Reviewerly/exHarmony huggingface dataset:
Modern targets for multilevel orders for social upbringing

The study of social upbringing has resumed recently and so far no complete target has been identified in scientific research. Based on the fact that there are social orders at the state, public and personal levels, an attempt is made to identify them. Social order regarding social education should be considered as a system for solving problems related to socialization and professionalization, solved by social and educational institutions. The presented review reflects the tendencies of scientific researches of targets for upbringing in domestic and foreign studies. Note that such studies are rare. The most priority aspects of research on the goals of education have been identified, reflecting a greater bias towards goal-setting in education, and educational goals remain insufficiently researched. The modern target thesis in education is for a person to be happy. The aim of the study was to identify targets for different levels of social orders for social education. It was assumed that social orders for social upbringing at different levels have homogeneity. The study of the general public was conducted to identify social orders at the societal and personal levels, and at the state level, social orders were studied through the analysis of regulatory documents. Differences in the formation of social orders for social education at the levels of the individual, society and the state are revealed, which is due to the social roles, functions and goals that various social institutions perform. In conclusion, it is indicated that the hypothesis put forward is partially confirmed. The materials of the study are of interest to the general pedagogical community concerning the understanding of the representation of social orders for social upbringing, in determining their leading guidelines for designing the process in question.
Information Practices of University’s Civil Servants in Managing Family Archives for Asset Declarations

PurposeThis study aims to elaborate on the information practices carried out by university’s civil servants in managing and preparing family archives for asset declaration purposes by applying a theory of everyday information practices. This study uses McKenzie’s two-dimensional model which consists of Active Seeking, Active Scanning, Non-directed Monitoring, and By Proxy modes, along with the Connecting and Interacting phases in each mode. MethodThis study is a descriptive qualitative study and utilizes the data analysis method. Research data was collected through semi-structured online interviews. The informants were selected based on purposive sampling. Results/FindingsThe results of this study indicate that only three modes of information practices were applied by the university’s civil servants for asset declaration purposes. The three modes are Active Seeking, Active Scanning, and By Proxy. ConclusionInformation practices for archive management are a part of daily life. It cannot be separated from context such as the type of archive and for what purpose. It is clear that archival study is related to information practices.
Random forest with SMOTE and ensemble feature selection for cervical cancer diagnosis

Cervical tumours are a leading cause of death worldwide, although they can be prevented by removing afflicted tissues early on. Recognising population weaknesses is necessary for inclusive cervical screening programs. STDs and smoking cause cervical cancer. Creating a cancer classifier requires complex learning. FS decreases a prediction system's inputs. Reducing model parameters and time improves performance. The goal is to create a new ensemble feature selection (EFS) and classifier for cervical cancer diagnosis. EFS, several FSs used. EFS mixes the results of single FS approaches, including entropy elephant herding optimisation (EEHO), entropy elephant herding optimisation (EBFO), and recursive feature elimination (RFE), to improve results. Bootstrap aggregates EFS results. Classifier approach is Random Forest with SMOT (SMOTE). UCI's cancer database has 32 features and four classes. Classification performance is calculated using a confusion matrix and precision, recall, f-measure, and accuracy. The classification algorithms use MATLAB. The proposed algorithm gives an enhanced accuracy value of 94.7552%, 94.5221%, 94.8718%, and 94.2890% for the Hinselmann, Schiller, Citology, and Biopsy tests, respectively.
Study on the Application of Real-Time Drone Monitoring in Ordos Open-Pit Coal Mine

In the process of intelligent mine construction in open-pit mine, in order to improve the safety monitoring ability of mine transportation system, solve the problems of large human interference and blind Angle detection by existing conventional monitoring methods, this paper establishes an open-pit mine monitoring data set, and proposes a real-time intelligent monitoring model based on UAV.The reasoning component with strong computing power and low power consumption is selected, and the lightweight object detection model is selected for the experiment.A quantitative standard of dynamic energy consumption detection by evaluation algorithm is proposed.Through experimental comparison, it is found that YOLOv4-tiny has the highest comprehensive grade in detection accuracy, speed, energy consumption and other aspects, which is suitable for application in the above model.
AI Application to Generate an Expected Picture Using Keywords with Stable Diffusion

Nowadays, artificial intelligence (AI) has a big impact in the field of painting.In contrast to the hand-painting and challenging personal creativity, AI applications practices to add noise, remove noise, restore image and conserve the present process after converting to data.The various AI image generator models, Diffusion model is the latest application consist of two main models, word-image mapping and diffusion algorithm.This article is introduced the mechanism of how images are generated and makes arguments about ethical issues of AI application of image generation.As a result, sophisticated legal restrictions should be implemented to prevent possible negative effects of AI models.
Automatic Exercise Generation for Exploring Connections between Mathematics and Music

