Reviewerly/exHarmony huggingface dataset:
Biogeochemical fractal characteristics of trace elements in the Shizhuyuan polymetallic mining area and their significance for prospecting

Shizhuyuan is a world-class W–Sn supergiant polymetallic deposit, with a large area of potential mineralization around it. Samples were taken of Dicranopteris dichotoma, which is widely distributed in the Shizhuyuan mining area. Biogeochemical and Number–Size fractal methods were used to evaluate the biogeochemical characteristics of trace elements in the samples and the feasibility of indicating subsurface mineral deposits. The results reveal that D. dichotoma not only has strong uptake of the active state of metal elements (Zn, Mo, Cd, Sn, etc.) in the soil, but also show the geochemical anomalies of Zn, Fe, Tl, Ag, Pb, U, As, Bi, Cd, Mo and Sn in the study area. The Number–Size fractal method can effectively identify the concentration populations of biogeochemical target elements and obtain convincing anomaly thresholds. Analysis of the metal content of D. dichotoma is an effective tool for biogeochemical exploration of Pb–Zn–U hydrothermal deposits in the study area. Zinc as well as U are target elements, and Tl appears to be an effective pathfinder element for Pb–Zn–U mineralization. In addition, 260–340 m of Line 3 # and 1100–1200 m of Line 8 # are the key areas for future exploration of Pb–Zn–U hydrothermal deposits.
Global Openness of Science in the Processes of Institutional Transformations of the Education System as a Factor in the Formation of Sustainable Development of Society

Scientific research is primarily due to the need to study the problem of compliance of strategic guidelines of higher education and its specific educational models with the needs of civilizational progress towards a sustainable future, peace, mutual understanding, mutual respect and care for the environment. Globalisation involves the fusion of all nations' societies, economies, and institutions (including those related to education and science) into a single socioeconomic system with the potential for improved political coordination between them. In modern globalism, an imitative homeostatic avalanche-like model of crisis degradation of a society, living in line with the principles of consumption, has been formed, threatening by falling intelligence development below the critical mark and consequently by cessation of technological progress and sharp increase of mortality compared to births. Higher education is becoming more globally and internationally integrated, which raises a number of new questions for theory and practise, such as which management patterns, forms, and techniques are universal and which are specific. The essential interdependence of the systemic and functional traits of the institutional dimension of global transformations of world development must be recognised and conceptualised, it is assumed. Global institutional transformations are currently taking place for the sustainable development of society in the context of the internationalization of higher education and science. One of the main features of the modern stage of development of world civilization is the globalization of all spheres of human social life - cultural, informational, economic. Various concepts of globalization are developed and arised. Science used to talk only about the internationalization of the economy, social and cultural life, and so on. However, today this concept has spread to all systemic and institutional levels and structures. Education institutions and establishments should teach how to work together in partnership, develop skills for sustainable development, develop critical thinking, share responsibility between students and the teaching team. Education institutions can be examples of sustainable lifestyles.
Evaluation of Air Combat Control Ability Based on Eye Movement Indicators and Combination Weighting GRA-TOPSIS

At present, expert scoring is mainly used to evaluate the air combat control ability, which is not accurate enough to effectively achieve the desired effect. In order to evaluate air battle managers’ air combat control ability more scientifically and accurately, using eye-tracking technology, a quantitative evaluation model is established based on eye movement indicators. Specifically, the air combat control ability was comprehensively assessed using the GRA-TOPSIS method based on the EW-CRITIC combination weighting. The model innovatively uses eye movement indicators as a vital evaluation basis. Firstly, it puts forth a comprehensive evaluation method by combining GRA with TOPSIS methods, using the EW and CRITIC methods for combined weighting, and giving full play to the advantages of various evaluation methods. Secondly, it not only effectively copes with the problem that the traditional evaluation method is deeply affected by subjectivity but also creatively provides a reasonable means for future training evaluation of air battle managers. Finally, the effectiveness and feasibility of the evaluation model are verified through case analysis.
A novel classification‐based shilling attack detection approach for multi‐criteria recommender systems

