Reviewerly/exHarmony huggingface dataset:
AIS - A Metric for Assessing the Impact of an Influencer’s Twitter Activity on the Price of a Cryptocurrency

Individual users on social media platforms like Twitter can significantly volatile assets, including cryptocurrencies. However, current research has overlooked this aspect, focusing on sentiment analysis that includes all posts from all users. Making it challenging to detect trends caused by individuals. To address this gap, we introduce the Asset Influence Score (AIS), a percentage-based metric that assesses the likelihood of a newly issued tweet aligning with periods of heightened trading activity. By analyzing price data and tweets concurrently, we identify correlations that enable to predict the likelihood of specific users’ tweets co-occurring with increased trading activity. Evaluating the AIS using a publicly available prototype and Twitter data from 2020 to 2023, we find that using the AIS as a buy signal outperforms buy-and-hold and technical trading strategies while maintaining high liquidity. Demonstrating the applicability of AIS in improving trading decisions and identifying key individuals on social media platforms.
19 A novel blockchain-based artificial intelligence application for healthcare automation

The integration of blockchain and artificial intelligence (AI) technology can facilitate a comprehensive healthcare automation platform that can leverage the power of both AI and blockchain. This platform not only creates trust, enabling transparency of patient data sharing and record keeping, but also allows AI and machine learning algorithms to improve the efficiency and accuracy of healthcare technologies. It can provide healthcare data privacy, ease the access and sharing of medical records and other confidential health information, and enable fast and secure medical imaging transfers.
Enhancing Fairness and Accuracy in Machine Learning Through Similarity Networks

Machine Learning is a powerful tool for uncovering relationships and patterns within datasets. However, applying it to a large datasets can lead to biased outcomes and quality issues, due to confounder variables indirectly related to the outcome of interest. Achieving fairness often alters training data, like balancing imbalanced groups (privileged/unprivileged) or excluding sensitive features, impacting accuracy. To address this, we propose a solution inspired by similarity network fusion, preserving dataset structure by integrating global and local similarities. We evaluate our method, considering data set complexity, fairness, and accuracy. Experimental results show the similarity network's effectiveness in balancing fairness and accuracy. We discuss implications and future directions.
Generative Target Tracking Method with Improved Generative Adversarial Network

Multitarget tracking is prone to target loss, identity exchange, and jumping problems in the context of complex background, target occlusion, target scale, and pose transformation. In this paper, we proposed a target tracking algorithm based on the conditional adversarial generative twin networks, using the improved you only look once multitarget association algorithm to classify and detect the position of the target to be detected in the current frame, constructing a feature extraction model using generative adversarial networks (GANs) to learn the main features and subtle features of the target, and then using GANs to generate the motion trajectories of multiple targets, finally fuzing the motion and appearance information of the target to obtain the optimal match. The optimal matching of the tracked targets is obtained. The experimental results under OTB2015 and IVOT2018 datasets demonstrate that the proposed multitarget tracking algorithm has high accuracy and robustness, with 65% less jumps and 0.25% more accuracy than the current algorithms with minimal identity exchange and jumps.
Levels of Readability of the Texts in the 7th Grade Turkish Textbook

The texts in the textbooks are one of the basic materials in terms of conveying elements such as achievement, value, and competence in the curriculum. The fact that the texts are suitable for the level of the students helps the students understand and analyze the text better. In this study, it is aimed at determining the readability levels of the texts in the 7th grade textbook and revealing their suitability for the students. In the study, the case design from the qualitative research method was preferred. The materials have been subjected to the subtractive readability process. The obtained data were analyzed by descriptive analysis. At the end of the study, it was determined that the level of the texts was at the level of anxiety; there was a differentiation between the readability scores according to the branches, and there were differences in the readability levels of the texts according to the gender variable. In this context, it is predicted that it will be more beneficial in terms of educational activities to be more careful while selecting or preparing texts for textbooks and to prefer texts according to the level of students.
Implementasi Model Pembelajaran PBL untuk Meningkatkan Hasil Belajar Matematika pada Siswa Kelas 1 SDN 23 Ampenan

