Reviewerly/exHarmony huggingface dataset:
Small-scale helmet detection based on improved YOLOv5 and moving object detection

In industrial human-computer interaction, the safety of human is particularly important, helmet wearing plays a crucial role in ensuring construction safety and security. In smart industrial systems, intelligent helmet detection is a fundamental task. However, in practical scenarios, safety helmets often appear as small objects in the monitoring scenes, posing challenges for traditional object detection methods. This paper addresses the enhancement of small-scale helmet detection in intelligent monitoring. We achieve this by combining unsupervised foreground detection with an improved object detection network. Experimental results demonstrate significant improvements in detection accuracy compared to the original YOLOv5 algorithm.
Use of mindmapping as a means of visualization in the process of teaching the discipline of international law

The article analyzes the feasibility of using mindmapping in the process of training specialists in international law. Possible ways to use mental maps during lectures, seminars and practical classes, as well as in distance learning have been identified and outlined. The analysis of scientific sources proved that oral presentation of the material is not enough to improve the level of students' knowledge. Visualization of information improves its perception and memorization by students. The main advantages of mindmapping include: improved concentration, systematic and structured material, the ability to process large amounts of information in a short period of time, the development of both professional and general competencies, the formation of creative thinking and others.In order to improve the perception of information in the classroom, in our opinion, it is necessary to use such methods of visualization, which in combination with the oral explanation of the lecture material maximize the quality of knowledge of future specialists in international law. Such innovative pedagogical technologies include scribing and mindmapping.Currently, there are no studies on the impact of the use of mental maps on improving the quality of knowledge of specialists in international law. Mental cards are ideal for use in higher education. Their main advantage is that mindmapping can be used during all types of training sessions. Combined with the brainstorming method, this method allows you to effectively solve complex professional practical problems. Students can create maps both individually and as part of working groups.All this suggests that the use of mindmapping during lectures, seminars and practical classes is methodologically sound and in combination with other innovative learning technologies can significantly improve the quality of students' knowledge.
Methods and means of formation of critical thinking in students during the study of safe disciplines

Currently, the problem of correct perception of information and its assimilation is acute, because every day students are forced to process a large amount of material, both educational and general, which significantly complicates memorization in class. In modern conditions, this problem has become more acute due to the pandemic, which has led to the need to organize the educational process with the help of distance learning tools and methods.&#x0D; The competence of students in terms of safety rules both during the work process and in emergency situations is formed during the study of safety disciplines. Also, the acquisition of this area of ​​knowledge should form in students practical skills to prevent emergencies and the correct response in case of their occurrence. That is why high-quality assimilation of material and the ability to use the knowledge gained during the study of these disciplines, in the future play an important role in shaping a safe social environment.&#x0D; The authors reviewed and analyzed the research of domestic and foreign experts on the formation of critical thinking during the educational process.&#x0D; The article defines the concept of "critical thinking", presents the process of applying technologies for the development of critical thinking in students while studying in higher education and provides ways to improve the perception of the material.&#x0D; According to the results of the research, the main methods of forming critical thinking in students during the educational process and, in particular, when studying the block of safety disciplines are determined. It is found out what means the teacher needs to promote the development of a rational perception of the material of security disciplines. It is proposed to use additional online resources that allow you to organize training with the use of multimedia technologies and ensure the simultaneous active interaction of both students with the teacher and between students during the lesson. The advantages and disadvantages of using certain resources are considered.
Exploring Activities to Handle Disruptive Behavior in English Language Classrooms of Private Schools of Nepal

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Effectiveness of the use of interactive learning media on student learning outcomes in the digestive system material class VIII MTS Muhammadiyah Kaluarrang

Interactive learning media is needed so that students are motivated to learn and find information related to what they are learning. This research aims to 1) Know the learning outcomes of class VIII MTs Muhammadiyah Kaluarrang taught by interactive learning media; 2) Know the learning outcames of class VIII students of MTs Muhammadiyah Kaluarrang who are taught without using interactive learning media; and 3) Know the effectiveness of using interactive learning media in class VIII MTs Muhammadiyah Kaluarrang. In this quasi-experimental study, a non-equivalent control group was used. All eighth-grade Muhammadiyah Kaluarrang students participated in this study. The purposive sampling method was used to select the sample, which consisted of 22 people from class VIII A, the experimental class, and class VIII B, the control class. Observation sheets and a test of learning outcomes are the instruments used. The homogeneity test, the normality test, and the hypothesis testing were utilized in the descriptive and inferential analyses. Students' average scores for learning outcomes were 61.22 in the control class and 82.27 in the experimental class, according to the study's findings. The significance level that was established was tcount 0.000&lt;0.05. Students who received instruction without the use of interactive learning media had significantly different learning outcomes than students who received instruction with interactive learning media. The implications of this research can be used as a reference and guide for the use of appropriate learning media for both online and offline learning in order to increase students' interest in and comprehension of learning activities.
Technological Forensic Auditing and Financial Crime Detection in Nigeria: A Study of Selected Deposit Money Banks