Abstract Recommender systems are emerging techniques guiding individuals with provided referrals by considering their past rating behaviors. By collecting multi‐criteria preferences concentrating on distinguishing perspectives of the items, a new extension of traditional recommenders, multi‐criteria recommender systems reveal how much a user likes an item and why user likes it; thus, they can improve predictive accuracy. However, these systems might be more vulnerable to malicious attacks than traditional ones, as they expose multiple dimensions of user opinions on items. Attackers might try to inject fake profiles into these systems to skew the recommendation results in favor of some particular items or to bring the system into discredit. Although several methods exist to defend systems against such attacks for traditional recommenders, achieving robust systems by capturing shill profiles remains elusive for multi‐criteria rating‐based ones. Therefore, in this study, we first consider a prominent and novel attack type, that is, the power‐item attack model, and introduce its four distinct variants adapted for multi‐criteria data collections. Then, we propose a classification method detecting shill profiles based on various generic and model‐based user attributes, most of which are new features usually related to item popularity and distribution of rating values. The experiments conducted on three benchmark datasets conclude that the proposed method successfully detects attack profiles from genuine users even with a small selected size and attack size. The empirical outcomes also demonstrate that item popularity and user characteristics based on their rating profiles are highly beneficial features in capturing shilling attack profiles.
Infrastructures by the users for users: Motivations, constraints, and consequences of user-driven infrastructuring of mobile phones

Users adapt infrastructures materially to fit their needs, they engage in maintenance and repair, and they learn about the inner workings of infrastructures. Different degrees of user engagement with infrastructures are empirically analysed using the case of user-developed alternative mobile operating systems. Some observations of user agency made already in early studies of the appropriation of media and technology were found to be still relevant: moral considerations motivate users to engage in infrastructuring and users actively negotiate their infrastructural attachments. But ‘acting on’ infrastructures is also different from ‘acting on’ devices: the users’ experiments with infrastructures require redundancy and they are inherently collective. Moreover, certain designs of infrastructures can enable and demand user-driven infrastructures, while others block it.
Opponent colour median-based scale invariant local ternary pattern: a new feature descriptor for moving object detection

This article presents a new colour-texture feature descriptor called opponent colour median-based scale invariant local ternary pattern (OCMD-SILTP) to deal with dynamic backgrounds, similar texture regions, moving cast shadow, and illumination changes. Here, the incoming image frames are first divided into several sub regions and then the colour-texture features are derived by applying OCMD-SILTP on the individual colour planes (R, G, and B) as well as opponent colour planes (R-G, R-B, and G-B). For opponent planes, the median of neighbourhood pixels of one color plane is used for thresholding the neighbourhood pixels of opponent color plane. Because of this, the proposed OCMD-SILTP colour-texture descriptors are more immune to scaling and video noises. The background changes are dynamically updated through OCMD-SILTP texture patterns using histogram intersection method. Experiments on publically available dataset prove the significance of proposed approach under complex environments.
Defending Byzantine attacks in ensemble federated learning: A reputation-based phishing approach

Emerging as a promising distributed learning paradigm, federated learning (FL) has been widely adopted in many fields. Nonetheless, a big challenge for FL in real-world implementation is Byzantine attacks, where compromised clients can mislead or poison the training model by falsifying or manipulating the local model parameters. To solve the abovementioned problem, we present a novel Byzantine robust-FL scheme via reputation, dubbed FLPhish, for defending Byzantine attacks under the Ensemble Federated Learning architecture (EFL). Specifically, we first develop a novel EFL architecture that allows FL to be compatible with different deep models from different clients. Second, a phishing method for EFL is crafted to identify possible Byzantine behaviors. Third, we devise a Bayesian inference-based reputation mechanism to measure each client’s confidence level and further identify Byzantine attackers. Last, we strictly analyze how the FLPhish scheme defends against Byzantine attacks. Extensive experiments demonstrate that the proposed FLPhish achieves outperformed efficacy in defending Byzantine attacks in EFL, respectively under different fractions of Byzantine attackers and different degrees of distribution imbalance.
The art of transfer learning: An adaptive and robust pipeline

Transfer learning is an essential tool for improving the performance of primary tasks by leveraging information from auxiliary data resources. In this work, we propose Adaptive Robust Transfer Learning (ART), a flexible pipeline of performing transfer learning with generic machine learning algorithms. We establish the nonasymptotic learning theory of ART, providing a provable theoretical guarantee for achieving adaptive transfer while preventing negative transfer. Additionally, we introduce an ART‐integrated‐aggregating machine that produces a single final model when multiple candidate algorithms are considered. We demonstrate the promising performance of ART through extensive empirical studies on regression, classification, and sparse learning. We further present a real‐data analysis for a mortality study.
Data

The initial step for a data scientist when addressing a business question is to identify the data type, as not all types can be employed in data mining analyses. Accordingly, the data scientist must select a suitable data type that corresponds to the data mining technique and classify the data into categorical and continuous types, regardless of the source of the data. Quality control is a significant factor for the data scientist, particularly if data collection was poorly administered or designed, leading to issues like missing values. Once the data scientist has acquired a relevant dataset, they should inspect the outliers associated with each feature to make sure the data is suitable for analysis. Observing outliers through data visualizations, such as scatter plots, is a common practice among data scientists, highlighting the crucial role of data type determination.
Multi-domain adaptation for cross-domain semantic slot filling

