Reviewerly/exHarmony huggingface dataset:
Secure Blockchain: Assessing Specific Security Threats

Blockchain technology originates from the Bitcoin system, and due to its great success it can draw awareness to other fields because of its global acceptable features like decentralization, anonymity, and audibility. Other than finance, blockchain technology has evolved in various applications like legal, supply chain, health care, etc. Though blockchain technology is a bit difficult to maintain and monitor, still a blockchain network is more secure than other technologies. In this work, we want to give some avenues of the blockchain security system and also highlight some specific security threats. The issues related to mining or fork problems have also been discussed. It is found that several unexpected fields contribute to security concerns rather than decentralized computing and any cryptographic issues.
An Overview of Logistics and Supply Chain Studies during COVID-19: Perspective from the Higher Educational Institutions

In the prevailing condition of the Coronavirus disease (COVID-19) pandemic, this study aims to understand problems and challenges in the use of online education technologies, to propose a strategy for the use of these technologies by Higher Education Institutions (HEIs) in Malaysia and mitigating the potential problems that may occur.During the period of the pandemic, teaching and learning must go online.At the wake of the pandemic, it was being observed that only a few are exposed to OTL.Through a wide-ranging evaluation of contextual synthesis and existent literature, an OTL mitigation strategy was identified.Six mitigation strategies behind the challenges of OTL in HEIs were determined namely strong internet access, compatible hardware and software, time synchronization between lecturers and students, the needs of plagiarism tools, psychological and financial obstacles solution, and OTL training.This study strives to aid as a groundwork for future research, predominantly in the logistics and supply chain education discipline.Likewise, these mitigation strategies could possibly offer valuable insights to HEIs providers in Malaysia on the necessity towards enhance the OTL in the future.This study encourages researchers to progressively integrate OTL and to realize the consequences of such efforts, which are deemed to safeguard the significance of e-learning in the 21st century during a crisis.
Formation of general professional competencies among students of agricultural universities through mathematical disciplines

Abstract The article describes the aspects of forming general professional competences (GPC-1) through the study of mathematical disciplines; reveals the features of the methodology for the application of problems (profile content problems, case tasks, problem-situations), which are the main means of assessing the formation of the corresponding indicators of competence achievement: AI-1, AI-2, AI-3, etc. The work analyzes textbooks on mathematical disciplines for the presence of problems for objective assessment of professional competence formation level (GPC-1), the problem of the development of the criteria content and assessment tools for formation of professional competences is identified. The authors give examples of problems in the discipline of Mathematics and Mathematical Statistics, designed to form skills for identifying and defining the problem, choosing research methods, analyzing, interpreting the result and transferring fundamental knowledge to the professional activity language.
New Hypothesis to Solve the Fermi Paradox

The study aims to formulate a new hypothesis to explain the Fermi paradox. The article considers development of civilisations with respect to electronic virtual reality (EVR). Special attention is paid to shifting the research from physical space to the world of EVR. The study is novel in that it puts forward the author’s hypothesis to solve the Fermi paradox based on the ontological and existential approach to the level of civilisation development. On the premise of such a prominent essential characteristic of human beings as freedom, the authors distinguish between the “hard” and “soft” world. The result of the study amounts to introducing the idea that civilisations which achieved a certain level of intellectual and moral development make a transition to EVR.
Learning a Deep Metric: A Lightweight Relation Network for Loop Closure in Complex Industrial Scenarios

The similarity metric in Loop closure detection (LCD) is still considered in an old fashioned way, i.e. to pre-define a fixed distance function, leading to a limited performance. This paper proposes a general framework named LRN-LCD, i.e. a Lightweight relation network for LCD, which combines the feature extraction module and similarity metric module into a simple and lightweight network. The LRN-LCD, an end-to-end framework, can learn a non-linear deep similarity metric to detect loop closures from different scenes. Moreover, the LRN-LCD supports image sequences as input to speed up the similarity metric in real-time applications. Extensive experiments on several open datasets illustrate that LRN-LCD is more robust to strong condition variations and viewpoint variations than the mainstream methods.
Maximally Entangled SU(1,1) Semi Coherent States

Abstract In this paper, we consider a special type of maximally entangled states namely by entangled SU(1,1) semi coherent states by using SU(1,1) semi coherent states(SU(1,1) Semi CS). The entanglement characteristics of these entangled states are studied by evaluating the concurrence.We investigate some of their nonclassical properties,especially probability distribution function,second-order correlation function and quadrature squeezing . Further, the quasiprobability distribution functions (Q-functions) is discussed.
Maintaining a “questing eye” at the natural language synchronization function to improve artificial intelligence

