Reviewerly/exHarmony huggingface dataset:
AGRICULTURAL BOOKS IN RARE AND VALUABLE COLLECTIONS OF THE LIBRARY NAMED AFTER D. R. NOVIKOV OF THE BELARUSIAN STATE AGRICULTURAL ACADEMY

The paper presents the experience of working with books on agriculture presented in the rare and valuable collection in the library n.a. D.R. Novikov of the Belarusian State Agricultural Academy. The functions, tasks and methods of this work are revealed within the framework of educational, socio-political and popularization activities. The initiatives aimed at improving the quality of educational activities of the university are described.
An Efficient User Protected Encryption Storage Algorithm Used in Encrypted Cloud Data

Cloud data security needs have become an ongoing issue that needs to be addressed urgently.The data encryption cloud is a valuable tool to ensure data security.The existing proxy server system allows many users to perform search operations to find the problem time lag and encryption of data that is not overflowing with cloud storage.Many researchers have been inspired by this work under the single data owner system and the multi-owner data system.As Protected Data Encryption Algorithm (PDEA), this algorithm is used to store data in a database provided by a single cloud service provider and cloud server to conduct data access with no actual data volume.Use simple data recovery and access procedures.Cloud storage, for companies to store data locally during this process.PDEA stores prefer active cloud storage services to lower prices and use a large amount of data to store.Cloud computing is based on rapid development and builds a secure search code from information recovery.Therefore, dual outsourcing of data in the cloud is cheaper, minimizing long-term storage and maintenance complexity.There are data integrity, reliability, security, and minimum guarantees available on the cloud server.
A Proposal to Lower Transaction Fees in Blockchain Transactions

The rate of trade in and out of cryptocurrency has grown tremendously in the last two years. Statista, for example, claims that in May 19, 2021, the trade volume in the entire cryptomarket was worth over 500 billion U.S. dollars, as compared to four billion two weeks prior to that. One aspect making it difficult […]
Introduction to Rethinking Law, Regulation, and Technology

This introductory chapter opens a now critical conversation between lawyers, regulators and technologists at the heart of which is the question of the role of today's (and tomorrow's) technologies in the governance of our communities. Starting with a rethinking of the Westphalian (rule-centred) idea of law (Chs 2-3), as a result of which both humans and their rules are potentially de-centred, five further exercises in rethinking the relationship between Law, Regulation, and Technology are introduced: namely, rethinking the ideals of legality, the Rule of Law, and legitimacy (Chs 4-6); rethinking the practice of law (Chs 7-9); rethinking the way in which law engages with new technologies (Chs 10-11); rethinking our attitudes to law (Chs 12-13); and rethinking the law schools (Chs 14-15).
The mentalese identity of the teacher

The text is focused on the issue and interpretation of the construction of the mentalese and professional identity of the teacher manifested in a certain coherence that configures a certain purpose, relating to the environment, to the openness to transcendence, to behaviour, to skills, to beliefs, to values, to role (identity), to spirituality being also called connection (beyond the self) - all this encompassing the bond you have with the entity you belong to (family, organization, etc.), the mission you have within it, the rules and laws according to which it operates.
A Literature Survey on Voice Assistance

Abstract: Artificial Intelligence has been in great use when it comes to day to day life. Computer science defines AI research as the study of brilliant agents. In almost any direction one turns today, some form of computer-based information processing technology intrudes, whether to the individual knowingly or not. Artificial Intelligence (AI) has already changed our lifestyle. AI device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Input to recommendation algorithm can be a database of user and items and output recklessly will be the recommendations. The user is the input into system by voice or text. This paper presents a new approach for smart search. Overall, in world there are many people who use assistant. The paper presents applications of virtual assistant and also describes provocation of applying virtual assistant technology. Keywords: Voice Assistance, Python, AI, Review, Text to Speech, Speech to Text, Voice Recognition.
Graph Summarization for Human-Understandable Visualization towards CVE Data Analysis

Graph is one of the data structures used to represent various real-world data effectively. It is easy to analyze complex relationships between objects through graphs, and the structure of data can be understood at a glance through visualization. As the size of the data increases, the scale of the graph representing the data also increases. However, for large-scale graphs, not only is it difficult to analyze patterns, but it is also expensive in terms of storage space and computational speed. In this case, by applying a graph summary method that can extract only the core information of the graph, the aforementioned problem can be solved. This paper studies various graph summarization techniques and applied them to the CVE data. For effective analysis, we first converted the CVE data into a graph, then summarized it with graph embedding and clustering methods. By visualizing the results, we confirmed that we could successfully derive a brief view of the data through this process.
Aggregating the Gaussian Experts' Predictions via Undirected Graphical Models

