Reviewerly/exHarmony huggingface dataset:
Third Industrial Revolution: 5G Wireless Systems, Internet of Things, and Beyond

Abstract Commercial 5G mobile communication installations are currently ongoing. A variety of reasons, notably rising business and consumer needs as well as the advent of much more cheap equipment, are driving 5G and IoT growth. Substantial carrier investments in 5G networks, frequency, and infrastructure, as well as the adoption of international standards, are indeed assisting in driving development and increasing investor interest in IoT. Today’s modern 5G mobile cellular systems are emerging beyond current 4G technology, which will remain to fulfill diverse applications. 5G, which is expected to last a long time, may meet present needs like intelligent power applications while also forecasting future use cases like self-driving automobiles. Mobile operators would need to guarantee to ensure its added versatility simultaneously present as well as future use cases need as companies oversee the growth of technology. Cautious providers would control their expenditures to assure customer service as infrastructures migrate to 5G. The majority of 5G use case scenarios fall into three broad segments: improved mobile broadband (eMBB), enormous IoT, as well as critical communications, within each set of performance, and bandwidth, including delay needs. While 4G would remain to be utilized for so many consumers and commercial IoT scenarios, 5G offers IoT features that 4G as well as other networks do not. This would include 5G's capacity to accommodate a massive amount of fixed and portable IoT systems with variable speeds, capacity, and service level needs. As the Internet of Things develops, the adaptability of 5G would become increasingly more important for organizations wanting to satisfy the stringent needs of vital connectivity. Because of 5G's ultra-reliability as well as reduced latency, self-driving vehicles, intelligent power infrastructures, better industrial automation, and some other demanding technologies are becoming a possibility. While 5G increases Internet bandwidth, cloud services, machine intelligence, as well as cloud technologies would all assist to manage huge data quantities created by IoT. Additional 5G advancements, like low latency, and non-public networking, including the core of 5G, would eventually help realize the goals of an IoT network that is worldwide and capable of sustaining connectivity that is larger in size.
The Similarities and Differences between Humanities and Social Sciences in Taiwan’s Initiative to Embrace Educational Digitization: Issues, Delivery, and Tools

This comprehensive study delves into technology integration competencies within humanities and social sciences (H&amp;SS) education, using the Technological Pedagogical Content Knowledge (TPACK) framework as its guiding lens. Through a careful analysis of course syllabi from higher education institutions in Taiwan, this research reveals distinctive patterns of emphasis across seven key TPACK knowledge domains. While both humanities and social sciences educators acknowledge the importance of integrating technology into their teaching and subject matter, subtle differences emerge. Humanities instructors tend to prioritize Pedagogical Content Knowledge (PCK), aligning this choice with their primary goal of conveying narratives and preserving cultural heritage. Conversely, their counterparts in social sciences lean more toward highlighting Technological Pedagogical Knowledge (TPK), reflecting the importance of understanding social phenomena in their field. This study underscores the pressing need for the development of tailored professional development initiatives and a revamp of pre-service teacher education programs, both of which should prioritize domain-specific TPACK competencies. This study highlights the critical importance of grounding training within authentic design tasks to effectively nurture TPACK. It points toward promising future research avenues, including investigations into the practical translation of TPACK understanding into classroom implementation and subsequent student outcomes. By shedding light on these distinctions, this research provides valuable insights for enhancing digital literacy and delivering technology-enriched learning experiences in the realm of H&amp;SS education.
Anonymous federated learning framework in the internet of things

Abstract With the continuous development of the internet of things (IoT), federated learning is being widely applied. This technology keeps data locally to protect data security. However, during the process of uploading gradients from local clients, there is a possibility of leaking sensitive information such as identities. To address this issue, this paper presents a secure and efficient anonymous federated learning framework. In our proposed, we first design a lightweight key‐sharing protocol based on elliptic curve cryptography (ECC) to generate shared keys and ensure secure communication. We then describe the improved process of federated learning, where clients communicate with the cloud server using pseudonyms to achieve anonymity. The security of our protocol is analyzed from both formal and informal perspectives, demonstrating that our proposed protocol satisfies “session key security.” We also employ the formal verification tool ProVerif to validate the security of the protocol in terms of mutual authentication and key configuration in the Dolve–Yao threat model. Finally, the computational and communication costs of the proposed anonymous federated learning framework are evaluated, showing that both the computational and communication expenses are relatively low. A comparison was made between the proposed federated learning process and three other federated learning processes, demonstrating the clear advantages of our proposed.
Improving Student Journal Visibility via the Directory of Open Access Journals

