Reviewerly/exHarmony huggingface dataset:
Detecting Anomalous Behavior of Users of Data Centers Based on the Application of Artificial Neural Networks

The paper discusses an approach to detecting anomalies in the behavior of users of data centers, using a specialized analytical unit based on artificial neural networks. It is proposed to use transaction log records of the databases that are part of the data center as data sets for analysis. An experimental evaluation of the proposed approach is made for several types of analytical units, which include several artificial neural networks. Experiments have demonstrated the high efficiency of the proposed approach.
CS-BERT: a pretrained model for customer service dialogues

Large-scale pretrained transformer models have demonstrated state-of-the-art (SOTA) performance in a variety of NLP tasks. Nowadays, numerous pretrained models are available in different model flavors and different languages, and can be easily adapted to one’s downstream task. However, only a limited number of models are available for dialogue tasks, and in particular, goal-oriented dialogue tasks. In addition, the available pretrained models are trained on general domain language, creating a mismatch between the pretraining language and the downstream domain launguage. In this contribution, we present CS-BERT, a BERT model pretrained on millions of dialogues in the customer service domain. We evaluate CS-BERT on several downstream customer service dialogue tasks, and demonstrate that our in-domain pretraining is advantageous compared to other pretrained models in both zero-shot experiments as well as in finetuning experiments, especially in a low-resource data setting.
Text Similarity Approach based on Semantic Networks and Words Description

Finding similarity in texts is important in many areas such as information retrieval, document compilation, word clarification, automatic recording of articles, and classification of short answers, machine translation, and text summarization. Calculating sentence similarity is not an easy task due to the differences and variations in natural language. Methods for calculating the similarity between texts depend on the semantic or grammatical aspects.  This paper discusses an approach for calculating the similarity between two sentences using semantic networks and word description. The text represented by a semantic network, which is composed of nodes and relationships. Similarity found by taking into consideration the description of the word in terms of its meaning and definition, and the grammatical aspects by measuring the similarity of parts of speech between the sentences.
Azure Sentinel Operations

With Azure Sentinel, a cloud-native SIEM service, many of the traditional areas demanding updates are no longer part of the required services. This includes routing maintenance for hardware, updates, patching, and more. Those resources could be migrated to other roles to support the overall success of the Security Operations Center (SOC). This is one of the legs of the triangle for people, processes, and technology.
초등 과학 온라인 수업 효과에 대한 교사들의 인식과 개선방안

The purpose of this study is to do a survey on teachers’ perception of the effectiveness of elementary science online courses and seek a way of the improvement. For this, 100 teachers who have taught elementary science online courses conducted a questionnaire consisted of three areas: general information of participant, teachers’ perception of the effectiveness of elementary science online remote class, the way of the improvement of elementary science online courses. The results were as follows: First, the elementary teachers thought the elementary science online courses have had a positive effect on scientific thinking ability, science process skill, scientific problem solving, scientific participation and lifelong learning ability. But online courses have negatively affected communication skill. Second, they thought that most of science curriculum units that are easy to apply online courses were in the fields of earth science and biology. However, physics and chemistry appeared to be the most difficult fields to apply online courses. Third, they thought that many contents should be developed so that students’ scientific skills could be developed, and a system for lending science exploration tools should be in place.
Analysis of Causal Relationships Effective Factors on the Green Supplier Selection in Health Centers Using the Intuitionistic Fuzzy Cognitive Map (IFCM) Method

The healthcare sector is one of the largest service industries with the highest potential to improve environmental performance. Hospitals as an important part of the healthcare system must act in a way that reduces their environmental consequences, which requires having a green supplier. The aim of this study was to identify the effective factors on the green supplier selection (GSS) in the hospital and to present an excellent model for analyzing the relationships between these factors. In this study, 14 concepts that effect the green supplier selection of a hospital have been extracted from in-depth literature and interviews entailing: financial capability, creativity and innovation, green technology, flexibility, organizational capability, commitment, trust on supplier, green quality, green transportation, environmental cooperation with customers, hazardous materials management, buy green, green warehouse and green packaging.In addition Intuitive fuzzy cognitive mapping approach was also used for data analysis and conclusion. The results showed that green technology index with 0.43 degree was the most influential and organizational capability index with 0.29 degree had the most influence over the other concepts. In addition, focusing on concepts like financial capability, trust on supplier and creativity and innovation process of green supplier selection of hospital. Taking these in consideration these factors should be given specific attention.
Wagon Wheel Approach of Artificial Intelligence for Managing Tourist Destinations in India