Mathematics is a branch of science that has an important role in the development of science and technology, both as a tool in the application of other fields of science and in the development of mathematics itself, where in the learning process there are many formulas, numbers and mathematical symbols. Mathematics is taught from elementary school age with the aim of training a person's thinking power, which makes them creative in solving problems, so that it can increase students' interest in learning. Based on the results of observations carried out at SDN 23 Ampenan, it was found that class I mathematics learning outcomes were very low under KKM and students lacked enthusiasm in the learning process. When learning is taking place there are still many students who do not pay attention to the teacher's explanation during learning because the teacher does not use a learning model and only uses a conventional model so that learning is teacher-centred. Thus, 80% of student learning outcomes are below the average or KKM and only 20% of students achieve the KKM score. Therefore, a learning model is needed that involves student activity during the learning process. Therefore, researchers took a problem based learning model to overcome this problem. This research is a type of classroom action research (PTK) because this research was conducted to observe or observe activities that were deliberately created and occurred in a class with an emphasis on perfecting or improving the learning process.
Quantum Algorithm for Lexicographically Minimal String Rotation

Abstract Lexicographically minimal string rotation (LMSR) is a problem to find the minimal one among all rotations of a string in the lexicographical order, which is widely used in equality checking of graphs, polygons, automata and chemical structures. In this paper, we propose an $$O(n^{3/4})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mrow> <mml:mn>3</mml:mn> <mml:mo>/</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> quantum query algorithm for LMSR. In particular, the algorithm has average-case query complexity $$O(\sqrt{n} \log n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msqrt> <mml:mi>n</mml:mi> </mml:msqrt> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , which is shown to be asymptotically optimal up to a polylogarithmic factor, compared to its $$\Omega \left( \sqrt{n/\log n}\right) $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Ω</mml:mi> <mml:mfenced> <mml:msqrt> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>/</mml:mo> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:msqrt> </mml:mfenced> </mml:mrow> </mml:math> lower bound. Furthermore, we show that our quantum algorithm outperforms any (classical) randomized algorithms in both worst and average cases. As an application, it is used in benzenoid identification and disjoint-cycle automata minimization.
ORGANIZATIONAL AND PEDAGOGICAL CONDITIONS OF USING WEB ORIENTED TECHNOLOGIES IN TEACHING HIGH SCHOOL STUDENTS OF SECONDARY EDUCATION ESTABLISHMENTS

The article characterizes the list of organizational and pedagogical conditions in computer science classes and examines their influence on the formation of digital competence through the use of web-based technologies in the education of high school students of secondary education institutions.The psychological and pedagogical conditions are considered as an important component in the formation of individual characteristics of high school students.The scientific and methodological conditions are analyzed as a leading component for informatics teachers during the preparation of students for correct work with hardware and software tools.The sanitary and hygienic conditions as a component of personality formation under the influence of the environment is highlighted. The main possibilities of optimizing the teaching of informatics through the use of web-oriented technologies to increase the efficiency of learning the educational material are revealed.The concept of "educational design" as high-quality E-learning content was formed.They studied scientific researches, works, experiments of scientists of this problem M. Leshchenko, Yu. Nosenko, A. Sukhikh, M. Yastrebova. The psychophysical characteristics of students and the main groups of factors that negatively affect their physical and mental health were outlined.It was found that compliance with organizational and pedagogical conditions forms a comprehensively developed person who strives for self-knowledge, improvement, is responsible for his actions when working on the World Wide Web.It is noted that the use of web-oriented technologies, which are based on organizational and pedagogical conditions, create optimal conditions for revealing the potential of the personality of high school students.The problems of the implementation of web-oriented technologies in organizational and pedagogical conditions in the education of high school students of secondary education institutions are highlighted and characterized. The danger of Internet addiction and disorder of students in the World Wide Web is presented. A tool for self-diagnosis of Internet addiction has been created for Internet users. Other dangers on the Internet, such as cyberphishing and cyberbullying, are highlighted. Their concepts have been formed.
<scp>Energy‐efficient sender‐initiated threshold‐based</scp> load balancing (<scp>e‐STLB</scp>) in cloud computing environment

Summary Cloud computing has transformed service delivery through its pay‐per‐use model, supporting diverse users with multiple heterogeneous Virtual Machines (VMs). However, energy consumption has emerged as a critical concern, necessitating cloud resource optimization for environment‐friendly practices. This research paper presents an innovative energy‐efficient threshold‐based sender‐initiated load‐balancing strategy (e‐STLB) to address this concern. The approach employs threshold values to trigger task migration between VMs, ensuring optimal performance while maximizing energy efficiency. The proposed strategy significantly reduces Makespan and increases Resource Utilization in an energy‐conscious manner. Experimental evaluations using Cloudsim 3.0 demonstrate that the e‐STLB outperforms other state‐of‐the‐art solutions, offering a compelling approach to sustainable cloud computing.
Psychosocial Predictors in College Writing

