Reviewerly/exHarmony huggingface dataset:
Blockchain-based Dependable Task Offloading and Resource Allocation for IIoT via Multi-Agent Deep Reinforcement Learning

Task offloading and resource allocation are fundamental and crucial for the edge computing-enhanced industrial Internet of things, where the security and credibility among massive heterogeneous devices are being challenged. This paper first proposes a novel blockchain consensus scheme named replicated and Byzantine fault tolerant, which can enhance the trust among nodes with the low communication cost. Then, with the objective of minimizing the task completion time, which includes credible verification, task offloading and transaction record, a joint task offloading and resource allocation problem with respect to blockchain verification ratio, offloading decision, communication and computing resources is formulated. Due to its non-convexity and the decentralized characteristic of blockchain, a multi-agent deep reinforcement learning algorithm with deterministic policy gradient is proposed to appropriate the optimal solution. Experiment results confirm the effectiveness of the proposed scheme in guaranteeing the timeliness and security.
Generalized Multi-Sensor Kalman Filters with Detectable Measurement Delays and Their Comparison

Considering applications involved with remote sensing and estimation, this work investigates Kalman filtering of measurement data from multiple sensors with time-varying delays, intermittent measurements, and out-of-order arrival of the sampled data. Relying on time-stamping in the existing network protocols, it is assumed that the delay magnitudes are detectable. Two approaches are proposed to derive functionally equivalent Kalman filtering methods for optimal estimation in presence of the communication effects. An example is presented to verify the filtering performance and compare between the computational loads of the two methods under various conditions.
MIM-GAN-based Anomaly Detection for Multivariate Time Series Data

The loss function of Generative adversarial network (GAN) is an important factor that affects the quality and diversity of the generated samples for anomaly detection. In this paper, we propose an unsupervised multiple time series anomaly detection algorithm based on the GAN with message importance measure (MIM-GAN). In particular, the time series data is divided into subsequences using a sliding window. Then a generator and a discriminator designed based on the Long Short-Term Memory (LSTM) are employed to capture the temporal correlations of the time series data. To avoid the local optimal solution of loss function and the model collapse, we introduce an exponential information measure into the loss function of GAN. Additionally, a discriminant-reconstruction score is composed of discrimination and reconstruction loss. The global optimal solution for the loss function is derived and the model collapse is proved to be avoided in our proposed MIM-GAN-based anomaly detection algorithm. Experimental results show that the proposed MIM-GAN-based anomaly detection algorithm has superior performance in terms of precision, recall, and F1-score.
The Use of Additive Decomposition and Deep Neural Network for Photovoltaic Power Forecasting

Predict photovoltaic (PV) power production is indispensable for security and reliability of the grid. In this article, a short-term forecasting method, namely trend decomposition two-dimensional convolutional neural network based on additive decomposition and convolution neural network (CNN) is proposed. Firstly, the additive decomposition model is deployed to decompose the PV power generation series to the long-term trend (LT), the seasonal trend (ST), and the random component. Then, three independent two-dimensional convolutional neural networks are designed to extract daily and hourly dependencies among the decomposed components. Finally, the prediction results of these networks are summed for the final forecast. The one-day-ahead forecasting capability of the presented method is evaluated with two case studies using real datasets gathered from Limburg and Luxembourg, Belgium. Analysis of the prediction's results indicates that the proposed method has higher accuracy compared to individual multi-layer perceptron, two-dimensional convolutional neural network, long short-term memory (LSTM), gated recurrent unit, and bidirectional LSTM networks.
Quantum-Enhanced Two-Photon Spectroscopy using Squeezed Light

We present our work towards a highly efficient quantum-enhanced two-photon spectroscopy method relying on the strong correlations generated in our squeezed light source. This method permits very few probing photons while maintaining a high SNR.
How to use machine learning and fuzzy cognitive maps to test hypothetical scenarios in health behavior change interventions: a case study on fruit intake

Abstract Background Intervention planners use logic models to design evidence-based health behavior interventions. Logic models that capture the complexity of health behavior necessitate additional computational techniques to inform decisions with respect to the design of interventions. Objective Using empirical data from a real intervention, the present paper demonstrates how machine learning can be used together with fuzzy cognitive maps to assist in designing health behavior change interventions. Methods A modified Real Coded Genetic algorithm was applied on longitudinal data from a real intervention study. The dataset contained information about 15 determinants of fruit intake among 257 adults in the Netherlands. Fuzzy cognitive maps were used to analyze the effect of two hypothetical intervention scenarios designed by domain experts. Results Simulations showed that the specified hypothetical interventions would have small impact on fruit intake. The results are consistent with the empirical evidence used in this paper. Conclusions Machine learning together with fuzzy cognitive maps can assist in building health behavior interventions with complex logic models. The testing of hypothetical scenarios may help interventionists finetune the intervention components thus increasing their potential effectiveness.
LIBERIA: Boakai Wins

