Reviewerly/exHarmony huggingface dataset:
Toward XAI &amp; Human Synergies to Explain the History of Art: The Smart Photobooth Project

The advent of Artificial Intelligence (AI) has brought about significant changes in our daily lives with applications including industry, smart cities, agriculture, and telemedicine. Despite the successes of AI in other "less-technical" domains, human-AI synergies are required to ensure user engagement and provide interactive expert knowledge. This is notably the case of applications related to art since the appreciation and the comprehension of art is considered to be an exclusively human capacity. This paper discusses the potential human-AI synergies aiming at explaining the history of art and artistic style transfer. This work is done in the context of the "Smart Photobooth" a project which runs within the AI & Art pavilion. The latter is a satellite event of Esch2022 European Capital of Culture whose main aim is to reflect on AI and the future of art. The project is mainly an outreach and knowledge dissemination project, it uses a smart photo-booth, capable of automatically transforming the user's picture into a well-known artistic style (e.g., impressionism), as an interactive approach to introduce the principles of the history of art to the open public and provide them with a simple explanation of different art painting styles. Whereas some of the cutting-edge AI algorithms can provide insights on what constitutes an artistic style on the visual level, the information provided by human experts is essential to explain the historical and political context in which the style emerged. To bridge this gap, this paper explores Human-AI synergies in which the explanation generated by the eXplainable AI (XAI) mechanism is coupled with insights from the human expert to provide explanations for school students as well as a wider audience. Open issues and challenges are also identified and discussed.
Association measures for interval variables

Symbolic Data Analysis (SDA) is a relatively new field of statistics that extends conventional data analysis by taking into account intrinsic data variability and structure. Unlike conventional data analysis, in SDA the features characterizing the data can be multi-valued, such as intervals or histograms. SDA has been mainly approached from a sampling perspective. In this work, we propose a model that links the micro-data and macro-data of interval-valued symbolic variables, which takes a populational perspective. Using this model, we derive the micro-data assumptions underlying the various definitions of symbolic covariance matrices proposed in the literature, and show that these assumptions can be too restrictive, raising applicability concerns. We analyze the various definitions using worked examples and four datasets. Our results show that the existence/absence of correlations in the macro-data may not be correctly captured by the definitions of symbolic covariance matrices and that, in real data, there can be a strong divergence between these definitions. Thus, in order to select the most appropriate definition, one must have some knowledge about the micro-data structure.
Audio Centered Game Development in Mobile VR

Audio is one of the essential elements of game design. Even in virtual reality applications, visuals have been thought to have more importance to create impressiveness. However, using its stereo or surround capabilities, it is possible to improve the effectiveness of the game, on the players. Also, creating an audio rich game increases its accessibility to a variety of people. This study involves a game setup with 3D audio used in virtual reality (VR), design of audio games, and Google Cardboard development. Using this game setup, a highly audio dependent mobile virtual reality game is developed and tested by 10 users. The performance of the users and feedback from the qualitative and quantitative comments were analyzed and reported. The main aim of this work is to show that audio can be a powerful game element, based on an explementary work involving a game developed with audio experience on its focus.
Smartphone Sensor-Based Fall Detection Using Machine Learning Algorithms

Human Activity Recognition and particularly detection of abnormal activities such as falls have become a point of interest to many researchers worldwide since falls are considered to be one of the leading causes of injury and death, especially in the elderly population. The prompt intervention of caregivers in critical situations can significantly improve the autonomy and well-being of individuals living alone and those who require remote monitoring. This paper presents a study of accelerometer and gyroscope data retrieved from smartphone embedded sensors, using iOS-based devices. In the project framework there was developed a mobile application for data collection with the following fall type and fall-like activities: Falling Right, Falling Left, Falling Forward, Falling Backward, Sitting Fast, and Jumping. The collected dataset has passed the preprocessing phase and afterward was classified using different Machine Learning algorithms, namely, by Decision Trees, Random Forest, Logistic Regression, k-Nearest Neighbour, XGBoost, LightGBM, and Pytorch Neural Network. Unlike other similar studies, during the experimental setting, volunteers were asked to have smartphones freely in their pockets without tightening and fixing them on the body. This natural way of keeping a mobile device is quite challenging in terms of noisiness however it is more comfortable to wearers and causes fewer constraints. The obtained results are promising that encourages us to continue working with the aim to reach sufficient accuracy along with building a real-time application for potential users.
Application Status and Prospect of Dot Matrix Digital Pen

