Reviewerly/exHarmony huggingface dataset:
Blockchain Technology

Blockchain technology starts to reconfigure all aspects of society to make it clear and beneficial for the legal system. The chapter introduces “The Blockchain Revolution” in categories 1.0, 2.0, and 3.0; in the form of analyzing the use of the technology that is being applied in new innovative business models, Blockchain 1.0 starts with the creation of the first blockchain and the introduction of the technology in the “Bitcoin Whitepaper,” the crypto-currency model, via Bitcoin's application in services related to cash, payments, and transfers. Blockchain 2.0 starts with the indication that using smart contracts on blockchains will be available via the development of syntax (i.e., “solidity” that would enable developers to create solutions with blockchain technology at the backend). The chapter explores the feature of the new disruptive business models-based blockchain technology as a new approach in delivering business products and services. In the chapter, the authors explore the new technologies raised in different fields of business.
PKMark: A Robust Zero-distortion Blind Reversible Scheme for Watermarking Relational Databases

In this paper, we propose a zero-distortion blind reversible robust scheme for watermarking relational databases called PKMark. Data owner can declare the copyright of the databases or pursue the infringement by extracting the water-mark information embedded in the database. PKMark is mainly based on the primary key attribute of the tuple. So it does not depend on the type of the attribute, and can provide high-precision numerical attributes. PKMark uses RSA encryption on the watermark before embedding the watermark to ensure the security of the watermark information. Then we use RSA to sign the watermark cipher text so that the owner can verify the ownership of the watermark without disclosing the watermark. The watermark embedding and extraction are based on the hash value of the primary key, so the scheme has blindness and reversibility. In other words, the user can obtain the watermark information or restore the original database without comparing it to the original database. Our scheme also has almost excellent robustness against addition attacks, deletion attacks and alteration attacks. In addition, PKMark is resistant to additive attacks, allowing different users to embed multiple watermarks without interfering with each other, and it can indicate the sequence of watermark embedding so as to indicate the original copyright owner of the database. This watermarking scheme also allows data owners to detect whether the data has been tampered with.
Intuitionistic mereology

Two mereological theories are presented based on a primitive apartness relation along with binary relations of mereological excess and weak excess, respectively. It is shown that both theories are acceptable from the standpoint of constructive reasoning while remaining faithful to the spirit of classical mereology. The two theories are then compared and assessed with regard to their extensional import.
Human/Personnel Security Threats: When Employees Turn Insider Threat Actors

Personnel security is a set of policies and processes (Addo et al., 2020) designed to protect employees (insiders) against unlawful use of their legitimate access to an organization's assets. Personnel security is one of the most important aspects of economic security, and its sound management allows enterprise risk to be reduced. Risks (Panchenko et al., 2018) that can be linked to personnel security manifests itself in several forms. Some of the forms are: understaffing, low motivation, lack of awareness of the enterprise's strategy, and limited goals related to short-term goals rather than long term strategic goals within an employees' functional tasks.
Confidence Interval About the Mean Using the TINV Function and Hypothesis Testing

This chapter explains how to find the 95% confidence interval about the mean for a set of data, and how to test hypotheses about your data using this confidence interval. You will learn how to estimate the population mean (average) for a group of people (or objects) at a 95% confidence level so that you are 95% confident that the population mean is between a lower limit of the data and an upper limit of the data. The formula for computing this confidence interval is presented, explained, and a sample problem is given using your calculator. You will then learn how to use Excel commands to determine the 95% confidence interval about the mean using Excel’s TINV function. The second half of this chapter explains hypothesis testing and how you can test hypotheses about your data using Excel commands to find the 95% confidence about the mean for your data. Seven steps are presented for this test, and you will be given specific explanations of how to write both the result and the conclusion of a hypothesis test. Alternative ways to summarize the result of a hypothesis test are also presented. Three practice problems are given at the end of the chapter to test your Excel skills, and the answers to these problems appear in Appendix A of this book. An additional practice problem is presented in the Practice Test given in Appendix B along with its answer in Appendix C of this book.
Inborn Ideas