Abstract Mathematics and Music are closely connected and their multifaceted relationship has been explored since ancient times. Pythagoras was one of the first who discovered and formalized one of those connections by studying the arithmetic of musical intervals, expressing them by numerical ratios and relating ratios to consonance/dissonance notions, being the Pythagorean tuning and scale based on arithmetic principles. Another interesting link between Mathematics and Music is the geometric approach to musical composition. Geometric patterns are present in different musical style compositions and some composing techniques, such as the 12-tone technique, use geometric transformations, involving also applications of modular arithmetic and set theory to Music. The aim of this work is to present existing relations between Mathematics and Music and to propose automatic exercise generation for exploring and studying those relations. The exercises are generated using the system MVGEN and the LaTeX package MusiXTeX. The generation process and the automatically generated exercises can be used for creating learning and assessment materials for education in Mathematics and Music, linking Science and Art.
An interactive method with gestures oriented toward the augmented reality electronic sand table

Traditional interaction techniques such as mouse and keyboard or gestures for a touch screen are utilized for a 2D electronic sand table，which don’t have enough degrees of freedom (DoFs) for operations on a 3D graph. Gestures interaction method has received increasing attention for 3D electronic sand tables with solid terrain construction and virtual-real fusion display techniques. This paper researches an interactive feedback method with gestures based on contextual information, gestures controlling virtual targets and a gesture interaction modal are designed. Users can control virtual targets with intuitive and feasible interaction methods through the real calculation of a gesture recognition engine and a gesture understanding engine, and the displaying status of the virtual targets can be updated in real time.
How to make use of pretrained models in few-shot classification

Few-shot learning(FSL) aims to generalize model to novel categoeries by few labelled samples, which is challenging for machine. Large-scaled pretrained models, especially vision transformers achieve excellent performances benefiting from numerous and diverse data. Researchers have exploited pretrained models in few-shot classification by simply updating the whole parameters and finetuning on few samples. In this paper, we explore two methods: vision prompt tuning and a reparameterization method called ‘scaling&&shift’ to leverage pretrained models in few-shot classification. Vision prompt tuning is for vision transformer only and we first evaluate the method in few-shot setting. ‘Scaling&&shift’ is originally applied in convolution neural networks(CNN). We extend it to vision transformer. The two methods are evaluated on standard benchmarks such as miniImageNet, CUB, CIFAR-FS, clipart and sketch. The results show that ‘scaling&&shift’ reaches the same level compared to updating the whole parameters. Vision prompt tuning is 0%~5% lower than updating the whole parameters over five datasets while it has quite smaller amount of parameters updated.
A privacy-preserving energy trading scheme in blockchain-based V2G networks

Vehicle to grid (V2G) network plays an important role, which can realize the electric energy transaction between electric vehicle (EV) and smart grid (SG). However, sensitive information on the identity of EVs and the transaction bills may be leaked. In this paper, a privacy-preserving energy trading scheme based on blockchain is proposed, which can protect private information such as transaction amount and identities of EVs. Specifically, elliptic curve encryption and digital signature are used to realize the confidentiality and integrity of transaction information, respectively. At the same time, we also use blockchain and smart contract technology to ensure the immutability of transaction amount between EVs and SG. Finally, we prove the security of the proposed scheme and evaluate its efficiency.
Artificially intelligent collusion caught under EU competition law

If businesses use artificial intelligence to track and determine prices, new possibilities for tacit collusion may arise. Although artificial intelligence does not have a human mind, such collusion remains punishable under EU competition law. The case law of the CJEU demonstrates that Article 101 TFEU applies in the absence of any meeting of minds in any literal sense, and that receipt of commercially sensitive information from a competitor may suffice. According to this case law, anticompetitive intent is not required to establish an anticompetitive agreement, though it is useful in interpreting ambiguous conduct. Finally, even if developers and users of artificial intelligence did not intend to engage in illegal behaviour, they may be held liable for the actions of their algorithms.
RECOMMENDATION SYSTEM USING JACCARD’S SIMILARITY

The following research paper puts forth the idea of a book recommendation system developed using machine learning techniques.The two approaches used in the system are content based filtering and collaborative filtering algorithms along with a hybrid model combining both approaches.The main working model involved taking real world datasets and running them through algorithms till the results achieved were more optimum and satisfactory for users based on their preferences and book browsing habits than the traditional recommender systems.
Sentence opinion mining model for fusing target entities in official government documents