The dot matrix digital pen has developed rapidly in recent years, and many products related to dot matrix digital pen have appeared in the market. This paper introduces the basic working mode and working principle of the dot matrix digital pen and shows the application of some dot matrix digital pen in the market in detail. Finally, it puts forward some ideas on the combination and development of dot matrix digital pen and artificial intelligence technology.
Demonstration of minisuperspace quantum cosmology using quantum computational algorithms on IBM quantum computer

Quantum computers promise to efficiently solve important problems that are intractable on a conventional computer. Quantum computational algorithms have the potential to be an exciting new way of studying quantum cosmology. In quantum cosmology, we learn about the dynamics of the universe without constructing a complete theory of quantum gravity. Since the universal wavefunction exists in an infinite-dimensional superspace over all possible 3D metrics and modes of matter configurations, we take minisuperspaces for our work by constraining the degrees of freedom to particular 3D metrics and uniform scalar field configurations. Here, we consider a wide variety of cosmological models. We begin by analyzing an anisotropic universe with cosmological constant and classical radiation. We then study the results for higher derivatives, Kaluza–Klein theories and string dilaton in quantum cosmology. We use IBM’s Quantum Information Science Kit (QISKit) python library and the Variational Quantum Eigensolver (VQE) algorithm for studying these systems. The VQE algorithm is a hybrid algorithm that uses the variational approach and interleaves quantum and classical computations in order to find the minimum eigenvalue of the Hamiltonian for a given system. The minimum eigenvalue of the Hamiltonian obtained will serve as a boundary condition for the given wavefuntion.
A Study on Universal Design of Musical Performance System

This paper describes the development of a new UI of musical performance system based on the results of workshops in a care home, and describes the extension of the UI for non-Western music. The new UI has only 8 strings which are numbered or colored, and they can be tuned for most major or minor scales so that users do not need to use sharp/flat. A new function was added to the score display system to transpose keys in order to generate the scores which can be used for the new UI. The new UI was evaluated by an experiment, and the result indicates that the new UI is easier than keyboard for the people who have little musical experience. Also, the two notations of duration of scores were evaluated by an experiment to know which is easy for those people. The score DB is enhanced based on the result of workshops in a care home. The new systems (string UI of musical performance system and score display system) were developed for elderly people and people with little musical experience. Then, the string UI was extended so that users can choose a number of strings and each string can be any pitch. By using this function, some UIs of non-Western musical instrument can be implemented.
Beyond Murphy’s Law: Applying Wider Human Factors Behavioural Science Approaches in Cyber-Security Resilience

Traditional approaches to cyber-security resilience, assuring the overall socio-technical system is secure from immediate known attacks and routes to potential future attacks, have relied on three pillars of people, process, and technology. In any complex socio-technical system, human behaviour can disrupt the secure and efficient running of the system with risk accumulating through individual and system-wide errors and compromised security behaviours that may be exploited by actors with malicious intent. Practitioners’ experience and use of different assessment methods and approaches to establish cyber-security vulnerabilities and risk are evaluated. Qualitative and quantitative methods and data are used for different stages of investigations in order to derive risk assessments and access contextual experience for further analyses. Organisational security culture and development approaches along with safety assessment methods are discussed in this case study to understand how well the people, the system, and the organisation interact. Cyber-security Human Factors practice draws on other application areas such as safety, usability, behaviours and culture to progressively assess security posture; the benefits of each approach are discussed. This study identifies the most effective methods for vulnerability identification and risk assessment, with focus on modelling large, dynamic and complex socio-technical systems, to be those which identify cultural factors with impact on human-system interactions.
A Personalized Brand Proposal Based on User’s Satisfaction and Curriculum Supported by an Intelligent Job Recommender System

One of the main challenges’ universities are confronted is the personalization of education services to improve quality mechanisms and strategies for supporting and assisting students when entering the workforce. Although many universities try to narrow the gap between academic life and job market, it is a highly challenging task to identify the right job for the right graduate. Market strives to find the most talented people and universities attempt to enrich students’ personal brands, but these do not always align. Pitfalls are found in obtaining proper information that harmonize employment offers, course content and graduate’s profile. This research places a transversal analysis of job mismatch in Latin American (LATAM) countries, builds a personalized brand based on satisfaction and course content and offers descriptions for an intelligent job recommender system. Proposal considers that providing a targeted job match implies by picking quantitatively relevant technical knowledge and transversal competencies of individual graduates and matching them to knowledge, skills and attitudes of employment offers and course content, in an efficient manner. Competencies from employment offers obtained with text mining are related to those from a current curriculum to help graduates bring about a personal brand for an appropriate job. Contribution of this research is the construction of a framework to construct match patterns that benefits graduates to meet professional success and to achieve personalization and optimization of the universities’ offered services that represents an incremental improvement.
A Novel Hybrid Pyramid Texture-Based Facial Expression Recognition