Book Review| April 01 2021 Inborn Ideas The Blind Storyteller: How We Reason About Human Nature, By Iris Berent. New York, NY: Oxford University Press, 2020. 304 pp. Hardcover, $29.95. Joachim I. Krueger Joachim I. Krueger Department of Cognitive, Linguistic & Psychological Sciences, Brown University, 190 Thayer St., Providence, RI 02912, E-mail: Joachim_Krueger@Brown.edu Search for other works by this author on: This Site Google The American Journal of Psychology (2021) 134 (1): 122–124. https://doi.org/10.5406/amerjpsyc.134.1.0122 Cite Icon Cite Share Icon Share Twitter Permissions Search Site Citation Joachim I. Krueger; Inborn Ideas. The American Journal of Psychology 1 January 2021; 134 (1): 122–124. doi: https://doi.org/10.5406/amerjpsyc.134.1.0122 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressThe American Journal of Psychology Search Advanced Search The text of this article is only available as a PDF. Copyright 2021 by the Board of Trustees of the University of Illinois2021 Article PDF first page preview Close Modal You do not currently have access to this content.
Improving Database Security in Cloud Computing

Distributed computing is an innovation that advances different configurable assets wherein the information is put away and overseen in a decentralized way. Nonetheless, since the information is out of the proprietor's control, concerns show up with respect to information secrecy. Encryption strategies have recently been proposed to furnish clients with privacy as far as re-appropriated capacity; be that as it may, a large number of these encryption calculations are powerless, empowering information security to be forced basically by a calculation. We propose a blend of encryption calculations and a dissemination framework to expand database secrecy. This plan separates a database over the cloud dependent fair and square of security gave by the encryption calculation used. We contrast the upsides of our plan and other existing calculations and guarantee that our plan offers a profoundly secure methodology that gives clients information secrecy and satisfactory execution.
Revisiting the impact of financial development on economic growth: the case of Turkey

This study re-examines the impact of financial development on economic growth using time series data covering the period 2006 to 2015 in Turkey. The existing literature on the relationship between economic growth and financial development has focused on various proxies for financial development. When utilising different proxies for financial development, the results of the models varied. The aim of this paper is to use one index, which represents financial development, without making any random decisions on the selection of financial development proxies. Our results confirm an unambiguously positive relationship between economic growth and financial development in Turkey. Using the bounds test for cointegration, this study finds that the effect of the Arab Spring is captured in the short-run dynamic model and it has a temporary impact on the instability of coefficients in terms of the link between economic growth and financial development in Turkey.
A New Image Encryption Method Based on Improved Cipher Block Chaining with Optimization Technique

Security of images in transmission medium is most prime issue found in literature. Encryption of images is a way to secure it from unauthorized access. The authors in this chapter insist on the encryption of images via block ciphers. Block ciphers works simultaneously as well as on chunks. In this chapter, an encryption method using improved cipher block chaining is proposed to encrypt RGB color images. For every encryption methodology, key generation process is the most important phase. The authors proposed sub-optimal key generation algorithm and this nature inspired optimization technique reveals complex keys, remains very useful for decision making in dynamic environment. Key generation is crafted as complex with this mathematical model that overcomes the predicament key problem exists in existing methods and upgrades quality of encryption. Results of the proposed algorithm show the efficiency and its resistance against various cryptanalytic attacks.
Introduction

This chapter covers the fundamentals of deep learning, we present the relevant knowledge in chronological order so as to fully introduce the history and development of deep learning; meanwhile, we review how to use MATLAB and TensorFlow as the typical platforms for developing deep learning applications. After the study of this chapter, we hope our readers could understand the concepts well and grasp the knowledge point of deep learning implementations. We will provide an overview of the core ideas and demonstrate our advanced understanding of the state-of-the-art theory and practice of deep learning and machine intelligence.
Linear Multi-Class Classification

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.
Framework Comparison of Neural Networks for Automated Counting of Vehicles and Pedestrians

This paper presents a comparison of three neural network frameworks used to make volumetric counts in an automated and continuous way. In addition to cars, the application count pedestrians. Frameworks used are: SSD Mobilenet re-trained, SSD Mobilenet pre-trained, and GoogLeNet pre-trained. The evaluation data set has a total duration of 60 min and comes from three different cameras. Images from the real deployment videos are included when training to enrich the detectable cases. Traditional detection models applied to vehicle counting systems usually provide high values for cars seen from the front. However, when the observer or camera is on the side, some models have lower detection and classification values. A new data set with fewer classes reach similar performance values as trained methods with default data sets. Results show that for the class cars, recall and precision values are 0.97 and 0.90 respectively in the best case, making use of a trained model by default, while for the class people the use of a re-trained model provides better results with precision and recall values of 1 and 0.82.
Promoting Library Services in a Digital Environment in Zimbabwe

