Reviewerly/exHarmony huggingface dataset:
Using Markov Random Field and Analytic Hierarchy Process to Account for Interdependent Criteria

The Analytic Hierarchy Process (AHP) has been a widely used multi-criteria decision-making (MCDM) method since the 1980s because of its simplicity and rationality. However, the conventional AHP assumes criteria independence, which is not always accurate in realistic scenarios where interdependencies between criteria exist. Several methods have been proposed to relax the postulation of the independent criteria in the AHP, e.g., the Analytic Network Process (ANP). However, these methods usually need a number of pairwise comparison matrices (PCMs) and make it hard to apply to a complicated and large-scale problem. This paper presents a groundbreaking approach to address this issue by incorporating discrete Markov Random Fields (MRFs) into the AHP framework. Our method enhances decision making by effectively and sensibly capturing interdependencies among criteria, reflecting actual weights. Moreover, we showcase a numerical example to illustrate the proposed method and compare the results with the conventional AHP and Fuzzy Cognitive Map (FCM). The findings highlight our method’s ability to influence global priority values and the ranking of alternatives when considering interdependencies between criteria. These results suggest that the introduced method provides a flexible and adaptable framework for modeling interdependencies between criteria, ultimately leading to more accurate and reliable decision-making outcomes.
Fuzzy Granulation for Feature Extraction in EEG-Based Stress Pattern Recognition

The study of different techniques for the characterization of biosignals tends to facilitate and promote improvement in the results of their analysis and classification. We propose electroencephalogram (EEG) characterization through fuzzy methodologies based on fuzzy partitions. The present work explores four different fuzzy granulation techniques to extract characteristics from EEG signals to perform multi-class stress recognition. Our experiments show that feature extraction through fuzzy granulation allows to achieve results that exceed the accuracy percentage in the classification of three different stress patterns previously reached in other works focused on the same classification problem with the same database.
Neighborhood sampling confidence metric for object detection

Object detection using deep learning has recently gained significant attention due to its impressive results in a variety of applications, such as autonomous vehicles, surveillance, and image and video analysis. State-of-the-art models, such as YOLO, Faster-RCNN, and SSD, have achieved impressive performance on various benchmarks. However, it is crucial to ensure that the results produced by deep learning models are trustworthy, as they can have serious consequences, especially in an industrial context. In this paper, we introduce a novel confidence metric for object detection using neighborhood sampling. We evaluate our approach on MS-COCO and demonstrate that it significantly improves the trustworthiness of deep learning models for object detection. We also compare our approach against attribution-guided neighborhood sampling and show that such a heuristic does not yield better results.
A Novel Method for Filtering a Useful Subset of Composite Linguistic Summaries

Selecting a subset of linguistic summaries and providing them in a user-friendly and compact form is a latent issue in the field of Linguistic Data Summarization. The paper proposes a method for filtering the most useful subset, for a given decision problem, from a set of composite linguistic summaries. Those summaries embody Evidence, Contrast or Emphasis relations, inspired by the Rhetorical Structure Theory. The summaries’ usefulness is determined according to the relevance of the attributes contained in each one. The strategy followed is based on first finding the Evidence relation whose nucleus contains the better possible representation of the problem attributes, then searching for a Contrast relation and an Emphasis relation that share that nucleus. The method output is a scheme that synthesizes and combines the texts of the three relations. The paper provides an illustrative example in which the most useful relations are found from a dataset of 63 crimes to solve a case of bank document forgery.
Design and Algorithm Research of a Weight Sorting Transport Robot

In warehousing logistics, the sorting and transportation efficiency of goods is still one of the important factors limiting the rapid development of logistics. At present, most regions still use manual sorting with low efficiency and high cost. Especially in some special work areas, such as high temperatures, severe electronic radiation, and areas with heavy long-term work tasks, urgent need for small robots to replace manual labor. In order to solve the inconvenience that small places such as logistics only use manual sorting, It is necessary to design a small-sized weight sorting and transportation robot, which can wait for receiving goods at a designated location, judge and identify the weight of goods by itself through PID intelligent control algorithm, which can move forward at a constant speed, and transport its weight to the designated position and unload it. Constant speed can make the trolley travel more smoothly and load and unload goods more smoothly, which is of great significance.
An Analysis of Sentiment: Methods, Applications, and Challenges