Distributed Gaussian process (DGP) is a popular approach to scale Gaussian processes to big data which divides the training data into some subsets, performs local inference for each partition, and aggregates the results to acquire global prediction. To combine the local predictions, the conditional independence assumption is used which basically means there is a perfect diversity between the subsets. Although it keeps the aggregation tractable, it is often violated in practice and generally yields poor results. In this paper, we propose a novel approach for aggregating the Gaussian experts&#x0027; predictions by Gaussian graphical model (GGM) where the target aggregation is defined as an unobserved latent variable and the local predictions are the observed variables. We first estimate the joint distribution of la-tent and observed variables using the Expectation-Maximization (EM) algorithm. The interaction between experts can be en-coded by the precision matrix of the joint distribution and the aggregated predictions are obtained based on the property of conditional Gaussian distribution. Using both synthetic and real datasets, our experimental evaluations illustrate that our new method outperforms other state-of-the-art DGP approaches.
A Presence Questionnaire for Understanding the Driving Simulator Experience

Presence is seen to be important in the Virtual Reality (VR) domain, as there is often a close link between an individual’s experience of a virtual environment and their subsequent performance/behaviour. Unfortunately, the generic presence questionnaires available in the VR literature have been developed considering tasks not necessarily relevant to the driving situation - for instance, relating to the participant’s ability to move objects, use touch, smell items, etc. This paper describes the evolution of a driving simulator experience questionnaire, designed specifically to enable researchers and practitioners to understand how study participants perceive the driving simulation environment in relation to real-world equivalent situations. The final questionnaire has been informed by 20 interviews and focus sessions, 5 expert reviews, and simulator studies involving 225 people across different fidelity simulators and research institutions. Specifically, 41 items are currently included, believed to be of importance for natural driving. Examples of questions relate to strategic - “I felt as if I had been on a journey”, tactical -“I was compelled to obey the displayed road signs…”, and control – “I had a strong sense of physically controlling the vehicle” elements of the driving task. Items are also included relating to social aspects of driving, e.g. “I was aware that other people were driving cars around me”. In next steps, we intend to evaluate the questionnaire by comparing ratings made by individuals with their subsequent driving behaviour/performance in a simulator.
Technically Challenging Human Factors Issues for a Policy Argumentation Approach

This work addresses the user requirements of an authoring environment interface for creation and appraisal of policy domains and models. This paper focuses on the technical considerations and challenges for the design phase requirements and modeling solutions during the first iterations for the user-driven development lifecycle. It presents the results of the focus group sessions as well as the main interaction tasks tested with the prototype version of the user interface. The technically challenging issues regarding the modeling of the interaction with complex visual structures such as tree-based hierarchies, mindmaps and graph representations are presented.
The Effect of Fear and Social Distancing on Chatbot Service Usage during a Pandemic: An Abstract

Social distancing is an important non-pharmaceutical intervention that policymakers rely heavily on to stop the spread of COVID-19. In spite of its benefits, the practice of social distancing has been associated with negative consequences, such as the loss of motivation (Williams et al. 2020) and increased levels of anxiety (Tuzovic and Kabadayi 2020). Given the mixed views on the practice, in this research, we seek to examine what factor would influence customers’ evaluations of the practice and how the evaluations affect their behavior of using service technologies (e.g., chatbot technologies), which can help organizations limit human-to-human contact during service encounters (Gursoy and Chi 2020; Shin and Kang 2020).To answer these research questions, we propose a theoretical model by drawing upon the theory of reasoned action (Ajzen and Fishbein 1980; Fishbein and Ajzen 1975) and the feeling-as-information literature (Schwarz 2012). To examine the hypothesized model, we conducted an experimental study and recruited 200 U.S. consumers to participate in the study. The results show general support for our hypothesized relationships such that in a utilitarian (hedonic) service situation, customers’ fear of being contaminated affects their usage of chatbot services via their positive attitudes (subjective norms) toward social distancing and then their perceived usefulness of chatbots in practicing social distancing.We seek to provide meaningful theoretical contributions and practical implications through this research. First, as the theory of reasoned action has largely overlooked the influence of situational and emotional factors, we extend this theory by examining the moderating influence of service situations and by suggesting fear as a possible antecedent to subjective norms and attitudes. Second, our research contributes to the literature on chatbot services by proposing that, in addition to the perceptions toward chatbots, the perceptions toward a social interaction practice can also influence customers’ willingness to interact with chatbot service agents. Third, we broaden the growing body of social distancing literature by answering Ali et al.’s (2021) call to examine how social distancing can influence customers’ behavior during service encounters. Finally, we offer practical implications for managing frontline encounters during a pandemic.KeywordsFearSocial distancingPerceived usefulnessChatbot servicesTheory of reasoned action
Would A Google Of AIs Be Able To Predict The Future?: Where Is Information Management Headed In A World Of Artificial Intelligence?