Africa Research Bulletin: Political, Social and Cultural SeriesVolume 60, Issue 11 p. 24227A-24228C Full Issue LIBERIA: Boakai Wins First published: 11 December 2023 https://doi.org/10.1111/j.1467-825X.2023.11325.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume60, Issue11December 2023Pages 24227A-24228C RelatedInformation
Expert teacher based on foundation image segmentation model for object detection in aerial images

Abstract Despite the remarkable progress of general object detection, the lack of labeled aerial images limits the robustness and generalization of the detector. Teacher–student learning is a feasible solution on natural image domain, but few works focus on unlabeled aerial images. Inspired by foundation models with the powerful generalization in computer vision field, we propose an expert teacher framework based on foundation image segmentation model called ET-FSM. Our approach provides the performance gains for the student detector by generating high-quality pseudo-labels for unlabeled aerial images. In the ET-FSM, we design the binary detector with expert guidance mechanism to sufficiently leverage the extra knowledge obtained from the foundation image segmentation model, which accurately detects object positions in the complex backgrounds. Also, we present the momentum contrast classification module to distinguish confused object categories in aerial images. To demonstrate the effectiveness of the proposed method, we construct an unlabeled aerial image dataset covering various scenes. The experiments are conducted on diverse types of student detectors. The results show that the proposed approach achieves superior performance compared to existing methods, and allows the student detector to achieve fully supervised performance with much less labeled aerial images. Our dataset and code are available at https://github.com/cq100/ET-FSM .
The Virtual Environment as a Setting for Culture Heritage Mediation

The fully immersive virtual reality experience leads to living new cognitive and scientific experiences giving an additional dimension and value to the public. New paths of reflection around valorization and mediation of heritage are crossed. The 360°VR Videos seem to offer innovative solutions to facilitate the involvement of communities in the definition and valorization of their inheritance. The main purpose of this paper is to study virtual reality devices and techniques in the context of heritage mediation. We propose to understand the nature of the mediations implemented and their potential uses for culture heritage mediation. Furthermore, we highlight the mechanisms of immersive storytelling in 360° video and the influence they have on the audience’s perception, particularly the sense of presence.
How Do Historians Interact with Digital Cultural Collections? A Study on Search and Selection Behavior

This paper aims to examine the search and information behavior of historians. The central question is, how historians achieve relevant results when they are using retrieval systems for their research and how they interact with these systems, especially when using digital source collections. Since problem-oriented information needs are predominant in academic research, a qualitative study on search and selection behavior was carried out. The results indicate that approaches are targeted-oriented and that scholars heavily rely on extended search functionalities and categorizations. Scholars arrive at relevant results through individually developed strategies and with a high degree of willingness to make use of the available options, even at the cost of a large amount of time. Transparency and quality can be identified as central topics in several steps of search and result evaluation. Hence, it is necessary that they are better addressed by digital cultural collections and further information systems.
Phone-Based Speech Recognition for Phonetic E-Learning System

The APTgt system is a web-based phonetics educational training tool that aims to improve teachers’ linguistics pedagogical experience and provide phonetic transcription training for students. Based on this system, we proposed an Automatic Speech Recognition (ASR) system to convert speech to a stream of phones, automatically generating the phonetic transcription of both standard speech and disordered speech. With the help of the phone recognizer, the instructors can generate a large number of phonetic transcription exams without manual transcription. This phone-level ASR system applied Mel-frequency cepstral coefficients (MFCCs) as features and bidirectional Long-Short Term Memory (LSTM) as an encoder. The Speech Exemplar and Evaluation Database (SEED) data set, including disordered and non-disordered speech, was used for training and further testing. The proposed recognizer will make our phonetic E-learning system more intelligent and better serve students with their performance on phonetic transcription.
A Design Space for Digital Augmentation of Reading

