Reviewerly/exHarmony huggingface dataset:
Measuring the Non-Transitivity in Chess

In this paper, we quantify the non-transitivity in chess using human game data. Specifically, we perform non-transitivity quantification in two ways—Nash clustering and counting the number of rock–paper–scissor cycles—on over one billion matches from the Lichess and FICS databases. Our findings indicate that the strategy space of real-world chess strategies has a spinning top geometry and that there exists a strong connection between the degree of non-transitivity and the progression of a chess player’s rating. Particularly, high degrees of non-transitivity tend to prevent human players from making progress in their Elo ratings. We also investigate the implications of non-transitivity for population-based training methods. By considering fixed-memory fictitious play as a proxy, we conclude that maintaining large and diverse populations of strategies is imperative to training effective AI agents for solving chess.
Masked Implementation of PIPO Block Cipher on 8-bit AVR Microcontrollers

PIPO is a lightweight block cipher and shows better performance than other block cipher algorithms on low-end microcontrollers (e.g. 8-bit AVR). In addition, PIPO block cipher can utilize the efficient masking method by minimizing the number of non-linear operations. Therefore, PIPO block cipher can prevent side-channel attacks, efficiently. In this paper, we propose an efficient first-order masking technique using a 2-byte random mask by taking an advantage of PIPO block cipher. We present a new OR operation masking technique. Among functions of PIPO, the masked S-layer with 23 AND operations, 5 OR operations, and 46 XOR operations is used. Operations of PIPO block cipher are implemented in AVR assembly languages. The proposed implementation showed 1.5\(\times \) faster performance enhancements compared to the unprotected C implementation in the encryption process and 2.2\(\times \) faster performance enhancements compared to the unprotected optimized assembly implementation.
A fuzzy proximity relation approach for outlier detection in the mixed dataset by using rough entropy-based weighted density method

Data mining is an emerging technology where researchers explore innovative ideas in different domains, particularly detecting anomalies. Instances in the dataset which considerably deviate from others by their common patterns are known as anomalies. The state of being ambiguous and not affording certainty of data exists in this world of nature. Rough Set Theory is a proven methodology which deals with ambiguity and uncertainty of data. Research works that have been done until this point were focused on numeric or categorical type, which fails when the attributes are mixed type. By using fuzzy proximity and ordering relations, the numerical data has been converted to categorical data. This article presented an idea for detecting outliers in mixed data where the weighted density values of attributes and objects are calculated. The proposed approach has been compared with existing outlier detection methods by taking the hiring dataset as an example and benchmarked with Harvard dataverse datasets to prove its efficiency and performance.
Wireless Sensor Network for Tactical Situation Assessment

This paper describes a complete solution of a new dropped wireless sensor network (called “abandoned” since it is never picked up) dedicated to intelligence operation. The sensor network, named SEXTANT for Smart sEnsor X for Tactical situation AssessmeNT and presented in this paper is the achievement of several years of research and development in the fields of data fusion and smart sensors at the French Aerospace Lab. The main contribution of this paper is the presentation of experimental results obtained with our Joint Tracking and Classification (JTC) algorithm. The originality of this algorithm relies on the use contextual information in the target tracking processing and data fusion for target classification processing.
ANALYSIS OF FLOOD-AFFECTED AREAS DUE TO EXTREME WEATHER IN PACITAN, INDONESIA

Extreme weather events such as tropical cyclones trigger floods in parts of Indonesia and result in massive damage. Tropical Cyclone Cempaka in 2017 is an example of such an event, which caused high levels of loss of life and property in Pacitan Regency. This area is located in the southern part of Java Island, an area prone to tropical cyclones. This paper aims to identify the specific area of Pacitan, Indonesia, that is affected to flood hazards triggered by extreme weather. The flood event during Cyclone Cempaka in 2017 is used as a case study, and the identification of flood-affected areas due to extreme weather was carried out by an analysis of Landsat 8 satellite imagery. Normalized Differentiation Vegetation Index (NDVI) was used to identify the inundated areas at that time, corrected by Normalized Difference Water Index (NDWI), slope, and landform. The flood-affected area included 11 villages with a total area of 412.684 hectares. Those villages are located in areas of landform resulting from fluvial processes with slope 0-2 and 2-7% which are prone to flooding. The results of the identification are appropriate for preparing mitigation strategies for the area.
Detection of Copy-Move Forgery in Audio Signal with Mel Frequency and Delta-Mel Frequency Kepstrum Coefficients