What is the current state of artificial intelligence (AI) in the world of scholarly communication? What impact does AI have on the practices and strategies of publishers, libraries, information technology companies, and researchers? What exactly is AI and what are those in the realm of scholarly communication actually thinking about it and doing with it? This Charleston Briefing seeks to provide some answers to these very important questions, offering both general essays on AI and more specific essays on AI in scholarly publishing, academic libraries, and AI in information discovery and knowledge building. The essays will help publishers, librarians, and researchers better understand the actual impact of AI on libraries and publishing so that they can respond to the potentially transformative impact of AI in a measured and knowledgeable manner. Charleston Briefings: Trending Topics for Information Professionals is a thought-provoking series of brief books concerning innovation in the sphere of libraries, publishing, and technology in scholarly communication. The briefings, growing out of the vital conversations characteristic of the Charleston Conference and Against the Grain, will offer valuable insights into the trends shaping our professional lives and the institutions in which we work. With Chapters By: Daniel W. Hook And Simon J. Porter Catherine Nicole Coleman And Michael A. Keller James W. Weis And Amy Brand Ruggero Gramatica Haris Dindo Todd A. Carpenter
A Novel Image Copy–Move Forgery Detection Algorithm Using the Characteristics of Local Descriptors

The image copy–move forgery can achieve the realistic effects without leaving any obvious manipulation traces because the copied source and pasted target belong to the homogenous sources. Image copy–move forgery detection (CMFD) is still a serious challenge issue for researchers, e.g. computational efficiency, occlusive (smoothing) object detection, and partial object detection, under various post-processing and geometrical transformations. This paper proposes a novel CMFD scheme using the characteristics of local descriptors. First, the proposed method deeply analyzes the structure and mines the inherent characteristics of the local descriptor (SURF) for feature extraction in the coarse and smooth regions. Subsequently, the proposed method takes the kernel features as coarse feature matching to decrease matching costs. Then, the found candidate keypoints with a relatively small quantity are used with complete features to perform fine keypoint matching for getting suspicious candidate keypoint pairs. Furthermore, based on the deep mining of the inherent local descriptor characteristics, the proposed method can indicate the forgery region localizations through keypoint pairs, even the forgeries suffering from various geometrical transformations. Then, the proposed method proposes adaptive dual-filtering algorithms of [Formula: see text]-means relying on keypoint characteristics, and spatial distance for hierarchical progress of feature filtering. Finally, Delaunay Triangulation relies on the true-positive keypoint pairs to perform the forgery region matting effectively and efficiently. The experimental results demonstrate that the proposed method can achieve the best F 1 scores compared to the state-of-the-art methods in most cases, especially in anti-scaling transformations.
Mastery of Reading Skills in Malay Language among Preschool Children during the Covid-19 Pandemic: Challenges for Teachers

The new norms of education during the COVID-19 pandemic caused many to worry that year one children would not be able to master reading skills. This study aims to identify the mastery of Malay language reading skills among preschool children from the perception of teachers and explore the challenges faced by preschool teachers when conducting teaching and facilitation at home (TFH). This qualitative study uses a case study method by interviewing four participants consisting of preschool teachers and teachers teaching year one pupils. The research participants were selected using the purposive sampling method. The interview protocol was self -administered by the researcher and data were collected through audio recording equipment by telephone conversations. Interview transcripts were analyzed thematically. The results of the analysis found that the majority of six-year-old children in preschool have not yet mastered reading skills in preparation for year one of primary school. Teachers have no satisfaction in teaching online and face many challenges to apply reading skills among children. The challenge is that teachers find it difficult to teach children in a ‘one to one’ manner and the children’s constant absence in class made it difficult to help them. Teachers had to allocate a large amount of time and had to seek the cooperation of parents to overcome this problem. This study also suggests that there are appropriate methods, books or equipment that can help engage children in learning to read.
Explanation as Model Reconciliation

In this chapter, we revisit the explicability score and investigate an alternate strategy to improve the explicability of the robot behavior, namely explanations. Rather than force the robot to choose behaviors that are inherently explicable in the human model, here we will let the robot choose a behavior optimal in its model and use communication to address the central reason why the human is confused about the behavior in the first place, i.e., the model difference. That is, the robot will help the human understand why the behavior was performed, by choosing to reveal parts of its model that were previously unknown to the human. This would allow us to overcome one of the main shortcomings of the plan generation methods discussed in Chapter 2, namely that there might not exist a plan in the robot model that has a high explicability score. In this scenario, the explicability score of the plan is only limited by the agent’s ability to effectively explain it. In this chapter, in addition to introducing the basic framework of explanation as model reconciliation under a certain set of assumptions, we will also look at several types of model reconciliation explanations, study some of their properties, and consider some simple approximations. In the coming chapters, we will further extend the idea of explanations and look at ways of relaxing some of the assumptions made in this chapter.
Explaining in the Presence of Vocabulary Mismatch

