) are one of the fastest growing sources of power production in the world today and there is a constant need to reduce the costs of operating and maintaining them. Condition monitoring (CM) is a tool commonly employed for the early detection of faults/failures so as to minimise downtime and maximize productivity. This paper provides a review of the state-of-the-art in the CM of wind turbines, describing the different maintenance strategies, CM techniques and methods, and highlighting in a table the various combinations of these that have been reported in the literature. Future research opportunities in fault diagnostics are identified using a qualitative fault tree analysis.


title: Autism Parenting Stress Index: Initial Psychometric Evidence

abstract: Data validating the Autism Parenting Stress Index (APSI) is presented for 274 children under age six. Cronbach’s alpha was .827. As a measure of parenting stress specific to core and co-morbid symptoms of autism, the APSI is unique. It is intended for use by clinicians to identify areas where parents need support with parenting skills, and to assess the effect of intervention on parenting stress. Mean parenting stress in the autism group was four times that of the typical group and double that of the other developmental delay group [F(2,272) = 153; p < 001]. An exploratory factor analysis suggested three factors impacting parenting stress: one relating to core deficits, one to co-morbid behavioral symptoms, and one to co-morbid physical symptoms.


Model of Occupational Performance and Engagement (


title: A novel approach for fast mining frequent itemsets use N-list structure based on MapReduce

abstract: Frequent Pattern Mining is a one field of the most significant topics in data mining. In recent years, many algorithms have been proposed for mining frequent itemsets. A new algorithm has been presented for mining frequent itemsets based on N-list data structure called Prepost algorithm. The Prepost algorithm is enhanced by implementing compact PPC-tree with the general tree. Prepost algorithm can only find a frequent itemsets with required (pre-order and post-order) for each node. In this chapter, we improved prepost algorithm based on Hadoop platform (HPrepost), proposed using the Mapreduce programming model. The main goals of proposed method are efficient mining frequent itemsets requiring less running time and memory usage. We have conduct experiments for the proposed scheme to compare with another algorithms. With dense datasets, which have a large average length of transactions, HPrepost is more effective than frequent itemsets algorithms in terms of execution time and memory usage for all min-sup. Generally, our algorithm outperforms algorithms in terms of runtime and memory usage with small thresholds and large datasets.


title: Development of a vibratory potato digger for small farms

abstract: The performance of the vibrating blade, attached to a two-wheeled and hand-steered 7.35 kw tractor, was improved by increasing frequency while reducing amplitude. Increased frequency assisted the travel speed of the machine by better feeding of materials into the digger blade and by enhancing soil separation. Vibrations with higher amplitude and frequency allowed the machine to travel faster for a given engine and transmission speed of tractor. Slippage of the wheels with lower amplitude and frequency was the major factor limiting the travel speed. The effects of vibrations on the unrecovered and damaged potatoes were not clear. The sum of unrecovered and damaged potatoes ranged from 1.25 to 8.35 percent


title: Big Data Stream Learning with SAMOA

abstract: Big data is flowing into every area of our life, professional and personal. Big data is defined as datasets whose size is beyond the ability of typical software tools to capture, store, manage and analyze, due to the time and memory complexity. Velocity is one of the main properties of big data. In this demo, we present SAMOA (Scalable Advanced Massive Online Analysis), an open-source platform for mining big data streams. It provides a collection of distributed streaming algorithms for the most common data mining and machine learning tasks such as classification, clustering, and regression, as well as programming abstractions to develop new algorithms. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Storm, S4, and Samza. SAMOA is written in Java and is available at http://samoa-project.net under the Apache Software License version 2.0.


title: Sensor fusion for precise autonomous vehicle navigation in outdoor semi-structured environments

abstract: This paper presents a guidance system for autonomous vehicles navigation in semi-structured outdoor environments. It integrates redundant encoders data and absolute positioning data provided by landmarks and artificial beacons. Natural features are localized using a laser range sensor, and magnetic sensing rulers were developed to detect magnetic markers buried in the ground. In the first fusion stage, data from four wheel encoders and one steering encoder are fused by means of an EKF, providing robust odometric information, namely in face of undesirable effects of wheels slippage. Next, a second fusion stage is processed for integrating odometric and absolute positioning data. Simulation and real experiments using a four-wheels actuated electrical vehicle are presented.


title: A location-based news article recommendation with explicit localized semantic analysis

abstract: The interest of users in handheld devices is strongly related to their location. Therefore, the user location is important, as a user context, for news article recommendation in a mobile environment. This paper proposes a novel news article recommendation that reflects the geographical context of the user. For this purpose, we propose the Explicit Localized Semantic Analysis (ELSA), an ESA-based topical representation of documents. Every location has its own geographical topics, which can be captured from the geo-tagged documents related to the location. Thus, not only news articles but locations are also represented as topic vectors. The main advantage of ELSA is that it stresses only the topics that are relevant to a given location, whereas all topics are equally important in ESA. As a result, geographical topics have different importance according to the user location in ELSA, even if they come from the same article. Another advantage of ELSA is that it allows a simple comparison of the user location and news articles, because it projects both locations and articles onto an identical space composed of Wikipedia topics. In the evaluation of ELSA with the New York Times corpus, it outperformed two simple baselines of Bag-Of-Words and LDA as well as two ESA-based methods. Rt10 of ELSA was improved up to 46.25% over other methods, and its NDCG@k was always higher than those of the others regardless of k.


title: A model driven framework to address challenges in a mobile learning environment

abstract: In this paper a review of the pedagogical, technological, policy and research challenges and concepts underlying mobile learning is presented, followed by a brief description of categories of implementations. A model Mobile learning framework and dynamic criteria for mobile learning implementations are proposed, along with a case study of one site that is used to illustrate how the proposed model can be applied. Implementation challenges including pedagogical challenges, technological challenges, policy challenges, and research challenges are described. These align well with the themes of EduSummIT 2013 that hosted the dialogue resulting in this paper.


