Reviewerly/exHarmony huggingface dataset:
Sonovisual: Transforming Image Description into Dynamic Narratives

Sonovisual is an innovative framework aimed at bridging the gap between static image descriptions and immersive, dynamic narratives. Traditional image descriptions are often limited to static textual representations, lacking the ability to fully convey the visual essence and emotional depth embedded within an image. This project introduces a novel approach that combines cutting-edge image recognition technologies with advanced natural language processing techniques, facilitating the seamless transformation of image features into engaging and vivid narratives. The core of the Sonovisual framework lies in its integration of sophisticated deep learning algorithms, enabling the extraction of intricate visual details and contextual information from images. Leveraging state-of-the-art machine learning models, the system can recognize objects, scenes, and intricate visual patterns, and subsequently generate descriptive narratives that not only encapsulate the visual content but also evoke a multisensory experience for the audience. Through the integration of adaptive storytelling techniques, Sonovisual dynamically adapts its narrative style and tone to cater to different audiences and contexts. By incorporating sentiment analysis and contextual understanding, the framework ensures that the generated narratives resonate with the emotional nuances and cultural sensitivities associated with the depicted imagery. The Sonovisual project holds promise for various applications, including enriching accessibility features for the visually impaired, enhancing educational materials with immersive visual narratives, and augmenting multimedia content with engaging storytelling elements. Additionally, its potential extends to fostering new avenues for creative expression and facilitating enhanced human-computer interaction through visually enriched storytelling experiences.
Optimal control of autonomous vehicle path tracking based on whale optimization algorithm

In order to solve the problem of accurate vehicle path tracking and address the issues of low convergence accuracy and susceptibility to local optima in Whale Optimization Algorithm (WOA), a nonlinear convergence factor is proposed and nonlinear inertia weights are introduced to improve the basic WOA. Firstly, the convergence factor in WOA is changed to a nonlinear convergence factor, and a nonlinear inertia weight is introduced to improve the convergence accuracy, local development ability, and global search ability. Then, this algorithm is combined with a fifth-degree polynomial. The simulation results show that the proposed method can solve the problem of vehicle path tracking effectively. And also, the vehicle can track the given path controlled by the proposed algorithm with higher accuracy and has stronger applicability. The study can help drivers easily identify safe lane-changing trajectories and area.
Application of the on-site P-wave earthquake early warning method based on site-specific ratios of S-waves to P-waves to the 2016 Kumamoto earthquake sequence, Japan

Abstract The on-site P-wave earthquake early warning (EEW) based on the site-specific ratios of S-waves to P-waves has been applied to large-sized offshore earthquakes, and the efficiency of the method has been validated. However, the method requires the P-waves including earthquake ground motions radiated from a large slip area while avoiding the inclusion of S-waves. In this study, we investigated the applicability of the on-site P-wave EEW method for ground motions near an earthquake source fault region, using strong-motion data observed during the 2016 Kumamoto earthquake sequence in Japan. At first, we examined the appropriate time-window length following the arrival of the P-waves. As a result, P-waves with a time-window length of 2.56 s after the arrival at most strong-motion stations were required at least to predict appropriately S-waves for the 2016 Kumamoto earthquake sequence, including the large-sized earthquakes. On the other hand, in the case of the large-sized earthquake as the mainshock ( Mj 7.3), the method can predict within a brief time of 0.5 to 2 s in the operational use that strong ground motions exceeding a certain threshold (e.g., acceleration of 150 cm/s 2 ) will come. Moreover, we found that the method was not strongly affected by the non-linearity of soil deposits due to strong ground motions during the 2016 Kumamoto earthquake sequence. The variability of the relationship between P- and S-waves at the seismic bedrock influenced by the source and path effects is larger than the variability of the relationships between P-/S-waves at the seismic bedrock and at the ground surface by the site effects, and therefore, it hides the effect of the non-linearity of soil deposits. Graphical Abstract
Web Page Ranking Based on Text Content and Link Information Using Data Mining Techniques