Natural language is considered a comprehensive phenomenon, which allows to interpret reality or improve interaction with it. It is questioned how meaning is constructed in search for the best alternatives to improve filters for deep learning or machine learning for artificial intelligence to develop conditions that reduce ambiguity. Language as a central axiomatic-logical mechanism reveals the characteristic of synchrony connecting several interdependent subsystems, so it depends on time. The signification process (comparison and synchronization) unites the formation of patterns in which the sum of the latter does not correspond to the final product. Vibration and pulsation are present in the physiological processes that exhibit periodicity, which creates the fluent form of language according to standards and rhythms. Periodicity is revealed as a systematic and universal character in a wide range of phenomena, whether in form, configuration, movement, or as a game of forces.
PreFace++: Faceted Retrieval of Prerequisites and Technical Data

While learning new technical material, a user faces difficulty encountering new concepts for which she does not have the necessary prerequisite knowledge. Determining the right set of prerequisites is challenging because it involves multiple searches on the web. Although a number of techniques have been proposed to retrieve prerequisites, none of them consider grouping prerequisites into interesting facets. To address this issue, we have developed a system called PreFace++ (http://eval_teknowbase_for_ir.apps.iitd.ac.in/prerequisites/) which assists a user in learning new topics. PreFace++ is an extension of our previous system PreFace. It takes a query as input and returns (i) a prerequisite graph, where the nodes represent prerequisites for the query and edges indicate prerequisite relationship, ii) a set of interesting facets towards understanding the query (iii) prerequisites for the query and the facet and iv) a set of research papers and posts relevant for the query and the facet to explore relationship between the query and the facet. The backbone of PreFace++ is TeKnowbase, which is a knowledge base in Computer Science.
Finding Records in Social Media: A Natural Language Processing Fundamentals Exploration

Social media postings are now routinely used as proof of activities, events, or transactions in news media, academic institutions, governments, judicial courts, commerce, and various other organizations. The need to preserve social media content as records has drawn the interest of academic researchers, industry professionals, and policy makers. Despite the importance of this research area, selection of records from a pool of social media content remains an area of low research activity. This paper explores the use of Natural Language Processing methods to classify and select records from a pool of tweets (twitter social media content). We experiment with various characteristics of the data and NLP parameters with the goal of determining optimal parameters for training a supervised machine learning classifier. This paper can serve as an aid for understanding the fundamental elements of automating the selection of social media records.
Teacher Development and Learning Ecologies: A Case Study Assisted by Atlas.ti

One of the main characteristics of the knowledge and information society that we find ourselves in is the increase in learning possibilities. This has contributed to the need for teachers to understand education as a process that combines formal, non-formal, informal, and self-directed learning. For that reason, the aim of this study was to determine how the different modalities of learning promote professional development in primary education teachers. This qualitative research, using a case study methodology, reviews the educational paths of five teachers who are teaching in various state schools in a city in the north of Spain. The data collection technique was a deep interview. Information was processed using content analysis strategies with the help of Atlas.ti software. The results showed that the teachers were engaged in various educational activities to keep themselves up to date. In addition, they largely valued self-directed learning and informal learning. This research indicates the value of the ecological perspective, as it allows the integration of all of the learning opportunities that arise in modern society and which therefore promote teachers’ professional development.
Weather prediction system and recommendation of plant varieties as an effort to minimize harvest failure with android-based Backpropagation Artificial Neural Networks

Abstract Based on Malang Regency agriculture data in 2017 presented by the Head of the Agriculture and Food Crops and Horticulture Office (DPTPH), there was a 2.5% decrease in yields. One hectare of paddy land can produce 7.71 tons during normal weather, but currently only produces 6.9 tons per hectare. Three farmers’ groups in Malang Regency (2018), are of the opinion that the current weather greatly influences crop failure. Weather prediction used so far using conventional benchmarks for planting, is no longer relevant to current reality. These estimates are often incorrect, resulting in crop failures, which have an impact on the farmer’s economy. This study aims to make the application of weather prediction systems and recommendations for suitable planting varieties based on data on temperature, humidity, time of sun exposure, wind speed, and rainfall using Backpropagation ANN method. Data used for Karangploso District weather prediction from 2009-2018. While the variables used in the application of Backpropagation ANN: temperature, humidity, time of sun exposure, wind speed, and rainfall. For output, the prediction of rainfall in the next 12 months and planting of varieties in accordance with the predicted rainfall, OPT and land type. From the calculation results of this application the MSE error value of 0.0299 is obtained.
Analysis of the Construction of College English Ecological Teaching Model in The Age of Artificial Intelligence