SVMs for Improved Branch Prediction ECS201A Computer Architecture, Prof. Matthew Farrens


title: Comparison of a 32-bit Vedic multiplier with a conventional binary multiplier

abstract: Binary multipliers and addresses are used in the design and development of Arithmetic Logic Unit (ALU), Digital Signal Processing (DSP) Processors, Multiply and Accumulate (MAC).The objective of this paper is to implement digital multipliers based on the concept of Vedic mathematics. In order to develop a digital multiplier, Urdhva-tiryakbyham sutra of Vedic mathematics is used to implement vertical and cross wise operations. Since these are digital multipliers, they are implemented on FPGA board and are tested through the 8 LED (s) in FPGA (Nexys 3). A 32-bit Vedic multiplier has been simulated in Xilinx ISE 13.4 and has been compared with a 32-bit binary multiplier.


title: Crowdsourced validation of a machine-learning classification system for autism and ADHD

abstract: Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) together affect >10% of the children in the United States, but considerable behavioral overlaps between the two disorders can often complicate differential diagnosis. Currently, there is no screening test designed to differentiate between the two disorders, and with waiting times from initial suspicion to diagnosis upwards of a year, methods to quickly and accurately assess risk for these and other developmental disorders are desperately needed. In a previous study, we found that four machine-learning algorithms were able to accurately (area under the curve (AUC)>0.96) distinguish ASD from ADHD using only a small subset of items from the Social Responsiveness Scale (SRS). Here, we expand upon our prior work by including a novel crowdsourced data set of responses to our predefined top 15 SRS-derived questions from parents of children with ASD (n=248) or ADHD (n=174) to improve our model’s capability to generalize to new, ‘real-world’ data. By mixing these novel survey data with our initial archival sample (n=3417) and performing repeated cross-validation with subsampling, we created a classification algorithm that performs with AUC=0.89±0.01 using only 15 questions.


title: Examining the Impact of Cell Phone Conversations on Driving Using Meta-Analytic Techniques

abstract: OBJECTIVE ::: The performance costs associated with cell phone use while driving were assessed meta-analytically using standardized measures of effect size along five dimensions. ::: ::: ::: BACKGROUND ::: There have been many studies on the impact of cell phone use on driving, showing some mixed findings. ::: ::: ::: METHODS ::: Twenty-three studies (contributing 47 analysis entries) met the appropriate conditions for the meta-analysis. The statistical results from each of these studies were converted into effect sizes and combined in the meta-analysis. ::: ::: ::: RESULTS ::: Overall, there were clear costs to driving performance when drivers were engaged in cell phone conversations. However, subsequent analyses indicated that these costs were borne primarily by reaction time tasks, with far smaller costs associated with tracking (lane-keeping) performance. Hands-free and handheld phones revealed similar patterns of results for both measures of performance. Conversation tasks tended to show greater costs than did information-processing tasks (e.g., word games). There was a similar pattern of results for passenger and remote (cell phone) conversations. Finally, there were some small differences between simulator and field studies, though both exhibited costs in performance for cell phone use. ::: ::: ::: CONCLUSION ::: We suggest that (a) there are significant costs to driver reactions to external hazards or events associated with cell phone use, (b) hands-free cell phones do not eliminate or substantially reduce these costs, and (c) different research methodologies or performance measures may underestimate these costs. ::: ::: ::: APPLICATION ::: Potential applications of this research include the assessment of performance costs attributable to different types of cell phones, cell phone conversations, experimental measures, or methodologies.


title: Challenges and opportunities of adopting business intelligence in SMEs: collaborative model

abstract: The increases of market competition, and the huge amount of data collected by business activities, raise the importance of using Business intelligence (BI) system. BI systems enable firms to benefit from the data and gain insights about the business opportunities, which will lead to a better decision and increase the profit. Large firms can use the existing BI solutions, as they have the financial power and the capacity to use them, whereas the small and medium sized enterprises are overlooked due to the lack of finance. This paper surveys some current BI solutions that can be used by SMEs in attempt to remedy the challenges of adopting BI in SME sector. In addition, a new proposed model is presented to potentially support SMEs to overcome the challenges of adopting BI in SME sector - It is expected that such model could extra level of reliability for SMEs support.


title: Dancing robots: integrating art, music, and robotics in Singapore’s early childhood centers

abstract: In recent years, Singapore has increased its national emphasis on technology and engineering in early childhood education. Their newest initiative, the Playmaker Programme, has focused on teaching robotics and coding in preschool settings. Robotics offers a playful and collaborative way for children to engage with foundational technology and engineering concepts during their formative early childhood years. This study looks at a sample of preschool children (N = 98) from five early childhood centers in Singapore who completed a 7-week STEAM (Science, Technology, Engineering, Arts, and Mathematics) KIBO robotics curriculum in their classrooms called, “Dances from Around the World.” KIBO is a newly developed robotics kit that teaches both engineering and programming. KIBO’s actions are programmed using tangible programming blocks—no screen-time required. Children’s knowledge of programming concepts were assessed upon completion of the curriculum using the Solve-Its assessment. Results indicate that children were highly successful at mastering foundational programming concepts. Additionally, teachers were successful at promoting a collaborative and creative environment, but less successful at finding ways to engage with the greater school community through robotics. This research study was part of a large country-wide initiative to increase the use of developmentally appropriate engineering tools in early childhood settings. Implications for the design of technology, curriculum, and other resources are addressed.


title: Forwarding in a content-based network

abstract: This paper presents an algorithm for content-based forwarding, an essential function in content-based networking. Unlike in traditional address-based unicast or multicast networks, where messages are given explicit destination addresses, the movement of messages through a content-based network is driven by predicates applied to the content of the messages. Forwarding in such a network amounts to evaluating the predicates stored in a router's forwarding table in order to decide to which neighbor routers the message should be sent. We are interested in finding a forwarding algorithm that can make this decision as quickly as possible in situations where there are numerous, complex predicates and high volumes of messages. We present such an algorithm and give the results of studies evaluating its performance.


