Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Smart Surveillance System

The role of CCTV cameras has been overgrown in this generation. CCTV cameras are installed all over the places for surveillance and security. Many surveillance systems still require human supervision. Recent advances in computer vision are, thus, seen as an important trend in video surveillance that could lead to dramatic efficiency gains. Various public places like shopping malls, supermarkets, ATMs, banks, and other places, where CCTV cameras are available, are the places we should concentrate on. Security can be characterized in various terms in various settings like robbery distinguishing proof, brutality recognition, odds of a blast, and so on. In jam-packed public places, the term security covers practically a wide range of strange occasions. So, it is important and challenging to build a model which detects these abnormal activities and generates some kind of alert. We used a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) which involve the concept of deep neural networks. It extracts the spatial–temporal features of the images and calculates the Euclidean distance between the original and reconstructed batch of images. We converted the training videos into images to train the model and calculated the loss between the images to identify the abnormality. To validate the proposed algorithm, 4 datasets as HOLLYWOOD, UCF101, HMDB51, and WEIZMANN are used for action recognition. The proposed technique performs better than the existing one. We made use of Jupiter notebook and Python frameworks.
Sudden position guide to collection management (ALCTS sudden position series, 2)

"Sudden position guide to collection management (ALCTS sudden position series, 2)." Journal of Electronic Resources Librarianship, 34(2), pp. 163–164
Arab Influencers and Social Media

Social media platforms such as Facebook, YouTube, Twitter and Instagram have paved the way for social, cultural and linguistic changes in the Arab region, including the rise of a new socio-cultural class of social media public figures, influencers. This chapter investigates the language of six Arab influencers from three Arab countries through the analysis of a corpus of their posts and tweets on Twitter and Instagram. The aim of the analysis is twofold: (a) to shed light on the language choices made by these influencers in relation to the use of a particular language variety in their online world (Arabic, English, a mix of the two) and (b) to showcase how such language choices establish the influencers’ digital identities. The analysis reveals interesting trends in language choice, with the Arabic language being dominant on Twitter but less so on Instagram, with more instances of code-switching between Arabic and English on Twitter, while Instagram has more cases of transliteration. This, in turn, reveals that virtual identities are flexible and vary according to the social media platform, targeted audience and themes/topics covered.
Methodological Issues in Research on Working Memory and L2 Reading Comprehension

The relationship between working memory (WM) and second language (L2) reading comprehension has received considerable attention for nearly three decades. Although studies in this line of research generally report a small to moderate relationship between WM and L2 reading comprehension, comparison of studies remains challenging due to the lack of specification of the kind of comprehension under investigation (e.g., textbase, situation model) and the means of comprehension assessment. In addition, inconsistencies in the usage, scoring and analysis of WM measures further complicate the interpretation of findings across studies. Thus, in this chapter, we examine L2 reading-WM studies, paying particular attention to methodological considerations surrounding the use and scoring of WM tasks and the assessment of comprehension. We argue that methodological decisions can have non-trivial effects on this line of research and provide task recommendations based on current theorizing in reading
Student Reactions to Bots on Course Q&amp;A Platform

Motivation Bots can alleviate the workload of instructors supporting students in large course Q&A platforms, but it's not clear whether students will be receptive to the use of automated assistants in this setting. Objectives We aim to observe student reactions when they encounter bot-generated follow-ups to Q&A board posts. We investigate the effect of revealing that a bot, rather than a human, is suggesting that the current post is a duplicate. Methods Our bot revealed or hid its bot identity when suggesting duplicate posts, with the condition selected randomly. We observed students' reactions in both conditions. A post-course survey was distributed to collect students' demographic data, previous experiences with bots, and attitudes toward our bot. Results We observed a slight increase in students' response rate when the bot hid its identity. We compared the positive response rate in both conditions and did not find evidence suggesting that students had less trust in bot-generated answers. From the survey, we only saw minimal direct evidence that students might mistrust the bot: 7 of 59 students reported worries about receiving an inaccurate bot-generated answer. Other students were concerned that they would not receive attention from an instructor. Discussion We did not find evidence that revealing the bot's identity has a negative impact on student reactions. However, future bot design should consider the emotional impact of deploying a bot as there may be negative emotional effects to receiving a bot-generated response.
Embedding Vendor Certifications in the Cloud Computing Curriculum