In this interview with Judith Barnsby, Directory of Open Access Journals (DOAJ), we look at how student-run journals could enhance their visibility by joining DOAJ. We highlight the general and student journal-specific application requirements for inclusion in DOAJ, known challenges with the application process, and recommendations for student journals that want to apply. The interview is conducted by Mariya Maistrovskaya, University of Toronto Libraries, the Interviewer.
Open your black box classifier

Abstract A priority for machine learning in healthcare and other high stakes applications is to enable end‐users to easily interpret individual predictions. This opinion piece outlines recent developments in interpretable classifiers and methods to open black box models.
Navigating privacy tensions when responding to online aggression at postsecondary institutions

Abstract Postsecondary institutions have a legal responsibility to ensure that students have access to a safe learning environment. While institutions adopt policies and hire administrators to protect students from harm, many are underprepared to support students when these harmful incidents happen online. This is of increased concern now that online aggression is pervasive across universities worldwide. While faculty, administrators and students agree that online aggression is a significant issue and that institutions ought to provide prevention and response services, there is concern that these efforts might violate privacy norms. We used the theory of privacy as contextual integrity (CI) to explore the tensions that postsecondary students and staff perceive regarding student privacy when responding to incidents of online aggression. To do so, we conducted focus groups with undergraduate students and student affairs administrators from a Historically Black College and University (HBCU) in the Mid‐Atlantic USA. Our analysis surfaced three considerations that inform students' and staff's decision to report an incident of online aggression: their closeness to the person making the post, their perception of the online post content as a real threat and their knowledge of an authority figure who could help resolve the situation. We used CI theory to explain how these considerations can inform institutional policy, practice and future research. Practitioner notes What is already known about this topic Online aggression is a pervasive issue at postsecondary institutions worldwide that can contribute to psychological, academic and developmental issues. Postsecondary students and staff are unsure of how to respond to incidents of online aggression. There is a gap in policies and procedures for responding to online aggression at postsecondary institutions. What this paper adds A novel use of Nissenbaum's (2010) theory of contextual integrity to understand students' and staff's perceptions of privacy. Students' and staff's decisions to intervene or report an online aggression incident are determined by their relationship to the perpetrator, the severity of the social media post and their knowledge of who to tell on campus. Students and staff are reluctant to inform the police out of fear of violence against the perpetrator. Implications for practice and/or policy Raise awareness about responding to online aggression incidents. Implement online bystander intervention training programs to increase awareness and self‐efficacy to intervene in unclear situations. Develop clear policies regarding online aggression, as well as a trustworthy procedure for how to respond.
Optimized aggregate height measurement by laser with nonlinear fitting

AbstractThe measurement of shape parameters of coarse aggregate is the premise to ensure the quality of concrete engineering. In the measurement of particle shape based on laser triangulation method, the selection of polynomial fitting parameters between different test scene displacement and image offset is often different. For coarse aggregate height measurement, an optimal laser triangulation method is proposed, and the best polynomial fitting times of aggregate measurement is obtained through data extraction and error analysis. Firstly, the laser streak information is collected from the aggregate surface based on the industrial camera, and the laser streak is processed by Gaussian filtering and color space transformation, and then the center line of the laser streak is obtained by the center method to obtain the corresponding aggregate height information. Finally, based on the comparison between the experimental measurements and the height values calculated by different polynomial fits, the laser triangulation solution method is optimized by different error factors analysis. The experimental results show that in the process of aggregate height measurement, the cubic term fitting solution is optimal with an average relative error of only 0.693%.Keywords: Particle heightlaser triangulation measurementnonlinear fittingshape feature Author contributionsWenyun Wang: Conceptualization, Methodology, Writing – review & editing. Wenhui Sun: Data curation, Software, Writing – original draft. Yingfu Guo: Visualization, Investigation. Jinglong Huang: Project administration.Disclosure statementNo potential conflict of interest was reported by the author(s). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.Data availability statementThe data that support the findings of this study are available from the corresponding author upon reasonable request.Additional informationFundingThis work was supported by the general program of the Hunan Natural Science Foundation (NO. 2021JJ30251), the youth program of the Hunan Natural Science Foundation (NO. 2020JJ5187), and the central and local science and technology development fund program (NO. 2022ZYT012).
Face Duplication Identifier Using Artificial Nerves