Digital multimedia security has taken a very important position with the developing technology. Detecting forgeries in audio signals is one of the most challenging application in the field of audio forensics. In this study, Mel Frequency and Delta Mel Frequency based methods are proposed to detect copypaste forgery in audio signals. Firstly, the proposed study YAAPT method was used as a pitch tracking algorithm to detect voiced/unvoiced segments from audio signals. Mel Frequency Kepstrum Coefficients (MFCC) and Delta-Mel Frequency Kepstrum Coefficients (Delta-MFCC) were used to extract features from the audio signal seperated into voiced/unvoiced segments. By averaging the obtained coefficients, 13 MFCC and 13 Delta-MFCC coefficients were obtained for each region. The Pearson correlation coefficient was then calculated to find the similarity between the segments. In the final stage, a threshold value was determined based on the obtained correlation matrix and it was determined whether copy-move forgeries were made in the audio signals.
INTERNET USER’S BEHAVIOR FROM THE STANDPOINT OF THE NEURAL NETWORK THEORY OF SOCIETY: PREREQUISITES FOR THE META-EDUCATION CONCEPT FORMATION

Abstract. An analysis of the behavior of Internet users from the point of view of their preferences in the choice of information sources and the effectiveness of their impact is presented. It is shown that the modern infocommunication space has undergone qualitative changes in the most recent time, and these transformations are already having a pronounced impact on higher education, mainly through the factor of competition between information sources. It is shown that these transformations can be interpreted as the evolution of the noosphere, which is considered as a global infocommunication network, in which non-trivial transpersonal information objects are formed. Their existence leads to the fact that the human intellect has a dual nature - both individual and collective principles are present in it at the same time. The latter is responsible for such phenomena as the collective unconscious, understood in the sense of Jung. It is shown that the neural network model of the noosphere makes it possible to formulate a similar concept of "professional collective unconscious", which is responsible for professional intuition, acts of creativity, etc. In turn, the existence of the professional collective unconscious forces us to radically reconsider the content of what is called training and move to the concept of meta-learning, which, among other things, involves stimulating transitions from one level of interaction with transpersonal information structures that make up the professional collective unconscious to another.
Ground view factor computation model for bifacial photovoltaic collector field: uniform and non-uniform surfaces

The photovoltaic collectors in a field are subject to three types of solar radiation: direct, diffuse, and reflected irradiance. The reflected irradiance received by solar photovoltaic (PV) depends on the view factor from solar PV to the ground. This view factor component is dominant for bifacial PV due to additional reflected irradiance gain, which can be achieved from the module's rear side. This paper proposes and verifies a finite element method based view factor computation model, which can handle both uniform and non-uniform ground surfaces. The unique contribution of this work is that it introduces a geometric progression based finite element mesh generation process that forms the quasi-uniform grid. The generated grid values are fitted into the computation model to calculate the view factor from bifacial photovoltaics to the ground, known as the ground view factor (GVF). The proposed computation model can achieve an accuracy of 99%. To keep accuracy at this level, the smallest element size of the coarse mesh should be within 0.1%–0.4% of surface width or length. Moreover, the geometric progression ratio of the fine and coarse mesh should be in the range of 1.001–1.002 and 1.01–1.04, respectively. The model is analysed under six different PV field variables: multiple reflective ground surfaces, the height of PV, tilted ground surface, PV position in the ground, length and width of the ground, and PV string length. For the different string sizes considered here, the view factor model's computation time varies from 180 s to 257 min for the iteration size of 7.67 billion to 765 billion. The view factor computation model will contribute to analyse reflected irradiance at the rear side of bifacial PV, which is essential to predict the energy generation accurately. The proposed model is also beneficial for urban planning and addressing heat gain of the building-integrated PV system and energy usage.
Glottocodes: Identifiers linking families, languages and dialects to comprehensive reference information

Glottocodes constitute the backbone identification system for the language, dialect and family inventory Glottolog (https://glottolog.org). In this paper, we summarize the motivation and history behind the system of glottocodes and describe the principles and practices of data curation, technical infrastructure and update/version-tracking systematics. Since our understanding of the target domain – the dialects, languages and language families of the entire world – is continually evolving, changes and updates are relatively common. The resulting data is assessed in terms of the FAIR (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship. As such the glottocode-system responds to an important challenge in the realm of Linguistic Linked Data with numerous NLP applications.
The Terms of “You(s)”: How the Term of Address Used by Conversational Agents Influences User Evaluations in French and German Linguaculture