All previous discussions on model-reconciliation explanations implicitly assume that the robot can communicate information about the model to the user. This suggests that the human and the robot share a common vocabulary that can be used to describe the model. However, this cannot be guaranteed unless the robots are using models that are specified by an expert. Since many of the modern AI systems rely on learned models, they may use representational schemes that would be inscrutable to most users. So in this chapter, we will focus on the question of generating model reconciliation explanations in the absence of shared vocabulary. Specifically, we will see how one could identify relevant model information in terms of concepts specified by a user. We will describe how the agent could use classifiers learned over such concepts to identify fragments of symbolic models specified in terms of such concepts that are sufficient to provide explanations. We will also discuss how one could measure the probability of the generated model fragments being true (especially when the learned classifiers may be noisy) and also how to identify cases where the user specified concepts may be insufficient.
Reliability of Knowledge Service Provider Attributes to Achieve Innovation in Green Products: An Analytical Study

The current study aimed to measure the reliability of knowledge service provider attributes of (Knowledge Embeddedness, knowledge distance) to achieve the innovation in green products of (Green Design, Green Operations) with a sample of employees of Al-Aitihad Industrial Company in Babylon Governorate, which included the study sample of the companys two employees (131) employees, Therefore, the problem of the study was formulated on the extent to which the characteristics of the knowledge provider to achieve innovation in green products in the research company. In order to achieve the studys objectives and indicate its problem, a hypothetical scheme was built to clarify the correlation and impact relationships between the study variables, and to elaborate on the formulation of special hypotheses measured in the statistical package of the program. (AMOS.V.27), also used a set of statistical methods of confirmatory working analysis, Cronbachs-alpha coefficient, and determination coefficient (R2), natural distribution of data, and descriptive statistics of computational medium and standard deviation, The results of the study showed the industrial companys awareness of the importance of attention to knowledge service provider attributes and innovation in green products to improve the performance of its work and preserve the environment. In addition to the companys interest in following environmental guidelines to instruct customers to pay attention to the environment and control the devices and tools that preserve the environment, as well as encouraging customers to take responsibility for their actions.
Acknowledgment of Reviewers 2021

VideourologyVol. 36, No. 1 AcknowledgmentFree AccessAcknowledgment of Reviewers 2021Published Online:25 Feb 2022https://doi.org/10.1089/vid.2021.28999.ackAboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail Critical evaluations of articles by expert reviewers make a vital contribution to ensuring the high quality of a journal's content. The editorial leadership of Videourology is very grateful for the support of the highly qualified peer reviewers who have dedicated their time and effort to reviewing our articles. We would like to show our appreciation by thanking the following individuals for their assistance with the review of articles for the journal in 2021.**Data through November 30, 2021.Varun AgarwalRajesh AhlawatTom AhleringIlter AlkanAhmed Al-KandariSero AndonianFatih AtugRiccardo AutorinoKetan BadaniPetar BajicSam BhayaniNaeem BhojaniMurat BinbayJames BorinMichael BorofskyFrancesco ChianconeMark CynkLeslie DeaneAhmed EL-NahasDan EunRobert FigenshauReza GhavamianJason GilleranNatasha GinzburgMantu GuptaSelcuk GuvenKelly HealyAshok HemalMilan HoraWilliam HuangSeung Hyun JeonNejdet KarsiyakaliAaron KatzMurat KeskeHyung Joon KimIsaac KimBodo KnudsenRuslan KoretsPeter KronenbergAngelish KumarCostas LallasTim LargeBenjamin LeeDavid LeeYong Seung LeeXuesong LiNick LiuMichael MetcalfePiruz MotamedinaMichael OstMichael PaleseRocco PapaliaAmelia PietropaoloManickam RamalingamPradeep RaoShancheng RenGideon RichardsMarcelino RiveraAndrés Rodríguez AlonsoOner SanliMichelle Jo SeminsHimanshu SharmaAlba Sierra del RioGiuseppe SimoneAbhishek SinghRene SoteloRobert SteinJay SulekRoger SurGopal TakDomenico VenezianoRamakrishna VenkateshYuyi YeowLee ZhaoFiguresReferencesRelatedDetails Volume 36Issue 1Feb 2022 InformationCopyright 2022, Mary Ann Liebert, Inc., publishersTo cite this article:Acknowledgment of Reviewers 2021.Videourology.Feb 2022.http://doi.org/10.1089/vid.2021.28999.ackPublished in Volume: 36 Issue 1: February 25, 2022Online Ahead of Print:February 14, 2022PDF download