Artificial intelligence (AI) has been one of the hottest topics in the software industry, especially lately. It has already proved its business potential for many major companies across different sectors. Recently, India has begun to take a keen interest in AI as well and is looking to use it extensively. This current study will discuss how AI can be used to manage tourist destinations effectively. It will focus on the Wagon Wheel Approach of Intelligent Management with Artificial Intelligence which uses an intelligent wheel with six spokes so that it's easy for personnel to manage the day-to-day operations of tourists visiting a destination with an artificial intelligence application deployed on top of it.
Concept, Design and Electronics for Control and Measurements of an Ecological Nephelometer

Engineering support of the natural and anthropogenic solid dust particles concentration measurement in the surface layer of the atmosphere implies the development and creation of a certain number of complex, but easy to manufacture and operate devices. The task takes on particular importance when considering dust as a means of transportation for various viruses and infections. The technical solutions we are considering boil down to the implementation of precision photometric measurements of light scattering by dust particles in liquid samples, the concentration of dust in which we increase to reliably measurable by photometry. The carried out research made it possible to formulate problems and propose solutions for a monitoring network. Technical solutions are implemented in working models and tested in urban dustiness of Tbilisi. (Abstract)
Query-Based Image Retrieval using SVM

Now a day’s image plays an important role to extract the information about the object in various industries. Many traditional methods have been employed to retrieve images. It interactively determines the user’s query by asking the user whether the image is relevant or not. In this information tech age, graphics have become a major portion of information processing. In the Image registration processing, the image plays an important part to extract the information regarding the item in a variety of fields including in tourism, medical and geological, weather systems calling. There are lots of Approaches individuals who are used to recover images. It interactively determines an individual's query by requesting the users whether the image will be relevant (similar) or maybe not. In content-based image retrieval (CBIR) system, effective company of this image database used to improve the functioning of the procedure. The research of content-based image retrieval (CBIR) technique has become a significant research topic. Being an individual, we've Studied and done investigation of various features in this manner or in mixes. We found that image Registration processing (IRP) is the key area in above mentioned industries. Various research papers through color feature and texture feature extraction were studied and concluded that point cloud data structure is best for image registration process using Iterative Closest Point (ICP) algorithm.
SAFRAN: An interpretable, rule-based link prediction method outperforming embedding models

Neural embedding-based machine learning models have shown promise for predicting novel links in knowledge graphs. Unfortunately, their practical utility is diminished by their lack of interpretability. Recently, the fully interpretable, rule-based algorithm AnyBURL yielded highly competitive results on many general-purpose link prediction benchmarks. However, current approaches for aggregating predictions made by multiple rules are affected by redundancies. We improve upon AnyBURL by introducing the SAFRAN rule application framework, which uses a novel aggregation approach called Non-redundant Noisy-OR that detects and clusters redundant rules prior to aggregation. SAFRAN yields new state-of-the-art results for fully interpretable link prediction on the established general-purpose benchmarks FB15K-237, WN18RR and YAGO3-10. Furthermore, it exceeds the results of multiple established embedding-based algorithms on FB15K-237 and WN18RR and narrows the gap between rule-based and embedding-based algorithms on YAGO3-10.
Generalized Funnelling: Ensemble Learning and Heterogeneous Document Embeddings for Cross-Lingual Text Classification

\emph{Funnelling} (Fun) is a recently proposed method for cross-lingual text classification (CLTC) based on a two-tier learning ensemble for heterogeneous transfer learning (HTL). In this ensemble method, 1st-tier classifiers, each working on a different and language-dependent feature space, return a vector of calibrated posterior probabilities (with one dimension for each class) for each document, and the final classification decision is taken by a metaclassifier that uses this vector as its input. The metaclassifier can thus exploit class-class correlations, and this (among other things) gives Fun an edge over CLTC systems in which these correlations cannot be brought to bear. In this paper we describe \emph{Generalized Funnelling} (gFun), a generalization of Fun consisting of an HTL architecture in which 1st-tier components can be arbitrary \emph{view-generating functions}, i.e., language-dependent functions that each produce a language-independent representation (view) of the document. We describe an instance of gFun in which the metaclassifier receives as input a vector of calibrated posterior probabilities (as in Fun) aggregated to other embedded representations that embody other types of correlations, such as word-class correlations (as encoded by \emph{Word-Class Embeddings}), word-word correlations (as encoded by \emph{Multilingual Unsupervised or Supervised Embeddings}), and word-context correlations (as encoded by \emph{multilingual BERT}). We show that this instance of \textsc{gFun} substantially improves over Fun and over state-of-the-art baselines, by reporting experimental results obtained on two large, standard datasets for multilingual multilabel text classification. Our code that implements gFun is publicly available.
Twenty Second Century Artificial Intelligence