This chapter documents the strategies that are employed by librarians in promoting library and information services in Zimbabwe. The study also sought to ascertain the perceptions of librarians towards promoting library service in Zimbabwe in a digital environment and assess the challenges faced by librarians when promoting library and information services in Zimbabwe in a digital environment. A study was done, and the participants were drawn from academics, the public, schools, and special libraries. An online questionnaire was posted on Survey Monkey and librarians were invited to participate from the Zimbabwe Library Association social media platforms. Data was analysed thematically using the objectives of the study. It was discovered that despite facing a number of challenges when promoting library and information services, participants were using various strategies to promote their services. The authors recommend continuing professional development of librarians to effectively deliver their services in a digital environment.
Universities and Cross-Cultural Management: Opportunities and Technologies for Improving the Quality of Services in Education

The relevance of the research topic is due to modern trends in world development, which indicate the need for the readiness of higher education for intercultural cooperation and cross-cultural interaction, contributing to the emergence of new elements and forms of cultural activity and correcting values, behaviors, and lifestyles (both universal and culture-specific). The purpose of this article is based on the justification of the theoretical foundations of the research problem to establish the conditions for the development of cross-cultural interaction and methods of university management associated with the formation of readiness of pedagogical process participants to cross-cultural interaction. The relevance of the search for new approaches to the organization of university cross-cultural management is noted in the article. The rating of readiness components for cross-cultural interaction, conditions of development of cross-cultural interaction, and methods used for the formation of skills of cross-cultural interaction are defined.
Development of Bluetooth, Xbee, and Wi-Fi-Based Wireless Control Systems for Controlling Electric-Powered Robotic Vehicle Wheelchair Prototype

In this study, multiple control systems were developed using commonly used wireless communication protocols like Bluetooth, Xbee, and Wi-Fi. Mechanical switch based control systems were initially designed using Xbee and Bluetooth protocols. Further, Android application based control systems were developed using Bluetooth and Wi-Fi protocols. A robotic vehicle was used as an electric-powered wheelchair prototype. Ultrasonic sensors and infrared sensors were integrated with the robotic vehicle for obstacle and pothole detection, respectively. The control systems were tested in an L-shaped corridor for identifying the suitable wireless communication protocol to efficiently guide the robotic vehicle through a scheduled navigational pathway. Both Xbee and Wi-Fi technology based control systems were able to guide the robotic vehicle through the corridor. But the implementation of Xbee communication protocol is economic. However, for shorter distances, Bluetooth technology may be used for cost-effective implementation.
On aggregating probabilistic evidence

Abstract Imagine a database—a set of propositions $\varGamma =\{F_1,\ldots ,F_n\}$ with some kind of probability estimates and let a proposition $X$ logically follow from $\varGamma $. What is the best justified lower bound of the probability of $X$? The traditional approach, e.g. within Adams’ probability logic, computes the numeric lower bound for $X$ corresponding to the worst-case scenario. We suggest a more flexible parameterized approach by assuming probability events $u_1,u_2,\ldots ,u_n$ that support $\varGamma $ and calculating aggregated evidence$e(u_1,u_2,\ldots ,u_n)$ for $X$. The probability of $e$ provides a tight lower bound for any, not only a worst-case, situation. The problem is formalized in a version of justification logic and the conclusions are supported by corresponding completeness theorems. This approach can handle conflicting and inconsistent data and allows the gathering both positive and negative evidence for the same proposition.
Application of Parallel Computing Thought in Training Model for Computer Talents