&lt;abstract&gt; &lt;p&gt;When drafting official government documents, it is necessary to firmly grasp the main idea and ensure that any positions stated within the text are consistent with those in previous documents. In combination with the field's demands, By taking advantage of suitable text-mining techniques to harvest opinions from sentences in official government documents, the efficiency of official government document writers can be significantly increased. Most existing opinion mining approaches employ text classification methods to directly mine the sentential text of official government documents while disregarding the influence of the objects described within the documents (i.e., the target entities) on the sentence opinion categories. To address these issues, this study proposes a sentence opinion mining model that fuses the target entities within documents. Based on the Bi-directional long short-term (BiLSTM) and attention mechanisms, the model fully considers the attention given by a official government document's target entity to different words within the corresponding sentence text, as well as the dependency between words of the sentence. The model subsequently fuses two by using feature vector fusion to obtain the final semantic representation of the text, which is then classified using a fully connected network and softmax function. Experimental results based on a dataset of official government documents show that the model significantly outperforms baseline models such as Text-convolutional neural network (TextCNN), recurrent neural network (RNN), and BiLSTM.&lt;/p&gt; &lt;/abstract&gt;
Fake Review Detection using Supervised and Semi-Supervised Learning with Natural Language Processing Techniques in Python

This research paper explores the use of supervised and semi-supervised learning techniques along with natural language processing in Python for detecting fake reviews. The study discusses the importance of detecting fake reviews and its impact on businesses and customers. The proposed approach involves extracting relevant features from text data using various natural language processing techniques and training supervised learning models such as logistic regression, support vector machines, and random forests. Additionally, a semi-supervised learning technique called self-training is employed to improve the model's performance using unlabeled data. The effectiveness of the proposed approach is evaluated on a dataset of reviews from Amazon and Yelp, and the results show that the models achieve high accuracy in detecting fake reviews. The study concludes that the proposed approach can be a useful tool for businesses and customers to identify and filter out fake reviews.
IS IT POSSIBLE TO FIND A BEHAVIOR PATTERN FOR CRYPTOCURRENCIES? A FIRST POINT OF VIEW

We used regression model to analyze daily COVID-19 world fatalities and daily Bitcoin closes and prices.We discovered that COVID-19 levels drove a rise in Bitcoin values, notably to residual values.We contribute to the rapidly developing body of research on COVID-19's financial implications, as well as the current debate over whether Bitcoin is a safe haven investment.Scholars and policymakers, as well as investment professionals interested in the financial implications of COVID-19 and cryptocurrencies, should be interested in our findings.
Application of Neural Network Algorithm in Translation System Construction

With the popularity of smart phones, people have a growing demand for translation. However, due to the complexity of translation tasks and long processing time, the existing translation systems cannot meet the growing needs of people. In real life, translation system plays a very important role. Translation is the second transmission of language, which is efficient, fast and accurate. AI Media Computer Graphics (AI for short) is a translation system developed based on machine intelligence technology, which consists of two parts: artificial intelligence and language services. So far, only Google Translation System has translation ability. This paper introduces a method to generate a translation system that meets the requirements by constructing a neural network algorithm. By calculating the similarity between different texts, it can be found that the largest similarity in the translation process is the alignment between the two texts and the alignment in semantics. Therefore, this paper uses deep learning to transform these differences into similarities and evaluate them.
Alternative Book Publishing: The Terminology Evolution

The article aims to clarify the content of the concept of “alternative book publishing” taking into account the historical retrospective, as well as to consider the formation of the terminology used in studying the development of this phenomenon in Russia. An analysis of scientific literature shows that the definition of “alternative” began to be used by Russian scientists in relation to printed works and publishing activities in the post-Soviet period against the background of the cardinal socio-political transformations taking place in the country. The term “alternative press” came into scientific usage to define the late 1980s—1990s press that was not controlled by the state, was in opposition and expressed public dissent. The researchers who introduced this term into the scientific community described it as a phenomenon of the transition period. In the first post-Soviet decade, the concept of “alternative book publishing” appeared and became widely used to refer to non-state, independent book publishing of the new stage of Russian history. However, according to the author, this term is also applicable to earlier historical periods, since publishing activities, alternative to the state book publishing, have a deep tradition in Russia. This paper offers an interpretation of the concept of “alternative book publishing” in relation to different stages of national history. The author traces the formation of the terms that characterize this phenomenon and reveals their content in different historical periods.
A Framework to Detect Digital Text Using OCR Machine Learning