A series of previous Working Groups has met at ITiCSE conferences to explore ways of incorporating cloud computing into courses and curricula, including mapping industry job skills to knowledge areas (KAs) and KAs to student learning objectives (LOs). The importance of industry-standard learning content and certification, produced by cloud vendors and others, was apparent throughout this work. This Working Group will focus on the role of certification within cloud computing curricula, from the viewpoints of a range of stakeholders: students, graduates, institutions, vendors and other certification providers; and employers. Areas for study will include: the scope of available certifications and their mapping to our KAs and LOs; approaches to integrating certification in academic cloud curricula, and challenges involved in doing so; and perceptions of the stakeholders of the role and value of certifications in evidencing employability. The outcome of the work will include a set of recommendations for best practice.
The Relationship Between Education and Economic Development In Iraq Using The Causality Test

This article highlights how evolutionary economics and innovation systems may be used to show how higher education contributes to economic development. Education strategy has occupied a special place among all nations throughout the history of humanity. In ancient China, the Kwan-Tzu book has been a century-old for the preference of investing in human beings through education, and recent international experience has shown that the real beginning of progress, development, and advancement begins when society reaches a certain level of education. Concerning education and development, the interest in education is because it is the fundamental basis on which the process of thoughtfully and adequately counting human resources is based. Furthermore, education provides the workforce with the skills of the crisis and develops it to enhance its contribution to economic and social activity. As part of sustainable development and decreasing poverty, education has a significant impact on the development of society and its culture. Developments in production processes, adapting imported technology to the country's specific circumstances, and formulation and follow-up of administrative plans are also facilitated.
Analyzing Customer Profitability Using Resource Consumption Accounting For Improving Firm Performance

Accuracy and quality of customer cost information depend on the selection and execution of a contemporary strategy to cost accounting and administration that is responsive to fierce competition between economic units and incorporates resource consumption accounting. RCA, the latest generation of cost accounting and contemporary management software, enables managers to make informed choices and establish effective plans. This research intends to illustrate the influence of resource consumption accounting in rationalizing customer costs in the bags / Hilla facility by lowering product prices, detecting idle energy, and identifying the factory's best customers. In the Al-Hilla / Al-Hilla / Al-Hilla plant, a case-study technique was applied. Through this process, an evaluation of the laboratory's actual expenses and the application of resource consumption accounting are made using the laboratory's existing data for the year 2018. The research concluded that the effectiveness of customer cost analysis is contingent on the technique used and that accounting for resource consumption is one of the most effective and accurate methods for cost rationalization. The resource consumption accounting highlights the economic unit is resources, the entwined relationships between them, and their optimum usage. Furthermore, it finds idle (untapped) energy and eliminates it from goods and services to minimize prices. Thus, the researcher's most significant suggestions affirm that enough attention is given to the application of resource consumption, accounting for their contribution to attaining this aim.
First Language Processing

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Matrix Approach to Solving Reachability Problems in Stochastic Petri Nets

The purpose of the research was to develop the theory of stochastic Petri nets and consider its practical application when studying discrete systems. The paper considers the possibility of solving the reachability problem in stochastic Petri nets by means of matrix equations widely used in Petri nets; describes the stages and features of generating matrix equations for stochastic networks; formulates the rules for introducing virtual elements, i.e., positions and transitions, into the stochastic Petri net to generate and solve matrix equations. Stochastic Petri nets different in structure and composition were used to explore the possibility of applying matrix equations. Findings of the research show that the reachability of the required states of the networks is determined through the firing of transitions, which are the solution of the matrix equation. Within the study, we interpreted the obtained results and developed an algorithm that allowed us to validate the assumption made and visually determine the restrictions on the use of matrix equations for various initial states of the simulated system. The results of the proposed algorithm are presented in graphical form on the examples of stochastic Petri nets that model the process of forensic handwriting analysis. The conclusion is made about the applicability of matrix equations in stochastic Petri nets and the need for further research in this area
The status of vietnamese word competency of Korea learners at some Vietnamese training institutions in Vietnam today

