Reviewerly/exHarmony huggingface dataset:
Optimizing Solar Energy Harvesting: Dual-Axis Solar Tracking and Simulation Analysis

This study explores the optimization of solar energy capture through the implementation of a dual-axis solar tracking system, coupled with advanced simulation using the PVsyst software. The objective is to enhance energy efficiency by precisely aligning photovoltaic panels with the sun's path. Our research investigates the performance of this system under varying environmental conditions and geographical locations. The PVsyst simulation offers valuable insights into energy generation potential, efficiency gains, and financial viability. Preliminary results indicate a substantial increase in energy yield compared to fixed panels, especially in regions with dynamic solar angles. The findings from this study underscore the significance of simulation-based approaches in assessing and maximizing the effectiveness of dual-axis.
CVE representation to build attack positions graphs

In cybersecurity, CVEs (Common Vulnerabilities and Exposures) are publicly disclosed hardware or software vulnerabilities. These vulnerabilities are documented and listed in the NVD database maintained by the NIST. Knowledge of the CVEs impacting an information system provides a measure of its level of security. This article points out that these vulnerabilities should be described in greater detail to understand how they could be chained together in a complete attack scenario. This article presents the first proposal for the CAPG format, which is a method for representing a CVE vulnerability, a corresponding exploit, and associated attack positions.
Tissue-Specific Color Encoding and GAN Synthesis for Enhanced Medical Image Generation

Medical image synthesis is important in diverse healthcare applications, such as computer-aided diagnosis, medical image analysis, and educational tools. While Generative Adversarial Networks (GANs) have shown remarkable success in generating natural images, their application to medical images often falls short in faithfully capturing essential anatomical features. In this paper, we introduce a new approach that focuses on tissue-specific color encoding to enhance medical image synthesis using GANs. Our method deviates from the conventional practice of directly training GANs on gray-scale medical images. Instead, we initiate the process by generating and encoding various gray-scale representations of distinct tissues into separate color channels within composite images. These tissue-specific color images are then utilized to train a GAN model. The GAN, once trained, excels in producing high-quality synthetic images for individual tissues, and when combined, these tissue images yield final synthesized images that better portray the intricate tissue characteristics found in medical data. We have conducted an experimental study to validate the effectiveness of our approach in comparison to alternative methods with both qualitative and quantitative assessments to evaluate the quality of synthesized individual tissues and their combined final results.
Accelerating Web-based Graph Visualization with Pixel-Based Edge Bundling

We present a novel web-based framework, named Pixel-Based Edge Bundling (PBEB), for effectively and interactively visualizing large graphs. Our framework combines an image-based edge-bundling method and a parallel texture-based processing scheme, allowing us to effectively and efficiently compute edge similarities using kernel density estimation and subsequently group these edges into bundles based on their similarities. We discuss several challenges related to developing large-graph visualization on web-based platforms. To accelerate the edge bundling process and enable interactivity in web-based environments, we leverage texture-based parallel processing, a standard feature of WebGL. Our framework optimizes an end-to-end process, from bundling to rendering, enabling practical and interactive visualization of large graphs in a web-based setting. We demonstrate the superior performance of our framework by conducting comparisons with existing web-based and CUDA-based edge-bundling methods using various standard graphics cards on different devices.
Terrorism and Hate Crime: From the Long Fuse to Hate Speech

In an information society, can jaw-jaw really be a form of war-war? Certainly, the ground has been sown for a clash of liberties: when the right of expression confronts the right to security. This chapter applies David Wall’s transformation test to the themes of so-called cyber terrorism and hate speech online. Has the digital made a difference? Clearly, digital networks can be used to distribute propaganda by those advocating politically motivated violence, and to spread hate speech; but do such affordances actually translate into the increased violence some seek to achieve? Digital networks can be used to trigger a bomb, but have not been used successfully to crash a plane or blow up a nuclear power station (as has been depicted in fiction). The internet can be used to recruit and plan violence, but such channels are not so anonymous and unregulated as might be imagined.
Conclusions

Digital access has offered new routes to commit fraud, circulate obscene content, groom, stalk and bully, surveil and download. Digital encryption conceals identities in relation to terrorist communications, fraud, hate speech, stalking, planting fake news, ‘scareware’ threats, online obscenity, bullying and trolling. Distributed networks and services afford jurisdictional evasions in relation to obscene and hateful content, fraudulent and extorting communications, peer-to-peer and torrent-based downloading and streaming. Digital communications may incite radicalization, polarization, normalization and/or disinhibition in relation to hate, abuse, dispute and theft. Yet, in relation to each of these, digital networks have alternative affordances that, when applied, may inhibit criminality and protect potential victims. Networked technologies are symmetrical in principle (affording greater levels of harm alongside greater levels of protection), but such symmetry in principle is not the same as symmetry in practice. The way digital technologies are used determines outcomes, not any logic of technology itself.
Blockchain-Based IoT for Precision Agriculture