The facial recognition system develops a basic identity verification system based on the natural features of human faces. The study included duplicate passport identification, which checks each person's facial accuracy through a sample of facial data. The data used in this study were 180 face samples at the training stage and 30 face samples at the testing stage. The face sample taken is a forward-facing face that is not obstructed by an object. Face image recognition in this study combines GLCM method, color moment, shape extraction and backpropagation algorithm. The test process recognition rate is 78.83%.
Adaptive visual servoing for the robot manipulator with extreme learning machine and reinforcement learning

Abstract In this study, a novel image‐based visual servo (IBVS) controller for robot manipulators is investigated using an optimized extreme learning machine (ELM) algorithm and an offline reinforcement learning (RL) algorithm. First of all, the classical IBVS method and its difficulties in accurately estimating the image interaction matrix and avoiding the singularity of pseudo‐inverse are introduced. Subsequently, an IBVS method based on ELM and RL is proposed to solve the problem of the singularity of the pseudo‐inverse solution and tune adaptive servo gain, improving the servo efficiency and stability. Specifically, the ELM algorithm optimized by particle swarm optimization (PSO) was used to approximate the pseudo‐inverse of the image interaction matrix to reduce the influence of camera calibration errors. Then, the RL algorithm was adopted to tune the adaptive visual servo gain in continuous space and improve the convergence speed. Finally, comparative simulation experiments on a 6‐DOF robot manipulator were conducted to verify the effectiveness of the proposed IBVS controller.
Implementation of Face Mask Detection Using Phyton Programming Language

Since the beginning of the pandemic in 2019, people in Indonesia have been required to wear masks. Although until now the pandemic has ended, the need to use masks is still very much needed such as to maintain health, avoid air pollution and others. In detecting mask users, an application is needed that can help human work. Currently, the Python programming language is widely used to build applications in the field of computer vision, one of which is this face mask detection application. This application will detect mask users, whether they are wearing a mask or not. This developed application uses the Yolo model using the Face Mask Detection dataset developed by Larxel, where the Yolo model can work on the dataset provided. The test results show that the Yolo model can recognize mask users with an accuracy value above 90%. The second experiment was carried out to detect several faces of mask users, the Yolo model can recognize mask users or not with an average accuracy value of 91.75%. For future research, it is also expected to use other models besides Yolo and make comparisons of several models and make improvements to the problems that exist in each model and use real time data.
Sistem Informasi Rental Mobil Pada Regulator Rent Car

Car rental is an activity of renting a vehicle in the form of a car with the terms and conditions determined by the rental party. Every time a rental transaction is made, the Car Rent Regulator always records data on each customer and transactions made in writing. In addition, the rental party also cannot determine the choice of the best available car. This study aims to build an information system that can be applied to Car Rent Regulators, so that the transaction process can be carried out more quickly and reduce the risk of data damage. This system will apply the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to determine the choice of the best car available at the rental. System information is created using the Java programming language and MySQL as a database. In designing the system, the Unified Modeling Language (UML) model is used to determine the structure and processes that occur in the system. The results of this study can facilitate and improve admin performance in conducting rental transactions, and calculations using the TOPSIS information system method can determine the choice of the best available car by entering values in the specified criteria. The results of calculations using the TOPSIS method show that the Avanza car is the best car choice available for rental.
How generous interface affect user experience and behavior: Evaluating the information display interface for museum cultural heritage

Abstract Museum web interfaces continue to be important in how users search for information and navigate through digital cultural heritage resources. A generous interface was deemed appropriate for displaying large amounts of digital cultural heritage. However, more example verification is required in this field of study. This study compared the traditional interface and the generous interface of the Liaoning Provincial Museum to investigate the role of a generous interface for cultural heritage collections. Forty second‐year design graduate students were randomly assigned to one of two interface groups: traditional interface or generous interface. Four variables were measured to compare the differences between the two interfaces for the participants. In addition, two parameters, holding time and average retrieval time, are recorded and used to evaluate and compare the impact of the two interfaces on user behavior. The generous interface, according to the findings, is more effective than the traditional interface in increasing participants' engagement with cultural heritage and is perceived to be more aesthetically pleasing. In addition, the generous interface has been proven to be more suitable for casual leisure users to explore cultural heritage information. This research provides the basis and reference for developing the web interface of cultural heritage collections.
Bayesian Multi-Task Variable Selection with an Application to Differential DAG Analysis

