Reviewerly/exHarmony huggingface dataset:
The Emergence of Web3 and Metaverse Technologies

This chapter seeks to examine the concepts Web 3.0 and Metaverse, where and how it is being applied to enhance library services, and the potential application by libraries to improve their resources and enhance their services. The concept of the Web 3.0 encompasses nonfungible tokens (NFT), decentralised finance (DeFi), decentralised autonomous organisations (DAO), decentralised applications (DApps), decentralised exchanges (DEX), game finance (GameFi), finance (GameFi). The concept of Metaverse (which comprises connectivity technologies, human interface, experience) is broadly based on distributed ledger technology (DLT). The types and features of the technologies shall be discussed in detail. This shall be followed by an exploration of evidence that libraries are applying these technologies to improve their resources and enhance their services consequently.
Role of Rural Librarians on the Provisions of Social Justice Education in South Africa

This chapter assesses the state of rural South Africa on the availability of strategies, infrastructure, programs, information sharing, and skills. The conditions of rural community libraries were affected by limited budgets allocated by the South African government. The majority of rural community libraries are illiterate, impoverished, or marginalized because of the apartheid system. The study recommend rural librarians to provide education to rural communities because of the need to social justice education. This is important during the COVID-19 pandemic as disadvantaged communities need information.
The Role of Library Digitization in Access and Preservation of Library Resources

Digitization implies conversion of a paper document to a digital document. The main reasons for library digitization are to enhance access and preservation of library resources. Despite the benefits of library digitization, it is disheartening to know that while libraries in developed countries are putting in efforts digitizing their resources, little effort is being put by libraries in developing countries to digitize their resources. Hence, this chapter attempts to create awareness of the importance of library digitization in the preservation and enhancement of access of library resources. In addition to discussing the roles of library digitization in preservation and enhancement of access of library resources, the chapter also discussed some digitization tools, some misconceptions of library digitization, stages of library digitization, and some challenges of library digitization. Some recommendations were also given in the chapter.
Emerging Technologies as a Panacea for Sustainable Provision of Library Services in Nigeria

The sustainability of library services is the contemporary trend within the library global ecosystem in which emerging technologies like artificial intelligence, blockchain technology, gamification, augmented reality, etc. are amply harnessed. These technologies have been adopted and are being used for the sustainable provision of library services in developed countries. But in a developing country like Nigeria, there are myriads challenges that are mitigating its adoption and use. The chapter discussed the sustainable library service delivery process driven by emerging technologies and situated the five laws of library science within the discourse to show its relevance in this era. The study revealed a lacuna that librarians are not evolving at the desirable pace of infusing emerging technologies into the nitty-gritty of service delivery. Therefore, the study recommends a change in disposition to these technologies, which are enablers and not a threat to librarians.
Exploring Key Factors Influencing Nursing Students’ Cognitive Load and Willingness to Serve Older Adults: Cross-sectional Descriptive Correlational Study

Virtual learning environments (VLEs) use a virtual environment to support learning activities. VLEs are commonly used to overcome the temporal and spatial restrictions of learning activities held in conventional face-to-face classrooms. In VLEs, students can participate in learning activities using the internet, and teachers can provide assistive learning tools during the process.The purpose of this study was to investigate the relationships among nursing students' mental load, cognitive load, and affective learning outcomes in terms of their willingness to serve older adults in an interaction-based educational virtual reality (VR) learning environment.This study used a cross-sectional method. A total of 130 students participated in interaction-based VR learning and completed related questionnaires. Descriptive and inferential statistics and stepwise regression for data analysis were used.The research results revealed that in the dimension of willingness to use VR learning materials, perceived usefulness received the highest score (mean 4.42, SD 0.45). In the dimension of nursing ability, students scored the highest in information management and application ability to care for case patients (mean 4.35, SD 0.54). Correlation analysis revealed that cognitive load during learning and willingness to serve older adults were negatively correlated, whereas willingness to use VR learning materials was positively correlated with nursing ability and willingness to serve older adults. Analyzing the regression coefficients of predictor variables revealed that willingness to use VR learning materials (β=.23; t2=2.89, P=.005) and cognitive load during learning (β=-.35; t2=-.4.30, P<.001) were predictive factors of nursing students' willingness to serve older adults.This study demonstrated that students' willingness to use VR learning materials and their cognitive load during learning affected their willingness to care for older adults. Therefore, the components of mental or cognitive load generate inconsistent predictive effects on affective variables and willingness to serve older adults.
Analysis and Design of Intelligent Tetris Game System Based on Gesture Recognition