The prevalence of writing anxiety among college students has been a pervasive concern across various academic disciplines, extending beyond Basic English courses. This study delved into the psychosocial factors contributing to writing-related challenges among students at the University of Cebu Main Campus during the 2016-2017 academic year. The data from 567 randomly selected college students enrolled in Writing in the Discipline classes were collected by implementing a descriptive-correlational research design and utilizing adapted and modified questionnaires. The research findings not only shed light on the relationship between students' profiles and their experiences of writing anxiety and motivation but also unveiled significant gender-based variations in the process and evaluation subscales of writing-related issues. Ultimately, this study underscored the substantial link between the students' chosen programs and the predictors of their writing challenges. Based on these insights, it is recommended to implement a strategic intervention program to alleviate writing anxiety and enhance motivation among college students, fostering improved writing outcomes across disciplines.
NMS-Free Oriented Object Detection Based on Channel Expansion and Dynamic Label Assignment in UAV Aerial Images

Object detection in unmanned aerial vehicle (UAV) aerial images has received extensive attention in recent years. The current mainstream oriented object detection methods for aerial images often suffer from complex network structures, slow inference speeds, and difficulties in deployment. In this paper, we propose a fast and easy-to-deploy oriented detector for UAV aerial images. First, we design a re-parameterization channel expansion network (RE-Net), which enhances the feature representation capabilities of the network based on the channel expansion structure and efficient layer aggregation network structure. During inference, RE-Net can be equivalently converted to a more streamlined structure, reducing parameters and computational costs. Next, we propose DynamicOTA to adjust the sampling area and the number of positive samples dynamically, which solves the problem of insufficient positive samples in the early stages of training. DynamicOTA improves detector performance and facilitates training convergence. Finally, we introduce a sample selection module (SSM) to achieve NMS-free object detection, simplifying the deployment of our detector on embedded devices. Extensive experiments on the DOTA and HRSC2016 datasets demonstrate the superiority of the proposed approach.
“Navigating through the digital swamp”: assessing SME propensity for online marketplaces

Abstract Digital technology has produced deep changes in the business world. However, companies have a hard time understanding consumers’ changing needs and new consumption patterns, especially small and medium-sized enterprises that currently face growing challenges in an era characterized by a lack of time and information overload. New mechanisms must be found that not only can keep businesses competitive in digital environments but can do so in innovative and sustainable ways. This study sought to address this issue by adopting a process-oriented approach and developing a decision-support tool for SMEs that are considering joining online marketplaces. The proposed analysis model helps these companies decide whether they are ready to take advantage of these digital platforms or whether they have to take action to improve in specific areas of their business operations. The model-building process relied on a combination of cognitive mapping and the best–worst method. An expert panel was recruited to identify the most relevant factors determining SME readiness to join online marketplaces. The model was applied to a set of real companies to assess their propensity for online marketplaces. The practical applicability of this decision-support system was then discussed in a consolidation session with a member of Portugal’s Agência Nacional de Inovação (National Innovation Agency), who acknowledged the strong potential of the proposed model.
Automated NFR testing in continuous integration environments: a multi-case study of Nordic companies

Abstract Context Non-functional requirements (NFRs) (also referred to as system qualities) are essential for developing high-quality software. Notwithstanding its importance, NFR testing remains challenging, especially in terms of automation. Compared to manual verification, automated testing shows the potential to improve the efficiency and effectiveness of quality assurance, especially in the context of Continuous Integration (CI). However, studies on how companies manage automated NFR testing through CI are limited. Objective This study examines how automated NFR testing can be enabled and supported using CI environments in software development companies. Method We performed a multi-case study at four companies by conducting 22 semi-structured interviews with industrial practitioners. Results Maintainability , reliability , performance , security and scalability , were found to be evaluated with automated tests in CI environments. Testing practices, quality metrics, and challenges for measuring NFRs were reported. Conclusions This study presents an empirically derived model that shows how data produced by CI environments can be used for evaluation and monitoring of implemented NFR quality. Additionally, the manuscript presents explicit metrics, CI components, tools, and challenges that shall be considered while performing NFR testing in practice.
Disentangled Prototypical Graph Convolutional Network for Phishing Scam Detection in Cryptocurrency Transactions