The book defines various new paths as nine different chapters. First, second and third chapters deal with “Artificial Human Optimization”, “Artificial Soul Optimization” and “Artificial God Optimization” respectively. Three new branches titled “Artificial Satisfaction”, “Deep Loving” and “Nature Plus Plus Inspired Computing” are shown in fourth, fifth and sixth chapters respectively. The seventh chapter describes “Artificial Heart Neural Networks” where algorithms are created by taking inspiration from both Heart and Brain. Two new branches “Artificial Excellence” and “Stories Inspired Optimization Algorithms” are created in last two chapters of this book.
Profile Video Design of Film & Television Study Program as a Promotional Media & Persuasion on Prospective Students of Widyatama University

In this new media era, video platforms have become a strategic part of promotional activities. Through videos, promotional activities of an organization such as higher education institutions, can contribute to massively spreading the organization's profile through various new media channels, such as Instagram, Facebook, Twitter, Websites and video sharing platforms such as Youtube. Video content is considered more attractive and interesting because it contains audio and visual content and is considered easier to understand. As an educational institution, the Film and Television (FTV) Study Program of the Faculty of Social and Political Sciences (FISIP) at Widyatama University views the importance of having a promotional video or profile video to provide information about the study program's profile and at the same time as a medium of persuasion to prospective students or of high school or vocational school graduates, who have the desire to continue their education in the applied sciences of cinematography or film and television production. Therefore, welcoming the new academic year 2021 – 2022, FTV Study Program of FISIP Widyatama University decided to produce a profile video containing the profile of the FTV study program and its activities, which were produced by lecturers in collaboration with students. The purpose of making the profile video is to provide information about the profile of the FTV Study Program with interesting content, to high school and vocational high school graduates in various regions in West Java and Indonesia. The target of making the profile video for the FTV Study Program is to make prospective students choose the FTV Study Program as their choice of further education. Therefore, the strategy taken by the FTV Study Program is to produce a video profile of the FTV Study Program before the announcement of the graduation of high school and vocational high school students, which is used as promotional and persuasive content for the marketing team of Widyatama University both offline and online.
AN ANALYSIS OF THINK-PAIR-SHARE STRATEGY ON GOOGLE CLASSROOM IN READING AT SMKN 1 KUTA SELATAN

This study aimed at analysing the implementation of TPS strategy on Google Classroom in reading at SMKN 1 Kuta Selatan. Descriptive qualitative was used as a research design. A purposive sampling technique was applied to choose 3 English teachers and 117 students as the research subjects. An observation sheet, closed-ended questionnaires, and semi-structured interview guide were used to collect the data. The results show that the teachers create many forums on Google Classroom to implement TPS strategy, namely; posting a text, providing some questions, asking the students to work in pairs, and collecting the results of their discussion. Teachers' perceptions show that 84.37% are very good and 15.63% are good. While students' perceptions show that 8.82% are very good, 70,6% are good, and 20,58% are enough. Increasing students’ enthusiasm and motivation to read a text became one of the teachers' reasons for using TPS strategy on Google Classroom.
Protection and Development of Hunan Huagu Opera

Hunan Huagu opera is intangible cultural heritage, is a high-quality cultural crystallization produced in the long history, and is a traditional culture with national characteristics. With the development of society, Hunan Huagu opera is facing development challenges and cultural transmission is hindered. Therefore, we need to analyze the development status of Huagu opera, find a better and better inheritance, and develop favorable methods of Hunan Huagu opera, so that Flower Drum Opera can return to the sight of the masses, so that more people can understand and pay attention to Huagu opera. Through the study of Hunan Huagu opera, the masses can be more active in inheriting, protecting and innovating the local Flower Drum Opera. While maintaining the traditional opera culture and national characteristics, Hunan Huagu opera will be developed and innovated, so that it has new vitality and can stand the test of time and wind and rain. Through the research of inheriting Huagu opera, we can find out the way to develop Huagu opera better.
The Cost of Information in the Blockchain: A Discussion of Routledge and Zetlin-Jones