Gesture recognition is an important way of human-computer interaction, which is of great significance to user experience. Aiming at the problem of traditional game intelligent interaction, this paper discusses the gesture recognition method based on Fourier descriptors, and this paper gives the architecture, working process, functional structure and communication interface of intelligent Tetris software. Experiments show that the system has the advantages of high recognition rate, excellent performance and good user experience.
Comparative analysis of photovoltaic energy production in the dry and rainy seasons: High altitude case study

The production of photovoltaic energy is currently of great importance; however, its use can be affected by seasonality due to the varied climatic conditions of the area. The objective of this research was to determine the comparative analysis of photovoltaic energy production in the dry and rainy seasons in the Peruvian Altiplano at high altitude. For the development of this research, a photovoltaic system was implemented to capture solar irradiation, a solar irradiation sensor connected to a programmable logic controller (PLC) for data acquisition, installed in varied climatic conditions in this area, and a principal component analysis (PCA) method was used to determine the differences in seasonal electricity production. The results of the dry season in peak sunshine hours (PSH) were 7.46 kWh/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /d and in the rainy season 6.05 kWh/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /d, being 18.86 % more in electricity production, due to climatic variability, presence of rain and days with partial cloud cover. Therefore, in the Peruvian Altiplano at high altitude in the months between July to October there is a higher energy production than in the months between November to February.
Graph-embedding-inspired article recommendation model

Intelligent recommendation systems(IRSs) need to face the problems of sparse data and cold start. As one of the intelligent recommendation scenarios, article recommendation has rich and various article information, which is a significant difference from other scenarios. However, the existing methods of article recommender do not fully consider the article information or ignore the implicit correlation between users and articles. Therefore, this paper proposes a Graph-Embedding-inspired Article Recommendation Model (GE-ARM): we capture the "User-Article" correlation feature embeddings by constructing a Bipartite Graph with Resource Allocation (RA-BG); considering the different contributions of the "Title, Abstract" and "Tag, Citation", design an Attention-based Dual-AutoEncoder, combining with Collaborative Filtering to fully capture the article feature embeddings; and update the correlation and article feature embeddings with Probabilistic Matrix Factorization to perform the final recommendation task. The experimental results highlight that GE-ARM outperforms the other known methods on four datasets.
Special issue: Selected papers of the 11th International Symposium on Games, Automata, Logics, and Formal Verification (GandALF 2020)

We introduce a new game-theoretic semantics (GTS) for the modal mu-calculus. Our so-called bounded GTS replaces parity games with alternative evaluation games where only finite paths arise; infinite paths are not needed even when the considered transition system is infinite. The novel games offer alternative approaches to various constructions in the framework of the mu-calculus. While our main focus is introducing the new GTS, we also consider some applications to demonstrate its uses. For example, we consider a natural model transformation procedure that reduces model checking games to checking a single, fixed formula in the constructed models. We also use the GTS to identify new alternative variants of the mu-calculus, including close variants of the logic with PTime model checking; variants with iteration limited to finite ordinals; and other systems where the semantic or syntactic specification of the mu-calculus has been modified in a natural way suggested by the GTS.
Role-oriented network embedding via anonymous walks

Role-oriented network embedding aims to preserve the structural similarity of nodes so that nodes with the same role stay close to each other in the embedding space. Role-oriented network embeddings have wide applications such as electronic business and scientific discovery. Anonymous walk (AW) has a powerful ability to capture structural information of nodes, but at present, there are few role-oriented network embedding methods based on AW. Our main contribution is the proposal of a new framework named REAW, which can generate the role-oriented embeddings of nodes based on anonymous walks. We first partition a number of anonymous walks starting from a node into the representative set and the non-representative set. Then, we leverage contrastive learning techniques to learn AW embeddings. We integrate the learned AW embeddings with AW’s empirical distribution to obtain the structural feature of the node, and finally we generate the node’s embedding through message passing operations. Extensive experiments on real network datasets demonstrate the effectiveness of our framework in capturing the role of nodes.
A Comparative Study of Machine Learning Algorithms for the Detection of Vulnerable Python Libraries