In the era of rapid development of artificial intelligence, it has brought new opportunities and challenges to the college English teaching model. Artificial intelligence has a very important role in many aspects of teaching. It can not only analyze the differences between language learners among individuals but also tailor suitable learning programs for students to become intelligent. Relying on artificial intelligence data increases the diversity of college English teaching, and further improves the quality, and enables better development in language teaching.
Diabetes Patients Hospital Re-admission Prediction Using Machine Learning Algorithms

The excess amount of blood glucose in the body leads to a chronic disease called diabetes. It causes severe damage to the eyes, kidneys, nerves, and other parts of body. Hospital re-admission is a scenario in which the patient gets re-admitted to the hospital after a certain duration of time. Diabetes patient’s hospital re-admission majorly impacts on the healthcare cost reduction as diabetic patients are more likely to get re-admitted than those without diabetes. The proposed work aims to predict the re-admission of diabetic patients and highlight the factors that lead to re-admission within 30 days of their discharge considering the database of 10-year administrative patients’ record using decision tree and AdaBoost classifiers. With all the preprocessing and feature selection techniques, the proposed approach has obtained an accuracy of 95%.
A Network Based On Deep Interest Network for Taobao Click-Through Rate Prediction

Improving the prediction accuracy of advertising click-through rate (CTR) is a very important task in the field of computing advertising. The higher the accuracy of click-through rate prediction, the more accurate the information provided to users will be, and the better the promotion effect of merchants will be, which will increase the profits of media platforms, DSP companies and merchants. In this paper, we propose a new model based on DIN (Deep Interest Network), which can make better use of data structure and achieve diversity and local interest to users through a network structure similar to attention. activation. Facts have proved that this method is effective and significantly better than traditional models. Experiments show that the model can bring more interpretability and obtain good AUC (area under the curve) performance.
Deep structural framework and genetic analysis of Gold Concentration Areas in the Northwestern Jiaodong Peninsula, China- A New Understanding based on High-Resolution Reflective Seismic Survey

The gold concentration areas in the northwestern Jiaodong Peninsula constituted an important gold metallogenetic region in Eastern China during the Mesozoic. The deep geological bodies' texture characteristic is important for exploring the resources thoroughly and understanding the metallogenic process. The detailed textures were revealed using high-resolution seismic profiles through the three major ore-controlling structures-Sanshandao fault zone, Jiaojia fault zone and Zhaoping fault zone. This study aims to establish a deep structural framework of this area. Based on their formation mechanism, the fault structures developed in the area can be divided into regional and local fault structures. The structural styles are characterised by superimposing their compressional, strike-slip and extensional multi-stage activities. The crust is cut by vertical structures corresponding to a left-lateral strike-slip fault system on the surface. Nearby these structures are the arc-shape structures formed by multi-stage magma intrusions into the upper crust. Bounded by the Tancheng–Lujiang and Muping–Jimo fault zones, the current Jiaodong block, developed a series of NE-trending strike-slip fault systems, was probably formed by the assemblage of several obliquely aligned blocks. The intensive magmatism and hydrothermal activity between the blocks induced large-scale mineralisation. It provides a new angle of view for understanding the cratonic destruction and large ore-concentration formed during the Mesozoic.
Research on the Security Criterion of S‐Boxes Against Division Property

As a generalized integral property, division property was proposed by Todo at EUROCRYPT 2015. We propose a new security criterion of S-boxes against division property and prove that it is invariant under permutation-xor equivalence. Based on the criterion, the division properties of some important 4-bit S-boxes are showed. Then, we apply it to improve the resistance of ciphers against division-property-based integral attacks while keeping the same security level against other attacks. Specifically, the resistance of the cipher PRESENT against division-property-based integral attack is improved by 2 rounds, and the resistance of the cipher LBlock against division-property-based integral attack is improved by 1 round.
Entropy-Based Variational Learning of Finite Generalized Inverted Dirichlet Mixture Model

Mixture models are considered as a powerful approach for modeling complex data in an unsupervised manner. In this paper, we introduce a finite generalized inverted Dirichlet mixture model for semi-bounded data clustering, where we also developed a variational entropy-based method in order to flexibly estimate the parameters and select the number of components. Experiments on real-world applications including breast cancer detection and image categorization demonstrate the superior performance of our proposed model.
THE STRENGTH OF TRANSVERS ACRYLIC RESIN WITH GLASS FIBER SOAKED IN TEA

