Reviewerly/exHarmony huggingface dataset:
Quantum Conditional Strategies and Automata for Prisoners’ Dilemmata under the EWL Scheme

Classical game theory is an important field with a long tradition of useful results. Recently, the quantum versions of classical games, such as the prisoner’s dilemma (PD), have attracted a lot of attention. This game variant can be considered as a specific type of game where the player’s actions and strategies are formed using notions from quantum computation. Similarly, state machines, and specifically finite automata, have also been under constant and thorough study for plenty of reasons. The quantum analogues of these abstract machines, like the quantum finite automata, have been studied extensively. In this work, we examine well-known conditional strategies that have been studied within the framework of the classical repeated PD game. Then, we try to associate these strategies to proper quantum finite automata that receive them as inputs and recognize them with a probability of 1, achieving some interesting results. We also study the quantum version of PD under the Eisert–Wilkens–Lewenstein scheme, proposing a novel conditional strategy for the repeated version of this game.
Probabilistic Planning for Robotics with ROSPlan

Probabilistic planning is very useful for handling uncertainty in planning tasks to be carried out by robots. ROSPlan is a framework for task planning in the Robot Operating System (ROS), but until now it has not been possible to use probabilistic planners within the framework. This systems paper presents a standardized integration of probabilistic planners into ROSPlan that allows for reasoning with non-deterministic effects and is agnostic to the probabilistic planner used. We instantiate the framework in a system for the case of a mobile robot performing tasks indoors, where probabilistic plans are generated and executed by the PROST planner. We evaluate the effectiveness of the proposed approach in a real-world robotic scenario.
Empirical comparison of text-based mobile apps similarity measurement techniques

Code-free software similarity detection techniques have been used to support different software engineering tasks, including clustering mobile applications (apps). The way of measuring similarity may affect both the efficiency and quality of clustering solutions. However, there has been no previous comparative study of feature extraction methods used to guide mobile app clustering. In this paper, we investigate different techniques to compute the similarity of apps based on their textual descriptions and evaluate their effectiveness using hierarchical agglomerative clustering. To this end we carry out an empirical study comparing five different techniques, based on topic modelling and keyword feature extraction, to cluster 12,664 apps randomly sampled from the Google Play App Store. The comparison is based on three main criteria: silhouette width measure, human judgement and execution time. The results of our study show that using topic modelling, in addition to collocation-based and dependency-based feature extractors perform similarly in detecting app-feature similarity. However, dependency-based feature extraction performs better than any other in finding application domain similarity (ρ = 0.7,p − value < 0.01). Current categorisation in the app store studied does not exhibit a good classification quality in terms of the claimed feature space. However, a better quality can be achieved using a good feature extraction technique and a traditional clustering method.
Analyzing energy poverty with Fuzzy Cognitive Maps: A step-forward towards a more holistic approach

Energy poverty is a widespread problem across Europe with serious socioeconomic, environmental, political and health implications. The primary contributing factors are related to low-income levels, high energy prices, and energy inefficient housing. However, household characteristics, political, and social circumstances, and other drivers such as severe weather conditions also play a vital role. Due to the complex nature of the problem, previous research efforts have usually focused on a limited number of factors. This paper comprises the first attempt to give a holistic picture of the problem of energy poverty, using Greece as a case study, by means of Fuzzy Cognitive Mapping (FCM). The FCM model provides an insight into the energy poverty system’s structure and function. Thus, it may prove useful to policy makers interested in developing and testing alternative measures for tackling energy poverty. There are, however, certain shortcomings and challenges, which should be taken into account for future work.
International practices in forensic speaker comparisons

A survey relating to current practices in forensic speaker comparison testing was recently undertaken of 39 laboratories and individual practitioners across 23 countries. Questions were organised around a number of themes, including the preliminary assessment and preparation of case materials, the checking of analysts' work, frameworks used for the expression of conclusions, the use of automatic speaker recognition systems, the use of reference populations, and awareness of cognitive bias. Developmental trends in this area of forensic speech science are established by comparing responses to the present survey with those to the authors' earlier survey published in 2011.
Research in Medical Imaging Using Image Processing Techniques