Detecting the existence of vulnerabilities within source code is an important step in improving the overall security of an organisation and reducing the possibility of an attacker breaching the IT system. This has led to the creation of different vulnerability detection tools and, therefore, to devoting efforts to the study of detection techniques to provide the best results. One of the techniques used for this purpose is those that use Machine Learning and Data Mining models, this being a booming field. Under this premise, this paper presents a comparison of the results obtained with Machine Learning models capable of classifying the vulnerability or non-vulnerability of a real-world source code in Python language.
Examination of Friction Ridge Impressions

The use of friction ridge impressions by government and private Forensic Service Providers (FSP) has been widespread for the last century. Although FSPs may vary in their specific policies and procedures, many FSPs have adopted the “ACE-V” approach: Analysis, Comparison, Evaluation, and Verification. During the Analysis stage, the key judgments relate to the utility (usefulness) of the mark and establishing search parameters. In the Comparison stage, the key judgments relate to target group selections, increasing search parameters, assessing the completeness of the exemplar prints, and assessing the level of similarity between the two impressions. In the Evaluation stage an opinion of source is provided, categorically or probabilistically. The Verification stage is the re-application of some or all the ACE process by another examiner and is generally regarded as a quality assurance step of the process. Examiners have demonstrated high accuracy rendering source “identification” opinions in large scale studies, however, accuracy rendering source exclusions has been significantly lower. Additionally, when studies intentionally included close non-matches, the rates of false identifications increased dramatically for those specific comparisons. Unpacking the causes of errors through a human-factors approach and promoting documentation standards across FSPs is expected to support the human-based examination of friction ridge impressions.
Multivaried acceptance of post-editing in China

Abstract Neural machine translation (NMT), proven to be productively and qualitatively competitive, creates great challenges and opportunities for stakeholders in both the market and the education contexts. This paper explores how English-Chinese NMT post-editing (PE) is accepted in China from the perspectives of attitude, practice, and training, based on an integrative digital survey with role-specific popup questions for translators and clients in the market setting, and for translation teachers and students in the education setting. Descriptive statistics and correlation analyses of the survey data suggest Chinese stakeholders’ generally moderate view of PE, with outsiders like clients being more optimistic about PE than are insiders like translators. In the market setting, most translators use PE to different degrees in translating primarily informative texts; here, affiliated translators report a more frequent usage, and employ more sophisticated tools than do part-time or freelance translators. Whereas translators, on the whole, fail to notify clients of their own PE usage, or to charge clients for PE and human translation (HT) differently, most clients express their willingness to accept high-quality PE output for the sake of saving cost and time. In the education setting, despite students’ concealed usage of PE to do HT assignments to varying degrees, and their wish to learn PE out of concern for their future career, PE is generally not taught in translation classrooms of Chinese universities in the form of teaching PE as a course or integrating PE content into traditional translation course.
Automating what? Scholastic products and instructional automation in virtual schooling

Schools produce multiple products and digitization articulates with them in different ways. In this paper we expand the frame for analyzing instructional automation by examining its implications for three scholastic products – embodied learning, grades and test scores, and the narratives that connect the two. We draw on data from interviews with 47 teachers in four full-time virtual elementary and secondary schools in the US to argue that at present most of the actual work of automation in virtual schools is focused on the production of marks and grades, and that the narrative products of digitization efforts – routinization discourses, potentials discourse, and ‘live’ teaching discourse, play key roles in shaping how we understand the connections between those products and student learning.
Reduction in and out of context

Abstract This article investigates reduced language in academic textchat. Previous work has suggested that frequency and context play a role in whether items can be reduced. It is demonstrated that students reduce more depending on the pedagogical set-up of the course, in that more reduction took place when the teacher-less pre-seminars came early in the course. Also, it was clear that high frequency items are more subject to reduction. When the topic is clear, items salient in such a discussion can be reduced. Also, the linguistic context of a full form of an item means that subsequent mentions can be reduced. Technical items vary in their behavior, and can be reduced or not. Another main conclusion, though, is that individuals also vary, and, when the context allows for it, can choose to reduce or not.
The fast image segmentation algorithms using dynamic programming for modals of image histograms