To comprehend a text readers can benefit from their capacity to bodily interact with the environment to construct the meaning of a text. Specifically, they use their perception, action, and emotion systems to gain a deeper understanding of the text. Readers’ mental simulations of perceptions, actions, and emotions associated with a text can therefore facilitate reading comprehension, motivation and learning. The digital augmentation of reading aims at the artificial enrichment of reading through digital media. This concept has the potential to digitally synchronise the text with the surrounding environment and to promote an embodied reading experience, effectively fostering comprehension and learning. To harness the potential, recent relevant work about digital augmentation of reading was reviewed and analysed in our ongoing research to construct a design space. The resulting design space aims to support researchers, designers or educators to systematically explore design parameters that enable the effective creation of embodied reading experiences. The under-explored configurations of the design space, as well as the most frequently used configuration patterns, are also identified to reveal trends and opportunities for future applications. The paper also exemplifies how the design space can be utilised.
Development of guiding virtual reality at the Sultanate Palace in North Maluku as cultural tourism content

Virtual reality is a new phenomenon that has rapidly developed over the past few years, while studies on the development of virtual reality in cultural objects such as sultanate palaces are still very few, so further studies are needed. This research aims to develop virtual reality as a guiding and promotional media for cultural tourism of the sultanate palace in North Maluku, especially the sultanate palace of Ternate and Bacan. The steps in developing Virtual reality consist of 3 steps. First, take the original image of the palace using drones and cameras. The second step is the development of 3D objects of the sultanate palace using Blender. The third step is application development using Unity and Virtual Reality tools: Smartphones and Cardboard. This research uses the SDLC Waterfall method. Testing in this study uses four stages of the testing process: (1) black box test, (2) media expert test, (3) expert test, and (4) UEQ (User Experience Questionnaire) test. The result of this research is that the availability of virtual guiding applications for the Bacan and Ternate Palaces has created an immersive cultural virtual tourism experience.
Cyber threat intelligence practices in the national sphere of government in South Africa

Over the past two decades, cybercrime and cybercriminals have had a great impact on the safety, security and protection of the public sector’s confidential data and information assets. The increased usage of the internet and new, advanced 4IR technologies, and the digitisation of government’s public sector goods and services have become a growing concern to the government and its public sector institutions. In the South African context, as a developing country, it unfortunately lags behind advanced and developed nations in terms of fully counteracting cybercrime and cybercriminal operations. Despite this challenge, the South African government has been making tremendous efforts in combatting cybercrime. The aim of this article is to contextualise the study and practice of cyber threat intelligence at the national sphere of government in the South African public sector, namely, the former Department of Energy (DoE), former Department of Science and Technology (DST) and the former Department of Environmental Affairs (DEA) a qualitative research approach was followed using a case study approach. Primary data was collected through semi-structured interviews with senior departmental management. The findings are presented according to the Threat Intelligence Lifecycle analysis which serves as an analytical framework. The study concludes that in order to achieve effective cybersecurity practices and principles in the public sector , the government must identify the top trends commonly associated with cybersecurity to be able to develop and implement counteractive strategies and approaches, as well as to improve security systems and programs to ensure that an organisation is sufficiently prepared for and protected against cybercriminals and cyberattacks.
An exploratory study on the challenges faced by South African municipalities in implementing the Municipal Standard Chart of Accounts: a case of the city of Tshwane

This study explored the challenges faced by municipalities in South Africa in the implementation of the Municipal Standard Chart of Accounts (mSCOA). The study adopted a qualitative approach, following an interpretive ontology, and a purposive sampling technique was used. The City of Tshwane in Gauteng Province was used as a unit of analysis; primary data was collected using virtual interviews as well as open-ended questionnaires, and secondary data was obtained in various documents on mSCOA implementation. The study revealed that there is a low mSCOA implementation level in the City of Tshwane. Furthermore, the study found that numerous challenges influenced the low implementation level. These challenges include resistance to change, a lack of adequate training on mSCOA implementation, a lack of personnel, a lack of pre-consultations, and poor system vendor skills transfer. The study recommends involving implementers in the concept and inception phases of any new reform.
Road Helmet Wear Detection Based on Improved YOLOv5s

At present, in order to protect the personal safety of riders, electric vehicle riders wearing helmets has become a mandatory requirement of the traffic control department. In order to improve the supervision of cyclists and protect the safety of cyclists. This paper proposes a road helmet detection algorithm based on improved YOLOv5s. Firstly, the SE attention module is embedded in the backbone network of YOLOv5s to solve the problem that small target detection is easy to miss, and secondly, the C3 residual module of the backbone network is replaced by the lightweight ShuffleNetV2 module to reduce the complexity of the network and improve the speed of target detection and identification. Finally, the network is trained and tested on self-made dataset and compared with the original model detection effect. The experimental results show that the detection precision, recall, and mAP0.5 are increased by 4.5%, 3.6, and 4.1%, respectively, after the improvement, and the improved YOLOv5s model can effectively improve the efficiency and timeliness of EV helmet recognition.
Electronic learning technologies as a modern means of educational activity