Slot filling is a crucial sub-task in the field of Spoken Language Understanding and aims to match the corresponding semantic slot for each word in the sequence. Slot prediction in an unknown domain requires a large amount of data in the domain for training, but in reality, there is often a lack of trainable samples in the unknown domain, which makes it difficult for the model to predict new domains. This is the biggest challenge of the cross-domain slot filling task. In recent years, the idea of transfer learning has been applied to cross-domain slot filling tasks. The current training method directly mixes the source domain data samples without considering the differences between the various domains in the source domain, which ignores the domain-invariant features contained in the source domain. In this paper, we proposed a cross-domain slot filling model based on multi-domain adaptation. First, we used the domain-adaptive domain projection layer to let the feature learner classify the domain-invariant information and domain-exclusive information into the specified dimension part of the vector, so as to realize the extraction of domain-invariant feature information, and then used the trainable linear transformation matrix to relieve the generalization burden of the feature learner. Experimental results show that our proposed models significantly outperform other methods on average F1-score.
OPTIMIZATION AND MATHEMATICAL MODELING OF FINANCIAL INFORMATION SYSTEMS IN THE DIGITAL ECONOMY

This article describes the points of the organization's financial information systems project and describes the results of the implementation of the financial information system design method in the conditions of the local fund of the financial organization. Mathematical modeling is used to select the optimal financial information system option from various options. An optimal solution is obtained with the help of mathematical modeling. Recommendations are made based on the results chart.
Arabic Opinion Mining Using Machine Learning Techniques: Algerian Dialect as a Case of Study

Social networking services such as Facebook, Twitter, and YouTube are fertile ground for analyzing texts, extracting opinions, and identifying feelings, due to a large number of texts and their diversity in all areas of life. In this manuscript, we apply four algorithms to classify tweets written in the Algerian dialect. To extract feelings, we used six features based on three polarities. In the presented work, we manually annotate a corpus of 2,891 texts and create an Algerian lexicon of idioms that contains 1328 annotated words. Our results show that there are improvements gained in the accuracy of the system, where we have achieved a better accuracy of 85.31.
Marketing and production activities of textile companies? Blockchain technology study

Blockchain technology has gained increasing popularity among the business community due to its numerous advantages for business such as decentralization, immutability, and transparency. Specifically, with a reliable transaction system, blockchain technology has developed information sharing and supply chain management in the textile industry. Although there is a growing number of research publications in the field of blockchain technology applications, there is a lack of systematic knowledge of the current literature regarding how blockchain technology is effectively applied to the textile industry, increasing the possibility of getting irrelevant, biased, or unclear information. Thus, the purpose of this study is to analyze, identify, and classify the existing literature to provide a comprehensive overview of the state of the art on using blockchain technology in the main activities of textile industry enterprises such as production and marketing. The researchers looked for publications and expert opinions articulating different applications of blockchain technology in marketing and production activities of the textile industry based on the analysis of academic databases selected and stakeholder interviews. The results highlighted that the most common categories of using blockchain technology in the textile industry are information sharing and supply chain traceability to integrate with the production and marketing activities of textile companies. The scientific contributions of this systematic mapping study are expected to be the classified portfolio of key publications and content taxonomy that demonstrates the state of the art in the whole research field. Finally, we concluded the paper with practical and theoretical implications and developed a future research agenda on closing the potential gap between blockchain-enhanced production and marketing activities of textile companies.
METHODS OF DETERMINING FAKE CONTENT USING ARTIFICIAL INTELLIGENCE

In this article, the ways and methods of detecting fake content are described by using artificial intelligence. The methods for detecting fake content using artificial intelligence are explained. The article also reveals the processes of using artificial intelligence in cases of detecting the falsity of photographs or texts.
Tackling bias in the data for breast cancer prediction using machine learning-based decision support