Thanks to the rapid expansion of the Internet, anyone can now access a vast array of information online. However, as the volume of web content continues to grow exponentially, search engines face challenges in delivering relevant results. Early search engines primarily relied on the words or phrases found within web pages to index and rank them. While this approach had its merits, it often resulted in irrelevant or inaccurate results. To address this issue, more advanced search engines began incorporating the hyperlink structures of web pages to help determine their relevance. While this method improved retrieval accuracy to some extent, it still had limitations, as it did not consider the actual content of web pages. The objective of the work is to enhance Web Information Retrieval methods by leveraging three key components: text content analysis, link analysis, and log file analysis. By integrating insights from these multiple data sources, the goal is to achieve a more accurate and effective ranking of relevant web pages in the retrieved document set, ultimately enhancing the user experience and delivering more precise search results the proposed system was tested with both multi-word and single-word queries, and the results were evaluated using metrics such as relative recall, precision, and F-measure. When compared to Google’s PageRank algorithm, the proposed system demonstrated superior performance, achieving an 81% mean average precision, 56% average relative recall, and a 66% F-measure.
Management of Mathematics Learning Based on Interactive Digital Worksheets to Improve Students' Critical Thinking Ability

The use of interactive digital technology in learning is an alternative strategy to create joyful learning activities. Therefore, interactive digital worksheet requires development by considering the critical thinking skills of elementary school students. This research determined the interactive digital media worksheet necessity based on critical thinking skills, compiling design, feasibility identification, and effectiveness test of the interactive digital media worksheet based on critical thinking skills for elementary school students. This research and development applied seven steps: (1) potential and problem identification; (2) data collection; (3) product design; (4) design validation; (5) design revision; (6) product test; and (7) product revision. The researchers collected the data with interviews, observations, questionnaires, and tests. The research results found the necessity of interactive digital student worksheets in managing mathematics learning to improve students' critical thinking skills. The interactive digital LKS design was based on critical thinking skills. The development considered the three components, such as (1) Planning; (2) implementing; and (3) evaluating. Planning consists of (1) the analysis of student characteristics; (2) the analysis of learning achievements (CP); (3) the determination of the learning objectives (TP) and the learning objective flow (ATP) and the assessment; (4) the selections of media, learning methods, and teaching materials. The implementation of the interactive digital student worksheets in learning was based on the problem-based learning model and formative and summative tests for the evaluation. The expert validation found the interactive digital worksheet was valid and reliable. The teacher responses reached the percentage of 87.5% while the student with 90%. The product test obtained an average of N-gain for the control group was 0.31 while the experimental group was 0.50, indicating an increment of students' critical thinking skills in mathematics. Thus, interactive digital worksheet implementation is useful in managing learning. The implementation was also useful for further development to manage various challenging activities and to realize meaningful learning.
Saudi EFL Students’ Perceptions Towards the Impact of YouTube on Improving Speaking Skills

The significance of integrating technology in education, especially the widely and frequently used platform “YouTube”, is found in its ability to provide accessible and various educational resources, meeting the needs of diverse learning styles and enhancing active engagement. Since YouTube is one of the preferred applications utilized by Saudi individuals, this study investigated the impact of using YouTube on enhancing the speaking skills of EFL male and female Saudi students from different Saudi universities. This study was conducted during the first semester of the academic year 2021- 2022. A questionnaire was used as the study instrument. It was adapted from Binmahboob’s (2020) study and modified according to the study’s purposes. The questionnaire contained 17 items divided into two sections. 79 EFL students from different Saudi universities participated. Fifty-nine of them were females, while 20 were males. The findings of the study revealed that YouTube videos had a high positive perspective from the participants. In other words, it can be concluded that YouTube videos enhance the speaking skills of EFL university students. Based on these finding, some recommendations and implications were provided.
On DeFi and On-Chain CeFi: How (Not) to Regulate Decentralized Finance

ABSTRACT This article proposes a framework to assess the factual decentralization of blockchain-based financial infrastructure, commonly referred to as ‘decentralized finance’ (DeFi). It discusses various centralization vectors along the DeFi architecture layers from a technological and legal point of view. Distinguishing between endogenous and inherited centralization, the importance of decentralization assessments for regulators is demonstrated. First, centralization vectors are a strong indication that a DeFi project may have (some) custodial properties warranting regulation. Second, centralization vectors reveal the avenues of control in projects, pointing out appropriate regulatory hooks. The article serves as an interdisciplinary introduction that analyses blockchain-based financial infrastructure and provides policymakers and regulators with a tool to identify the differences between truly independent, neutral infrastructure and fake decentralization. It concludes that the former may have very beneficial properties and can contribute towards a more open and transparent financial system, while the latter is a blockchain-based form of financial intermediation and should be regulated as such.
Regional Perspective of Using Cyber Insurance as a Tool for Protection of Agriculture 4.0