Localized Langerhans cell histiocytosis masquerading as Brodie's abscess in a 2-year-old child: a case report


title: Emergence of grid-like representations by training recurrent neural networks to perform spatial localization

abstract: Decades of research on the neural code underlying spatial navigation have revealed a diverse set of neural response properties. The Entorhinal Cortex (EC) of the mammalian brain contains a rich set of spatial correlates, including grid cells which encode space using tessellating patterns. However, the mechanisms and functional significance of these spatial representations remain largely mysterious. As a new way to understand these neural representations, we trained recurrent neural networks (RNNs) to perform navigation tasks in 2D arenas based on velocity inputs. Surprisingly, we find that grid-like spatial response patterns emerge in trained networks, along with units that exhibit other spatial correlates, including border cells and band-like cells. All these different functional types of neurons have been observed experimentally. The order of the emergence of grid-like and border cells is also consistent with observations from developmental studies. Together, our results suggest that grid cells, border cells and others as observed in EC may be a natural solution for representing space efficiently given the predominant recurrent connections in the neural circuits.


title: Voluntary changes in step width and step length during human walking affect dynamic margins of stability

abstract: Abstract “Cautious” gait is generally characterized by wider and shorter steps. However, we do not clearly understand the relationship between step characteristics and individuals’ stability. Here, we examined the effects of voluntarily altering step width (SW) and step length (SL) on individuals’ margins of stability. Fourteen participants completed three 3-min treadmill walking trials during three SL (short, normal with metronome, and long) and three SW (narrow, normal and wide) manipulation conditions. SL manipulations yielded significant changes in mean anterior–posterior (AP) margins of stability (MOSap) (p


title: A Multi-stage Dynamic Game-Theoretic Approach for Multi-Workflow Scheduling on Heterogeneous Virtual Machines from Multiple Infrastructure-as-a-Service Clouds

abstract: Distributed computing systems such as clouds continue to evolve to support various types of scientific applications, especially scientific workflows, with dependable, consistent, pervasive, and inexpensive access to geographically-distributed computational capabilities. Scheduling multiple workflows on distributed computing systems like Infrastructure-as-a-Service (IaaS) clouds is well recognized as a fundamental NP-complete problem that is critical to meeting various types of Quality-of-Service (QoS) requirements. In this paper, we propose a multi-objective optimization workflow scheduling approach based on dynamic game-theoretic model aiming at reducing workflow make-spans, reducing total cost, and maximizing system fairness in terms of workload distribution among heterogeneous cloud virtual machines (VMs). We conduct extensive case studies as well based on various well-known scientific workflow templates and real-world third-party commercial IaaS clouds. Experimental results clearly suggest that our proposed approach outperform traditional ones by achieving lower workflow make-spans, lower cost, and better system fairness.


title: Bayesian statistical model checking with application to Simulink/Stateflow verification

abstract: We address the problem of model checking stochastic systems, i.e.~checking whether a stochastic system satisfies a certain temporal property with a probability greater (or smaller) than a fixed threshold. In particular, we present a novel Statistical Model Checking (SMC) approach based on Bayesian statistics. We show that our approach is feasible for hybrid systems with stochastic transitions, a generalization of Simulink/Stateflow models. Standard approaches to stochastic (discrete) systems require numerical solutions for large optimization problems and quickly become infeasible with larger state spaces. Generalizations of these techniques to hybrid systems with stochastic effects are even more challenging. The SMC approach was pioneered by Younes and Simmons in the discrete and non-Bayesian case. It solves the verification problem by combining randomized sampling of system traces (which is very efficient for Simulink/Stateflow) with hypothesis testing or estimation. We believe SMC is essential for scaling up to large Stateflow/Simulink models. While the answer to the verification problem is not guaranteed to be correct, we prove that Bayesian SMC can make the probability of giving a wrong answer arbitrarily small. The advantage is that answers can usually be obtained much faster than with standard, exhaustive model checking techniques. We apply our Bayesian SMC approach to a representative example of stochastic discrete-time hybrid system models in Stateflow/Simulink: a fuel control system featuring hybrid behavior and fault tolerance. We show that our technique enables faster verification than state-of-the-art statistical techniques, while retaining the same error bounds. We emphasize that Bayesian SMC is by no means restricted to Stateflow/Simulink models: we have in fact successfully applied it to very large stochastic models from Systems Biology.


title: The DStar Method for Effective Software Fault Localization

abstract: Effective debugging is crucial to producing reliable software. Manual debugging is becoming prohibitively expensive, especially due to the growing size and complexity of programs. Given that fault localization is one of the most expensive activities in program debugging, there has been a great demand for fault localization techniques that can help guide programmers to the locations of faults. In this paper, a technique named DStar (D*) is proposed which can suggest suspicious locations for fault localization automatically without requiring any prior information on program structure or semantics. D* is evaluated across 24 programs, and is compared to 38 different fault localization techniques. Both single-fault and multi-fault programs are used. Results indicate that D* is more effective at locating faults than all the other techniques it is compared to. An empirical evaluation is also conducted to illustrate how the effectiveness of D* increases as the exponent * grows, and then levels off when the exponent * exceeds a critical value. Discussions are presented to support such observations.


title: A Positive and Unlabeled Learning Algorithm for One-Class Classification of Remote-Sensing Data

abstract: In remote-sensing classification, there are situations when users are only interested in classifying one specific land-cover type, without considering other classes. These situations are referred to as one-class classification. Traditional supervised learning is inefficient for one-class classification because it requires all classes that occur in the image to be exhaustively assigned labels. In this paper, we investigate a new positive and unlabeled learning (PUL) algorithm, applying it to one-class classifications of two scenes of a high-spatial-resolution aerial photograph. The PUL algorithm trains a classifier on positive and unlabeled data, estimates the probability that a positive training sample has been labeled, and generates binary predictions for test samples using an adjusted threshold. Experimental results indicate that the new algorithm provides high classification accuracy, outperforming the biased support-vector machine (SVM), one-class SVM, and Gaussian domain descriptor methods. The advantages of the new algorithm are that it can use unlabeled data to help build classifiers, and it requires only a small set of positive data to be labeled by hand. Therefore, it can significantly reduce the effort of assigning labels to training data without losing predictive accuracy.


