Reviewerly/exHarmony huggingface dataset:
The Use of Innovative Technologies in Education: analysis of effectiveness and implementation at different levels of education

Integrating innovative technologies into the educational process can significantly enhance students' understanding and engagement with the subject matter. The article deals with the issue of innovative educational technologies implementation into biology teaching at different levels of education. The paper aims to research the most used and effective innovative technologies for biology teaching in Ukrainian education establishments. To achieve the aim of the research, a set of methods was used: theoretical: analysis of psychological, pedagogical, and methodical sources; empirical: questionnaires, interviews; graphics. The research results have found that students get an opportunity to solve a number of academic problems in the course of modern educational technologies using. It has been revealed that the most effective innovative technologies for biology teaching in Ukrainian education institutions are simulation software, virtual reality and augmented reality, online laboratories, gamification, online collaborative platforms, mobile apps, personalized learning platforms, data analysis tools, and biotechnology kits. The results show that mostly young teachers (1-10 years of work experience) implement modern education technologies. The research made it possible to determine the most effective mobile applications for learning biology that are designed to provide students with interactive and engaging ways to learn biology concepts on their smartphones and tablets. The results point to the online collaborative platforms that are most used in the educational process: Google Workspace for Education (secondary education) and Moodle (higher education).
Multi‐objective Firefly algorithm combining logistic mapping and Cauchy mutation

Summary In the process of evolution, the multi‐target Firefly algorithm has a single learning strategy and is easy to fall into premature convergence, resulting in poor comprehensive performance of the algorithm. To solve this problem, a multi‐objective firefly algorithm (MOFA‐LM) combining logistic mapping and Cauchy mutation is proposed in this article. To improve the uneven distribution of the initial population, the initial population with good ergodicity is generated by logistic mapping. Aiming at the problem that the population is easy to fall into local optimal, Cauchy mutation can generate relatively large disturbance when fireflies are updating their positions, which makes it easier to jump out of the local optimal value with a wide range of individual optimization, effectively improve the optimization accuracy of the algorithm, overcome the premature convergence of the algorithm and maintain the convergence of the population. In the experimental part, the proposed algorithm is compared with some multi‐objective optimization algorithms in 19 benchmark test functions, and the effectiveness of the strategy added by MOFA‐LM is verified. The results show that MOFA‐LM has obvious advantages over other algorithms in improving population convergence and distribution.
ANOMALY DETECTION IN CRYPTOCURRENCY TRANSACTIONS WITH ACTIVE LEARNING

Cryptocurrencies have gained tremendous popularity in recent years, with the rise of Bitcoin and other altcoins. However, this surge in popularity has also attracted fraudulent activities, such as scams, phishing, and money laundering. Particularly,
A Systematic Review on Various Task Scheduling Algorithms in Cloud Computing

Task scheduling in cloud computing involves allocating tasks to virtual machines based on factors such as node availability, processing power, memory, and network connectivity. In task scheduling, we have various scheduling algorithms that are nature-inspired, bio-inspired, and metaheuristic, but we still have latency issues because it is an NP-hard problem. This paper reviews the existing task scheduling algorithms modelled by metaheuristics, nature-inspired algorithms, and machine learning, which address various scheduling parameters like cost, response time, energy consumption, quality of services, execution time, resource utilization, makespan, and throughput, but do not address parameters like trust or fault tolerance. Trust and fault tolerance have an impact on task scheduling; trust is necessary for tasks and assigning responsibility to systems, while fault tolerance ensures that the system can continue to operate even when failures occur. A balance of trust and fault tolerance gives a quality of service and efficient task scheduling; therefore, this paper has analysed parameters like trust and fault tolerance and given research directions.
Focus on Physics: Aha! Moments in Sun and Moon Measurements

"Focus on Physics: Aha! Moments in Sun and Moon Measurements." The Science Teacher, 90(5), pp. 14–15 Additional informationNotes on contributorsPaul G. HewittPaul G. Hewitt (pghewitt@aol.com) is the author of Conceptual Physics, 13th edition; Conceptual Physical Science, 6th edition, coauthored with daughter Leslie Hewitt and nephew John Suchocki; and Conceptual Integrated Science, 3rd edition, with coauthors Suzanne Lyons, John Suchocki, and Jennifer Yeh.
A model parameter update strategy for enhanced asynchronous federated learning algorithm