In this article, we have conducted a survey on the word competency of Korean students who have been or are studying Vietnamese at some training institutions in Vietnam recently. The survey was conducted in 2 phases, with 2 main survey tools: Vocabulary and word capacity survey on the topic of "human body parts"; The final test of level B, Vocabulary - Grammar, through 2 forms of survey: face-to-face and online (send link). Results obtained: 156 questionnaires and 94 tests were performed. After that, we assessed the word competency of the learners according to 2 criteria: according to the ability levels: Proficient, Satisfactory, Limited, Not yet capable; and by score: Excellent, Fair, Average, Weak. From there, we propose some solutions in Vietnamese language training for Koreans in Vietnam today.
Supplemental Material: New insights into the source of gold in the Youjiang basin, SW China

&lt;p&gt;Text: Analytical Methods. Figure S1: Petrographic characteristics of volcanic facies including tuff layers, volcanic agglomerates and ignimbrites in the Middle-Permian carbonate platform, Qinglong area, Youjiang basin. Tables S1–S5.&lt;/p&gt;
An Application of Machine Learning Algorithms and a Staking Ensemble Method for Mass Appraisal of Apartments

Purpose The goal is to find an optimal data-driven model for apartment price appraisal system. We compares the predictive power of several machine learning algorithms and the ensemble model by using 17,779 apartment transaction data in Gangnam from 2006 to 2017. Methods We use three tree-based machine learning techniques, random forest, Adaboost, and Catboost. Also, an ensemble model is developed through the stacking technique using the above algorithm. 5-fold cross validation is used as model validation techniques. Results By applying the algorithm, it was found that the ensemble model constructed from stacking method performs better than individual machine learning models. Conclusion The results illustrate that the quality of mass appraisals or apartment price indices can be improved by using the ensemble model.
Trajectories of Innovative Transformations of the University of the Xxi Century: Prospects and Risks. Book Review: Vanhemping E.g., Ivanishcheva N.a., Petrenko E.i. Design of Education in the “University 4.0” Paradigm

The monograph Vanhemping E.G., Ivanishcheva N.A., Petrenko E.I. “Design of education in the University 4.0 paradigm” continues the cycle of books devoted to the analysis of transformations of education in the context of the challenges of the XXI century. Global trends in the development of education, philosophical and ideological foundations of educational transformations in the era of globalization, institutional aspects of transformational processes in the field of higher education are considered. Much attention is paid to the processes of internationalization of higher education and the role of transnational corporations in the field of higher education. The authors focus on the problems of training teachers of a new formation in the conditions of modernization of the education system. We are talking, first of all, about the formation of the “global competence” of future teachers, which is largely determined by the availability of resources of an innovative educational environment. A special role is given to extracurricular technologies, especially digital ones.
VEŠTAČKA INTELIGENCIJA I PATENTNO PRAVO

Artificial intelligence is a field of technology that is developing intensively. Along with the development of artificial intelligence, the issue of its patent protection has become topical. Artificial intelligence systems are based on highly developed algorithms and mathematical models, phenomena with which patent law is traditionally in conflict. This issue is not just a national or European problem. There is also an intensive debate in the United States about the patentability of artificially intelligent systems. The author deals with the question of whether artificially intelligent systems can enjoy patent protection. The paper analyzes the case of "DABUS" which refers to an international patent application in which the artificially intelligent system DABUS is listed as the inventor. Numerous intellectual property offices around the world (eg American, British, German, Australian, EPO) have rejected such a patent application. On the other hand, the Federal Court of Australia has ruled that under the Australian Patent Act AI could be listed as the inventor. Recognition of AI as the inventor (not the owner) of inventions generated by artificial intelligence can have certain consequences, including in the field of copyright.
Digital Leadership

Every company, regardless of size and industry, is in the digital technology business—whether they realize it or not. The authors define digital leadership as having practical skills to lead or guide other individuals, teams, or entire organizations. The main point they are making is that such leadership is impossible without a basic technical understanding of the impact of digital technologies on the business. This does not mean that digital leaders should be software developers, system integrators, or advanced data analysts. However, they should know what is technically feasible to shape and communicate their vision.
Anatomy of Learned Database Tuning with Bayesian Optimization

