Reviewerly/exHarmony huggingface dataset:
Research on the improvement of EdgeCloudSim's task scheduling framework

In cloud-edge collaborative working mode, application services are affected by complex components, configuration and deployment conditions and other multi-dimensional factors. Therefore, it’s important to use simulation tools to conduct experiments reasonably and effectively. EdgeCloudSim is a simulation test platform widely used in edge computing. However, EdgeCloudSim's built-in task scheduling algorithm FF(First Fit) has problem of queuing and blocking, which makes some tasks fail to obtain computing resources in time. Besides, the processing time and load are not ideal. Another algorithm LL(Least load) optimizes above problems, but VM list needs to be traversed every time while scheduling. So the decision-making efficiency is low. Furthermore, EdgeCloudSim ignores migration cost of tasks. This paper improves the task scheduling framework of EdgeCloudSim for above problems and proposes a two-stage cloud-edge task scheduling strategy based on MH-LL(Max Heap-Least Load) algorithm and PP-LL(Position Probability-Least Load) algorithm. When resources of scheduled edge cloud are insufficient, task will be migrated to other edge clouds according to the migration cost calculated by distance factor &alpha; and distance coefficient d. Comparing with the original platform by experiments, the improved platform has an average optimization of 1.38%, 2.978s, 0.129s, and 19.26% in task failure rate, service time, delay, and load. It can improve work efficiency and system performance.
A stacked auto-encoder and SVDD-based detection method for radar signal

Aiming at the difficulty of radar signal detection in low signal-to-noise ratio (SNR) condition with traditional methods, a stacked auto-encoder (SAE) and support vector data description (SVDD) based detection method is proposed. Firstly, the radar signal with noise is extracted by SAE to obtain the representative features. Secondly, the SVDD is trained with the extracted features to obtain a spherical discriminative boundary for classification offline. Finally, the trained SAE-SVDD used as the one-class classifier to detect the signal by minimizing both the reconstruction error and the hypersphere volume simultaneously in a real-time manner. Simulation results indicate that the proposed algorithm can extract and identify the radar signal under noise condition effectively with a good robustness. It has practical significance for improving the accuracy of radar signal detection under low SNR.
Autothnography on The experience of running a Village Curriculum in a Small School in a Rural Village

The purpose of this study is to examine an elementary school teacher’s educational experience of running a small-scale village curriculum in rural and fishing villages using the autoethnography method. in the search for meaning and value. The research results are briefly presented as follows. First, is there really learning in a classroom without questions? The contemplation of “Future” led to the conclusion that the true meaning of learning should begin with helping and caring for the existence and life of children. Second, the answer to their concerns was transferred to a small school in a farming and fishing village, where they got out of textbooks and experienced classroom lessons that they had long dreamed of through classes with children’s curiosity. Third, it was found that Saetbyeol, who started making eye contact with friends through village outings, expanded her interests to the surroundings and learned not only empathy but also how to build relationships. The researcher, who was convinced of the educational experience through the village outing, ran a village curriculum with children who had the power to learn from early March, and the educational experience conducted as a class with the teaching staff, parents, and village elders on the theme of Chuseok was a solidarity with the village community. It was an important practice to create a cultural foundation through. Fourth, the researcher who visited the village he lived in as a child saw who he was through the place and experienced recovery through the process of comforting himself while facing his painful past.
Investigating the Intersection of Cybercrime and Machine Learning: Strategies for Prevention and Detection

Cybercrime is a growing problem that has gained increased attention in recent years due to the growing use of technology and the internet. With the increasing use of machine learning in various fields, including cybersecurity, it is important to examine the intersection of these two domains. This review paper aims to provide an overview of the evolution of cybercrime and its use of machine learning techniques. It also identifies various types of cybercrime that utilize machine learning and the challenges and limitations of detecting and preventing these crimes. The paper also suggests strategies for prevention and detection, both technical and non-technical, that could be used to combat the growing problem of cybercrime. While non-technical strategies include user education, strong passwords, and cooperation between the public and private sectors, technical techniques employ machine learning-based threat detection systems, anomaly detection, and encryption. In conclusion, this research emphasizes the significance of comprehending the relationship between cybercrime and machine learning to create successful preventive and detection solutions.
Fusion with temporal and spatial attention for video human pose estimation