Federated learning addresses the “data islands” issue resulting from privacy concerns, enabling multiple users to train a model collaboratively without sharing their private datasets. However, in the model training process of federated learning, effectively coordinating the communication between participants and server is essential to improve the training efficiency. Traditional synchronous and asynchronous federated learning algorithms cannot meet the requirements of convergence efficiency and precision stability at the same time. In order to balance algorithm efficiency and model stability, this paper proposes a model parameter update strategy for an enhanced asynchronous federated learning algorithm (EAFL). This strategy requires the server to store the latest global model parameters, the last round of global model parameters, and the update part of both, and judge the issued model type according to the sensitivity difference of the client. The client executes different local update processes according to different model types to improve the training efficiency and maintain the stability of the training accuracy. The experimental results show that the EAFL algorithm achieves better training results on MNIST and CIFAR-10 data sets than the traditional federated learning parameter update algorithm. This improvement in training results can provide new thoughts to solve computer vision tasks such as object detection with scattered devices.
HUBUNGAN INTENSITAS MEMBACA DENGAN KETERAMPILAN MENULIS KARANGAN NARASI SISWA KELAS V SEKOLAH DASAR

Reading and writing are very important skills in education. Indonesia is ranked 62nd out of 70 countries, or is in the bottom 10 countries that have a low literacy rate. This study aims to determine how big the relationship between reading intensity and narrative essay writing skills of elementary school students. The method used is correlation research with a quantitative approach. The population in this study were all fifth grade elementary school students. The sampling technique uses saturated samples, where all members of the population are used as research samples. The data collection techniques in this study used indirect communication techniques and measurement techniques, namely using a reading intensity questionnaire and a narrative essay writing skills test. The data analysis technique used is the Pearson Product Moment Correlation. The results showed that there was a significant correlation (correlation) between reading intensity and narrative essay writing skills. With a correlation coefficient of 0.6419 (classified as high), which means that reading intensity plays a very important role in influencing narrative essay writing skills with a contribution of 41.21%. From the results of the research that has been done, there is a relationship between students' reading intensity and narrative essay writing skills.
SECURING IOT HEALTHCARE APPLICATIONS AND BLOCKCHAIN: ADDRESSING SECURITY ATTACKS

The Internet of Things (IoT) describes the connection of bodily devices as "things" that can communicate with other systems and devices through the Internet and exchange statistics (data or information), facilitating the exchange of data with other systems and devices. These devices have sensors, software, and various components designed to exchange data seamlessly within the IoT network. Securing and protecting the data transmitted over the Internet from unauthorized access is imperative to ensuring the integrity and confidentiality of the information. IoT Smart health monitoring systems, integral components of the IoT landscape, are susceptible to various attacks. These include denial of service (DoS), fingerprint, router, select, forwarding, sensor, and replay attacks, all of which pose significant threats to the security of these systems. As such, there is a pressing need to address and mitigate the vulnerabilities associated with IoT healthcare applications. This paper aims to explore the significant role of IoT devices in healthcare systems and provide an in-depth review of attacks that threaten the security of IoT healthcare applications. The study analyses the existing literature on the vulnerabilities present in smart health monitoring systems and the potential application of blockchain technology as a robust solution to enhance the security of IoT healthcare applications. This research reveals critical vulnerabilities in IoT healthcare applications and highlights blockchain's effectiveness in mitigating them, providing insights for robust security measures and strategic decision-making in secure healthcare systems. This paper provides valuable insight and recommendations for policymakers, researchers, and practitioners involved in the domain of the IoT healthcare system.
Cyborgism And Social Enhancement: Shaping A New Rhetoric for Woman’s Participation in Contemporary Society as Represented in William Gibson’s Neuromancer (1984)

In A manifesto for Cyborgs: Science, technology, and socialist feminism in the 1980s (2015), Australian scholar Donna Haraway develops a new feminist philosophy based on the possibilities offered by the cyborg, a cybernetic hybrid organism representing the coupling of organism and the machine, which is turned into an instrument for achieving all human aspirations. Drawing from this vision, we show through the study of Neuromancer (1984) by American writer William Gibson, that the trajectory of Molly Million, Gibson’s female protagonist is consistent to the cybor figure, and as such it is instrumental to the emancipation of contemporary woman’s life.
A combination network of CNN and transformer for interference identification