In recent years, IoT has been increasingly applied in agriculture to transform traditional farming practices into smart and precision agriculture (PA) that are more efficient, productive, and sustainable. However, its implementation in agriculture faces several challenges, including network coverage, reliability, lack of flexibility, and scalability. To address these challenges, current research has focused on developing new communication protocols and technologies, along with several IoT architectural design patterns, especially those based on SOA, which play a crucial role in designing service-oriented solutions. This chapter presents comprehensive and impactful solutions for blockchain-based IoT applications in PA. It proposes novel models combining IoT, blockchain, fog and cloud computing for the development of decentralized applications with independent, autonomous, and interoperable functionalities and services based on the SOA approach. Also, technical challenges, research directions, and the recent advances towards an optimized blockchain-based IoT ecosystem for PA are presented.
THE CHALLENGES TO LEARN MALAY LANGUAGE AMONG FOREIGN UNIVERSITY STUDENTS THROUGH ONLINE LEARNING

The Malay Language is Malaysia's national language and learning it is very essential. Since the Covid-19 epidemic, most educational institutions have used online learning as one of their teaching strategies. Unfortunately, studying and reading a language through online instruction can be challenging, and some students may find this way to be very tedious. This research investigates the difficulties that foreign students and lecturers may encounter when learning and teaching Malay language online. To address the research issues, a qualitative method was employed in this study, which drew information from secondary sources such books, chosen articles, and official reports. The initial results indicate that there are numerous foreign students and numerous difficulties for both foreign students and instructors, as they are unable to acquire the language as effectively as they could in completely physical classes. In addition, students struggle to concentrate and face other challenges such as poor internet access, sluggish online videos, and difficulty understanding instructors' pronunciation of the Malay language.
M2-Mixer: A Multimodal Mixer with Multi-head Loss for Classification from Multimodal Data

In this paper, we propose M2-Mixer, an MLP-Mixer based architecture with multi-head loss for multimodal classification. It achieves better performances than the convolutional, recurrent, or neural architecture search based baseline models with the main advantage of conceptual and computational simplicity. The proposed multi-head loss function addresses the problem of modality predominance (i.e., when one of the modalities is favored over the others by the training algorithm). Our experiments demonstrate that our multimodal mixer architecture, combined with the multi-head loss function, outperforms the baseline models on two benchmark multimodal datasets: AVMNIST and MIMIC-III with respectively, on average, + 0.43% in accuracy and 6. 4 times reduction in training time and + 0.33% in accuracy and 13. 3 times reduction in training time, compared with previous best performing models.
Interaction in Metaverse: A Survey

Human-computer interaction (HCI) emerged with the birth of the computer and has been upgraded through decades of development. Metaverse has attracted a lot of interest with its immersive experience, and HCI is the entrance to the Metaverse for people. It is predictable that HCI will determine the immersion of the Metaverse. However, the technologies of HCI in Metaverse are not mature enough. There are many issues that we should address for HCI in the Metaverse. To this end, the purpose of this paper is to provide a systematic literature review on the key technologies and applications of HCI in the Metaverse. This paper is a comprehensive survey of HCI for the Metaverse, focusing on current technology, future directions, and challenges. First, we provide a brief overview of HCI in the Metaverse and their mutually exclusive relationships. Then, we summarize the evolution of HCI and its future characteristics in the Metaverse. Next, we envision and present the key technologies involved in HCI in the Metaverse. We also review recent case studies of HCI in the Metaverse. Finally, we highlight several challenges and future issues in this promising area.
Recommending Related Research Papers by Topic Modelling

The modern world generates information at an ever-increasing rate. This information explosion also includes the number of published academic research papers. Researchers have access to a variety of digital libraries to retrieve information. Digital libraries often offer access to a number of journal articles and books. Although digital libraries have search mechanisms, it still takes much time to find related research papers. To ease the process of searching for related research papers, The main objective of this study was to develop a model that uses machine learning techniques to recommend related research papers based on an input research paper. The model is implemented and demonstrated using a prototype. The prototype was used to graphically define a model for the creation of a system to recommend related research papers.
Detecting Offensive Posts on Social Media

