Reviewerly/exHarmony huggingface dataset:
Advancing Towards Sustainable Supply Chain Management Using IoT and Blockchain Technology

The central theme of this chapter is to provide an overview of new evolving technologies such as radio frequency identification (RFID), the Internet of Things (IoT), service-oriented computing, and blockchain for advancing supply chain management (SCM) towards sustainability. First, the chapter evaluates the capabilities of Industry 4.0 for sustainability under different technology uses. The chapter expands beyond Industry 4.0 by discussing the capabilities that blockchain technology can offer for increasing sustainability under three main areas: (i) enhance visibility across the entire product lifecycle; (ii) decreasing development and operational costs; and (iii) providing sustainability monitoring and the benefits of having strategic data-sharing practices that help companies concentrate with customers and engage in novel partnerships. Finally, the chapter presents an information system architecture consisting of IoT and blockchain technology to maintain data security and transparency.
New Results on Boolean Functions with High Nonlinearity and Low Transparency Order

We improve the steepest descent algorithm and increase the double threshold parameter, which significantly improves the algorithm’s efficiency. And we design a new cost function so that in terms of search, various characteristics of Boolean functions can be taken into account simultaneously. Applying our algorithm, there are excellent results regarding the 9, 10, 11, and 12 variables. We find a Boolean function with a nonlinearity of 242 in 9 variables and the whole search space. Previously, this result only appeared in the rotational symmetry class. The best-achieved nonlinearity result for permutation (6, 5, 1, 4, 7, 2, 3, 0, 8) and (0, 7, 2, 5, 4, 1, 3, 6, 8) class is 238 and 239 introduced by Kavut in Information and Computation (2010). Still, applying our algorithm, we obtain a balanced Boolean function with a nonlinearity of 240 under the same permutation, indicating that our method is more general. Among the 11 variables, a Boolean function with a higher nonlinearity and a lower transparency level and the absolute value spectrum are maintained at a lower level. The algorithm performs well when considering all aspects of the property. There are similarly promising results in even-numbered variables.
A Novel High-Efficiency Password Authentication and Key Agreement Protocol for Mobile Client-Server

With the development of wireless technology, people increasingly rely on mobile devices. Since most mobile devices transmit sensitive information via insecure public channels, it is important to design multiauthentication key agreement protocols for security protection. Traditional scholars tend to use traditional public-key cryptosystems (PKCs) in their protocols to improve security. High-cost operations (e.g., elliptic curve point multiplication and bilinear pairing) were widely used in their scheme but were not suitable for mobile devices because of limited computing resources. In this study, we designed a novel high-efficiency multiauthentication and key agreement protocol and demonstrate its security in the random oracle model. Compared with other protocols, our proposed scheme only uses string concatenation operations, one-way hash functions, and XOR operations. In addition, our protocol requires much fewer computing resources to achieve the same level of security.
<b>Sociolinguistic variation, or lack thereof, in the use of the Italian subjunctive:</b> mood selection with factive and semi-factive governors

Abstract This paper deals with various aspects of the use of the subjunctive in spoken Italian. Firstly, a brief overview is provided of the interplay of linguistic and extra-linguistic factors underlying the selection of the subjunctive in various syntactic structures. Special attention is paid to the differences in subjunctive use in factual and non-factual contexts, pointing out that the former are less studied and less affected by normative injunctions than the latter. The focus then falls on factual contexts, in particular subordinate clauses which depend on factive and semi-factive governors. The investigation is based on KIParla, a corpus of spoken Italian consisting of different types of interactions with speakers of various origins and social backgrounds. It is argued that in the factual context at hand the subjunctive fulfills a specific set of functions, mostly related to the speaker’s stance towards the content of the subordinate clause. Finally, a statistical analysis is performed using conditional inference trees and random forests in order to uncover the linguistic and extra-linguistic conditioning of subjunctive use. The analysis shows that in the context under observation it is only linguistic factors that significantly condition mood selection; the use of the subjunctive is substantially insensitive to socio-stylistic and geographic variation, and therefore a socially shared pattern of variation has emerged.
<b>The new</b> <b> <i>ne</i>: an incipient restandardization process in contemporary Italian</b>