The article examines the results of the application of electronic learning technology, which were presented at the All-Ukrainian Internet Conference "Electronic Learning Technologies" during 2016-2021.
Use of information and communication technologies in studying the school course of mathematics

The article is devoted to the use of information and communication technologies (ICT) in remote mathematics lessons, highlighting the problems of using ICT, highlighting the advantages and disadvantages of using ICT during the educational process, as well as analyzing the results of research on the problems that teachers have when using information and communication technologies in preparation for separate parts of studying mathematics during distance learning.
Informed Feature Selection for Data Clustering of CSP Plant Production

To make concentrating solar power (CSP) more cost competitive, rigourous optimizations must be run to improve plant design and operations. However, these optimizaitons rely on time consuming annual simulations that solve an electricity dispatch scheduling problem to maximize plant revenue. To reduce the runtime of annual dispatch simulations of CSP plants, a data clustering approach is utilized. This approach assumes that like days of revenue and electricity generation can be identified using weather and price data. Although weather and price are important factors for electricity production, this work investigates how thermal energy storage (TES) inventory at the beginning of a day, denoted as Si, can be used as a supplemental feature to group like days. A framework for creating and training a deep neural network to predict Si is proposed. This model is validated and assessed using eleven sets of testing data that were not used during training. Then, the data clustering approach is performed three seperate times with features of weather and price along with either Si from the neural network, Si from the full annual simulation, or no Si. Ultimately, the results suggest that using Si as an additional clustering feature improves the data clustering simulation accuracy by 1.4%.
Performance and Improvement of Linguistic Data Analysis on Different Languages Using Deep Learnedness Techniques

India is an assorted country with various kinds of societies in each edge of the country. This is an organization of 30 states and 8 association regions with various dialects and particular social legacy. Thus, there have been many discussions on the beginning of the constitution, and the worry of the public language. It is said about the language of India, "The language expressed in India changes very much like the flavour of water changes in India each couple of kilometres". Nonetheless, India does not have a public language as there is an impressive contrast between an authority language and a public language. infusion synopsis of message archives generally comprises of positioning the record string of words and separating the highest-level string of words subject to the rundown dimension requirements. We investigate and commitment of different directed acquisition calculations to the string of words positioning assignment. That is the reason, we present an original string of words positioning philosophy in view of the similitude score between an up-and-comer string of words and touchstone rundowns. The famous direct relapse framework accomplished the high-grade outcomes in undeniably assessed datasets. Moreover, the direct relapse framework, which included Part-of-Speech supported highlights, beat the same with factual elements as it were. The proposed framework beat than the current framework with boundaries/measurements as precision (81.43%), blunder rate (0.13), val_loss (0.41), val_accuracy (0.50), size of dataset utilized in research (1.30 GB), No. of ages (50), Time-intricacy (O(n2)) and execution time (1012 ms).
Study on the Resilience Enhancement and Renewal Strategy of Traditional Village Public Space in the Post-Epidemic Era: A Study Considering Cuanxia Village in China as an Illustration

Background: The rapid emergence of the COVID-19 epidemic has prompted a reevaluation of the current physical space environment. After the pandemic, the fragmented rural tourist era has returned, and new ideas for the development of public space in traditional villages have emerged, with resilient designs for rural public space planning and design taking into account disaster conditions. Methods: This study examines the link between the idea of resilience and the public space of traditional villages, the present application of the concept of resilience in their spaces, and the features of shifting spatial demands during an epidemic. Results: This article provides techniques for maximizing and enhancing resilience at three levels: responding to the catastrophe, adjusting to the disaster, and seeking development in the epidemic, to reshape the public space in traditional communities. Cuanxia Village in west Beijing is utilized as a practical object for the application of empirical evidence in order to increase the resilience of village public space and the catastrophe adaption and response capabilities of traditional villages. Conclusions: This research proposes a public space design method that integrates with normalized epidemics, enhances the catastrophe response and recovery capacities of conventional rural public spaces, and makes an essential multidisciplinary theoretical contribution.