While social media platforms bring people closer together and facilitate the rapid spread of information, they also face the challenge of offensive and harmful content, such as bullying, discrimination, and hate speech. Most recent research has focused on detecting text-based offensive language in social media. However, audio-based detection methods are still underdeveloped. This paper addresses this gap by proposing a novel deep learning-based model, CLS-CNN, for identifying objectionable audio. We collect a video dataset for training machine learning models for detecting offensive language. We compare the performance of CLS-CNN with traditional machine learning methods using the collected dataset. Results showed that the CLS-CNN model achieved an accuracy of 88% on our collected dataset and outperformed other models. The model was also evaluated using three publicly available text-based datasets. It shows that CLS-CNN achieves at least a 2% accuracy improvement compared to the other published schemes using the same dataset.
Network Traffic Forensics and Traceability Technology Based on Blockchain

With the development of network information technology, Cyber attacks keep cropping up and the means are becoming more complex and diverse. Network attacks often use technologies such as network springboards and botnets to resist tracking. The existing single-node forensics traceability method from the perspective of defense has the problem of fragmentation and serious fragmentation of evidence data, making many valuable data become islands of information, which can not form a highly reliable attack chain and is difficult to be used for accountability of network protection. What's more, there are performance bottlenecks and security risks, which also lead to frequent user data leakage incidents. To solve these problems, this paper proposes a network traffic forensics and traceability technology based on blockchain which based on the decentralization and non-tampering characteristics of blockchain and we combined it with the off-chain database to realize the on-chain and off-chain distributed data storage library. At the same time, SM3 encryption algorithm and SM2 digital signature algorithm are used to protect the security and integrity of stored data, and Bloom filter is used to realize efficient retrieval and traceability. On the premise of protecting the security of storage information, the cost of forensics and traceability is reduced, and the efficiency of data utilization is improved. Compared with the existing forensic traceability methods, it is verified that this model has certain advantages and good scalability in some aspects. The effectiveness of the proposed method is verified by comparative experiments, and the performance is evaluated as well.
Fraud, Extortion and Identity Theft

Micro-fraud has increased vastly online. There has been a huge escalation of online fraud. In addition, more traditional forms of large-scale fraud are also made easier through digital channels to digitally held accounts. Overall levels of fraud are on the increase because overall levels of financial transaction have increased, and overall levels of online fraud reflect this. Scope to steal a person’s identity increases online precisely because online forms of identification are more ‘alienable’. The separation of identify and identification is intensified in online domains, even if the distinction between personality and persona has longer roots. While ‘identity theft’ is significantly escalated online, capacity to appropriate someone else’s identity has its limits. Encryption is never absolute, but in transactions where both parties have an interest in maintaining confidentiality, digital keys are more secure than physical ones. Where sharing keys with non-trusted others takes place, digital encryption falls apart.
Optimization of Diversified Teaching Mode of Vocal Singing in Colleges and Universities by Integrating Audio-visual and Multi-sensory Senses

Abstract To cultivate students’ perception of music culture through audio-visual multi-sensory methods in college vocal singing teaching is a new way to understand music works and cultivate music culture consciousness. The flipped classroom is the basis for this paper, which aims to innovate the teaching of vocal singing in colleges and universities. The linear prediction of Mel’s cepstral coefficient is used to extract the auditory features of the music symbols of the vocal singing video in the flipped classroom so that the students can master the relevant skills and pronunciation of vocal singing. To help students understand vocal singing in its specific context, the time-frequency diagram is utilized to analyze the singer’s movements in the vocal singing video. The self-attention mechanism is used to fuse auditory and visual features, which is validated and analyzed for its effectiveness, teaching effect, and satisfaction. The results show that when the added noise signal-to-noise ratio reaches 50dB, the recognition rate of the LPMFCC algorithm for vocal singing music signals is above 80%. Utilizing the flipped classroom for vocal singing teaching, the average score of students’ vocal singing increased to 9.71, and more than 80% of the students believed that the flipped classroom teaching mode could enhance their independent learning ability. The flipped classroom teaching mode, which integrates audio-visual and multi-sensory teaching methods, can enrich the teaching methods of vocal singing in colleges and universities and further enhance students’ interest in vocal singing.
Human Behavior Recognition Based on CNN-LSTM Hybrid and Multi-Sensing Feature Information Fusion