Medical imaging is the procedure used to attain images of the body parts for medical uses in order to identify or study diseases.There are millions of imaging procedures done every week worldwide.Medical imaging is developing rapidly due to developments in image processing techniques including image recognition, analysis, and enhancement.Image processing increases the percentage and amount of detected tissues.This chapter presents the application of both simple and sophisticated image analysis techniques in the medical imaging field.This chapter also summarizes how to exemplify image interpretation challenges using different image processing algorithms such as k-means, ROI-based segmentation, and watershed techniques.
Effects of displays on a direct reaching task: A comparative study of head mounted display and stereoscopic widescreen display

This study was conducted to investigate the virtual display effects on direct interaction performance metrics such as accuracy, task completion time, and comfort. Eighteen participants performed tapping (pointing) tasks in the coronal plane by directly reaching for tapping targets at three egocentric distance levels, with three indices of difficulty at each egocentric distance. The position data and severity of cybersickness symptoms were collected with a motion system and a symptom questionnaire, respectively. The results indicated that accuracy was higher with the stereoscopic widescreen display than with the head mounted display. However, no significant differences in task completion time, throughput, and cybersickness were observed between the two VR displays. In addition, increasing the egocentric distance improved accuracy and lengthened the task completion time, whereas increasing the task difficulty lengthened the task completion time but did not affect the accuracy. The findings are important and informative for users in choosing between the two virtual reality displays. Generally, the stereoscopic widescreen display can be recommended for tasks requiring high egocentric distance accuracy in the coronal plane. Furthermore, developers may refer to these findings in designing interfaces that allow a more natural way of interaction for users.
Enhanced path smoothing based on conjugate gradient descent for firefighting robots in petrochemical complexes

The firefighting robot system (FFRS) comprises several autonomous robots that can be deployed to fire disasters in petrochemical complexes. For autonomous navigation, the path planner should consider the robot constraints and characteristics. Specifically, three requirements should be satisfied for a path to be suitable for the FFRS. First, the path must satisfy the maximum curvature constraint. Second, it must be smooth for robots to easily execute the trajectory. Third, it must allow reaching the target location in a specific heading. We propose a path planner that provides smooth paths, satisfy the maximum curvature constraint, and allows a suitable robot heading. The path smoother is based on the conjugate gradient descent, and three approaches are proposed for this path planner to meet all the FFRS requirements. The effectiveness of these approaches is qualitatively and quantitatively evaluated by examining the generated paths. Finally, the path planner is applied to an actual robot to verify the suitability of the generated paths for the FFRS, and planning is applied to another type of robot to demonstrate the wide applicability of the proposed planner.
A rough-granular approach to the imbalanced data classification problem

More than two decades ago the imbalanced data problem turned out to be one of the most important and challenging problems. Indeed, missing information about the minority class leads to a significant degradation in classifier performance. Moreover, comprehensive research has proved that there are certain factors increasing the problem's complexity. These additional difficulties are closely related to the data distribution over decision classes. In spite of numerous methods which have been proposed, the flexibility of existing solutions needs further improvement. Therefore, we offer a novel rough–granular computing approach (RGA, in short) to address the mentioned issues. New synthetic examples are generated only in specific regions of feature space. This selective oversampling approach is applied to reduce the number of misclassified minority class examples. A strategy relevant for a given problem is obtained by formation of information granules and an analysis of their degrees of inclusion in the minority class. Potential inconsistencies are eliminated by applying an editing phase based on a similarity relation. The most significant algorithm parameters are tuned in an iterative process. The set of evaluated parameters includes the number of nearest neighbours, complexity threshold, distance threshold and cardinality redundancy. Each data model is built by exploiting different parameters' values. The results obtained by the experimental study on different datasets from the UCI repository are presented. They prove that the proposed method of inducing the neighbourhoods of examples is crucial in the proper creation of synthetic positive instances. The proposed algorithm outperforms related methods in most of the tested datasets. The set of valid parameters for the Rough–Granular Approach (RGA) technique is established.
An experimental evaluation of large scale GBDT systems