Abstract The paper deals with an ongoing change in the clitic paradigm of contemporary Italian, namely the incipient overextension of functions of the clitic ne . In the standard variety ne can only stand for prepositional phrases consisting of di + [noun phrase] and da + [noun phrase], such as those found in genitival, partitive, and locative constructions or in passive ‘by’-phrases. In contemporary Italian, on the other hand, ne is increasingly appearing as a substitute of a + [noun phrase] when used as the second argument of intransitive bivalent verbs, such as accennare (a) ‘to mention’, appassionarsi (a) ‘to get passionate (about)’, and sopravvivere (a) ‘to survive’. The latter use has not received much attention in the literature. This overextension is observed in a number of verbs, drawing on data from four corpora of written and spoken Italian, and is discussed as an incipient change from above, originating in formal registers of Italian and moving downwards into the neo-standard and (albeit rarely) colloquial varieties. The change seems to have been triggered and favored by the fact that the standard variants for that slot of the paradigm, namely locative vi and ci , are avoided because they are considered too obsolete or low-prestige respectively, and also by the fact that these clitics, when combined with others, especially in pronominal verbs, might be judged agrammatical by some speakers.
Parallel Finite Field Algorithms for Communications and Sensor Networks

Sensor network integrates sensor technology, embedded computing technology, modern network and wireless communication technology, distributed information processing technology, etc. It can monitor, perceive and collect the information of various environments or monitoring objects in real time through various integrated micro sensors, and process the information through the embedded system. The perceived information is transmitted to the user terminal by multi-hop relay through the random ad-hoc wireless communication network. The coding of communications and sensor networks affects the efficiency of communication. The main coding technology is based on finite field. Among operations in finite fields, divisions are time-consuming operations since they are generally computed via inversions and multiplications. We present techniques to exploit extend binary trees for fast divisions in finite fields. We implement the finite field algorithm on Field Programmable Logic Arrays (FPGAs) in a parallel way. The implementation result from FPGAs shows that the parallel finite field algorithm can speed up the process of coding of communications and sensor networks.
Multi-view Graph Representation Learning Beyond Homophily

Unsupervised graph representation learning (GRL) aims at distilling diverse graph information into task-agnostic embeddings without label supervision. Due to a lack of support from labels, recent representation learning methods usually adopt self-supervised learning, and embeddings are learned by solving a handcrafted auxiliary task (so-called pretext task). However, partially due to the irregular non-Euclidean data in graphs, the pretext tasks are generally designed under homophily assumptions and cornered in the low-frequency signals, which results in significant loss of other signals, especially high-frequency signals widespread in graphs with heterophily. Motivated by this limitation, we propose a multi-view perspective and the usage of diverse pretext tasks to capture different signals in graphs into embeddings. A novel framework, denoted as Multi-view Graph Encoder (MVGE), is proposed, and a set of key designs are identified. More specifically, a set of new pretext tasks are designed to encode different types of signals, and a straightforward operation is proposed to maintain both the commodity and personalization in both the attribute and the structural levels. Extensive experiments on synthetic and real-world network datasets show that the node representations learned with MVGE achieve significant performance improvements in three different downstream tasks, especially on graphs with heterophily.
Detection of Hateful Social Media Content for Arabic Language

Social media is a common medium for expression of views, discussion, sharing of content, and promotion of products and ideas. These views are either polite or obscene. The growth of hate speech is one of the negative aspects of the medium and its emergence poses risk factors for society at various levels. Although there are rules and laws for these platforms, they cannot oversee and control all types of content. Thus, there is an urgent need to develop modern algorithms to automatically detect hateful content on social media. Arab society is not isolated from the world, and the usage of social media by its members has highlighted the importance of automated systems that help build an electronic society free of hate and aggression. This article aims to detect hate speech based on Arabic context over the Twitter platform by proposing different novel deep learning architectures in order to provide a thorough analytical study. Also, a comparative study is presented with a different well-known machine learning algorithm, as well as other state-of-the-art algorithms from the literature to be used as a beacon for interested researchers. These models have been applied to the Arabic tweets dataset, which included 15K tweets and 14 features. After training these models, the results obtained for the top two models included an improved bidirectional long short-term memory with an accuracy of 92.20% and a macro F1-score of 92% and a modified convolutional neural network with an accuracy of 92.10% and a macro F1-score of 91%. The results also showed the superiority of the performance of the deep learning models over other models in terms of accuracy.
Towards Addressing Ambiguous Interactions and Inferring User Intent with Dimension Reduction and Clustering Combinations in Visual Analytics