Financial crime has been one of the current challenges faced by organizations especially deposit money banks. To ameliorate this ugly development, technology-enabled forensic auditing was introduced in addition to conventional auditing. This study examined the impact of technological forensic auditing on financial crime detection in Nigeria; a study of selected deposit money banks in Nigeria. The theories reviewed in the study are the strain theory, the theory of inspired confidence, and the credibility theory. The study adopted the survey design. The population of the study is made up of the accounting staff of five selected deposit money banks in Delta state namely; Zenith, First Bank, United Bank for Africa (UBA), Fidelity Bank, and Sterling Bank with a statistic of fifty-eight (58) staff/respondents. Data for the study were generated through the distribution of well-structured questionnaires to the corresponding respondents. Data was analyzed with the multiple linear regression method. The study found that robotic process automation, textual analysis, and data analytics contribute significantly to detecting financial crime in selected deposit money banks in Nigeria. It is therefore the recommendation of this study that forensic audit firms and deposit money banks should strategically employ the usage of software applications that can permit data analytics, textual analysis, and robotic process automation to redeem the credibility image of the forensic auditors’ report.
Global Path Planning for Differential Drive Mobile Robots Based on Improved BSGA* Algorithm

The global path planner is an important part of the navigation system for autonomous differential drive mobile robots (DDMRs). Aiming at the problems such as long calculation time, large number of search nodes, and poor smoothness of path when A* is applied to global path planning, this study proposes an improved bidirectional search Gaussian-A* (BSGA*) algorithm. First, the Gaussian function is introduced to realize the dynamic weighting of the heuristic function, which reduces the calculation time. Secondly, the bidirectional search (BS) structure is adopted to solve the problem of nodes’ repeated search when there are large obstacles between the starting point and the target point. Finally, a multi-layer turning point filter strategy is proposed to further smooth the path. In order to verify the performance of the improved BSGA* algorithm, experiments are carried out in simulation environments with the size of 15 × 15 and 30 × 30, respectively, and compared with the five common global path planning algorithms including ant colony optimization (ACO), D* lite algorithm, and genetic algorithm (GA). The results show that the improved BSGA* algorithm has the lowest calculation time and generates the shortest and smoothest path in the same environment. Finally, the program of the improved BSGA* algorithm is embedded into the LEO ROS mobile robot and two different real environments were built for experimental verification. By comparing with the A* algorithm, Dijkstra algorithm, ACO, D* lite algorithm, and GA, the results show that the improved BSGA* algorithm not only outperforms the above five algorithms in terms of calculation time, length, and total turning angle of the generated paths, but also consumes the least time when DDMR drives along the generated paths.
The Integration of Artificial Intelligence Into Database Systems(AI-DB Integration Review)

In recent times, it can be deduced that a distributed workforce is the future of work, and the future is now. Therefore, it is essential to know that AI-DB integration is not only for the effective application of Artificial intelligence technology, and the development of database technology, but also for the next generation of computing, which will support future Intelligent Information Systems, and allow work to be more effective and productive. Hence, AI-DB Integration will contribute generally to the infrastructure of science and technology, businesses, and humanitarian applications of computers. With all the potential contributions in play, AI-DB Integration is considerably more important than might be assumed from its contribution to the enhancement of AI and DB technologies alone. In this review, different concepts were discussed by emphasizing some key areas like the design of Intelligent Database Interfaces (IDIs), Learnable databases, and Smart Query. The three fundamental areas geared us to investigate how AI enhances database efficiency by optimizing query performance, automating routine management tasks, and fortifying data security. Also, the paper presents short-term and long-term application areas where AI and databases have converged, providing a comprehensive overview of progress, challenges, and opportunities. The outcome of this review expresses some authors' or experts’ opinions on the need for and importance of AI-DB integration and on the future generation of computing.
Data-level Hybrid Strategy Selection for Disk Fault Prediction Model Based on Multivariate Gan