title: Broadband Balanced Frequency Doublers With Fundamental Rejection Enhancement Using a Novel Compensated Marchand Balun

abstract: In this paper, a novel compensation technique is proposed to improve the imbalance of a Marchand balun due to the unequal odd- and even-mode phase velocities of the coupled lines. Using this method, the fundamental rejection of the balanced doubler with the Marchand balun can be effectively enhanced. Two single-balanced doublers using the improved Marchand balun are designed, fabricated, and measured to verify the concept in CMOS processes. One doubler for 15–36-GHz possesses $-$ 10-dB conversion gain with the 3-dB bandwidth of 82.4% and the fundamental rejection of 33 dB. The other doubler for 95–150 GHz achieves $-$ 7.9-dB conversion gain with the 3-dB bandwidth of 45% and the fundamental rejection of 30 dB. With the proposed compensation technique, these frequency doublers feature wide bandwidths and high fundamental rejections.


title: Harmonic Performance Evaluation of CMOS SOI SPDT Switch with Embedded Lateral Substrate Model

abstract: This paper describes a single pole, double throw (SPDT) CMOS SOI switch in 180nm Technology developed for the GSM 900MHz RF switch applications. Silicon-on-Insulator (SOI) CMOS FETs have many properties which are desirable for RF switch applications. By being manufactured on an insulator substrate, the bulk parasitic capacitances typical of CMOS FETs are eliminated. The SOI FET has a very low Ron-Coff product, allowing for low insertion loss and high isolation in high frequency applications. Despite the low breakdown voltage intrinsic to Si, SOI FETs can be stacked in series to withstand high voltages. This work discuss Harmonic Performance and Power handling behavior of SOI CMOS Switch with non-linear lateral substrate model as a function of gate and body biasing voltages.


title: Interpretation of Natural Language Rules in Conversational Machine Reading

abstract: Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. However, many real-world question answering problems require the reading of text not because it contains the literal answer, but because it contains a recipe to derive an answer together with the reader's background knowledge. One example is the task of interpreting regulations to answer "Can I...?" or "Do I have to...?" questions such as "I am working in Canada. Do I have to carry on paying UK National Insurance?" after reading a UK government website about this topic. This task requires both the interpretation of rules and the application of background knowledge. It is further complicated due to the fact that, in practice, most questions are underspecified, and a human assistant will regularly have to ask clarification questions such as "How long have you been working abroad?" when the answer cannot be directly derived from the question and text. In this paper, we formalise this task and develop a crowd-sourcing strategy to collect 32k task instances based on real-world rules and crowd-generated questions and scenarios. We analyse the challenges of this task and assess its difficulty by evaluating the performance of rule-based and machine-learning baselines. We observe promising results when no background knowledge is necessary, and substantial room for improvement whenever background knowledge is needed.


Thring Signatures and their Applications to Spender-Ambiguous Digital Currencies.


title: Proof of Activity: Extending Bitcoin's Proof of Work via Proof of Stake [Extended Abstract]y

abstract: We propose a new protocol for a cryptocurrency, that builds upon the Bitcoin protocol by combining its Proof of Work component with a Proof of Stake type of system. Our Proof of Activity protocol offers good security against possibly practical attacks on Bitcoin, and has a relatively low penalty in terms of network communication and storage space.


title: Deep Multi-patch Aggregation Network for Image Style, Aesthetics, and Quality Estimation

abstract: This paper investigates problems of image style, aesthetics, and quality estimation, which require fine-grained details from high-resolution images, utilizing deep neural network training approach. Existing deep convolutional neural networks mostly extracted one patch such as a down-sized crop from each image as a training example. However, one patch may not always well represent the entire image, which may cause ambiguity during training. We propose a deep multi-patch aggregation network training approach, which allows us to train models using multiple patches generated from one image. We achieve this by constructing multiple, shared columns in the neural network and feeding multiple patches to each of the columns. More importantly, we propose two novel network layers (statistics and sorting) to support aggregation of those patches. The proposed deep multi-patch aggregation network integrates shared feature learning and aggregation function learning into a unified framework. We demonstrate the effectiveness of the deep multi-patch aggregation network on the three problems, i.e., image style recognition, aesthetic quality categorization, and image quality estimation. Our models trained using the proposed networks significantly outperformed the state of the art in all three applications.


title: Application of Public Ledgers to Revocation in Distributed Access Control

abstract: There has recently been a flood of interest in potential new applications of blockchains, as well as proposals for more generic designs called public ledgers. Most of the novel proposals have been in the financial sector. However, the public ledger is an abstraction that solves several of the fundamental problems in the design of secure distributed systems: global time in the form of a strict linear order of past events, globally consistent and immutable view of the history, and enforcement of some application-specific safety properties. This paper investigates the applications of public ledgers to access control and, more specifically, to group management in distributed systems where entities are represented by their public keys and authorization is encoded into signed certificates. It is particularly difficult to handle negative information, such as revocation of certificates or group membership, in the distributed setting. The linear order of events and global consistency simplify these problems, but the enforcement of internal constraints in the ledger implementation often presents problems. We show that different types of revocation require slightly different properties from the ledger. We compare the requirements with Bitcoin, the best known blockchain, and describe an efficient ledger design for membership revocation that combines ideas from blockchains and from web-PKI monitoring. While we use certificate-based group-membership management as the case study, the same ideas can be applied more widely to rights revocation in distributed systems.