Direct manipulation interactions on projections are often incorporated in visual analytics applications. These interactions enable analysts to provide incremental feedback to the system in a semi-supervised manner, demonstrating relationships that the analyst wishes to find within the data. However, determining the precise intent of the analyst is a challenge. When an analyst interacts with a projection, the inherent ambiguity of interactions can lead to a variety of possible interpretations that the system can infer. Previous work has demonstrated the utility of clusters as an interaction target to address this “With Respect to What” problem in dimension-reduced projections. However, the introduction of clusters introduces interaction inference challenges as well. In this work, we discuss the interaction space for the simultaneous use of semi-supervised dimension reduction and clustering algorithms. We introduce a novel pipeline representation to disambiguate between interactions on observations and clusters, as well as which underlying model is responding to those analyst interactions. We use a prototype visual analytics tool to demonstrate the effects of these ambiguous interactions, their properties, and the insights that an analyst can glean from each.
Digital Literacy Among Teacher Educators in Ethiopia

This study was initiated to assess the status of digital competencies among teacher educators in Ethiopia. A questionnaire, adopted from an instrument previously developed to measure teachers' preparedness to use digital technology has been used to gather information. Findings indicate that teacher educators demonstrated marked strength in certain domains of competence (e.g., preparedness to use digital technologies; and attitude towards use of digital technologies); and had serious limitations in others (e.g., awareness about the shortcomings of digital technologies). With regard to factors that affect the utilization of digital technologies, teacher educators identified three factors as most important: lack of facilities, supporting resources, and training. The chapter ends by making recommendations based on the findings of the study.
Paradigm Shift From Traditional to Communicaiton and Technology Acceptance Models for the Survival of Family Businesses in Oman

The deadly outbreak of different variants of covid-19 has not only created havoc on millions of lives and public health but has severely impacted the global market and shattered many countries' economies. A general phenomenon of shortage in public income, rise in unemployment, and breakdown in manufacturing and service industries have been noticed worldwide. Transportation, tourism, and accommodation industries have been badly affected. Oman has not been exempted from covid-19 mitigation measures. Through case studies, the authors explain the traditional resources of Omani family businesses and their adaptive approach to new media technology, resilience, and strategic communication to respond to the crisis created by the pandemic. This chapter will contribute as a reference document for the researchers to conduct a comparative study.
Linking Culture, Pedagogy, and Digitalization for Education and Sustainable Development in Tribal Areas

This chapter discusses the role of education and sustainable development in Pakistan, especially in tribal areas. Forty-six teachers and 28 parents from 15 primary schools were interviewed for this study, and data has also been collected via observational technique. A mixed methods approach employs both qualitative and quantitate techniques. To achieve quality education at any level, especially at primary level, teachers and students should aim at creating the link between culture, pedagogy, and digitalization. Teachers should be competent in the implementation of technology, that is, the use of computers, and have multimedia skills in order to find and adopt pedagogies relevant to class environments. This chapter looks at the current situation Pakistan, and especially in the tribal areas of Pakistan, and suggests recommendations for the incorporation of ESD goals in the single national curriculum, the development of teacher education programs, incentives for teachers and (female) students, and the facilitation of electricity, computer technology, and internet access for all schools.
Hybrid KD-NFT: A multi-layered NFT assisted robust Knowledge Distillation framework for Internet of Things

The Internet of Things (IoT) concept has increasingly gained popularity among different industrial and scientific fields by leveraging the development of modern electronic chip manufacturing and networking hardware and techniques. As the combination of end devices and networking techniques, it becomes more and more possible that people can organize an IoT system to serve more extensive industrial and scientific needs by running a higher level of machine learning models, such as Federated Learning (FL) and Knowledge Distillation (KD) architectures. However, security over network communications is vital when the whole system spreads to massive scales in networking, including data integrity and robustness over external malicious attacks, which would significantly affect the system’s overall modal training effectiveness. In this paper, we propose an ungraded version of the Non-Fungible Token (NFT) assisted Knowledge Distillation framework, aiming at leveraging the blockchain features on data security to solve the intrinsic robustness defects in a naive KD architecture and reducing overall processing time by adding a local blockchain layer. Our major contributions can be concluded as the following (1) a revised version Hybrid KD-NFT framework from our previously proposed State-of-the-art (SOTA) framework, which improves the overall effectiveness and reduces the overall latency when compared to its original version; (2) the proposal and implementation of a hybrid (combination of private and public chain) multi-layer blockchain architecture; and (3) conducting more sets of experiments and performing more comprehensive comparisons over existing other KD frameworks in more metrics to prove our proposed Hybrid KD-NFT framework is a valid work.
Classroom Virtual Reality for Students With Disabilities: A Preliminary Guide to Available Virtual Content