Background: Conversational agents (CAs) are a novel approach to delivering digital health interventions. In human interactions, terms of address often change depending on the context or relationship between interlocutors. In many languages, this encompasses T/V distinction —formal and informal forms of the second-person pronoun “You”—that conveys different levels of familiarity. Yet, few research articles have examined whether CAs' use of T/V distinction across language contexts affects users' evaluations of digital health applications. Methods: In an online experiment ( N = 284), we manipulated a public health CA prototype to use either informal or formal T/V distinction forms in French (“tu” vs. “vous”) and German (“du” vs. “Sie”) language settings. A MANCOVA and post-hoc tests were performed to examine the effects of the independent variables (i.e., T/V distinction and Language) and the moderating role of users' demographic profile (i.e., Age and Gender) on eleven user evaluation variables. These were related to four themes: (i) Sociability, (ii) CA-User Collaboration, (iii) Service Evaluation, and (iv) Behavioral Intentions. Results: Results showed a four-way interaction between T/V Distinction, Language, Age, and Gender, influencing user evaluations across all outcome themes. For French speakers, when the informal “T form” (“ Tu” ) was used, higher user evaluation scores were generated for younger women and older men (e.g., the CA felt more humanlike or individuals were more likely to recommend the CA), whereas when the formal “V form” (“ Vous” ) was used, higher user evaluation scores were generated for younger men and older women. For German speakers, when the informal T form (“ Du” ) was used, younger users' evaluations were comparable regardless of Gender, however, as individuals' Age increased, the use of “ Du” resulted in lower user evaluation scores, with this effect more pronounced in men. When using the formal V form (“ Sie” ), user evaluation scores were relatively stable, regardless of Gender, and only increasing slightly with Age. Conclusions: Results highlight how user CA evaluations vary based on the T/V distinction used and language setting, however, that even within a culturally homogenous language group, evaluations vary based on user demographics, thus highlighting the importance of personalizing CA language.
Combining Citation Network Information and Text Similarity for Research Article Recommender Systems

Researchers often need to gather a comprehensive set of papers relevant to a focused topic, but this is often difficult and time-consuming using existing search methods. For example, keyword searching suffers from difficulties with synonyms and multiple meanings. While some automated research-paper recommender systems exist, these typically depend on either a researcher&#x2019;s entire library or just a single paper, resulting in either a quite broad or a quite narrow search. With these issues in mind, we built a new research-paper recommender system that utilizes both citation information and textual similarity of abstracts to provide a highly focused set of relevant results. The input to this system is a set of one or more related papers, and our system searches for papers that are closely related to the entire set. This framework helps researchers gather a set of papers that are closely related to a particular topic of interest, and allows control over which cross-section of the literature is located. We show the effectiveness of this recommender system by using it to recreate the references of review papers. We also show its utility as a general similarity metric between scientific articles by performing unsupervised clustering on sets of scientific articles. We release an implementation, ExCiteSearch (bitbucket.org/mmmontemore/excitesearch), to allow researchers to apply this framework to locate relevant scientific articles.
BiQUE: Biquaternionic Embeddings of Knowledge Graphs

Knowledge graph embeddings (KGEs) compactly encode multi-relational knowledge graphs (KGs).Existing KGE models rely on geometric operations to model relational patterns.Euclidean (circular) rotation is useful for modeling patterns such as symmetry, but cannot represent hierarchical semantics.In contrast, hyperbolic models are effective at modeling hierarchical relations, but do not perform as well on patterns on which circular rotation excels.It is crucial for KGE models to unify multiple geometric transformations so as to fully cover the multifarious relations in KGs.To do so, we propose BiQUE, a novel model that employs biquaternions to integrate multiple geometric transformations, viz., scaling, translation, Euclidean rotation, and hyperbolic rotation.BiQUE makes the best tradeoffs among geometric operators during training, picking the best one (or their best combination) for each relation.Experiments on five datasets show BiQUE's effectiveness.
A Novel Framework for Unification of Association Rule Mining, Online Analytical Processing and Statistical Reasoning

