Reviewerly/exHarmony huggingface dataset:
3D Single Person Pose Estimation Method Based on Deep Learning

In view of the significant effects of deep learning in graphics and image processing, research on human pose estimation methods using deep learning has attracted much attention, and many method models have been produced one after another. On the basis of tracking and in-depth study of domestic and foreign research results, this paper concentrates on 3D single person pose estimation methods, contrasts and analyzes three methods of end-to-end, staged and hybrid network models, and summarizes the characteristics of the methods. For evaluating method performance, set up an experimental environment, and utilize the Human3.6M data set to test several mainstream methods. The test results indicate that the hybrid network model method has a better performance in the field of human pose estimation.
Reproducible modelling and simulating security vulnerability scanners evaluation framework towards risk management assessment of small and medium enterprises business networks

Objectives: Risk Management has been recognized as a critical issue in computer infrastructures, especially in medium to large scale organizations and enterprises. The goal of this research report is to provide a practical comprehensive virtual machine based framework for assessing the performance of vulnerability scanners applied to such enterprises, focused to small and medium size ones towards a risk evaluation analysis. Moreover, the purpose of this paper is to compare three of the most well-known free vulnerability scanners (Nessus, OpenVAS, Nmap Scripting Engine) with regards to how they can be used to systematise the process of Risk Assessment in an enterprise, based on the herein presented experimental evaluation framework involving virtual machine testing. Method: The proposed methodology is based on developing a framework for suitable setup and usage of virtual machines making risk analysis practical and being capable of comparing different vulnerability scanners. Findings: The herein developed framework is shown to be efficient with regards to comparison and selection of candidate risk analysis software with easily accessed and affordable infrastructure. Novelty: Although there might be few other similar comparisons of vulnerability scanners in the literature, the main herein contribution is the provision of a practical and above all easily reproducible framework for small business enterprises to establish proper selection procedures of such security software without spending a lot of money for expensive testing infrastructure. Keywords: Vulnerability Scanning; risk assessment; nessus; OpenVAS; Nmap scripting engine
The Study Of Historical And Cultural Heritage: As An Epistemological And Socio-Philosophical Problem

The article discusses some epistemological aspects of the study of historical and cultural heritage, as well as socio-philosophical issues of the relationship of historical and cultural heritage with the life of man and society. Epistemology is an area of philosophy concerned with the nature and justification of human knowledge. A growing area of interest for psychologists and educators is that of personal epistemological development and epistemological beliefs: how individuals come to know, the theories and beliefs they hold about knowing, and the manner in which such epistemological premises are a part of and an influence on the cognitive processes of thinking and reasoning.
Unsupervised Anomaly Detection in Parole Hearings using Language Models

Each year, thousands of roughly 150-page parole hearing transcripts in California go unread because legal experts lack the time to review them.Yet, reviewing transcripts is the only means of public oversight in the parole process.To assist reviewers, we present a simple unsupervised technique for using language models (LMs) to identify procedural anomalies in long-form legal text.Our technique highlights unusual passages that suggest further review could be necessary.We utilize a contrastive perplexity score to identify passages, defined as the scaled difference between its perplexities from two LMs, one fine-tuned on the target (parole) domain, and another pre-trained on out-of-domain text to normalize for grammatical or syntactic anomalies.We present quantitative analysis of the results and note that our method has identified some important cases for review.We are also excited about potential applications in unsupervised anomaly detection, and present a brief analysis of results for detecting fake TripAdvisor reviews.
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference

Standard Convolutional Neural Networks (CNNs) designed for computer vision tasks tend to have large intermediate activation maps. These require large working memory and are thus unsuitable for deployment on resource-constrained devices typically used for inference on the edge. Aggressively downsampling the images via pooling or strided convolutions can address the problem but leads to a signifi�cant decrease in accuracy due to gross aggregation of the feature map by standard pooling operators. In this paper, we introduce RNNPool, a novel pooling operator based on Recurrent Neural Networks (RNNs), that efficiently aggregates features over large patches of an image and rapidly downsamples activation maps. Empirical evaluation indicates that an RNNPool layer can effectively replace multiple blocks in a variety of architectures such as MobileNets, DenseNet when applied to stan�dard vision tasks like image classification and face detection. That is, RNNPool can significantly decrease computational complexity and peak memory usage for inference while retaining comparable accuracy. We use RNNPool with the standard S3FD [50] architecture to construct a face detection method that achieves state-of-the-art MAP for tiny ARM Cortex-M4 class microcontrollers with under 256 KB of RAM.
Effective Feature Subset Identification Using Adaptive Bee Colony Algorithm