Automated analysis of facial expressions is one of the most interesting and challenging problems in many areas such as human-computer interaction. Facial images are affected by many factors, such as intensity, pose and facial expressions. These factors make facial expression recognition problem a challenge. The aim of this paper is to propose a new method based on the pyramid local binary pattern (PLBP) and the pyramid local phase quantization (PLPQ), which are the extension of the local binary pattern (LBP) and the local phase quantization (LPQ) as two methods for extracting texture features. LBP operator is used to extract LBP feature in the spatial domain and LPQ operator is used to extract LPQ feature in the frequency domain. The combination of features in spatial and frequency domains can provide important information in both domains. In this paper, PLBP and PLPQ operators are separately used to extract features. Then, these features are combined to create a new feature vector. The advantage of pyramid transform domain is that it can recognize facial expressions efficiently and with high accuracy even for very low-resolution facial images. The proposed method is verified on the CK+ facial expression database. The proposed method achieves the recognition rate of 99.85% on CK+ database.
Inequality, Information Technology and Inclusive Education in Sub-Saharan Africa

This study examines linkages between inequality, information and communication technology (ICT) and inclusive education in order to establish inequality thresholds that should not be exceeded in order for ICT to promote inclusive education in 42 countries in sub-Saharan Africa for the period 2004-2014. The empirical evidence is based on the Generalized Method of Moments. The following findings are established. First, a Gini coefficient and an Atkinson index of respectively, 0.400 and 0.625 are income inequality thresholds that should not be exceeded in order for internet penetration to positively influence inclusive education. Second, a Gini coefficient, an Atkinson index and a Palma ratio of respectively, 0.574, 0.676 and 9.000 are thresholds of income inequality that if exceeded, fixed broadband subscriptions will no longer positively affect inclusive education. As a main policy implication, the established inequality thresholds should not be exceeded in order for ICT to promote inclusive education in sampled countries. Other implications in the light of Sustainable Development Goals (SDGs) are discussed.
Signal Classification Algorithms over Time Selective Channels

In this work, we propose a general framework to design a signal classification algorithm over time selective channels for wireless communications applications. We derive an upper bound on the maximum number of observation samples over which the channel response is an essential invariant. The proposed framework relies on dividing the received signal into blocks, and each of them has a length less than the mentioned bound. Then, these blocks are fed into a number of classifiers in a parallel fashion. A final decision is made through a well-designed combiner and detector. As a case study, we employ the proposed framework on a space-time block-code classification problem by developing two combiners and detectors. Monte Carlo simulations show that the proposed framework is capable of achieving excellent classification performance over time selective channels compared to the conventional algorithms.
Opinion Mining of Restaurant Reviews and Comparison of Different Classifiers

The growth of Internet world wide, social media and networking sites, blogs, discussion forums, e-commerce websites has gained a huge importance these days and has provided platform for users to express and share their views on entities and their features and aspects. These days, many users give their reviews, good or bad about food items, services and ambience in the restaurants to express their actual opinion. The true reviews are very important in order to take decisions to other users. Retrieving and analyzing these many opinions manually are difficult and tiresome. So, the need of time is an automated tool or technique to overcome this problem. Opinion mining is a method to analyse these reviews and classifies them as positive, negative, and neutral. We presented lexicon-based sentiment analysis, that is, classical and VADER method for opinion mining of restaurant reviews and sentence relevance score-based method for opinion summarisation. We also implemented different machine learning classifier algorithms for the classified data. This detailed analysis hopefully provides insights into choosing the most appropriate restaurant.
Selecting and Training Young Cyber Talent: A European Cybersecurity Challenge Case Study