Data class imbalance is a common problem in classification problems, where minority class samples are often more important and more costly to misclassify in a classification task. Therefore, it is very important to solve the data class imbalance classification problem. The SMART dataset exhibits an evident class imbalance, comprising a substantial quantity of healthy samples and a comparatively limited number of defective samples. This dataset serves as a reliable indicator of the disc's health status. In this paper, we obtain the best balanced disk SMART dataset for a specific classification model by mixing and integrating the data synthesised by multivariate generative adversarial networks (GAN) to balance the disk SMART dataset at the data level; and combine it with genetic algorithms to obtain higher disk fault classification prediction accuracy on a specific classification model.
Estimating Cloud Computing Round-Trip Time (RTT) Using Fuzzy Logic for Inter-Region Distances

Cloud computing is widely considered a transformative force in the computing world and is poised to replace the traditional office setup as an industry standard. However, given the relative novelty of these services and challenges such as the impact of physical distance on Round-Trip Time (RTT), questions have arisen regarding system performance and associated billing structures. The primary objective of this study is to address these concerns. We aim to alleviate doubts by leveraging a fuzzy logic system to classify distances between regions that support computing services and compare them with the conventional web hosting format. To achieve this, we analyse the responses of one of these services, like Amazon Web Services, across different distance categories (near, medium, and far) between regions and strive to conclude overall system performance. Our tests reveal that significant data is consistently lost during customer transmission despite exhibiting superior round-trip times. We delve into this issue and present our findings, which may illuminate the observed anomalous behaviour.
A Survey of Methods for Handling Disk Data Imbalance

Class imbalance exists in many classification problems, and since the data is designed for accuracy, imbalance in data classes can lead to classification challenges with a few classes having higher misclassification costs. The Backblaze dataset, a widely used dataset related to hard discs, has a small amount of failure data and a large amount of health data, which exhibits a serious class imbalance. This paper provides a comprehensive overview of research in the field of imbalanced data classification. The discussion is organized into three main aspects: data-level methods, algorithmic-level methods, and hybrid methods. For each type of method, we summarize and analyze the existing problems, algorithmic ideas, strengths, and weaknesses. Additionally, the challenges of unbalanced data classification are discussed, along with strategies to address them. It is convenient for researchers to choose the appropriate method according to their needs.
Deliberative Security: Policy Making for the Police Based on Deliberative

The paper puts forward of approache which will help to promote Evidence Based Policy (EBP) in decision about of security policy.The aim of this paper is to identify lessons and approaches from EBP in developing democratic security. In a deliberative security framework, the policy formulation process not only emphasizes the protection and fulfillment of human rights, but also provides public space for society to be actively involved in the formulation and evaluation of policies in the security sector. This article uses the Evidence Based Policy (EBP) approach which to informs the policy process, rather than aiming to directly affect the eventual goals of the policy. Security is related to socio-political values and security is a process of negotiation and contestation, so a process involving society or deliberative security is needed.
Natural Language Chatbots in Biomedical Translations in Nigeria

In recent years, technology integration into the healthcare sector has witnessed significant advancements, with natural language chatbots emerging as a promising tool for improving communication and accessibility in various domains. This article explores the evolving role of natural language chatbots in the context of biomedical translation in Nigeria. Three objectives were formulated to guide the study. The design methodology use for the study is Natural language chatbots for medical diagnosis. The method employs a literature review approach to synthesize relevant literature on the impacts of Natural Language Chatbots in Biomedical Translations in Nigeria. This methodology is suitable for the research objectives as enables the identification and analysis of extant knowledge and best practices in the field. The challenges of Biomedical, translational practice in Nigeria were discussed. The historical development of Natural language chatbots globally was traced. Finally, the role of natural language chatbots in biomedical translation in Nigeria was examined. The study concludes that by addressing language barriers and improving patient-provider interactions, chatbots can enhance healthcare delivery and accessibility. Finding indicates that a successful implementation requires a comprehensive understanding of Nigeria's linguistic, cultural, and technological landscape. The recommendations include implementing ethical guidelines and best practices to mitigate potential risks and ensure responsible use of the Natural Language Chatbots. The study also recommended human involvement in Natural language chatbots for medical diagnosis to ensure human judgment invest in training and reskilling programs for workers whose jobs may be impacted by the adoption of Natural Language Chatbots in Biomedical Translations in Nigeria.
Mixing Histopathology Prototypes into Robust Slide-Level Representations for Cancer Subtyping