title: Micellar solubilization of biopolymers in hydrocarbon solvents. ii. the case of horse liver alcohol dehydrogenase

abstract: The chemical characterization of horse liver alcohol dehydrogenase solubilized in isooctane via reverse micelles formed by the anionic surfactant di (2-ethyl-hexyl) sodium sulfosuccinate (AOT) and water (0.6 to 4% v/v) is presented. The enzyme’s catalytic activity toward acetaldehyde reduction is markedly dependent upon w0 = [H2O]/[AOT], and upon the pH of the stock aqueous solution (pHst), from which the hydrocarbon enzyme solution is prepared. Kinetically, the micellar solution appears to follow a normal Michaelis-Menten behavior, with a turnover number which, under the optimal conditions (w0 = 42, pHst = 8.8), appears to be higher than in bulk water. The affinity between enzyme and NADH, as judged from direct binding studies (quenching of the protein fluorescence), is much reduced with respect to water if concentrations refer to the water pool of the micelles, and comparable to water if concentrations refer to the overall volume (hydrocarbon plus water pool). Also, the Km values are much higher if concentrations refer to the water pool. Ultraviolet absorption studies show that the aromatic chromophores are not significantly perturbed on going from a water solution to the micellar solution. The essentially aqueous environment of the protein in the reverse micelles is confirmed by fluoresence studies. Circular dichroism studies show that the enzyme’s conformation in the micelles is similar to that in water; however, under certain conditions, small but significant changes of the main chain folding seem to occur, which do not impair enzymatic activity. The spectroscopic properties of NADH in the hydrocarbon phase (fluorescence and circular dichroism) are also investigated. The potential of the LADH-NADH system for technical applications (oxidoreduction of lipophylic substrates) is discussed.


title: Who Killed Albert Einstein? From Open Data to Murder Mystery Games

abstract: This paper presents a framework for generating adventure games from open data. Focusing on the murder mystery type of adventure games, the generator is able to transform open data from Wikipedia articles, OpenStreetMap, and images from Wikimedia Commons into WikiMysteries. Every WikiMystery game revolves around the murder of a person with a Wikipedia article, and populates the game with suspects who must be arrested by the player if guilty of the murder or absolved if innocent. Starting from only one person as the victim, an extensive generative pipeline finds suspects, their alibis, and paths connecting them from open data, transforms open data into cities, buildings, nonplayer characters, locks and keys, and dialog options. This paper describes in detail each generative step, provides a specific playthrough of one WikiMystery where Albert Einstein is murdered, and evaluates the outcomes of games generated for the 100 most influential people of the 20th century.


title: Advanced Wi-Fi attacks using commodity hardware

abstract: We show that low-layer attacks against Wi-Fi can be implemented using user-modifiable firmware. Hence cheap off-the-shelf Wi-Fi dongles can be used carry out advanced attacks. We demonstrate this by implementing five low-layer attacks using open source Atheros firmware. The first attack consists of unfair channel usage, giving the user a higher throughput while reducing that of others. The second attack defeats countermeasures designed to prevent unfair channel usage. The third attack performs continuous jamming, making the channel unusable for other devices. For the fourth attack we implemented a selective jammer, allowing one to jam specific frames already in the air. The fifth is a novel channel-based Man-in-the-Middle (MitM) attack, enabling reliable manipulation of encrypted traffic. These low-layer attacks facilitate novel attacks against higher-layer protocols. To demonstrate this we show how our MitM attack facilitates attacks against the Temporal Key Integrity Protocol (TKIP) when used as a group cipher. Since a substantial number of networks still use TKIP as their group cipher, this shows that weaknesses in TKIP have a higher impact than previously thought.


title: A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-based Discovery

abstract: In this paper, we present a model which incorporates biomedical knowledge graph, graph embedding and deep learning methods for literature-based discovery. Firstly, the relations between entities are extracted from biomedical abstracts and then a knowledge graph is constructed by using these obtained relations. Secondly, the graph embedding technologies are applied to convert the entities and relations in the knowledge graph into a low-dimensional vector space. Thirdly, a bidirectional Long Short-Term Memory network is trained based on the entity associations represented by the pre-trained graph embeddings. Finally, the learned model is used for open and closed literature-based discovery tasks. The experimental results show that our method could not only effectively discover hidden associations between entities, but also reveal the corresponding mechanism of interactions. It suggests that incorporating knowledge graph and deep learning methods is an effective way for capturing the underlying complex associations between entities hidden in the literature.


Computer and natural language texts – a comparison based on longrange correlations


title: Using popular social network sites to support requirements elicitation, prioritization and negotiation

abstract: Social networks have changed our daily life and they have the potential to significantly influence and support Requirements Engineering (RE) activities. Social network-based RE approaches will allow us to overcome limitations of traditional approaches and allow end users to play a more prominent role in RE. They are key stakeholders in many software projects. However, involving end users is challenging, particularly when they are not within organizational reach. The goal of our work is to increase end user involvement in RE. In this paper we present an approach where we harness a social network to perform RE activities such as elicitation, prioritization and negotiation. Our approach was applied in three studies where students used Facebook to actively participate in RE activities of different projects. Although there are limitations, the results show that a popular social network site can support distributed RE.


title: A framework for truthful online auctions in cloud computing with heterogeneous user demands

abstract: The paradigm of cloud computing has spontaneously prompted a wide interest in market-based resource allocation mechanisms by which a cloud provider aims at efficiently allocating cloud resources among potential users. Among these mechanisms, auction-style pricing policies, as they can effectively reflect the underlying trends in demand and supply for the computing resources, have attracted a research interest recently. This paper conducts the first work on a framework for truthful online cloud auctions where users with heterogeneous demands could come and leave on the fly. Our framework desirably supports a variety of design requirements, including (1) dynamic design for timely reflecting fluctuation of supply-demand relations, (2) joint design for supporting the heterogeneous user demands, and (3) truthful design for discouraging bidders from cheating behaviors. Concretely speaking, we first design a novel bidding language, wherein users' heterogeneous demands are generalized to regulated and consistent forms. Besides, building on top of our bidding language we propose COCA, an incentive-Compatible (truthful) Online Cloud Auction mechanism based on two proposed guidelines. Our theoretical analysis shows that the worst-case performance of COCA can be well-bounded. Further, in simulations the performance of COCA is seen to be comparable to the well-known off-line Vickrey-Clarke-Groves (VCG) mechanism [11].