Database Management System (DBMS) tuning is central to the performance of the end-to-end database system. DBMSs are typically characterised by hundreds of configuration knobs that impact various facets of their behavior and planning abilities. Tuning such a system is a prohibitively-challenging task due to the obfuscated knob inter-dependencies and the intimidating size of the design space. The general vendor recommendation is to sequentially tune each knob, which further exacerbates the time-consuming nature of the task. To overcome this, recent work in the realm of self-driving database systems proxy the design problem through Machine Learning. Among the most prominent proxies in self-managing databases literature is the Bayesian-inference proxy. The purpose of this proxy, or surrogate in Bayesian Optimisation parlance, is to learn the inter-knob relationships and how they relate to the overall performance, independent of any human guidance. To this end, one of the goals of this work is to shed light on the common design patterns we identify in Bayesian-driven DBMS tuning agents. Second of all, we aim to provide a handbook for implementing such agents through the lens of a new tuning framework that leverages a multi-regression proxy.
Modern hybrids - optimization using local search, population-based search, and machine learning

This talk summarizes our 15+ years of work on the use of Machine Learning for Search & Optimization. I review the four main approaches that we invented during this time. Since learning during search takes effort, it should not surprise that we designed three of these approaches for a particular target range of total function evaluations: from few tens of dozens, to thousands, to many hundreds of thousands of function evaluations. The last hybrid I review regards a surrogate-based approach for optimization under stochastic uncertainty. The wonder of this research area is that each of these four methods defines the state of the art in its respective area, giving significant empirical evidence that learning to optimize can be highly effective.
Puzzle

The anchoring effect and the Einstellung effect are two cognitive biases studied in different areas of psychology: respectively, decision making and problem solving. In both cases, the activation of an irrelevant representation creates a “mental set” effect. This article presents an overview of the apparent similarities and dissimilarities between these two phenomena. Exploring the relationship between them offers promising prospects for understanding the underlying mechanisms. We hypothesize that the two effects are based on analogous cognitive mechanisms and propose several lines of research aimed at testing this hypothesis.
Remote teaching in times of social distancing: a study in the accounting and actuarial sciences courses

Purpose: Considering the imposed social isolation measures to contain the spread of the Sars-Cov-2 virus, the cause of COVID-19 pandemic, the in-person classes started to be held remotely. Those measures had an impact on education, mainly because they had to be adopted quickly, with no time for preparation. In our current study, we investigate how remote teaching has been occurring in the undergraduate courses of Accounting and Actuarial Sciences at a Public Higher Education Institution (PHEI), in times of social distancing.&#x0D; Methodology: An e-survey was carried out with teachers and students, in order to obtain information on the subject. The collected data were processed using statistical techniques to generate the results.&#x0D; Results: It came across that the studied PHEI did not have resources such as a Flipped classroom, Digital Whiteboard or any other technological tools to support remote classes, since the remote teaching modality became a need during the COVID-19 pandemic. The PHEI launched, via public notices, programs for digital inclusion in order to acquire mobile data and equipment, such as computers/smartphones/tablets for students; however, only few of the students could have access to those resources.&#x0D; Contributions of the Study: Our research results contribute to the state-of-the-art regarding undergraduate courses, by investigating their evolution towards remote learning. Such contributions provide information and knowledge on this subject to the national scientific literature, and give support and guidance to other courses offered by public and/or private higher education institutions.
Symbolic and Numeric Challenges in Neural Network Verification Methods

We study finite-state controllers (FSCs) for partially observable Markov decision processes (POMDPs) that are provably correct with respect to given specifications.The key insight is that computing (randomised) FSCs on POMDPs is equivalent to-and computationally as hard as-synthesis for parametric Markov chains (pMCs).This correspondence allows to use tools for synthesis in pMCs to compute correct-by-construction FSCs on POMDPs for a variety of specifications.Our experimental evaluation shows comparable performance to well-known POMDP solvers. *Supported by the DFG RTG 2236 "UnRAVeL".can infer the likelihood of the system being in a certain state.This likelihood is called the belief state.Executing an action leads to an update of the belief state according to new observations.The belief state together with an update function form a (typically uncountably infinite) MDP, referred to as the belief MDP (Shani et al., 2013).