The digitalization of the agricultural industry is manifested through the active use of innovative technologies in all its areas. Agribusiness owners have to constantly improve their security to meet new challenges. In this context, the existing cyber risks of the agrarian industry were assessed and their classification by possible consequences, such as data theft or alteration, cyber terrorism, cyber warfare, software hacking or modification, the blocking of markets and transactions on them, was proposed. Cyber insurance is an effective tool for minimizing the likelihood of cyber incidents and for comprehensive post-incident support, with the involvement of cybersecurity specialists. An algorithm for cooperation between an agricultural company and an insurance company when concluding a cyber risk insurance contract is proposed, which takes into account the needs and wishes of insurers at each stage of the interaction. To assess the need to use cyber insurance in agriculture 4.0, a methodology has been developed to evaluate the regional characteristics of cybersecurity and the digitalization of agribusiness. The results of the study show a heterogeneous need for this tool in different regions of the world.
Reservoir Computing Using Interfacial Memristors with Native SiO<sub><i>x</i></sub> Nanostructures Modified by Room-Temperature Plasma Oxidation

We present an interface-type memristor utilizing native silicon oxide nanostructures achieved through controlled plasma oxidation engineering at room temperature. This process effectively reduces the concentration of oxygen vacancies on the surface of the native silicon oxide, facilitating oxygen-vacancy-mediated interfacial resistive switching with excellent COMS compatibility and low switching variability (1.03% for C2C and 3.96% for D2D variability). Moreover, this dynamic memristor exhibits favorable nonlinearity and short-term memory (STM) effects, which are crucial properties for the development of memristor-based reservoir computing (RC) systems. Compared with the conventional neural network, the RC system requires fewer trainable weights to achieve an accuracy of 96.7% for the Iris classification task. By the addition of virtual nodes to the reservoir with masks, the accuracy can be further increased to 100%. A simple digit recognition task is also employed, and noisy images that are not included in the original training set can be recognized correctly. These results highlight the great potential of our device for constructing reservoir computing hardware.
Privacy Protection Method for Blockchain Transactions Based on the Stealth Address and the Note Mechanism

Blockchain is a distributed ledger technology that possesses characteristics such as decentralization, tamper resistance, and programmability. However, while blockchain ensures transaction openness and transparency, transaction privacy is also at risk of being exposed. Therefore, this paper proposes the blockchain transaction privacy protection method based on the stealth address and the note mechanism to address the privacy leakage risk in blockchain public environments. Firstly, the proposed method generates a random seed known only to the parties involved based on the Diffie–Hellman key exchange protocol, ensuring the privacy of transactions. Then, it utilizes the Note Commitments table to maintain the binding relationship between the stealth address and the corresponding note, enabling efficient transfer and verification of note ownership. The uniqueness of the stealth address is utilized as an invalidation identifier for notes in the Nullifier table, ensuring efficient verification of the correctness of note invalidation identifiers. Additionally, this method employs Pedersen commitment and Bulletproofs range proof to generate proof of the legality of transaction amounts, enabling the concealment of transaction amounts and facilitating private transactions between the parties involved. Finally, this paper presents a detailed performance analysis, implementation, and testing of the method. From the results, it can be concluded that the method proposed can effectively prevent fraudulent behavior by various transaction participants and ensure the security, privacy, and integrity of the transaction. Critical processes consume only milliseconds, and the related commitments and proofs are also minimal, which is crucial for controlling transaction costs. At the same time, this method achieves a completely decentralized privacy transaction solution.
Biomedical named entity recognition based on reinforcement learning label correction

Biomedical named entity recognition is of great significance in the field of natural language processing.Deep learning approaches are mainly used at present, and the BERT-BiLSTM-CRF model is one of them. Although the BiLSTM structure in this model can capture bi-directional long-distance dependencies in sentences, there is the problem that the model performance can easily reach the bottleneck by simply adjusting the hyperparameters or changing the network structure, and the quality of its recognition results can hardly be further improved. To address the above problems, this paper adds the label correction process based on deep reinforcement learning to the BERT-BiLSTM-CRF model to form the BERT-BiLSTM-CRF-DRL model. The intelligent body Agent based on deep reinforcement learning is used as a label corrector, and the label correction threshold is set, so that the uncertainty in the annotation results of the BERT-BiLSTMCRF model is greater than the label correction threshold. The labels with uncertainty greater than this threshold in the results are processed for correction. The experimental results show that the F1 values on the datasets BC4CHEMD, BC5CR-CHEM, and NCBI-DISEASE reach 91.07%, 94.02%, and 89.56%,, which are all improved compared with the existing advanced biomedical named entity recognition models.
An Improved Fault Diagnosis Algorithm for Highly Scalable Data Center Networks