title: A STUDY ON FARSI HANDWRITING STYLES FOR ONLINE RECOGNITION

abstract: Knowing varieties of writing a letter in a word or a subword in different handwriting styles is very beneficial in recognition specifically for online recognition. In this paper, TMU-OFS dataset consisting of 1000 frequent Farsi subwords is employed to study Farsi handwriting styles. The subwords are grouped based on their delayed strokes and their main bodies, separately. The handwriting styles in this dataset are analyzed and the wrongly spelled or incorrect structural samples are extracted. Finally, the second version of the dataset is introduced by considering the handwriting styles. The preliminarily results show a significant improvement in recognition of subwords based on their styles.


title: Astley S: Classification of breast tissue by texture analysis

abstract: The identification of glandular tissue in breast X-rays (mammograms) is important both in assessing asymmetry between left and right breasts, and in estimating the radiation risk associated with mammographic screening. The appearance of glandular tissue in mammograms is highly variable, ranging from sparse streaks to dense blobs. Fatty regions are generally smooth and dark. Texture analysis provides a flexible approach to discriminating between glandular and fatty regions. We have performed a series of experiments investigating the use of granulometry and texture energy to classify breast tissue. Results of automatic classifications have been compared with a consensus annotation provided by two expert breast radiologists. On a set of 40 mammograms, a correct classification rate of 80% has been achieved using texture energy analysis.


title: Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs

abstract: Convolutional neural networks (CNN) are the current stateof-the-art for many computer vision tasks. CNNs outperform older methods in accuracy, but require vast amounts of computation and memory. As a result, existing CNN applications are typically run on clusters of CPUs or GPUs. Studies into the FPGA acceleration of CNN workloads has achieved reductions in power and energy consumption. However, large GPUs outperform modern FPGAs in throughput, and the existence of compatible deep learning frameworks give GPUs a significant advantage in programmability. Recent research in machine learning demonstrates the potential of very low precision CNNs -- i.e., CNNs with binarized weights and activations. Such binarized neural networks (BNNs) appear well suited for FPGA implementation, as their dominant computations are bitwise logic operations and their memory requirements are reduced. A combination of low-precision networks and high-level design methodology may help address the performance and productivity gap between FPGAs and GPUs. In this paper, we present the design of a BNN accelerator that is synthesized from C++ to FPGA-targeted Verilog. The accelerator outperforms existing FPGA-based CNN accelerators in GOPS as well as energy and resource efficiency.


title: Autobiographical memory after temporal lobe resection: neuropsychological and MRI volumetric findings

abstract: This study examined the contribution of medial temporal lobe (MTL) structures in autobiographical memory. While some investigators have reported a temporal gradient in memory performance, characterized by retrieval difficulties limited to recent periods of life [Squire and Alvarez (Retrograde amnesia and memory consolidation: a neurobiological perspective. Curr Opin Neurobiol 1995; 5: 169–77)], others have suggested that this impairment involves all life-time periods [Nadel and Moscovitch (Memory consolidation, retrograde amnesia and the hippocampal complex. Curr Opin Neurobiol 1997; 7: 217–27)]. In this study, autobiographical memory was assessed in 22 patients who had undergone a left ( n = 12) or a right ( n = 10) MTL resection for the relief of epileptic seizures and in 22 normal control participants. For this purpose, we used an autobiographical memory task (TEMPau, Piolino et al. , 2003) across four time periods covering the subjects’ entire lifespan. For each period, an overall autobiographical memory score (AM score) was obtained, from which a strictly episodic score (SE score), characterized by specificity and richness of details, was computed. For all events recalled, Remember responses justified by specificity of factual, spatial and temporal contents (jR responses) were measured using the Remember/Know paradigm. MRI volumetric analyses performed on the medial (i.e. hippocampus, temporopolar, entorhinal, perirhinal and parahippocampal cortices) and lateral temporal (i.e. superior, middle and inferior temporal gyri) lobe structures stated that the resection mainly included MTL structures. AM and SE scores were impaired in patients with right and left MTL resections as compared to normal controls across all time periods, reflecting the patients’ particular difficulty in producing specific and detailed memories across all periods. This impairment was associated with poor autonoetic consciousness, revealed by the small number of jR responses across all periods. Results of correlation analysis between MRI volume measures of temporal lobe structures and autobiographical memory scores suggest that the right MTL structures are particularly responsive in reliving the encoding context regardless of remoteness. Our results support the bilateral MTL contribution to episodic autobiographical memory covering the entire lifespan, which is consistent with the multiple trace theory of MTL function [Moscovitch et al. (Functional neuroanatomy of remote episodic, semantic and spatial memory: a unified account based on multiple trace theory. J Anat 2005; 207: 35–66.)].


title: Localized image watermarking based on feature points of scale-space representation

abstract: This paper proposes a novel method for content-based watermarking based on feature points of an image. At each feature point, the watermark is embedded after scale normalization according to the local characteristic scale. Characteristic scale is the maximum scale of the scale-space representation of an image at the feature point. By binding watermarking with the local characteristics of an image, resilience against affine transformations can be obtained easily. Experimental results show that the proposed method is robust against various image processing steps including affine transformations, cropping, filtering and JPEG compression.