Sentiment analysis involves contextually examining text to identify and extract subjective information from source material. It aids businesses in comprehending the public sentiment surrounding their brand, product, or service while monitoring online discussions. Nevertheless, analyzing social media content is often limited to basic sentiment analysis and simple count-based metrics. Devices that allow the collection of huge amounts of unstructured, opinionated data are becoming increasingly connected with humans. Everyday-activity-related comments and evaluations have been obtained as a result of the advances in Internet-based services like social media platforms and blogs. This study supplies a comprehensive assessment of sentiment analysis approaches to provide academics with a global perspective on the analysis of feelings and its associated domain, applications, and challenges. To comprehend the applications of sentiment analysis, this article provides a detailed explanation of the technique for performing this activity. To comprehend the benefits and drawbacks of each method, they are then evaluated, compared, and discussed. To establish future perspectives, the difficulties of sentiment analysis are finally evaluated.
Soluzioni per il <i>markup</i> dell’apparato critico: il caso della <i>Latin Grammarians Collection</i>

Starting with the case of the Latin Grammarians Collections , currently under development using the Cadmus system as part of the PRIN 2017 The Transmission of Ancient Linguistics , ERC AdG-2019 PAGES and Thesaurus dubii sermonis project (Sapienza Università di Roma 2018-von Humboldt Stiftung 2020), this article aims to reflect on the challenges posed by the markup of the critical apparatus. The initial section begins by providing a status quaestionis concerning the digitisation of Latin grammatical texts and the annotation of the critical apparatus. Subsequently, the second part delves into the workflow for marking up the critical apparatus using Cadmus.
Assessing self-directed English language learning strategies of YouTube content creators

The emerging interest in self-directed learning (SDL) in the context of language learning has focused less on specific learning strategies selected by language learners based on their discoveries. Anchored on Boyatzis' Theory of Selfdirected Learning (2001) and Classifications of Language Learning Strategies by O'Mally and Chamot (1990), and guided by Clarke's and Braun (2017) procedure of Thematic Analysis, the study aims to assess self-directed English language learning strategies of YouTube content creators. The observational method was used to assess 55 videos which met the predetermined criterion. The results have shown that the YouTube content creators utilized primarily cognitive strategies, followed by socio-affective strategies, then metacognitive strategies. Further investigation has drawn out the advantages and disadvantages of the learning strategies used. The research also determined how the learning strategies used by the content creators could be useful in English classrooms. Despite the successful experiences of the YouTube content creators in self-directed language learning, there are several factors to consider when determining how these can be effective for students learning English.
Visibility and exploitation in social networks

Abstract Social media is not a neutral channel. How visible information posted online is depends on many factors such as the network structure, the emotional volatility of the content, and the design of the social media platform. In this paper, we use formal methods to study the visibility of agents and information in a social network, as well as how vulnerable the network is to exploitation. We introduce a modal logic to reason about a social network of agents that can follow each other, post, and share information. We show that by imposing some simple rules on the system, a potentially malicious agent can take advantage of the network construction to post an unpopular opinion that may reach many agents. The network is presented both in static and dynamic forms. We prove completeness, expressivity, and model checking problem complexity results for the corresponding logical systems.
Education Testing System by Artificial Intelligence

The article describes the possibilities of using and modifying existing machine learning technologies in the field of natural language processing for the purpose of designing a system for automatically generating control and test tasks (CTT). The reason for such studies was the limitations in generating theminimumrequired amount ofCTtomaintain student engagement in game-based learning formats, such as quizzes, and others. These limitations are associated with the lack of time resources among training professionals for manual generation of tests. The article discusses the applied research of the Large Language Model (LLM) and Generative pre-trained transformer (GPT) technologies for the development of a system for automatic generation of tests for the purpose of its implementation in the BoxBattle gamified learning platform. The result of such applied research can be a system for automatic generation of tests, which will reduce the time for developing tests. As a result, this will allow teachers to free up time to implement a personalized approach to teaching and develop students’soft skills.
Biophysical approach to modeling reflection: basis, methods, results

The approach used by physics is based on the identification and study of ideal objects, which is also the basis of biophysics, in combination with von Neumann heuristic modeling and functional fractionation according to R.Rosen is discussed as a tool for studying the properties of consciousness. The object of the study is a kind of line of analog systems: the human brain, the vertebrate brain, the invertebrate brain and artificial neural networks capable of reflection, which is a key property characteristic of consciousness. Reflection in the broad sense of the word, understood as an internal representation of the external world, is characteristic of a wide range of animals, and some of them (bumblebees, fish) even demonstrate reflection in the narrow sense of the word, understood as an inner self-representation. This complex behavior is realized by miniature brains of ~1 million neurons. The use of simple recurrent neural networks (RNNs) to obtain answers to general questions is illustrated. For example, it has been shown a small RNS is able to pass delayed matching to sample (DMTS) test, forming an individual dynamic representation of the received stimulus, allowing decoding by a special external neural detector. . It has been demonstrated in the reflexive game “even-odd”, the RNS has a huge advantage over a multi-layered neural network, with the same and a larger number of neurons – reflection defeats regression. It was found that the asymmetry of outcomes in the odd-even game, which was explained by various causes, including psychological ones – “it’s easier to catch up than to run away”, is reproduced in the game of two RNNs. Obviously, there are no psychological causes here and the advantage of the player playing for “even” is explained by the more complex strategy of the “odd” player – he needs to predict the opponent’s move and choose the opposite one.
Social Character of Artificial Intelligence Technologies