We study the Bayesian multi-task variable selection problem, where the goal is to select activated variables for multiple related datasets simultaneously. We propose a new variational Bayes algorithm which generalizes and improves the recently developed "sum of single effects" model of Wang et al. Motivated by differential gene network analysis in biology, we further extend our method to joint structure learning of multiple directed acyclic graphical models, a problem known to be computationally highly challenging. We propose a novel order MCMC sampler where our multi-task variable selection algorithm is used to quickly evaluate the posterior probability of each ordering. Both simulation studies and real gene expression data analysis are conducted to show the efficiency of our method. Finally, we also prove a posterior consistency result for multi-task variable selection, which provides a theoretical guarantee for the proposed algorithms. Supplementary materials for this article are available online.
Robust gradient‐based neural networks for solving online the discrete periodic Lyapunov matrix equations

Abstract Here, a gradient‐based neural network (GNN) model is constructed for solving the discrete periodic Lyapunov matrix equation (DPLME) associated with discrete‐time linear periodic systems. In practical applications, the recurrent neural network model should not only converge rapidly, but also be able to tolerate noise. However, the influence of noise on GNN models was seldom considered in the past. In order to improve the convergence and robustness of the GNN model, a novel type of non‐linear activation function is applied to the GNN model. Compared with the traditional activation functions, the activation function used here makes the GNN model to achieve fixed‐time convergence. Besides, when disturbed by bounded noise, the unique positive definite solution of the DPLME can still be obtained by using the GNN model. Finally, simulation experiment is performed to verify the effectiveness and superiority of the proposed GNN model.
Blockchain assisted encryption scheme for intellectual share estimation using medical research data

Summary Recently, blockchain utilization with advanced encryption and decryption has gained improved attention over diverse applications. Especially in the medical field, big data storage requires an effective medium for managing user data with high confidentiality. The existing research presents diverse encryption algorithms, but certain drawbacks include sensitive information leakage, ineffective data integrity verification, and increased computational cost and time. The proposed work develops an effective blockchain mechanism for secured data storage to conquer these limitations. This article secures the medical research data from different team members for intellectual share estimation. Initially, the registration process involves user details such as name, contact number, age and so on. Next, a digital signature is generated for every registered user using the SHA‐256 hash algorithm. During data uploading, the data is encrypted using elliptic curve Diffie Hellman encryption scheme to promote security. The audio and video files are stored in inter planetary file system, and texts are stored in the blockchain. The users can retrieve the stored data from the blockchain using the encryption key. The identities and associated public keys are verified using a smart contract to validate the user certificate. Next, a decision form is made to show the polling mechanism using item preference ranking for other users viewing the data. The proposed mechanism ensures intellectual shares for all the users associated with a specified project. The performances are analyzed using PYTHON, and the encryption time obtained in the proposed model is 0.85 s at 140 security bit level.
A novel static and dynamic hand gesture recognition using self organizing map with deep convolutional neural network

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Merging operators on stratified belief bases equipped with argumentative inference

AbstractThis work considers the formalisation of the merging process of stratified belief bases, where beliefs are stored in different layers or strata. Their strata are ranked, following a total order, employing the value the agent using the belief base assigns to these beliefs. The agent uses an argumentation mechanism to reason from the belief base and obtain the final inferences. We present two ways of merging stratified belief bases: the first is defined by merging two strata without belief preservation, and the second by merging two strata with belief preservation. For these operators, as layers merge, it is possible that some information has to be deleted from the stratum to maintain consistency. The latter merge operator will be defined as performing their actions so that information removed from the stratum is preserved after the merging by moving it to a stratum containing information perceived as having a lower value and where those beliefs may trigger a new revision, possibly starting a cascading effect.KEYWORDS: Argumentationbelief revisionstratified belief bases AcknowledgmentsWe would like to thank to the anonymous referees for very valuable comments on earlier versions of this paper.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Given a set of sentences S, a minimal inconsistent subset of S is a set S′ such that S′⊆S, S′⊢⊥, and if S″⊂S′ then S″⊬⊥. Making use of the notation introduced in Definition 4.1 this postulate can be formulated in the following alternative way: If (K∪A)⊥⊥⊥=(H∪B)⊥⊥⊥ then (K∪A)∖(K∘A)=(H∪B)∖(H∘B).Additional informationFundingThis work is partially supported by Universidad Nacional del Sur (UNS) [grant numbers PGI 24/N055 and PGI 24/ZN37] and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) from Argentina.