title: Context-Aware Wireless Sensors for IoT-Centeric Energy-Efficient Campuses

abstract: Energy Efficiency is becoming a world-wide concern and attracting increasing interest in both industry and academia. A smart building is at the cornerstone of energy-efficiency as it represents the main constituent in a smart micro-grid. To promote energy-efficiency in buildings, an Energy Management System (EMSs) that controls HVAC appliances is indispensable. Based on the Plan-Do- Check-Act (PDCA) cycle, an EMS needs to handle data acquisition, data analysis, and acting. In this paper, we present a real-world EMS deployed in a real-world building. We used wireless sensors, along with microcontrollers, to implement the networking component that connects data readers, actuators, and the control plane where the database lies. To test our platform, we sense rooms' temperature and react upon corresponding heaters. The presented solution is promoted for a wide deployment covering a whole university campus.


title: Publication Trends Over 10 Years of Computational Thinking Research

abstract: The current study aimed to review studies on computational thinking (CT) indexed in Web of Science (WOS) and ERIC databases. A thorough search in electronic databases revealed 96 studies on computational thinking which were published between 2006 and 2016. Studies were exposed to a quantitative content analysis through using an article control form developed by the researchers. Studies were summarized under several themes including the research purpose, design, methodology, sampling characteristics, data analysis, and main findings. The findings were reported using descriptive statistics to see the trends. It was observed that there was an increase in the number of CT studies in recent years, and these were mainly conducted in the field of computer sciences. In addition, CT studies were mostly published in journals in the field of Education and Instructional Technologies. Theoretical paradigm and literature review design were preferred more in previous studies. The most commonly used sampling method was the purposive sampling. It was also revealed that samples of previous CT studies were generally pre-college students. Written data collection tools and quantitative analysis were mostly used in reviewed papers. Findings mainly focused on CT skills. Based on current findings, recommendations and implications for further researches were provided.


A Privacy-Preserving Deep Learning Approach for Face Recognition with Edge Computing


A Singing Robot Realized by a Collaboration of VOCALOID and Cybernetic Human HRP-4 C


title: Understanding Intra-Class Knowledge Inside CNN

abstract: Convolutional Neural Network (CNN) has been successful in image recognition tasks, and recent works shed lights on how CNN separates different classes with the learned inter-class knowledge through visualization. In this work, we instead visualize the intra-class knowledge inside CNN to better understand how an object class is represented in the fully-connected layers. ::: To invert the intra-class knowledge into more interpretable images, we propose a non-parametric patch prior upon previous CNN visualization models. With it, we show how different "styles" of templates for an object class are organized by CNN in terms of location and content, and represented in a hierarchical and ensemble way. Moreover, such intra-class knowledge can be used in many interesting applications, e.g. style-based image retrieval and style-based object completion.


title: Planning algorithms for s-curve trajectories

abstract: Although numerous researches on s-curve motion profiles have been carried out, up to date, no systematic investigation on the general model of polynomial s-curve motion profiles is considered. In this paper, the model of polynomial s-curve motion profiles is generalized in a recursive form. Based on that, a general algorithm to design s-curve trajectory with jerk bounded and time-optimal consideration is proposed. In addition, a special strategy for planning s-curve motion profiles using a trigonometric model is also presented. The algorithms are implemented on a linear motor system. Experimental results show the effectiveness and promising application ability of the algorithms in s-curve motion profiling.


title: Modelling Irony in Twitter: Feature Analysis and Evaluation

abstract: Irony, a creative use of language, has received scarce attention from the computational linguistics research point of view. We propose an automatic system capable of detecting irony with good accuracy in the social network Twitter. Twitter allows users to post short messages (140 characters) which usually do not follow the expected rules of the grammar, users tend to truncate words and use particular punctuation. For these reason automatic detection of Irony in Twitter is not trivial and requires specific linguistic tools. We propose in this paper a new set of experiments to assess the relevance of the features included in our model. Our model does not include words or sequences of words as features, aiming to detect inner characteristic of Irony.


title: CAUSATION, ACTION, AND COUNTERFACTUALS

abstract: The central aim of many empirical studies in the physical, behavioral, social, and biological sciences is the elucidation of cause-effect relationships among variables. It is through cause-effect relationships that we obtain a sense of a "deep understanding" of a given phenomenon, and it is through such relationships that we obtain a sense of being "in control," namely, that we are able to shape the course of events by deliberate actions or policies. It is for these two reasons, understanding and control, that causal thinking is so pervasive, popping up in everything from everyday activities to high-level decision-making: For example, every car owner wonders why an engine won't start; a cigarette smoker would like to know, given his/her specific characteristics, to what degree his/her health would be affected by refraining from further smoking; a policy maker would like to know to what degree anti-smoking advertising would reduce costs of health care; and so on. Although a plethora of data has been collected on cars and on smoking and health, the appropriate methodology for extracting answers to such questions from the data has been fiercely debated, partly because some fundamental questions of causality have not been given fully satisfactory answers.


title: Probabilistic visual search for masses within mammography images using deep learning

abstract: We developed a deep learning-based visual search system for the task of automated search and localization of masses in whole mammography images. The system consists of two modules: a classification engine and a localization engine. It first classifies mammograms as containing a mass or no mass using a deep learning classifier, and then localizes the mass(es) within the image using a regional probabilistic approach based on a deep learning network. We obtained 85% accuracy for the task of identifying images that contain a mass, and we were able to localize 85% of the masses at an average of 0.9 false positives per image. Our system has the advantages of being able to work with an entire mammography image as input without the need for image segmentation or other pre-processing steps, such as cropping or tiling the image, and it is based on deep learning with unsupervised feature discovery, so it does not require pre-defined and hand-crafted image features.