Virtual reality (VR) is an innovative technology that is rapidly gaining popularity in and out of the classroom. With improvements in hardware and software technology and the push for one-to-one computing, teachers have access to innovative VR supports and resources. These innovative technologies have the potential to improve instruction for diverse learners in today’s classroom, particularly students with identified disabilities. VR, as an interactive and visual tool, provides teachers with the ability to support and enhance traditional classroom materials for students, particularly those with disabilities. However, teacher time is often compromised and finding new technology can be daunting. This article intends to assist educators in utilizing VR in the classroom, with minimal effort and money. Using a detailed vignette, we guide the reader through a detailed example of how VR apps can be located and implemented for students with disabilities who struggle with motivation and engagement through experiential learning. Finally, we offer additional apps and their delivery devices as a starting point for teachers to begin creating more immersive and interactive content through VR technology.
Bound the Parameters of Neural Networks Using Particle Swarm Optimization

Artificial neural networks are machine learning models widely used in many sciences as well as in practical applications. The basic element of these models is a vector of parameters; the values of these parameters should be estimated using some computational method, and this process is called training. For effective training of the network, computational methods from the field of global minimization are often used. However, for global minimization techniques to be effective, the bounds of the objective function should also be clearly defined. In this paper, a two-stage global optimization technique is presented for efficient training of artificial neural networks. In the first stage, the bounds for the neural network parameters are estimated using Particle Swarm Optimization and, in the following phase, the parameters of the network are optimized within the bounds of the first phase using global optimization techniques. The suggested method was used on a series of well-known problems in the literature and the experimental results were more than encouraging.
Are automated static analysis tools worth it? An investigation into relative warning density and external software quality on the example of Apache open source projects

Abstract Automated Static Analysis Tools (ASATs) are part of software development best practices. ASATs are able to warn developers about potential problems in the code. On the one hand, ASATs are based on best practices so there should be a noticeable effect on software quality. On the other hand, ASATs suffer from false positive warnings, which developers have to inspect and then ignore or mark as invalid. In this article, we ask whether ASATs have a measurable impact on external software quality, using the example of PMD for Java. We investigate the relationship between ASAT warnings emitted by PMD on defects per change and per file. Our case study includes data for the history of each file as well as the differences between changed files and the project in which they are contained. We investigate whether files that induce a defect have more static analysis warnings than the rest of the project. Moreover, we investigate the impact of two different sets of ASAT rules. We find that, bug inducing files contain less static analysis warnings than other files of the project at that point in time. However, this can be explained by the overall decreasing warning density. When compared with all other changes, we find a statistically significant difference in one metric for all rules and two metrics for a subset of rules. However, the effect size is negligible in all cases, showing that the actual difference in warning density between bug inducing changes and other changes is small at best.
Reference‐based Screentone Transfer via Pattern Correspondence and Regularization

Abstract Adding screentone to initial line drawings is a crucial step for manga generation, but is a tedious and human‐laborious task. In this work, we propose a novel data‐driven method aiming to transfer the screentone pattern from a reference manga image. This not only ensures the quality, but also adds controllability to the generated manga results. The reference‐based screentone translation task imposes several unique challenges. Since manga image often contains multiple screentone patterns interweaved with line drawing, as an abstract art, this makes it even more difficult to extract disentangled style code from the reference. Also, finding correspondence for mapping between the reference and the input line drawing without any screentone is hard. As screentone contains many subtle details, how to guarantee the style consistency to the reference remains challenging. To suit our purpose and resolve the above difficulties, we propose a novel Reference‐based Screentone Transfer Network (RSTN). We encode the screentone style through a 1D stylegram. A patch correspondence loss is designed to build a similarity mapping function for guiding the translation. To mitigate the generated artefacts, a pattern regularization loss is introduced in the patch‐level. Through extensive experiments and a user study, we have demonstrated the effectiveness of our proposed model.