In this study, a machine learning (ML)-based decision support approach was developed to identify breast cancer likelihood in patients, based on their background and physiological data. Two ML models, Naïve Bayes and Logistic Regression were used to evaluate the Breast Cancer Surveillance Consortium dataset that had about 9:1 ratio of non-cancer cases ('Class 0') to cancer cases ('Class 1'). We manually built both balanced and unbalanced training datasets and a non-overlapping testing dataset using a stratified sampling method. For each model, we partitioned the prediction results on testing set into two groups, the 'Agree' group included cases where balanced and unbalanced ML predictions agreed, and the remaining cases come under 'Disagree' group. Sensitivity and Positive Predictive Value were used as the prediction performance measures. For Naïve Bayes, the sensitivity of Class 1 in regular versus 'Agree' group increased from 0.687 to 0.936 and for Logistic Regression, it increased from 0.358 to 0.8306. This indicates the 'Agree' group predictions were more accurate and could be labeled as high-confidence ML predictions. The 'Agree' group consisted of 89% cases in the testing set, so the improved prediction performance was applicable for a large portion of the testing dataset.
Deep Bidirectional Transformers for Arabic Dialect Identification

The rising adoption of social media has led to the widespread dissemination of online textual data. Arabic is among the top five most popular languages worldwide (Arabic is spoken by a total of about 360.2 million people worldwide as a native language). In this regard, Arabic-text data available on social media are presented using different Arabic dialects, such as the Gulf, Iraqi, Egyptian, Levantine, and North Africa dialects. Particularly, identifying the Arabic dialect used in text is of significant value for several natural language processing tasks, such as machine translation, text generation, word correction, and information retrieval. Arabic-dialect identification is a multiclass classification problem in which classes represent different Arabic dialects. In this study, we investigated the performance of two bidirectional deep learning models for Arabic-dialect classification: MARBERT and ARBERT. We analyzed the performance of the models on two publicly available datasets: the Arabic Online Commentary dataset and the Social Media Arabic Dialect Corpus. Extensive experiments were conducted, encompassing binary dialect classification, three-way dialect classification, and multi-way dialect classification. The results indicate that MARBERT consistently achieved higher F1-scores than ARBERT, which can be attributed to the significant differences between the two models, including their architectures, training mechanisms, and data sources.
Twitter User Sentiment Analysis Of TIX ID Applications Using Support Vector Machine Algorithm

The cinema is a place to watch movies using the big screen. Various comments on the TIX ID application service can be used to reference the company's evaluation material in assessing the level of service quality that has been provided, so that later the TIX ID application can be used optimally by application users and the company, as for the purpose of this study to find out responses and find out the sentiment analysis stage on twitter social media using the vector machine support algorithm for the TIX ID application. This algorithm is commonly used for text mining by going through the data collection stage, cleaning and labelling data stage, training and testing data sharing stage with 3 comparison scenarios, namely 70:30, 80:20, and 90:10 using 3 kernels, namely dot, radial, and polynomial, then through the text preprocessing stage, the TF-IDF word weighting stage, the data modeling stage, and the evaluation stage. The preprocessing stage consists of transform case, tokenize, and stopwords filters. The result of this study is that the support vector machine algorithm has an accuracy value of 74.17%. The research concludes that the support vector machine algorithm with a ratio of 80:20 training and testing data ratio scenario produces the highest accuracy.
Climate Games, the Blue Humanities, and Listening to the Deep-Sea Ecosystems in Games in a Time of Ecological Crisis

ABSTRACT Abzû, released in 2016 by Giant Squid Studios, is an underwater adventure exploration game where the player plays as an unspeaking protagonist, known as the Diver, who communicates only through sonic “pings” as they navigate the unexplored depths of the ocean. In this discussion of the aquatic ecosystem modeling and sound design of Abzû and its relationship to narrative and game mechanics, the author applies approaches from the blue humanities and Stefan Helmreich’s concept of the “alien ocean,” a term he reclaims from the marine biologists who used it to describe the “strangeness” of the creatures they were investigating, to address how players interact with and come to understand in-game deep-sea soundscapes that are beyond human perception and experienced by playing as an AI exploration entity. The article considers how inequities in power and agency are translated into constructions of knowledge and the epistemological framing and scoring the nonhuman “other” (and what that communicates about being human) in environmental art that explores environments uninhabited by human life. In Abzû, the author looks at what the framework of the blue humanities and blue media can shape the discussion of ecological game sound design.
Technical Issues: Creating a Troubleshooting System with Microsoft Outlook

As electronic resources routinely account for a sizable portion of library budgets, electronic resources librarians are under increasing pressure to ensure constant access to these resources. Therefore, developing troubleshooting skills and resources is critical for maintaining electronic access for patrons. This article emphasizes the significance of troubleshooting in electronic resources librarianship by highlighting articles on troubleshooting, the NASIG Core Competencies, and the subsequent need for troubleshooting systems that integrate with email. Therefore, this article details how to create an Outlook-Based Troubleshooting System to assist electronic resources librarians in efficiently resolving access issues.