With the increasing popularization of cybersecurity concepts due to ever increasing cybersecurity incidents, it is no secret that countries worldwide are investing heavily in cybersecurity education to build the necessary talent pool to cope with future cybersecurity challenges. At the same time, different cybersecurity competitions are being organized to identify leading countries in cybersecurity domains. One such competition is European Cyber Security Challenge in which countries across Europe participate annually to showcase their cyber capabilities. In this research, we present a first of a kind study that focused on identifying selection and training strategies of national cybersecurity teams for international cybersecurity competitions. In the study, we identified countries with major talent pools, their selection and training methodologies, and the training platforms they used.
Solid Semantics for Abstract Argumentation Frameworks and the Preservation of Solid Semantic Properties

In this paper, we propose solid admissibility that is a strengthened version of Dung's admissibility to obtain the most acceptable set of arguments. Besides, other solid extensions based on solid admissibility are defined. Such extensions not only include all defenders of its elements but also exclude all arguments indirectly attacked and indirectly defended by some argument(s). Furthermore, we characterize solid extensions with propositional formulas. Using these formulas, we aggregate solid extensions by using approaches from judgment aggregation. Especially, although no quota rule preserves Dung's admissibility for any argumentation framework, we show that there exist quota rules that preserve solid admissibility for any argumentation framework.
On the Hierarchical Community Structure of Practical Boolean Formulas

Modern CDCL SAT solvers easily solve industrial instances containing tens of millions of variables and clauses, despite the theoretical intractability of the SAT problem. This gap between practice and theory is a central problem in solver research. It is believed that SAT solvers exploit structure inherent in industrial instances, and hence there have been numerous attempts over the last 25 years at characterizing this structure via parameters. These can be classified as rigorous, i.e., they serve as a basis for complexity-theoretic upper bounds (e.g., backdoors), or correlative, i.e., they correlate well with solver run time and are observed in industrial instances (e.g., community structure). Unfortunately, no parameter proposed to date has been shown to be both strongly correlative and rigorous over a large fraction of industrial instances. Given the sheer difficulty of the problem, we aim for an intermediate goal of proposing a set of parameters that is strongly correlative and has good theoretical properties. Specifically, we propose parameters based on a graph partitioning called Hierarchical Community Structure (HCS), which captures the recursive community structure of a graph of a Boolean formula. We show that HCS parameters are strongly correlative with solver run time using an Empirical Hardness Model, and further build a classifier based on HCS parameters that distinguishes between easy industrial and hard random/crafted instances with very high accuracy. We further strengthen our hypotheses via scaling studies. On the theoretical side, we show that counterexamples which plagued flat community structure do not apply to HCS, and that there is a subset of HCS parameters such that restricting them limits the size of embeddable expanders.
University Students’ Attitudes towards the Use of YouTube in the EFL Classroom

Noticing the generally lower-than-desirable English language proficiency level with which Spanish students start university, I considered trying a different approach in which the use of a familiar application would be introduced in the classroom. The main objective of this work is to explore Primary Education degree programme students’ attitudes towards the integration of YouTube in the English language classroom. To this end, 46 third-year intermediate-level students participated in the study. It adopted a mixed-method approach, using a combination of qualitative and quantitative methods. The results show that, although participants mentioned some drawbacks, they reacted positively to the use of the platform. Given these results and the benefits offered, the integration of YouTube in the foreign language classroom is highly recommended.
A new hybrid method for epithermal gold exploration using multi-sensor satellite data in Sistan and Baluchestan Province (Iran)

Remote sensing data provide a useful piece of information for initial identification of alteration zones, lithological units and structures in relation to mineralization, saving time and cost. In this research, silicic, argillic and propylitic alteration zones with gold mineralization in Taftan area in Sistan and Baluchestan province (Iran) were identified by thermal infrared (TIR), shortwave infrared (SWIR) and visible-near infrared (VNIR) bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat-8 data. Detecting altered and mineralized zones of epithermal gold were conducted using a conventional hybrid method of band ratio in false coloured composite (BR-FCC), and a new hybrid method including, constrained energy minimization in false coloured composite (CEM-FCC). The hybrid methods of the BR-FCC (BRs in RGB) identified the silicic area and places, containing clay and secondary iron minerals. Furthermore, the advanced argillic, argillic and propylitic alteration zones detected and derived in altered areas specifically to the northwest of Taftan, in Kharestan and Bidester deposits using the hybrid methods of the CEM-FCC (CEMs in RGB). Results of image processing showed two new prospective altered places to the northeast of Kharestan deposit and to the northwest of Taftan volcano.