The irrelevant features, along with redundant features, severely affect the accuracy of the learning machines. Feature subset selection as the process of identifying and removing many irrelevant and redundant features. The overall process of the optimal feature selection method is divided into two main steps, such as, i) preprocessing (ii) Optimal feature selection using clustering and tree generation. At first, preprocessing is done in the input micro array dataset. Then the Possibilistic fuzzy c-means clustering algorithm with optimal minimum spanning tree algorithm is applied on the high dimensional micro array dataset to select the important features. Here the proposed method is optimally select the features with the help of Adaptive artificial bee colony algorithm.
Supporting Comedy Writers: Predicting Audience’s Response from Sketch Comedy and Crosstalk Scripts

Sketch comedy and crosstalk are two popular types of comedy.They can relieve people's stress and thus benefit their mental health, especially when performances and scripts are high-quality.However, writing a script is time-consuming and its quality is difficult to achieve.In order to minimise the time and effort needed for producing an excellent script, we explore ways of predicting the audience's response from the comedy scripts.For this task, we present a corpus of annotated scripts from popular television entertainment programmes in recent years.Annotations include a) text classification labels, indicating which actor's lines made the studio audience laugh; b) information extraction labels, i.e. the text spans that made the audience laughed immediately after the performers said them.The corpus will also be useful for dialogue systems and discourse analysis, since our annotations are based on entire scripts.In addition, we evaluate different baseline algorithms.Experimental results demonstrate that BERT models can achieve the best predictions among all the baseline methods.Furthermore, we conduct an error analysis and investigate predictions across scripts with different styles. 1 * The research was conducted during non-working time.The idea of this research was inspired by a discussion with my friend about an entertainment TV programme in which the comedians mentioned the difficulties of producing a highquality script.
A Definitional Implementation of the Lax Logical Framework LLFP in Coq, for Supporting Fast and Loose Reasoning

The Lax Logical Framework, LLFP, was introduced, by a team including the last two authors, to provide a conceptual framework for integrating different proof development tools, thus allowing for external evidence and for postponing, delegating, or factoring-out side conditions. In particular, LLFP allows for reducing the number of times a proof-irrelevant check is performed. In this paper we give a shallow, actually definitional, implementation of LLFP in Coq, i.e. we use Coq both as host framework and oracle for LLFP. This illuminates the principles underpinning the mechanism of Lock-types and also suggests how to possibly extend Coq with the features of LLFP. The derived proof editor is then put to use for developing case-studies on an emerging paradigm, both at logical and implementation level, which we call fast and loose reasoning following Danielsson et alii [6]. This paradigm trades off efficiency for correctness and amounts to postponing, or running in parallel, tedious or computationally demanding checks, until we are really sure that the intended goal can be achieved. Typical examples are branch-prediction in CPUs and optimistic concurrency control.
PENERAPAN ASSOCIATION RULE DATA MINING UNTUK REKOMENDASI PRODUK KOSMETIK PADA PT. FABIANDO SEJAHTERA MENGGUNAKAN ALGORITMA APRIORI

In some companies that have sales transaction data and this data will increase from day to day so that it will accumulate and become garbage if it is not managed and utilized properly. Sales transaction data is one thing that can be used to increase product sales. Not only to increase product sales but also to provide product recommendations for each sale. As in the product stock setting section, it can provide recommendations for the number of products so that problems such as over stock will not occur which will cause the amount in a product to expire. In this study, an association rule data mining will be implemented for cosmetic product recommendations using the Apriori algorithm. Testing the results of using data mining and the Apriori algorithm is carried out to find out that the results of the study can find association rules from existing datasets to recommend cosmetic products. The association rule method is used in the search for product attachment patterns for sales strategies in policy decision making. So that it can be seen that the cosmetics that are often purchased by consumers, based on the rules generated from the data contained in the database. Tests were carried out using the Rapidminer 9.5 application. The results obtained from this test are that there are 16 rules (rules) that will be used for decision making in cosmetic product recommendations.
Realistic painting as a cultural phenomenon: Academic reflection, museum practices, cultural industries