A machine learning method is now considered capable of accurately segmenting images. However, one significant disadvantage of this strategy is that it requires a lengthy training phase and an extensive training dataset. This article uses an image segmentation by histogram thresholding approach that does not require training to overcome this difficulty. This article proposes straightforward and time-optimal algorithms, which are guaranteed by mathematical proofs. Furthermore, we experiment with the proposed algorithms using 100 images from a standard database. The results show that, while their performances are not significantly different, the two proposed methods are roughly 10 and 20 times faster than the most simple and optimal method, Brute Force. They also show that the proposed algorithms can deal with bimodal images and images with various shapes of the image histogram. Because our proposed algorithms are the most efficient and effective. As a result, they can be used for real-time segmentations and as a pre-processing approach for multiple object segmentation.
Analysis of Block Recombination and Lazy Interpolation Methods and Their Applications to Saber

Since the beginning of the National Institute of Standards and Technology (NIST), The Post-Quantum Cryptog-raphy (PQC) Standardization Process, efficient implementations of lattice-based algorithms have been studied extensively. Lattice-based NIST PQC finalists use polynomial or matrix-vector multiplications on the ring with type <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{Z}_{q}[x]/f(x)$</tex> . For convenient ring types, Number Theoretic Transform (NTT) can be used to perform multiplications as done in Crystals-KYBER among the finalists of the NIST PQC Standardization Process. On the other hand, if the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q$</tex> value of the scheme is a power of 2, as in NTRU and Saber, which are among the other lattice-based finalists, NTT can not be used explicitly. Hence multiplications are performed by the combination of Toom-Cook and Karatsuba algorithms. Recently, a novel technique called lazy interpolation [IACR Transactions on Cryptographic Hardware and Embedded Systems 2020.2: 222–244 (2020)] has been introduced to increase the performance of Toom-Cook and Karatsuba algorithms. This paper shows that the block recombination method [IEEE Transactions on Computers 63(9): 2273–2287 (2014)] is equivalent to lazy interpolation and can be used efficiently on multiplication algorithms. On the practical side, we compare different hybrid multiplications algorithms, then implement the block recombination method for Saber. Performance results are given in cycle values on general-purpose Intel processors with C implementation. Our work speeds up key generation, encapsulation, and decapsulation parts of Saber than the previous C implementations in the literature with a rate of between 10% - 13%.
A Framework for Global Optimization of Linear Layers in SPN Block Ciphers

In this paper, we design a new framework that can utilize the current global optimization heuristics for solving the straight-line program (SLP) problem. We combine Paar1, Paar2, BP (Boyar-Peralta), BFI, RNBP (Random-Boyar Peralta), A1, A2, XZLBZ, and LWFWSW (backward search) state-of-the-art heuristics by taking the XOR (exclusive OR) count metrics into consideration. Thus, by using the proposed framework, optimal circuit implementations of a given diffusion (or linear) layer can be found with fewer XOR gate counts.
How to Exploit Biham-Keller ID Characteristic to Minimize Data

In this work, we examine the following question: How can we improve the best data complexity among the impossible differential (ID) attacks on AES? One of the most efficient attacks on AES are ID attacks. We have seen that the Biham-Keller ID characteristics are frequently used in these ID attacks. We observe the following fact: The probability that a given pair with a wrong key produce an ID characteristic is closely correlated to the data usage negatively. So, we maximize this probability by exploiting a Biham-Keller ID characteristic in a different manner than the other attacks. As a result, we mount an ID attack on 7-round AES-192 and obtain the best data requirement among all the ID attacks on 7-round AES. We make use of only <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{58}$</tex> chosen plaintexts.
Age-related Eye Tracking Study of Reading COVID-19 Texts with Negative Elements

The worldwide COVID-19 pandemic leads to the development of stress disorders, increased anxiety in the society. One of the strongest factors leading to the development of anxiety, stress in society during a pandemic is the Mass Media. The mechanisms of stressogenic effects of Mass Media remain not completely clear. The aim of this study was to evaluate age-specific characteristics of gaze behavior related to the perception of anxiety-provoking information. The study was performed basing on the 189 volunteers (164 participants aged between 17 and 22 years old (students, control group), 25 people aged between 59 and 71 aged 59 to 71 (experimental group). We surveyed participants to determine their level of stress, depression, and anxiety and analyzed eye-tracking data during the text perception by using web-tracking (EyePass). Results showed the significant age-related differences in gaze behavior while reading text with negative text elements. Aged adults had shorter median fixation duration. There was no difference between groups in the amount of the fixation. We can assume that except age factor other ones might contribute to our result, namely the occupation of participants, professors at the Institute of Journalism, with developed professional skills (reading pattern, method of information perception) but from other side higher vulnerability to adverse COVID-19 outcomes compared to younger adults.