The volatility of crypto currencies hinders their ability to be media of exchange or stores of value, leading to the implementation of exchange-rate pegs in an attempt to stabilize these currencies. This strategy has been used by crypto currencies such as US Dollar Tether, Steem Backed Dollar and TrueUSD; and was previously adopted in countries such as Brazil, Mexico and Argentina. However, an exchangerate peg is vulnerable to speculative attacks if it is not 100% backed by reserves, as discussed in Obstfeld (1996). Using insights from the bank-run literature, Routledge and Zetlin-Jones (2018) build on Green and Lin (2003) and propose a model of speculative attacks. They show that adjustments to the exchange rate can prevent speculative attacks in equilibrium. They also show how to implement such contracts using blockchain technology. In this discussion paper, I provide a cautionary tale. I show also in a version of Green and Lin (2003) that the information content in the blockchain prevents agents from attaining all the gains from risk sharing— highlighting the downsides of too much public information.
STRUCTURAL FEATURES OF THE ABILITY TO PROJECT SELF-REALIZATION OF THE FUTURE HIGHER SCHOOL TEACHER

The article highlights the state of studying the problem of the ability to project self-realization of the future teacher of higher education. The author considers the specifics of this concept in the process of professional training of a higher school teacher and carries out its psychological and pedagogical structural analysis (ability, selfrealization, self-realization of the teacher, projecting, project activity) in modern scientific reflection. The domestic and foreign scientific literature on the specification of the concept of “ability” is analyzed. The state of elaboration of the question on the essence of professional self-realization of a higher school teacher in the scientific literature is highlighted. The concept of “project” is considered as part of the concept of the ability to project selfrealization of a higher school teacher in the existing scientific interpretations. The author concretizes a methodological platform consisting of humanistic, holistic-emergent, individual, synergetic, personal-activity, praxeological, project, and technological approaches to ensure the effectiveness of solving the issue of ability to project selfrealization of future teachers.&nbsp;&nbsp
(Invited) A Highly Sensitive MEMS Accelerometer Module for Measuring Micro Muscular Sounds

This paper introduces a high-sensitivity accelerometer module to measure micro muscular sounds. The feature of our microelectromechanical systems (MEMS) accelerometer is the use of high-density gold proof mass structure fabricated by the multi-layer metal technology for reducing the Brownian noise. The noise floor of the proposed module is 10-dB better than that of a commercial one. The measurement results showed that our module can capture small muscular sounds which are buried in the noise of the commercial accelerometer.
Considerations on the Application of Information Technology Tools in Building the Accounting of Entities

In this paper, in a simplified manner, we try to highlight some problems that have arisen in the context of the digitization of accounting as a booming process, similar to other industries. The reasons for the lower applicability can be found in the exceptionally high regulatory requirements regarding the validity and integrity of accounting data. The whole accounting system is built so that counterfeiting is impossible or at least very expensive. To achieve this, it relies on mechanisms of mutual control, checks and balances. Most of them are manual tasks, which require a lot of work and are far from automated. Blockchain technology can be the next step for accounting: instead of keeping separate records based on transactions, companies can record their transactions directly in a common register. Because all entries are distributed and cryptographically sealed, falsifying or destroying them to hide the activity is virtually impossible.
Educación Matemática y COVID-19 en las Américas: limitaciones, adaptaciones, y lecciones aprendidas

Examples of the impact of the pandemic on mathematics education and the diversity of responses generated in the Latin American region are described through 20 cases and 2 reflections (from ethic and dimensions of curriculum) from 14 countries. The cases describe specific contexts and approaches generated in four areas of mathematics education: gener- ating and using resources; mathematics teacher education, the family-school relationship, and the adaptations of extra-curricular activities. From these contributions, we can infer three implications considering the limitations, characteristics of the adaptations and lessons learned. Firstly, that the use of digital technologies, to maintain communication between students and teachers, is generating new ways of thinking and representing mathematics to be learned and about the contexts in which it has to be learned. Second, teacher trainers are articulating new initiatives in initial teacher education in response to the need of mathematics teacher’s technological training. Finally, the need to take into account issues of equity and social justice in making mathematics education for all.
NEAT: Neural Attention Fields for End-to-End Autonomous Driving

Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel representation that enables such reasoning for end-to-end imitation learning models. NEAT is a continuous function which maps locations in Bird's Eye View (BEV) scene coordinates to waypoints and semantics, using intermediate attention maps to iteratively compress high-dimensional 2D image features into a compact representation. This allows our model to selectively attend to relevant regions in the input while ignoring information irrelevant to the driving task, effectively associating the images with the BEV representation. In a new evaluation setting involving adverse environmental conditions and challenging scenarios, NEAT outperforms several strong baselines and achieves driving scores on par with the privileged CARLA expert used to generate its training data. Furthermore, visualizing the attention maps for models with NEAT intermediate representations provides improved interpretability.