Statistical reasoning was one of the earliest methods to draw insights from data. However, over the last three decades, association rule mining and online analytical processing have gained massive ground in practice and theory. Logically, both association rule mining and online analytical processing have some common objectives, but they have been introduced with their own set of mathematical formalizations and have developed their specific terminologies. Therefore, it is difficult to reuse results from one domain in another. Furthermore, it is not easy to unlock the potential of statistical results in their application scenarios. The target of this paper is to bridge the artificial gaps between association rule mining, online analytical processing and statistical reasoning. We first provide an elaboration of the semantic correspondences between their foundations, i.e., itemset apparatus, relational algebra and probability theory. Subsequently, we propose a novel framework for the unification of association rule mining, online analytical processing and statistical reasoning. Additionally, an instance of the proposed framework is developed by implementing a sample decision support tool. The tool is compared with a state-of-the-art decision support tool and evaluated by a series of experiments using two real data sets and one synthetic data set. The results of the tool validate the framework for the unified usage of association rule mining, online analytical processing, and statistical reasoning. The tool clarifies in how far the operations of association rule mining and online analytical processing can complement each other in understanding data, data visualization and decision making.
Training Plan for the Continuity of Non-Presential Education in Six Peruvian Universities during COVID-19

This article shows the response offered by the UTEC-UNED-TECSUP Consortium to six Peruvian public (national) universities aimed at strengthening the digital competences of their communities, made up of managers, teachers, students, and support technicians. The contextual and situational diagnosis, which covered organizational, technological, and competence dimensions, revealed a series of cross-cutting needs related to technological skills that prevented the training or mobilization of the digital competences necessary for progress in the other dimensions under study. The response was an online training plan, consisting of three training programs and eighty-three courses. The pedagogical strategy was based on the scaling of competence achievements that ended with problem-solving and applications in daily activities. The accompaniment was carried out through virtual tutorials, distributed via synchronous and asynchronous sessions. In total, 5034 were involved-347 were teachers and managers, 4932 were students, and 25 were technical staff. The pedagogical and socio-emotional limitations of the university community, as well as the scarcity of technological resources and poor connectivity, meant that the plan was only partially implemented. Moreover, the short and intense period of development to which the universities were subjected was also a factor.
Concepts and Techniques

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Pancalis: Android-based leaning media for early reading in new normal

Early reading is an essential skill that is needed to be accelerated through application-based media. This study aims to develop Pancalis as a medium for the online teaching of early reading. This study used the Research and Development (RD) approach using the ADDIE model. The subjects were two experts as well as 18 teachers and five students of elementary schools. They were established using the purposive sampling technique. The data collection technique used questionnaires and scales. The data analysis used descriptive statistics. The results show that: 1) at the analysis stage through literature study, it can be concluded that Pancalis as an Android-based application is an alternative learning media for the teaching of early-reading; while the result of field study shows teachers and students need learning media for early reading that can accommodate distance learning; 2) at the design stage, the Pancalis as an Android-based application for early-reading has been produced; 3) while at the end of the development stage, an assessment was carried out through an expert validation scale and it was found that the Pancalis is very suitable for the teaching of early-reading; while the practicality response questionnaire of teachers and students shows that Pancalis is very practical to use.
Lyapunov Exponent Enhancement in Chaotic Maps with Uniform Distribution Modulo One Transformation

Most of the chaotic maps are not suitable for chaos-based cryptosystems due to their narrow chaotic parameter range and lacking of strong unpredictability. This work presents a nonlinear transformation approach for Lyapunov exponent enhancement and robust chaotification in discrete-time chaotic systems for generating highly independent and uniformly distributed random chaotic sequences. The outcome of the new chaotic systems can directly be used in random number and random bit generators without any post-processing algorithms for various information technology applications. The proposed Lyapunov exponent enhancement based chaotic maps are analyzed with Lyapunov exponents, bifurcation diagrams, entropy, correlation and some other statistical tests. The results show that excellent random features can be accomplished even with one-dimensional chaotic maps with the proposed approach.
Balanced Spectral Feature Selection

In many real-world unsupervised learning applications, given data with balanced distribution, that is, there are an approximately equal number of instances in each class, we often need to construct a model to reveal such balance. However, in many data, especially the high-dimensional ones, the data in the original feature space often do not present such balance due to the redundant and noisy features. To tackle this problem, we apply an unsupervised spectral feature selection method to select some informative features, which can better reveal the balanced structure of data. Although spectral feature selection is one of the most popular unsupervised feature selection methods and has been widely studied, none of the existing spectral feature selection methods consider the balance property of data. To address this issue, in this article, we propose a novel balanced spectral feature selection (BSFS) method, which not only selects the discriminative features but also picks those to reveal the balanced structure of data. To the best of our knowledge, this is the first spectral feature selection method considering balance structure of data. By introducing a balanced regularization term, we integrate the balanced spectral clustering and feature selection into a unified framework seamlessly. At last, the experiments on benchmark datasets show that the proposed one outperforms the conventional feature selection methods in both clustering performance and balance, which demonstrates the effectiveness and efficiency of the proposed method.