Gradient boosting decision tree (GBDT) is a widely-used machine learning algorithm in both data analytic competitions and real-world industrial applications. Further, driven by the rapid increase in data volume, efforts have been made to train GBDT in a distributed setting to support large-scale workloads. However, we find it surprising that the existing systems manage the training dataset in different ways, but none of them have studied the impact of data management. To that end, this paper aims to study the pros and cons of different data management methods regarding the performance of distributed GBDT. We first introduce a quadrant categorization of data management policies based on data partitioning and data storage. Then we conduct an in-depth systematic analysis and summarize the advantageous scenarios of the quadrants. Based on the analysis, we further propose a novel distributed GBDT system named Vero, which adopts the unexplored composition of vertical partitioning and row-store and suits for many large-scale cases. To validate our analysis empirically, we implement different quadrants in the same code base and compare them under extensive workloads, and finally compare Vero with other state-of-the-art systems over a wide range of datasets. Our theoretical and experimental results provide a guideline on choosing a proper data management policy for a given workload.
Metagraph-Based Learning on Heterogeneous Graphs

Data in the form of graphs are prevalent, ranging from biological and social networks to citation graphs and the Web. In particular, most real-world graphs are heterogeneous, containing objects of multiple types, which present new opportunities for many problems on graphs. Consider a typical proximity search problem on graphs, which boils down to measuring the proximity between two given nodes. Most earlier studies on homogeneous or bipartite graphs only measure a generic form of proximity, without accounting for different “semantic classes”-for instance, on a social network two users can be close for different reasons, such as being classmates or family members, which represent two distinct semantic classes. Learning these semantic classes are made possible on heterogeneous graphs through the concept of metagraphs. In this study, we identify metagraphs as a novel and effective means to characterize the common structures for a desired class of proximity. Subsequently, we propose a family of metagraph-based proximity, and employ a learning-to-rank technique that automatically learns the right parameters to suit the desired semantic class. In terms of efficiency, we develop a symmetry-based matching algorithm to speed up the computation of metagraph instances. Empirically, extensive experiments reveal that our metagraph-based proximity substantially outperforms the best competitor by more than 10 percent, and our matching algorithm can reduce matching time by more than half. As a further generalization, we aim to derive a general node and edge representation for heterogeneous graphs, in order to support arbitrary machine learning tasks beyond proximity search. In particular, we propose the finer-grained anchored metagraph, which is capable of discriminating the roles of nodes within the same metagraph. Finally, further experiments on the general representation show that we can outperform the state of the art significantly and consistently across various machine learning tasks.
On Public Decentralized Ledger Oracles via a Paired-Question Protocol

Blockchain technology enables the operation of fully decentralized applications without the need for a central authority to manage the execution of the underlying process. However, a critical limitation in the technology today is the inability for such applications to query information external to the blockchain. Applications must make use of a decentralized oracle, i.e. a trusted source of external information. In this work we propose the paired-question decentralized oracle protocol, designed to extract true answers from the public. When querying the oracle, a user submits pairs of antithetic questions and voting users answer them for the chance to receive rewards. This new protocol lends itself to a simple formal analysis, and it is shown to strongly incentivize a Nash equilibrium of truthful reporting. This paper also discusses a number of extensions to the base protocol to improve its cost-effectiveness, security, and applicability.
Toward Bridging Microexpressions From Different Domains

Recently, microexpression recognition has attracted a lot of researchers' attention due to its challenges and valuable applications. However, it is noticed that currently most of the existing proposed methods are often evaluated and tested on the single database and, hence, this brings us a question whether these methods are still effective if the training and testing samples belong to different domains, for example, different microexpression databases. In this case, a large feature distribution difference may exist between training (source) and testing (target) samples and, hence, microexpression recognition tasks would become more difficult. To solve this challenging problem, that is, cross-domain microexpression recognition, in this paper, we propose an effective method consisting of an auxiliary set selection model (ASSM) and a transductive transfer regression model (TTRM). In our method, an ASSM is designed to automatically select an optimal set of samples from the target domain to serve as the auxiliary set, which is used for subsequent TTRM training. As for TTRM, it aims at bridging the feature distribution gap between the source and target domains by learning a joint regression model with the source domain samples and the auxiliary set selected from the target domain. We evaluate the proposed TTRM plus ASSM by extensive cross-domain microexpression recognition experiments on SMIC and CASME II databases. Compared with the recent state-of-the-art domain adaptation methods, our proposed method has a more satisfactory performance in dealing with the cross-domain microexpression recognition tasks.
WSQF: Comprehensive Software Quality Evaluation Framework and Benchmark Based on SQuaRE