Blockchain technology has generated an influx of transaction data and complex interactions, posing significant challenges for traditional machine learning methods, which struggle to capture high-dimensional patterns in transaction networks. In this paper, we present the disentangled prototypical graph convolutional network (DP-GCN), an innovative approach to account classification in Ethereum transaction records. Our method employs a unique disentanglement mechanism that isolates relevant features, enhancing pattern recognition within the network. Additionally, we apply prototyping to disentangled representations, to classify scam nodes robustly, despite extreme class imbalances. We further employ a joint learning strategy, combining triplet loss and prototypical loss with a gamma coefficient, achieving an effective balance between the two. Experiments on real Ethereum data showcase the success of our approach, as the DP-GCN attained an F1 score improvement of 32.54%p over the previous best-performing GCN model and an area under the ROC curve (AUC) improvement of 4.28%p by incorporating our novel disentangled prototyping concept. Our research highlights the importance of advanced techniques in detecting malicious activities within large-scale real-world cryptocurrency transactions.
AR, VR, and the Metaverse in Teaching: An Advocacy for Precise Differentiation

Augmented reality, virtual reality, mixed reality, and extended reality are becoming prominent in science education. However, in many reports on the matter, there is no clear differentiation between these different technologies. We comment on potential distinctions to avoid confusion and recommend a clear separation and naming of technology, as well as interactive and immersive level.
The Impact of using Blockchain on the Auditing Profession

Rapid technological changes cause several challenges for established processes and approaches to the auditing. A special role in this context is assigned to the blockchain as a key driving force in changes in auditing. The aim of the article is to identify the key features of blockchain’s impact on auditing. The research employs economic and statistical methods, namely trend analysis, in-depth semi-structured interviews, assessment of the economic effect to identify the main changes in auditing under the influence of blockchain technologies. The experience of using blockchain was studied through a sample of 27 auditors from Azerbaijan, Israel, and Jordan. The respondents indicated changes in the operational work of auditors (88,9% of respondents) among the main areas of influence of blockchain technologies. The surveyed auditors also consider it necessary to improve auditors’ IT skills under the influence of blockchain (88,9% of respondents). The respondents emphasize the appropriateness to change the audit methodology under the influence of blockchain (77,8% of respondents). The surveyed auditors see the prospect in using real-time auditing (55,6% of respondents) and other higher value-added services (44,4% of respondents). Data from the financial statements of the 4 largest auditing companies were analyzed to determine the economic effect of the impact of blockchain on auditing. It was determined that the average annual growth rate under the basic scenario is 6.9% for 2023-2025, or $72.1 billion on average per company in 2025. The prospects for further research are studying the directions of strategic and operational transformations in auditing because of the influence of blockchain in terms of audit methodology, organizational structure of audit companies.
DataPLAN: A Web-Based Data Management Plan Generator for the Plant Sciences

Research data management (RDM) combines a set of practices for the organization, storage and preservation of data from research projects. The RDM strategy of a project is usually formalized as a data management plan (DMP)—a document that sets out procedures to ensure data findability, accessibility, interoperability and reusability (FAIR-ness). Many aspects of RDM are standardized across disciplines so that data and metadata are reusable, but the components of DMPs in the plant sciences are often disconnected. The inability to reuse plant-specific DMP content across projects and funding sources requires additional time and effort to write unique DMPs for different settings. To address this issue, we developed DataPLAN—an open-source tool incorporating prewritten DMP content for the plant sciences that can be used online or offline to prepare multiple DMPs. The current version of DataPLAN supports Horizon 2020 and Horizon Europe projects, as well as projects funded by the German Research Foundation (DFG). Furthermore, DataPLAN offers the option for users to customize their own templates. Additional templates to accommodate other funding schemes will be added in the future. DataPLAN reduces the workload needed to create or update DMPs in the plant sciences by presenting standardized RDM practices optimized for different funding contexts.
Fast algorithms for band-limited extrapolation by over sampling and Fourier series

Abstract In this paper, fast algorithms for the extrapolation of band-limited signals are presented by the sampling theorem and Fourier series in the case of over sampling. Assume the band-limited signal is known in a finite interval. We update the signal outside the interval by the Shannon sampling theorem in the case of over sampling. Then we obtain a fast algorithm in the form of Fourier series instead of the Fourier transform in the Papoulis–Gerchberg algorithm. Gibbs phenomena is analyzed in the method. An algorithm is presented to control the Gibbs phenomena, and some examples are given in the experimental results.