Fault detection and localization are vital for ensuring the stability of data center networks (DCNs). Specifically, adaptive fault diagnosis is deemed a fundamental technology in achieving the fault tolerance of systems. The highly scalable data center network (HSDC) is a promising structure of server-centric DCNs, as it exhibits the capacity for incremental scalability, coupled with the assurance of low cost and energy consumption, low diameter, and high bisection width. In this paper, we first determine that both the connectivity and diagnosability of the m-dimensional complete HSDC, denoted by HSDCm(m), are m. Further, we propose an efficient adaptive fault diagnosis algorithm to diagnose an HSDCm(m) within three test rounds, and at most N+4m(m−2) tests with m≥3 (resp. at most nine tests with m=2), where N=m·2m is the total number of nodes in HSDCm(m). Our experimental outcomes demonstrate that this diagnosis scheme of HSDC can achieve complete diagnosis and significantly reduce the number of required tests.
Advancing (U-Th)/He Zircon Dating: Novel Approaches in Sample Preparation and Uncertainty Reduction

Abstract The laser microprobe (U-Th)/He dating method is a new and efficient technique that utilizes an interoperable and integrated suite of instruments, including the excimer laser system, quadrupole helium mass spectrometer, and quadrupole inductively coupled plasma mass spectrometer. To demonstrate the applicability of this new method, we applied both the conventional and laser microprobe techniques to the Sri Lanka zircon (LGC-1). We obtained twenty-two (U-Th)/He ages on nine shards using the laser microprobe method, showing an average (U-Th)/He age of 471.1 ± 16.6 Ma (1σ). This result is generally consistent with the mean conventional age (484.1 ± 9.6 Ma) for twenty-two zircon fragments. Both are nearly equal to the age value (~476 Ma) predicted by the He diffusion model and the thermal history model of Sri Lanka highland. The variations in the laser microprobe-derived ages are most likely caused by the uncertainties in volume measurements, which is also common in other studies. We used the Mahalanobis distance technique to reduce the volume measurement bias by identifying and eliminating abnormal data.
Structural Topic Modelling-based Systematic Review for Service Robots’ Fault Diagnosis: Content, Popularity and Regional Differences

Abstract The market for service robots is expanding as labor costs continue to rise. Faced with intricate working environments, fault detection and diagnosis are crucial to ensure the proper functioning of service robots. The objective of this review is to systematically investigate the realm of service robots’ fault diagnosis through the application of Structural Topic Modeling (STM). A total of 289 papers were included, culminating in ten topics, including advanced algorithm application, data learning-based evaluation, automated equipment maintenance, actuator diagnosis for manipulator, non-parametric method, distributed diagnosis in multi-agent systems, signal-based anomaly analysis, integrating complex control framework, event knowledge assistance, mobile robot particle filtering method. These topics spanned service robot hardware and software failures, diverse service robot systems, and a range of advanced algorithms for fault detection in service robots. Asia-Pacific, Europe, and the Americas, recognized as three pivotal regions propelling the advancement of service robots, were employed as covariates in this review to investigate regional disparities. The review found that current research tends to favor the use of artificial intelligence algorithms to address service robots’ complex system faults and vast volumes of data. The topics of algorithms, data learning, automated maintenance, and signal analysis are advancing with the support of artificial intelligence, gaining increasing popularity as a burgeoning trend. Additionally, variations in research focus across different regions were found. The Asia-Pacific region tends to prioritize algorithm-related studies, while Europe and the Americas show a greater emphasis on robot safety issues. The integration of diverse technologies holds the potential to bring forth new opportunities for future service robot fault diagnosis. Simultaneously, regional standards about data, communication, and other aspects can streamline the development of methods for service robots’ fault diagnosis.&amp;#xD;
Towards optimal learning: Investigating the impact of different model updating strategies in federated learning

With rising data security concerns, privacy preserving machine learning (ML) methods have become a key research topic. Federated learning (FL) is one such approach which has gained a lot of attention recently as it offers greater data security in ML tasks. Substantial research has already been done on different aggregation methods, personalized FL algorithms etc. However, insufficient work has been done to identify the effects different model update strategies (concurrent FL, incremental FL, etc.) have on federated model performance. This paper presents results of extensive FL simulations ran on multiple datasets with different conditions in order to determine the efficiency of 4 different FL model update strategies: concurrent, semi-concurrent, incremental, and cyclic-incremental. We have found that incremental updating methods offer more reliable FL models in cases where data is distributed both evenly and unevenly between edge nodes, especially when the number of data samples across all edge nodes is small.