The article considers modern transformations of the ideas concerning subject’s cognitive abilities towards object because of the emergence and development of artificial intelligence (AI) technologies. The developments of scientists and engineers from National Research University of Electronic Technology (Moscow, Russia) in the field of artificial intelligence have been taken as a foundation and material of this research. Their analysis allows making a conclusion that the humanity is rather far from the realization of ‘strong artificial intelligence’. We need a qualitative breakthrough in AI material and technological basis for it. Applying ‘model-dependent realism’ by S. Hawking in the course of the speculations in the article, the author concludes that the purpose of cognition in modern science is not to describe an objective reality, but to organize its subjective perception by some definite way. So, the purpose of cognition is the perfection of cognition models permitting a subject ‘to capture’ the reality. This idea is completely confirmed by Maturana and Varela’s autopoiesis theory and von Foerster’s ‘second-order cybernetics’. The article drafts some prospects of moving away from post-nonclassical scientific thinking in its current understanding, because the development of AI technologies results in ‘blurring’ borders of subjectivity in the cognition process. The author concludes that the modern world is moving in another epistemological paradigm where a new scientific revolution is inevitable.
Heuristic modeling of reflection in reflexive games

The functioning of a subject in a changing environment is most effective from the point of view of survival if the subject can form, maintain and use internal representations of the external world for decision-making. These representations are also called reflection in a broad sense. Using it, one can win in reflexive games since an internal representation of the enemy allows predicting their future moves. The goal is to assess the reflexive potential of heuristic model objects – artificial neural networks – in the reflexive games “Even-Odd” (or “Matching pennies”) and “Rock-Paper-Scissors”. We used homogeneous fully connected neural networks of small sizes (from 8 to 45 neurons). Games were played between neural networks with different configurations and parameters (size, step size for modifying weight coefficients). A set of reflexivity criteria is presented, corresponding to different levels of consideration: neuronal, behavioral, formal. The transitivity of formal success in the game is shown. The most successful configurations, however, may not meet other criteria of reflexivity. We hypothesize that the best compliance with the criteria and, as a consequence, universal success in reflection tasks is achievable for heterogeneous configurations with a structure in which the formation of hierarchical systems of attractors is possible.
the The Improvement of Fbg, Ang-1, VEGF, BDNF in Postoperative Patients with Brain Trauma Through Target Task Oriented Phrase Training

Objective:It aims to explore the effect of target task oriented phase training on fibrinogen (Fbg), angiopoietin (Ang-1), vascular endothelial growth factor (VEGF), serum brain derived neurotrophic factor (BDNF) and quality of life in postoperative patients with brain trauma. Methods:142 patients with brain trauma who were operated in neurosurgery of our hospital from March 2020 to March 2023 are chosen and separated into two groups by random number table. The control group (n=71) receive routine postoperative training, and the experimental group (n=71) receive target task oriented training based on the control group, and the serum cell levels of nursing for 3, 7 and 14 days are compared. Improvement of limb function and quality of life after 2, 4, and 6 weeks of nursing care is observed. Results: Before nursing, the comparison in serum factor levels, limb function scores, and quality of life scores between the two groups was with P&gt;0.05; After 3, 7, and 14 days of nursing, the Fbg of the control group was higher than that of the experimental group; The Ang-1, VEGF, and BDNF levels in the experimental group were higher than those in the control group (P&lt;0.05); After 2, 4, and 6 weeks of nursing care, the FMA scores of the upper and lower limbs in the control group were lower than those in the experimental group, with P&lt;0.05; The scores in the physiological, environmental, psychological, and social fields of thecontrol group were lower than those of the experimental group, with P&lt;0.05. Conclusion: The application of target task oriented phase training in patients with brain trauma after surgery can help promote the serum levels of Fbg, Ang-1, VEGF, and BDNF, improve limb function, and enhance quality of life.
d - Lucky Face Labeling of Polytopes