Communication interference identification is critical in electronic countermeasures. However, existed methods based on deep learning, such as convolutional neural networks (CNNs) and transformer, seldom take both local characteristics and global feature information of the signal into account. Motivated by the local convolution property of CNNs and the attention mechanism of transformer, we designed a novel network that combines both architectures, which make better use of both local and global characteristics of the signals. Additionally, recognizing the challenge of distinguishing contextual semantics within the one-dimensional signal data used in this study, we advocate the use of CNNs in place of word embedding, aligning more closely with the intrinsic features of the signal data. Furthermore, to capture the time-frequency characteristics of the signals, we integrate the proposed network with a cross-attention mechanism, facilitating the fusion of temporal and spectral domain feature information through multiple cross-attention computational layers. This innovation obviates the need for specialized time-frequency analysis. Experimental results demonstrate that our approach significantly improves recognition accuracy compared to existing methods, highlighting its efficacy in addressing the challenge of communication interference identification in electronic warfare.
Human Activity Recognition (HAR) in Healthcare

Developments in the medical and technological fields have led to a longer life expectancy [...]
THE IMPLEMENTATION OF CARTOON VIDEO TO IMPROVE THE IMPLEMENTATION OF CARTOON VIDEO TO IMPROVE THE EIGHTH-GRADE STUDENTS’ PRONUNCIATION SKILL AT SMP NEGERI 12 DENPASAR

Pronunciation is one of the essential nouns that are based on oral and basic forms skill to speak English and other languages. Learning English pronunciation can help students to pronounce words and sentences correctly. Clear pronunciation makes it easy for students to understand and produce intelligible sounds, therefore pronunciation learning has become an important aspect in teaching. This research used the Classroom Action Research (CAR) methodology. The result showed that the best implementation of cartoon videos was proven to improve students’ pronunciation skills. In order to achieve the best implementation of cartoon video to improve students’ pronunciation skill, there were three factors that must be considered: Learning system, speech rate, and the students’ frequency in practicing pronunciation. Besides, the findings also showed that most of the students had a positive perception of the use of cartoon videos in learning pronunciation. Most of the students felt helped in learning pronunciation using cartoon videos. They also felt more interested and motivated in pronunciation learning process. Therefore, it can be concluded that the cartoon videos was the suitable media to teach pronunciaciton and had been proven to improve students’ pronunciation skills through its best implementation.
Development of Audio-Visual Learning Media on Molecular Gastronomy Material

This research aims to: 1) Develop audio-visual learning media on molecular gastronomy topics, 2) Determine the feasibility of audio-visual learning media on molecular gastronomy topics. This study is a research and development (R&amp;D) study through the 4D development model consisting of define, design, develop, and disseminate. The results of the development of audio-visual learning media on molecular gastronomy topics can be described as follows: a) The media has a duration of 12 minutes and 15 seconds, b) The learning media format is in the form of an mp4 video sized at 420.1 MB, c) The sections of the media are divided into the opening stage, aperception material, core material containing explanations about the definition of molecular gastronomy, techniques in molecular gastronomy, practice of making imitation caviar using gelification techniques, evaluation quizzes, and conclusion, d) The media dissemination is done by uploading it through the YouTube platform on the BOGA UNY channel with the URL https://youtu.be/61-ZqZIXkSA. The results of the feasibility assessment of audio-visual learning media on molecular gastronomy topics by subject matter experts received an average score of 100%, which falls into the highly feasible category. Evaluation by media experts resulted in an average score of 95.19%, also classified as highly feasible. The assessment by students yielded an average score of 84.29% on a small scale and 89.71% on a large scale, both falling into the highly feasible category. Based on the feasibility test results, the audio-visual learning media on molecular gastronomy topics is declared highly suitable for use as a learning medium.
Virtual Pipeline Flight Based Adaptive Dynamic Obstacle Avoidance Algorithm for UAVs

Due to the occlusion of buildings in three-dimensional space, it is difficult for UAV trajectory planning to find an optimal route. In this paper, by artificially creating a virtual pipe of the UAV cluster, the corner points and the obstacles that cannot be detected in advance in the virtual pipe are divided into dynamic obstacles and unknown obstacles, and a dynamic obstacle avoidance algorithm is added to the trajectory control in the virtual pipe, and a UAV adaptive obstacle avoidance algorithm with better implementation effect is designed. The algorithm can detect nearby dynamic obstacles and other fixed obstacles in the original trajectory path in real time to avoid collision, which has great practical significance.
Tampering Detection and Localization on Copy-Move Images Using Deep Learning Approaches

Images can be used as legal evidence in forensics, journalism, and other fields. Image tampering is modifying images using modern technologies, which might create false evidence. There are many tampering with images like copy-move image forging, image slicing, and recoloring. Now the detection of those types of images whether tampered with or not and the process of recognizing copied and pasted portions for the copy-move tampered images is a challenging task. In this connection, this work employed CNN, ResNet-50, and VGG-16 for the detection of tampering images. These models were trained on CASIAv2 and COMOFOD datasets. Moreover, error-level analysis techniques and Resizing are used to preprocess for detection. SIFT, and Gray Scaling were applied to preprocess the Copy-Move images to identify similar features in the image. Furthermore, preprocessed images are fed to the DBSCAN algorithm to locate the portions of the image where it is copied and pasted in copy-move tampered images. From the results, it was observed that the CNN model trained on the CASIAv2 dataset outperformed all other models. Hence, it was tested on the MICC-F220 dataset followed by DBSCAN for localization of tampered images.
Data Analytics of Restaurants using AI