The deep learning algorithm used in this paper to explain optical character recognition Deep learning and character recognition have recently caught the attention of numerous scholars.In many classification and recognition problems, deep neural networks operate at the cutting edge.Ocular character recognition is referred to as OCR.utilizes a character's optical picture as input and outputs that character.Numerous uses for it exist, such as robotics, traffic monitoring, and the digitization of printed materials.OCR can be implemented using Convolutional neural networks are examples of deep neural network designs (CNN),which is a well-known example.Traditional CNN classifiers can classify pictures using the soft-max layer by learning the most important 2D characteristics that are present in the medical images.
ITALIAN DIALECT CLASSIFICATIONS

This paper provides a historical overview and a critical analysis of Italian dialect classifications.After various empirically-based classifications proposed since the 14 th century, five scientific classifications were formulated.Ascoli (1882/1885) adopted a genealogical classification, arranging Italian dialects in three macrogroups (Gallo-Italic dialects and Sardinian; Venetian, dialects of central Italy, southern dialects and Corsican; Tuscan).This distribution, with some adjustments regarding the position of Veneto, Sardinian and Corsican dialects, also returns in Merlo's ethnic classification (1924; 1933), that underlines the effects of substrates.Rohlfs (1937) proposes a similar three-part classification using a geolinguistic approach based on the areal diffusion of eighteen linguistic phenomena.Devoto's proposal (1970) follows a quantitative model, by measuring the structural affinity between eleven dialectal systems.Pellegrini's classification (1973, 1977), accepted by most contemporary scholars, is based on the contextual application of sociolinguistic and geolinguistic criteria.He subdivides Italian dialects in five systems (northern dialects, Friulian, Tuscan, central-south dialects, Sardinian), each of which is further divided into subsections.
A Novel Method Using Deep Learning Technique for Automatic Grading and Classification of Interferometry Video Frames for Dry Eye Analysis

Maintaining good ocular health is vital to overall health, wellness and well-being.Dry eye is a syndrome that affects people of all ages if the blink rate is not up to the mark.A normal mean blink rate of up to 22 blinks/min has been observed under relaxed conditions.Tears protect eyes from irritants and infections and keep the eyes wet.A thin layer of tear film is spread across the outer layer of the eye with each blink. Dry eye occurs when eyes do not produce enough tears or good qualityof tears.There are several diagnostic techniques available in the market to detect dry eye syndrome.One of the techniques is tear film interferometry, which is based on interferometric patterns developed under extended white light source over outer most layer of tear film, called lipid layer.It is a non-invasive technique used to evaluate the quality of the tear film.Interferometric patterns develop over lipid layer can be classified into five different dry eye grades: grade 1, grade 2, grade 3, grade 4 and grade 5 based on color and distribution of fringes.This work intends to propose a novel method for automatic classification of interferometry video frames to detect the dry eye condition using a combination of computer vision and deep learning technique based on sequential convolutional neural network architecture.The proposed novel technique has achieved an overall accuracy of 86.25% and the results look promising.
Different Together: Design for Radical Placemaking

This work responds to isolating urban places, and contributes new ways for thinking about placemaking. Progressing through autoethnography and prototyping, we critique design proposals with Lefebvre’s theory of utopia. There inhabitants can enjoy and shape their place together without risking depletion of their abilities and motivations to do so. The critique produces political sensibilities that help us make sense of common tensions among inhabitants, landowners, and visitors, and generate possible responses. The critique process itself illustrates how designing through critique with theory can help us think in new ways. This paper contributes a display of how design with critical theory can happen, ultimately to support our abilities and motivations to envision and make places of social flourishing that can respond to our socio-environmental crises.
ModSandbox: Facilitating Online Community Moderation Through Error Prediction and Improvement of Automated Rules

Despite the common use of rule-based tools for online content moderation, human moderators still spend a lot of time monitoring them to ensure they work as intended. Based on surveys and interviews with Reddit moderators who use AutoModerator, we identified the main challenges in reducing false positives and false negatives of automated rules: not being able to estimate the actual effect of a rule in advance and having difficulty figuring out how the rules should be updated. To address these issues, we built ModSandbox, a novel virtual sandbox system that detects possible false positives and false negatives of a rule and visualizes which part of the rule is causing issues. We conducted a comparative, between-subject study with online content moderators to evaluate the effect of ModSandbox in improving automated rules. Results show that ModSandbox can support quickly finding possible false positives and false negatives of automated rules and guide moderators to improve them to reduce future errors.