Conventional quality evaluations of software concentrate on specific quality characteristics. Moreover, the measurement data are limited to specific products and organizations. Consequently, the present state of product quality and qualityin-use characteristics are not fully understood, preventing effective decision-making for software stakeholders. To alleviate this problem, ISO/IEC defined international standards called the SQuaRE (Systems and software Quality Requirements an Evaluation) series for comprehensive quality measurement and evaluation. However, these standards remain rather general and abstract, making them difficult to apply. In this paper, we establish a SQuaRE-based comprehensive software quality evaluation framework, Waseda Software Quality Framework (WSQF), which concretizes many product quality and qualityin-use measurement methods originally defined in the SQuaRE series. By applying the WSQF to 21 commercial ready-to-use software products, we revealed the status of software product quality. A resulted comprehensive benchmark includes trends of the quality measurement values, relationships among quality characteristics, relationship between quality-in-use and product quality, and relationship between the quality characteristics and product contexts within the limits of an application.
Exploiting Social Review-Enhanced Convolutional Matrix Factorization for Social Recommendation

To deal with the inherent data sparsity and cold-start problem, many recommender systems try to exploit the textual information for improving prediction accuracy.Due to the significant progress of deep learning techniques, neural network-based content modeling methods have been investigated in recent studies.However, most of these existing methods often assume that users are independent and identically distributed (i.i.d), and the social influence is not considered.However, in the real world, we always turn to our friends for recommendations, and the closer the friendship between the friends, the greater the social impact is.These methods only exploit the reviews from an item perspective and rarely consider the user's reviews to capture the user's interests, but in reality, users often express their preferences by posting different reviews to different items.Based on the above-mentioned observations, we propose a social-enhanced content-aware recommendation method by fusing the social network, item's reviews, and user's reviews in a unified framework.Specifically, to better model the item's reviews, we first introduce the convolutional matrix factorization (ConvMF) as our basic recommendation framework, which utilizes convolutional neural network (CNN) to capture the deeper understanding of the content context.Then, to consider the user's social influence, we integrate the user's social network into ConvMF by a shared user latent factor, which can bridge the user's social interests and user's general preferences in the same latent space.To model the user's reviews, similar to ConvMF, we exploit another CNN to learn a deeper understanding of the user's posted contents.Finally, we conduct experiments on the real-world dataset Yelp to demonstrate the effectiveness of our method.The experimental results indicate that our proposed method can effectively model the social and the review information and outperforms other related methods in terms of root mean squared error (RMSE) and mean absolute error (MAE).
The Ethical Limits of Blockchain-Enabled Markets for Private IoT Data

This paper looks at the development of blockchain technologies that promise to bring new tools for the management of private data, providing enhanced security and privacy to individuals. Particular interest presents solutions aimed at reorganizing data flows in the Internet of Things (IoT) architectures, enabling the secure and decentralized exchange of data between network participants. However, as this paper argues, the promised benefits are counterbalanced by a significant shift towards the propertization of private data, underlying these proposals. Considering the unique capacity of blockchain technology applications to imitate and even replace traditional institutions, this aspect may present certain challenges, both of technical and ethical character. In order to highlight these challenges and associated concerns, this paper identifies the underlying techno-economic factors and normative assumptions defining the development of these solutions amounting to technologically enabled propertization. It is argued that without careful consideration of a wider impact, such blockchain applications could have effects opposite to the intended ones, thus contributing to the erosion of privacy for IoT users.
Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification

Microarray technology plays a significant role in cancer classification, where a large number of genes and samples are simultaneously analysed. For the efficient analysis of the microarray data, there is a great demand for the development of intelligent techniques. In this article, the authors propose a novel hybrid technique employing Fisher criterion, ReliefF, and extreme learning machine (ELM) based on the principle of chaotic emperor penguin optimisation algorithm (CEPO). EPO is a recently developed metaheuristic method. In the proposed method, initially, Fisher score and ReliefF are independently used as filters for relevant gene selection. Further, a novel population-based metaheuristic, namely, CEPO was proposed to pre-train the ELM by selecting the optimal input weights and hidden biases. The authors have successfully conducted experiments on seven well-known data sets. To evaluate the effectiveness, the proposed method is compared with original EPO, genetic algorithm, and particle swarm optimisation-based ELM along with other state-of-the-art techniques. The experimental results show that the proposed framework achieves better accuracy as compared to the state-of-the-art schemes. The efficacy of the proposed method is demonstrated in terms of accuracy, sensitivity, specificity, and F-measure.