title: Spatial and Linguistic Aspects of Visual Imagery in Sentence Comprehension

abstract: There is mounting evidence that language comprehension involves the activation of mental imagery of the content of utterances (Barsalou, 1999; Bergen, Chang, & Narayan, 2004; Bergen, Narayan, & Feldman, 2003; Narayan, Bergen, & Weinberg, 2004; Richardson, Spivey, McRae, & Barsalou, 2003; Stanfield & Zwaan, 2001; Zwaan, Stanfield, & Yaxley, 2002). This imagery can have motor or perceptual content. Three main questions about the process remain under-explored, however. First, are lexical associations with perception or motion sufficient to yield mental simulation, or is the integration of lexical semantics into larger structures, like sentences, necessary? Second, what linguistic elements (e.g., verbs, nouns, etc.) trigger mental simulations? Third, how detailed are the visual simulations that are performed? A series of behavioral experiments address these questions, using a visual object categorization task to investigate whether up- or down-related language selectively interferes with visual processing in the same part of the visual field (following Richardson et al., 2003). The results demonstrate that either subject nouns or main verbs can trigger visual imagery, but only when used in literal sentences about real space—metaphorical language does not yield significant effects—which implies that it is the comprehension of the sentence as a whole and not simply lexical associations that yields imagery effects. These studies also show that the evoked imagery contains detail as to the part of the visual field where the described scene would take place.


title: MADE: Masked Autoencoder for Distribution Estimation

abstract: There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples. We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder's parameters to respect autoregressive constraints: each input is reconstructed only from previous inputs in a given ordering. Constrained this way, the autoencoder outputs can be interpreted as a set of conditional probabilities, and their product, the full joint probability. We can also train a single network that can decompose the joint probability in multiple different orderings. Our simple framework can be applied to multiple architectures, including deep ones. Vectorized implementations, such as on GPUs, are simple and fast. Experiments demonstrate that this approach is competitive with state-of-the-art tractable distribution estimators. At test time, the method is significantly faster and scales better than other autoregressive estimators.


title: Preference elicitation as an optimization problem

abstract: The new user coldstart problem arises when a recommender system does not yet have any information about a user. A common solution to it is to generate a profile by asking the user to rate a number of items. Which items are selected determines the quality of the recommendations made, and thus has been studied extensively. We propose a new elicitation method to generate a static preference questionnaire (SPQ) that poses relative preference questions to the user. Using a latent factor model, we show that SPQ improves personalized recommendations by choosing a minimal and diverse set of questions. We are the first to rigorously prove which optimization task should be solved to select each question in static questionnaires. Our theoretical results are confirmed by extensive experimentation. We test the performance of SPQ on two real-world datasets, under two experimental conditions: simulated, when users behave according to a latent factor model (LFM), and real, in which only real user judgments are revealed as the system asks questions. We show that SPQ reduces the necessary length of a questionnaire by up to a factor of three compared to state-of-the-art preference elicitation methods. Moreover, solving the right optimization task, SPQ also performs better than baselines with dynamically generated questions.


Sentiment Analysis using Imperfect Views from Spoken Language and Acoustic Modalities


title: Association of PD-1, PD-1 Ligands, and Other Features of the Tumor Immune Microenvironment with Response to Anti-PD-1 Therapy

abstract: PURPOSE ::: Immunomodulatory drugs differ in mechanism-of-action from directly cytotoxic cancer therapies. Identifying factors predicting clinical response could guide patient selection and therapeutic optimization. ::: ::: ::: EXPERIMENTAL DESIGN ::: Patients (N = 41) with melanoma, non-small cell lung carcinoma (NSCLC), renal cell carcinoma (RCC), colorectal carcinoma, or castration-resistant prostate cancer were treated on an early-phase trial of anti-PD-1 (nivolumab) at one institution and had evaluable pretreatment tumor specimens. Immunoarchitectural features, including PD-1, PD-L1, and PD-L2 expression, patterns of immune cell infiltration, and lymphocyte subpopulations, were assessed for interrelationships and potential correlations with clinical outcomes. ::: ::: ::: RESULTS ::: Membranous (cell surface) PD-L1 expression by tumor cells and immune infiltrates varied significantly by tumor type and was most abundant in melanoma, NSCLC, and RCC. In the overall cohort, PD-L1 expression was geographically associated with infiltrating immune cells (P < 0.001), although lymphocyte-rich regions were not always associated with PD-L1 expression. Expression of PD-L1 by tumor cells and immune infiltrates was significantly associated with expression of PD-1 on lymphocytes. PD-L2, the second ligand for PD-1, was associated with PD-L1 expression. Tumor cell PD-L1 expression correlated with objective response to anti-PD-1 therapy, when analyzing either the specimen obtained closest to therapy or the highest scoring sample among multiple biopsies from individual patients. These correlations were stronger than borderline associations of PD-1 expression or the presence of intratumoral immune cell infiltrates with response. ::: ::: ::: CONCLUSIONS ::: Tumor PD-L1 expression reflects an immune-active microenvironment and, while associated other immunosuppressive molecules, including PD-1 and PD-L2, is the single factor most closely correlated with response to anti-PD-1 blockade. Clin Cancer Res; 20(19); 5064-74. ©2014 AACR.


title: One Sentence One Model for Neural Machine Translation

abstract: Neural machine translation (NMT) becomes a new state-of-the-art and achieves promising translation results using a simple encoder-decoder neural network. This neural network is trained once on the parallel corpus and the fixed network is used to translate all the test sentences. We argue that the general fixed network cannot best fit the specific test sentences. In this paper, we propose the dynamic NMT which learns a general network as usual, and then fine-tunes the network for each test sentence. The fine-tune work is done on a small set of the bilingual training data that is obtained through similarity search according to the test sentence. Extensive experiments demonstrate that this method can significantly improve the translation performance, especially when highly similar sentences are available.


title: Using Word Embeddings for Visual Data Exploration with Ontodia and Wikidata

abstract: One of the big challenges in Linked Data consumption is to create visual and natural language interfaces to the data usable for non-technical users. Ontodia provides support for diagrammatic data exploration, showcased in this publication in combination with the Wikidata dataset. We present improvements to the natural language interface regarding exploring and querying Linked Data entities. The method uses models of distributional semantics to find and rank entity properties related to user input in Ontodia. Various word embedding types and model settings are evaluated, and the results show that user experience in visual data exploration benefits from the proposed approach.