Background: In acrylic removable dentures, brokenness often occurs in the midline region of the maxilla. Resistance to fracture depends on the physical properties of the material namely the transverse strength. This can be prevented by the addition of glass fibers. Tea is consumed by many people, acrylic resin dipped in steeping tea will reduce the strength of acrylic resin. How the physical properties of acrylic resin are modified by the addition of glass fiber effect when immersed in a tea solution. Purpose: This study was to determine the strength of transverse acrylic resin with the addition of glass fibers soaked in tea. Method: Rectangular acrylic resin with a size of 10 x 65 x 2.5 mm. The number of samples is 24. divided into 4 groups. namely 2 groups without glass fiber and 2 groups with glass fiber. The sample is measured by transverse strength using an autograph (Shimadzu) tool, then data is analyzed using ANOVA. Result: Acrylic resin without glass fiber immersed in tea solution decreases its transverse strength. while the acrylic resin was given glass fiber the strength value of the transversion does not differ significantly either soaked or not. Conclusion: Acrylic resin which is given glass fiber and soaked in tea does not reduce the strength of the transversion. This is due to the glass fiber filling the cavity in the acrylic resin.
Understanding the Effect of Distance Learning Teaching Experiences on Transformational Instructor-Leadership on STEM education

The major paradigm shifts in educational instruction from the traditional face-to-face learning to distance or blended learning is an effective pedagogical approach/strategy used by all levels of educational institutions in the international community due to the COVID-19 pandemic. According to Qureshi and Shahzadi (2013), a key to success is transformational leadership in open distance learning. These authors asserted that the thrust for developing online education is because of the need to provide education for all, to enrich people's lives, to spread higher education and make and guide the future leaders to drive economic growth in terms of economy scale; that among all leadership styles, transformational leadership leads to positive changes in those who follow and prove themselves energetic, enthusiastic, and passionate as leaders (Qureshi & Shahzadi, 2013). In this perspective, this scholarly writing seeks to highlight the effect of distance learning teaching experiences on STEM instructors' transformational leadership in higher education. To accomplish this, (1) I define and explain: distance learning, transformational leadership, transformative leadership in education, transformative learning, and STEM education; (2) review pertinent literature; and lastly, (3) conclude on the topic.
Decision support system algorithm for the beneficiary of uninhabitable housing funds

Abstract Poverty is one of the big problems facing the people of Indonesia. This problem evidenced by the existence of 3.4 million Indonesians living in settlements that are not livable. This problem is also a concern of the government by providing financial assistance for homes that are not livable. But in reality, the available aid is often not on target, not on a budget, and not on time. The purpose of this research is to implement the El Choination El Choix Traduisant La Realite (ELECTRE) algorithm as a decision support system to provide recommendations for potential recipients who deserve assistance from the government. System testing is done by inputting 46 prospective beneficiary data with predetermined criteria. The results showed the ELECTRE algorithm was able to provide recommendations with an accuracy rate of 86.9%. This result indicates that the decision support system using the ELECTRE method can use as an alternative to the efficiency of determining the prospective beneficiaries of uninhabitable housing in Indonesia.
Bayesian System Inference on Shallow Pools

IR test collections make use of human annotated judgments. However, new systems that surface unjudged documents high in their result lists might undermine the reliability of statistical comparisons of system effectiveness, eroding the collection’s value. Here we explore a Bayesian inference-based analysis in a “high uncertainty” evaluation scenario, using data from the first round of the TREC COVID 2020 Track. Our approach constrains statistical modeling and generates credible replicates derived from the judged runs’ scores, comparing the relative discriminatory capacity of RBP scores by their system parameters modeled hierarchically over different response distributions. The resultant models directly compute risk measures as a posterior predictive distribution summary statistic; and also offer enhanced sensitivity.
Deep Learning Based Stabbing Action Detection in ATM Kiosks for Intelligent Video Surveillance Applications

The role of computer vision algorithms for the analysis of human action as well as their behavior has been gaining more importance since they could be applied for detecting even the complex action of a person stabbing others. Instinctive detection of such violent action restricts the crimes and guarantees the protection of the public. To address this social threat, this paper presents an intelligent video analytics for detecting the stabbing action of ATM surveillance applications by assuming static camera environment. The dataset collection is the significant contribution towards this complex action detection framework. By means of MAMR (Motion Assisted Matrix Restoration) foreground extraction and labelling have been done, and then hierarchical-based You Look Only Once (YOLO) v3 detector has been employed for further detection of ‘knife-in-hand’ of the suspect. Further, Lucas Kannade (LK) and Weber Local Descriptor (WLD)-based motion detection is executed using the hybrid network YOLO v3 (Darknet-53) in this video analytics. Finally, in the proposed hybrid-based hierarchical, the results of these Two Stream (TS) detection results are concatenated by averaging their classification scores to confirm the stabbing action and they have been validated using the mean Average Precision (mAP) metric on ATM and Non-ATM surveillance videos.