To address the human activity recognition problem and its application in practical situations, a CNN-LSTM hybrid neural network model capable of automatically extracting sensor data features and memorizing temporal activity data is designed and improved by integrating CNN and gated recurrent units as a variant of RNN. A multi-channel spatiotemporal fusion network-based two-person interaction behavior recognition method is proposed for two-person skeletal sequential behavior recognition. Firstly, a viewpoint invariant feature extraction method is used to extract two-player skeleton features, then a two-layer cascaded spatiotemporal fusion network model is designed, and finally, a multi-channel spatiotemporal fusion network is used to learn multiple sets of two-player skeleton features separately to obtain multi-channel fusion features, and the fusion features are used to recognize the interaction behavior, and the weights are shared among the channels. Applying the algorithm in the paper to the UCF101 dataset for experiments, the accuracy of the two-person cross-object experiment can reach 96.42% and the accuracy of the cross-view experiment can reach 97.46%. The method in the paper shows better performance in two-player interaction behavior recognition compared to typical methods in this field.
Skip Connection Model for SAR Ship Semantic Segmentation

In this paper, we propose a new encoder-decoder model to enhance the performance of semantic segmentation in satellite synthetic aperture radar (SAR) images. Traditional semantic segmentation suffers from significant information loss during feature compression and expansion, primarily because of its shallow structure. In particular, SAR ship images are noisy and difficult to interpret due to the class imbalance problem. This issue reduces segmentation accuracy and hinders proper object distinction. The proposed model addresses these problems by incorporating multiple layers of features to create a connected structure with the decoder, thereby enhancing the preservation of feature information. In addition, the Gaussian filter is used to address the issue of texture bias problem and improve the accuracy of semantic segmentation. This connection method reduces information loss in the feature learning process and addresses the issue of texture bias problem. Compared to the existing techniques including U-Net, feature pyramid networks, and LinkNet, the experimental results of semantic segmentation of SAR ships demonstrate that the proposed method achieves an intersection over union of 78.2%, which is higher than U-Net’s 67.8%. Additionally, the proposed model also achieves the highest Dice coefficient.
Andaman Token: A Feasibility Study of Blockchain Adoption in the Tourism Industry

This paper investigates investment costs and challenges of implementing digital money in the tourism industry. Electronic cash offers financial transparency and transaction efficiency by eliminating the reliance on physical cash via a voucher system. Also, it enables travelers to make instant payments for bookings, accommodations, and services through digital platforms, thus simplifying cross-border transactions. However, recent difficulties encountered by Russian tourists in Thailand highlight the negative impact of restricted financial transactions on tourism revenue, underscoring the need for further examination and solutions. Embracing digital money enhances customer experiences, streamlines financial operations, and paves the way for a connected and cashless future of travel. Additionally, this research explores the properties and considerations surrounding the implementation of the Andaman token in blockchain-based platforms, including technical-operational costs and user understanding. It contributes to a deeper understanding of utilizing vouchers in different contexts and evaluating the potential of blockchain solutions for the tourism industry.
The Implementation of Supervised Learning Recommender System to Enhance Reading Interest of Visitors in Regional Libraries

In the era of digitization, regional libraries face challenges in sustaining the reading interest of their visitors. One solution that could be implemented to enhance the reading interest of visitors in regional libraries is by applying the supervised learning recommender system method based on the profiling of previous book borrowers. This study aims to implement this method on the borrowing history of books by visitors to regional libraries in order to recommend books that align with their reading interests, thereby increasing the number of borrowed books, facilitating visitors in finding the books they need, and providing the best service such as user experience to the visitors. The method used in this research is item-based collaborative filtering with the K-Nearest Neighbor (K-NN) algorithm and cosine similarity distance to find similar book titles based on the titles previously borrowed by book borrowers. The visitor’s borrowing history data in this study consists of Bibliographic Data from book borrowers in 2022 at regional libraries. The data underwent preprocessing to enhance accuracy, resulting in quantitative data comprising attributes such as borrower ID, book ID, and result. The model evaluation method used a confusion matrix on the training data compared to the testing data to determine the accuracy, precision, recall, and f-measure values generated by the created K-Nearest Neighbor (K-NN) model. The result of this study is a recommender system model that shows the top 10 book titles based on the borrowing history of previous book titles entered by the visitor. The performance measurements of the recommendation model yield the following results: the model’s accuracy stands at 68.53%, signifying its ability to accurately classify all samples. Precision is recorded at 99.79%, indicating that the model tends to have minimal false positives (FP). The recall rate is 68.61%, suggesting that the model excels at detecting positive samples compared to negative ones. The F-measure registers at 81.32%, showcasing a fairly effective trade-off between precision and recall. The implementation of this model can be used as a reference for regional libraries in providing book recommendations favored by visitors and assisting visitors in selecting books aligned with their interests based on the profiling of previous borrowers, thus increasing the number of books borrowed.