The article is devoted to the dynamics of realism as a cultural paradigm in Russia’s visual arts. Realism in Russian visual arts is a vibrant phenomenon of contemporary culture. The study indicates a correlation between a research interest in realism in visual arts and social context. The article explores the forms of realism in fine art in the museum space. A realistic painting is a significant part of the cultural heritage. It creates the visual range of the exhibition space and unites the country’s museums thematically. The article discusses the marketing trends in realism in visual arts. Realistic paintings are part of both the elite and mass art market. The article provides reasons for the popularity of realism in visual arts in modern popular culture, namely, in cultural industries. Popular culture actively exploits the potential of the realistic painting — the popularity of the artist’s name, the clarity of the visual image, the richness of references to human experience, i. e. childhood memories, illustrations in school textbook, memories of expositions in the country’s leading museums. The study identified the most stable trends in domestic research of realism in Russia’s visual arts (art history, aesthetics, sociology of culture, cultural studies). The subject of discussion is the definition of realism in art. Theorists of art tend to supplement the basic term “realism” with definitions that shift the emphasis and add nuances to the interpretation of realism. The article also highlights the expansion of the field of research in humanities.
Sentence Boundary Detection on Line Breaks in Japanese

For NLP, sentence boundary detection (SBD) is an essential task to decompose a text into sentences.Most of the previous studies have used a simple rule that uses only typical characters as sentence boundaries.However, some characters may or may not be sentence boundaries depending on the context.We focused on line breaks in them.We newly constructed annotated corpora, implemented sentence boundary detectors, and analyzed performance of SBD in several settings.* Both authors contributed equally. 1 https://www.gsk.or.jp/files/catalog/GSK2007-C/GSK2007C_README.utf8.txt 2 In this paper, we use "⏎" to show line breaks and "
" to show sentence boundaries.1. Is it possible to train a sentence boundary detector on line breaks with annotated corpora?(Section 4.2)
Design of a GSM-Based Skimming Reporting System for Automated Teller Machines

In recent years, there has been the unpleasant advent of a new type of credit card fraud called Automated Teller Machine (ATM) skimming. This type of fraud poses a substantial threat to the banking sector because its modus operandi is quite subtler than other known types of ATM fraud. It consists of a criminal implanting a disguised dummy card reader very similar to the ATM’s original card reader at the ATM. This is done to intercept the ATM card data of any unsuspecting customer who tries to withdraw cash. This paper seeks to design a system which will be able to detect and report such devices before they cause harm. The objective of this research was achieved by designing a skimmer incorporating the use of a metal detector for detecting new electronic components within the ATMs card slot region, an ultrasonic sensor for detecting unfamiliar skimmer overlays and the processing power of a microcontroller to coordinate theses sensors which monitor the status of the ATM terminal’s original card reader and send a Short Message Service (SMS) text message whenever the system detects that a skimmer has been attached to the ATM terminal. This concept of skimming detection was designed, tested and simulated under several operating conditions in Proteus 8.0 simulation software to prove the detection method’s efficacy. The simulation results showed that the proposed system provided a decent theoretical skimmer detection technique. However, other factors such as metal detector oscillator instability and the difficulty in accurately modelling the composition of ATM skimmers served as this design’s major drawbacks.Keywords : ATM, Skimming, GSM, Microcontroller
Automatic selection of binarization method from images with serial numbers on industrial products

The article deals with the automatic selection of the binarization method using advanced methods of artificial intelligence.The input images to the algorithms are images of serial numbers from industrial environments, for example on iron and steel billets, slabs, etc.The surface of these products is in most cases severely damaged by industrial processes, such as traces of cut, rust, noise, surface roughness, etc.Text recognition is a very common topic nowadays.All investigated solutions are based on the fact that each image is binarized by a single defined method and the accuracy of recognition is given only by the quality of learning of the neural network.Especially in an industrial environment, it is difficult to create a universal method for unambiguous methods for text recognition.The innovation described in this article is the automatic selection of the binarization method (from the Bradley, Niblack, Sauvola methods etc.), which increases the accuracy already in the phase before the text recognition itself, which with the subsequent correct combination of filters leads to an overall increase in accuracy.
Improving Learning Outcomes by Using Quizizz Educational Game Assisted Learning Video Media

&lt;p&gt;&lt;em&gt;This study aims to determine the increase in learning outcomes in online learning through learning video media assisted by quiziz educational games. This research is a classroom action research with 29 students of class V SDN Adiwerna 01 as the research subjects. This research was conducted in 2 cycles, each cycle of 2 meetings. Each meeting consists of 4 stages, namely planning, implementing, observing and reflecting. The implementation of the research stages is slightly different from classroom learning because learning is carried out separately or students are in their respective homes. Based on the results of the research, in the first cycle, the average student learning outcomes were 72.07 with 72% student completeness success, still below the 85% success indicator. Whereas in cycle II the average student learning outcomes were 82, 07 and the completeness success increased to 93% in this case it was more than the 85% achievement indicator. So from cycle I and cycle II there was an increase in student learning outcomes. So, in this study it can be concluded that using video learning media assisted by quiziz educational games can improve student learning outcomes in online learning science subjects human digestive organs material.&lt;/em&gt;&lt;/p&gt;
Experience of the Russian Higher Education System’s Operating During the Pandemic