Objectives: Our main focus in this study is to apply the concept of d-lucky face labeling to a family of polytopes like and . Methods: The results were obtained by the method of application of formula to the graphs. Findings: We have found the d-lucky face number and proved the graphs are Euler lucky graphs. Novelty: The concept of d-lucky face labeling and Euler lucky graphs are new findings. The d-lucky face labeling differs from other face labeling by its formula. 2020 Mathematics Subject Classification: 05C78 Keywords: Euler lucky number, Polytopes, Lucky number, d-Lucky labeling, d-Lucky face labeling
Protection of Digital Archives in the Archives of Bosnia and Herzegovina

One of the basic tasks of every archives, in addition to collecting and making archival records available, is to protect them from damage and loss. Records, regardless of whether they are „traditional“ or digital, are subject to deterioration. Daily careless handling and improper storage conditions contribute to increasing the risk of their permanent disappearance. The author will discuss current experiences of the Archives of Bosnia and Herzegovina in manipulating digital records, especially in the aspect of preservation and protection, taking into account sharp contrast between historical importance of the fonds that are stored and the outdated and technically insufficient IT equipment which is currently in use.
SEM analysis of agreement with regulating online hate speech: influences of victimization, social harm assessment, and regulatory effectiveness assessment

In an era where digital interactions are increasingly prevalent, the challenge of effectively regulating online hate speech has emerged as a crucial societal concern. Balancing the regulation of such speech with the preservation of online freedom of expression is a delicate task, requiring broad consensus among internet users. This study delves into the various factors shaping public attitudes towards the regulation of online hate speech in South Korea. An online survey of 1,000 Internet users provided the data for a structural equation model. Our findings reveal that experiences of victimization by hate speech, online activity such as content uploading, assessment of social harm caused by online hate speech, and assessment on the effectiveness of regulatory measures all play significant roles in garnering support for regulation. Notably, online activity correlates strongly with increased encounters with hate speech. This, in turn, leads to a more profound understanding of its social harm and, consequently, a heightened inclination to support regulatory measures. These insights underscore the growing urgency to address online hate speech, especially as online activity continue to intensify. This study contributes to the discourse on online hate speech regulation by highlighting the complex interplay of personal experience, perceived harm, and efficacy of regulation in shaping public consensus.
Analisis Sentimen Berdasarkan Topik Terkait Wabah Covid-19 di Twitter Menggunakan Latent Dirichlet Allocation (LDA) dan Naive Bayes Classifier (NBC)

In 2020 WHO determined that the Corona Virus (COVID-19) was a pandemic. The global spread of the COVID-19 outbreak has made Twitter one of the most widely used tools to publish and find information. This study aims to form a modeling of topics related to the COVID-19 outbreak on the Twitter social media platform and analyze positive and negative sentiments in each topic that has been obtained by combining the two Latent Dirichlet Allocation (LDA) and Naïve Bayes Classification (NBC) methods. Beginning with modeling the topic using the Latent Dirichlet Allocation so that the topics that have been obtained will be searched for the sentiment value of each topic using the Naïve Bayes Classifier method. This study succeeded in combining the two methods with a fairly good accuracy of 89%. In topic modeling, 5 ideal topics were obtained and it can be seen that the most discussed topic is booster vaccination. The results of the classification using NBC show that the topic of booster vaccination has more negative sentiments than positive sentiments.
PERBANDINGAN METODE DECISION TREE DAN NAIVE BAYES CLASSIFIER PADA ANALISIS SENTIMEN PENGGUNA LAYANAN PT PERUSAHAAN LISTRIK NEGARA (PLN)

Background : PLN is a state-owned company that is tasked with supplying electricity to all regions of Indonesia which certainly cannot be separated from the various obstacles experienced, to find out public sentiment on the services that have been provided, an analysis is carried out to determine public sentiment. The results of these sentiments are created in the dashboard using the Flask framework by comparing the Naive Bayes and Decision tree methods. To create a sentiment analysis dashboard for PT. PLN and make a research analysis model using a comparison of the Naive Bayes Classification and Decision tree methods. The method used in this research is Naive Bayes and Decision tree. The data obtained with a total of 40,745 Tweet data taken in the period 1 May 2022 - 4 June 2022 with the keyword "PLN". Making a dashboard that displays the results of the analysis where there is a menu to display the data and each analysis process. The use of 900 training data and 300 testing data resulted in the Naive Bayes method getting an accuracy of 83% on the training data and 80% for the Testing data, while the Decision tree method got an accuracy of 77% on the Training data and 56% on the Testing data. The analysis obtained for the method in this study also shows that the Naive Bayes method is better for classifying large amounts of data than the Decision tree. The sentiment generated by the highest number is negative, with most of the Tweets being complaints about the response to complaints and handling of damage reported by the public.