Compared with serial computing, parallel computing can execute multiple instructions at a time, which can increase computing speed and expand the scale of solutions, thereby solving large and complex computing problems. The training of computer talents is also a complex system engineering. Therefore, we propose a parallel dual-system training model for computer talents with industry-education integration and quality-development. Different from general parallel computing, the model we proposed has both temporal and spatial parallelism. The temporal parallelism is reflected in the two system lines being carried out at the same time in the training process, while the spatial parallelism is reflected in the staggering of the two system lines in the same course. In industry-education integration system, we establish the cooperation between schools and enterprises in building a series of curriculums, including mobile application (MA), artificial intelligence (Al), cloud computing (CC), big data (BD), innovation and entrepreneurship (IES), internships at enterprises (IE), to effectively improve students’ technological foresight and mastery. Moreover, the quality-development system consists of the compulsory projects integrated with theoretical courses and the elective projects integrated with extracurricular practice, so as to improve students’ comprehensive quality. In this model, the credits of courses in industry-education system accounts for 37.5%, quality-development system accounts for 32.7%, and other courses account for 29.8%. Practical results show that by applying this talent training model, the counterpart employment rate of computer science graduates in 2021 has increased by 10%, and the number of postgraduate admissions has increased by 80%, compared with graduates in 2020, the number of professional competition awards has increased by 15%, compared with students in 2020.
Image noise level estimation via kurtosis test

Noise level estimation is a long-standing problem in image processing. The challenge arises from the estimation being easily affected by texture information. We propose an innovative noise level estimation method via the kurtosis test, which is a normalized fourth-order moment. The proposed method consists of two stages: the first one is to determine the image patches with normality using the kurtosis test; the noise level is then estimated from these selected normal patches in the second stage, in which the average of the standard deviations is used as the final estimation. Experimental results show that the proposed method outperforms the state-of-the-art estimation techniques in terms of noise level estimation and guided denoising.
Semantic prosody of Slovene adverb–verb collocations: introducing the top-down approach

This paper presents a corpus-driven Sinclairian analysis of five high-frequency Slovene verbs covering the lexical paradigm ‘to express orally’ in combination with their premodifying adverbs of manner. One of the main goals of the paper is to establish how frequent the phenomenon of semantic prosody actually is among high-frequency lexical items (here, adv-v pairs). A methodology aiming to provide an answer to this question has been proposed featuring the top-down approach (i.e., in order of decreasing frequency of occurrence). It involves setting up the widest possible parameters of searching for so-called ‘extended units of meaning’ and their semantic prosody amongst the most frequent lexical patterns in a language. A total of twenty-six adv-v pairs have been examined. Results indicate a strong correlation between the frequency of multi-word lexical items and their tendency to develop semantic prosodies: high-frequency collocations are thus more likely to have semantic prosodies compared to their lower-frequency counterparts. Overall, results also corroborate the trend of semantic prosody to be found with mainly negative meanings and to a lesser extent in neutral meanings, while no positive semantic prosody has been determined in this study.
An Unsupervised Detection Method for Multiple Abnormal Wi-Fi Access Points in Large-Scale Wireless Network

The probability of a single access point (AP) failure is very small. In addition, APs communicate with each other; therefore, it is considered that these failures have little impact on the wireless network. Only when a large number of APs are abnormal offline, do we consider that the wireless network is faulty and needs to be recovered immediately. Network breakdown, network congestion, and AP management software shutdown may cause numerous APs in aborted status. In this article, we utilize DBSCAN algorithm to detect abnormal Wi-Fi APs. Compared with other research works, our proposed unsupervised method can distinguish between normal and abnormal offline APs. This study proposes a new date dimension to calculate the number of online APs together with the time dimension, and it provides new insights to set up thresholds of online APs automatically. Experimental results show that this 3-D model based on date and time is more accurate than the traditional 2-D model only based on time. With regard to the sampling method of random forest, this paper carries out repetitive random sampling to form small sample sets and finally to obtain the mean feature plane, which can reduce the interference of abnormal points to our algorithm.
Rethinking Model-Based Gaze Estimation

Over the past several years, a number of data-driven gaze tracking algorithms have been proposed, which have been shown to outperform classic model-based methods in terms of gaze direction accuracy. These algorithms leverage the recent development of sophisticated CNN architectures, as well as the availability of large gaze datasets captured under various conditions. One shortcoming of black-box, end-to-end methods, though, is that any unexpected behaviors are difficult to explain. In addition, there is always the risk that a system trained with a certain dataset may not perform well when tested on data from a different source (the "domain gap" problem.) In this work, we propose a novel method to embed eye geometry information in an end-to-end gaze estimation network by means of a "geometric layer". Our experimental results show that our system outperforms other state-of-the-art methods in cross-dataset evaluation, while producing competitive performance over within dataset tests. In addition, the proposed system is able to extrapolate gaze angles outside the range of those considered in the training data.