At present, there are 4.3 million restaurants worldwide. With such a huge number of restaurants, the level of competition between them to attract interested customers will increase, thereby affecting the sales market the most. Various surveys have shown that customers rely a lot on online reviews and ratings given by previously visited customers and then try a new restaurant or cuisine. This is because customer reviews affect the sustainability of the restaurant business. There are various websites and applications like Zomato, Trip Advisor, and similar models that focus only on customer satisfaction services, but not regarding business satisfaction services/models. If some of them still include the business side of these services, they would not give their 100 percent as customer services are much more in demand. Taking insights from these, this paper focuses mainly on the concept of reviews and ratings to create a web platform that will provide enormous options according to the expectations and needs of the business consumer/consultancy companies and help them analyze the entire restaurant market in an area based on what type of cuisine is the most tried, number of ratings and many more important parameters. This can be a successful venture between businesses and restaurants, which will help in expanding the restaurant community according to the real-time data of a particular region/area.
Prompt Learning for Subjective and Objective Recognition of Text

Subjective expressions generally contain the user's emotional opinions and thus can better represent the sentiment information of the originating text reviews, while objective descriptions usually appear in declarative sentences that do not reflect the sentiment polarity of the comment text. Therefore, subjective and objective recognition techniques for text are necessary for natural language processing tasks (e.g. opinion mining, sentiment analysis). However, most existing deep learning-based methods need large human-annotated data, but labeling large amounts of high-quality emotional texts is labor-intensive. The lack of labeled data has become a bottleneck restricting the deep algorithm. To cope with the shortage of labeled data, we employ Prompt Learning strategy in Subjective and Objective recognition task of text, thus proposing a simple PLSO method. PLSO can effectively inherit the abundant prior knowledge from the pre-trained model and learn high-level semantic features of text by reducing the differences between upstream and downstream tasks. Meanwhile, we introduce triplet loss as a regular term in the training objective, which guides the model to capture the category distribution of data in joint training. Benefiting from the above advantages, the PLSO approach significantly outperforms the other baseline methods in the data sparsity scenario, demonstrating its superiority and effectiveness.
IMPLEMENTATION OF RISK ASSESSMENT TO IDENTIFY DAMAGE OF SEMARANG STATIC ARCHIVE PAPERS IN ANRI

Risk Assessment is a process of identifying and analyzing risks to determine the level of threat/danger to archive damage. The results of the Risk Assessment activities can be used to formulate archive preservation policies and strategies in carrying out preservation actions both preventively and curatively. The Risk Assessment activity is an activity that is expected to make it easier to determine steps in prevention, repair, maintenance of physical archives in general, including the information contained therein and can be used to formulate policies. The purpose of writing this final project is to determine the parameters observed in the risk assessment process of Semarang static archival paper, and determine the extent of the damage so that it can be used as a reference to determine whether restoration and reproduction are necessary. The risk assessment steps carried out consist of preparation, implementation and evaluation. Parameters observed in the risk assessment process of Semarang static archive paper included the shape of the binding and the causes of damage to archival documents caused by chemicals, pests and water stains. The results of the archival paper risk assessment show that the document has a high level of damage and is very vulnerable and easily broken so restoration activities are needed. The factors that cause the most damage to archival paper are insects, ink corrosion, foxing stains and acidification
Analysis of Residual Solvent Problems In Cooking Oil Packaging Products at PT X

The packaging printing production process cannot be separated from the problems that affect the product yield, and solvent residue is one of them. Quality control or quality control is needed in a business to achieve the desired results and minimize problems so that these products can be sold to customers. Product quality has several weaknesses, such as residual solvent, sealing and leaking. Apart from the disadvantages mentioned, the most important critical point is the high residual solvent content. The way to solve the problem at the critical point of remaining solvent is to clean the chamber, exos and chiller and increase the temperature of the chamber so that the evaporation of the remaining solvent remains, and gas chromatography examination to assist in sample verification. Based on the problems identified, product quality results at PT X can reduce problems in products with high solvent residues. PT X is a manufacturer, marketer and distributor of cooking oil products.