Whole-slide image analysis via the means of computational pathology often relies on processing tessellated gigapixel images with only slide-level labels available. Applying multiple instance learning-based methods or transformer models is computationally expensive as, for each image, all instances have to be processed simultaneously. The MLP-Mixer is an under-explored alternative model to common vision transformers, especially for large-scale datasets. Due to the lack of a self-attention mechanism, they have linear computational complexity to the number of input patches but achieve comparable performance on natural image datasets. We propose a combination of feature embedding and clustering to preprocess the full whole-slide image into a reduced prototype representation which can then serve as input to a suitable MLP-Mixer architecture. Our experiments on two public benchmarks and one inhouse malignant lymphoma dataset show comparable performance to current state-of-the-art methods, while achieving lower training costs in terms of computational time and memory load. Code is publicly available at https://github.com/butkej/ProtoMixer.
A two-stage decision-support system for floating debris collection in reservoir areas

The accumulation of floating debris is one of the main challenges of water conservancy projects, which may pollute the vulnerable ecosystem of the reservoir area and impose significant risks on waterway transportation and dam operations. Due to the dynamicity and uncertainty caused by water flow, the collection of floating debris is much more complicated than on-land waste collection. In this paper, we propose a two-stage decision-support system to optimize the task allocation and routing decisions for floating debris collection in the reservoir area, where the first stage is proactive planning based on historical/observed data and the second stage is reactive planning based on real-time data. The primary objective is to minimize the total collection cost while simultaneously ensuring the accumulation areas with high risks are prioritized in the daily collection plan, and both genetic algorithm and simulated annealing algorithm are used to solve the optimization problems. The proposed method is validated with a real-world case study at Wushan County in the Three Gorges Reservoir area. The computational results show that the level of time-dependent penalty cost on service priority, the types of collection ships, and the number and locations of unloading points are important influencing factors to the cost and responsiveness. Furthermore, the proposed two-stage decision-support system can help effectively optimize the operational planning of floating debris collection in reservoir areas.
Measuring instruments and quality of student achievements

The paper discusses the quality and methods of measurement of student achievements in the first grade in secondary education. It addresses questions such as what needs to be conducted in order to perform a correct measurement, why it is important to measure the achievements, who the obtained results are for etc. These are only some questions the paper aims to answer. Furthermore, the paper analyzes the teachers’ role in the core process of the quality measurement of student achievements as well as their role in the process of preparing students with the aim of achieving the designated and set quality. Our focus in the paper is on how the standards are defined, who participates in the process of creating the standards, who approves them, and finally how the validity of the set quality standards is measured since the teaching process quality standards have been in the center of interest lately. Finally, we analyze the impact of input results and whether it is important to plan methods of teaching and learning in accordance with the input results in order to achieve better output results.
INCREASING TEACHER COMPETENCE IN UTILIZING CHROMEBOOKS FOR LEARNING

The development of science and technology encourages education practitioners to continuously innovate learning in welcoming the industrial era 4.0. Learning in this era requires all elements of education to master 21st century competencies, namely critical thinking, creative, innovative, collaborative and communicative. For this reason, teachers are expected to be able to improve their competence, one of which is in mastering Information and Communication Technology (ICT) in learning. To increase teacher competence in using chromebooks for learning, this school action research will be carried out. This research was carried out at SDN 01 Sidorejo in November 2021 with the research subjects being all 10 teachers at SD Negeri Sidorejo 01. The data collection technique for this school action research is through qualitative data obtained from observation, interviews and document study. This school action research was carried out in 2 cycles. Each cycle includes the following stages: (1) planning, (2) implementation, (3) observation, and (4) reflection. The results of this school action research show that the teacher's ability to use Chromebooks for learning in cycle 1 reached a score of 79.6 and in cycle 2 it was 81.4. The impact of activities on students has shown success with increasing student abilities reaching a score of 85.8 and student wellbeing reaching 88.2. So it can be concluded that increasing teacher competence in using chromebooks for learning can be concluded as successful.