The article presents the experience of the higher education system of the Russian Federation during the global spread of the SARSCOV-2 virus and the related coronavirus disease .Radical changes are described and difficulties of transition to distance learning in the education system are updated.The article considers the results of a large-scale study conducted by expert teams of thirteen universities aimed at assessing and monitoring the emergency situation in which the entire Russian education system found itself.The article analyzes the readiness of teachers and students to switch to distance learning based on the study of the opinions of the university communities, conducted sociological surveys.Various modes of organization of the educational process (correspondence, remote, mixed) are considered.The possibilities and limitations of digital technologies in the educational process of Russian universities in remote mode are shown.The importance of improving computer literacy and developing digital competencies of all participants in educational relations is emphasized.
DOC2VEC OR BETTER INTERPRETABILITY? A METHOD STUDY FOR AUTHORSHIP ATTRIBUTION

In this work, we perform a method study for the problem of authorship attribution in Russian and English. The datasets used consist of 324 works written in Russian and 207 works in English. We propose a set of text representation models that reflect various linguistic phenomena, in particular, morphological and syntactic ones. One distinctive feature of the proposed models is that they are interpretable. These models are used individually and in combination against a Doc2Vec baseline. For Russian, some of our models outperform Doc2Vec, but this does not happen in the case of English, for various reasons. However, the proposed models can also be used together with Doc2Vec, dramatically improving its performance: by 16.79% in the case of Russian and by 7.2% for English. Additionally, we experiment with two different methods for separating texts into blocks of K sentences (contiguous and bootstrapped) and performed parameter tuning of K. Finally, we conduct a feature importance analysis and show which linguistic markers of author style are the most pertinent for Russian, English and for both these languages. All code used in this work is made freely available to the community.
The undergraduate students’ beliefs about English language learning; a survey study in Madurese context

The discussion in line with students’ beliefs about language learning is still popular nowadays since it is believed that the student’s beliefs may influence the language learning process, such as motivation, learning style, and strategies, etc. which later ultimately affected the success of language learning. This study was conducted to investigate how students of the English program at IAIN Madura (State Islamic Institute of Madura), Indonesia, express their beliefs about English language learning. This study was implemented in terms of survey study in which the data was primarily gathered by administering the questionnaire entitled Beliefs About Language learning Inventory (BALLI) of Horwitz’s (1987). The questionnaires were administered to students of English teaching and learning program through Google form. About 144 undergraduate students in the first year have participated in this study. The finding revealed the students’ beliefs in terms of percentage of agreements in all area of BALLI, namely language aptitude, the strategy of learning and communication, the nature of learning language, motivation and expectation in learning, and the difficulty faced by students
PENALARAN BERBASIS KASUS UNTUK MENDIAGNOSA PENYAKIT INFEKSI MENULAR SEKSUAL (IMS) MENGGUNAKAN ALGORITMA WEIGHTED EUCLIDEAN DISTANCE

Case-based reasoning is a methodology for solving problems by utilizing previous experience. In this study the authors apply case-based reasoning to diagnose sexually transmitted infection using the weighted Euclidean distance method. Source of the knowledge base was obtained by collecting medical record of patients with sexually transmitted infections in 2016-2017. The process of finding a solution starts with eliminating irrelevant data using the C4.5 method and continues with the calculation of the similarity value using the Weighted Euclidean Distance algorithm. This system can diagnose 5 types of sexually transmitted infections based on 123 existing symptoms. System result in the form of sexually transmitted infections based on symptoms experienced by the patient, treatment solution and presentation of similarities between new cases and old cases. Based on the result of testing with 127 cases of sexually transmitted infections obtained result: testing uses the K-Fold Cross Validation scenario, the total data is divided into 10fold and the testing process is divided into 2 parts, namely testing using indexing and testing without using indexing. For testing using the highest accuracy indexing obtained at 90.84% in the second fold, and the average accuracy of the entire fold is 88.55% with the average time generated 9498 ms (millisecond), while testing without using the highest accuracy indexing obtained by 63.03% in the second fold, and the average accuracy of the entire fold is 53.48% with the average time generated 9975 ms (millisecond).&#x0D;