In this paper, deep learning method is applied to study the estimation of human joint points in complex multi-person videos. Although there are many excellent research results on human pose estimation from single frame images, but as a combination of multiple frames of continuous images, video contains more complex temporal information. So human pose estimation for videos is more challenging. In order to make use of temporal and spatial continuity in video sequence, temporal attention and spatial attention are used in this paper. To better consider the amount of information in each frame and aggregate information across multiple features. Temporal attention fusion is adopted by calculating the element correlation between the features of the current frame and adjacent frames; in order to take advantage of the spatial relationship within the frame, spatial attention is used to achieve efficient fusion. Through experimental evaluation, the method in this paper can recover the accurate human pose skeleton with high accuracy.
Fusion System for Blockchain Asset Securitization Risk Control Using Adaptive Deep Learning-Based Framework

Feature engineering methods, which entail identifying and extracting useful features from big datasets, can be used to enhance the precision of asset securitization. It might be difficult to securitize assets that produce multiple receivables, such as consumer or company debt. In order to overcome these difficulties, companies might think about adopting a fusion system that integrates feature engineering with distributed ledger technologies such as blockchain. Businesses can benefit from implementing a fusion system like the Deep learning-based Adaptive Online Intelligent Framework (DLAOIF) since it allows for better decision-making, less wasted time and money, and less chance of fraud. Financial asset tracking on a blockchain can help investors keep a closer eye on asset performance and related risks, while also decreasing their reliance on credit rating agencies. Blockchain's high data security standards and elimination of regulatory bottlenecks in the securitization process also make it a useful tool for easing the burden of due diligence.
Incremental Pedestrian Attribute Recognition via Dual Uncertainty-Aware Pseudo-Labeling

Incremental pedestrian attribute recognition (IncPAR) aims to learn novel person attributes continuously and avoid the catastrophic forgetting, which is an essential problem for image forensic and security applications, e.g., suspect search. Different from the conventional continual learning for visual classification, we formulate the IncPAR as a problem of multi-label continual learning with incomplete labels (MCL-IL), where the training samples in a novel task are annotated with only a few categories of interest but may implicitly contain other attributes of previous tasks. The incomplete label assignments is a challenging and frequently-encountered issue in real-world multi-label classification applications due to a number of reasons, e.g., incomplete data collection, moderate budget for annotations, etc. To tackle the MCL-IL problem, we propose a self-training based approach via dual uncertainty-aware pseudo-labeling (DUAPL) to transfer the knowledge learned in previous tasks to novel tasks. Specially, both kinds of uncertainties, i.e., aleatoric uncertainty and epistemic uncertainty, are modeled to mitigate the negative influences of noisy pseudo labels induced by low quality samples and immature models learned by inadequate training in early tasks. Based on the DUAPL, more reliable supervision signals can be estimated to prevent the model evolution from forgetting attributes seen in previous tasks. For standard evaluations of MCL-IL methods, two benchmarks on IncPAR, termed RAP-CL and PETA-CL, are constructed by re-organizing public human attribute datasets. Extensive experiments have been performed on these benchmarks to compare the proposed method with multiple baselines. The superior performance in terms of both recognition accuracies and forgetting ratios demonstrate the effectiveness of the proposed DUAPL for IncPAR.
Sharing Mode and Construction of Cloud based on University Education Resources Data

In recent years, with the rapid development of economy, China has been giving priority to education and increasing investment in education. In the context of educational informatization, the co construction and sharing of digital educational resources has become a key link in the development of educational informatization, and has been attracting much attention. This paper mainly studies the sharing mode and construction of cloud based on university education resources data. This paper uses the cloud platform network to build an education cloud platform system, and based on this system to build an education cloud sharing teaching mode. From the perspective of teaching process, the whole mode is divided into three parts: preliminary preparation, teaching activities, learning evaluation. This paper tests the education cloud platform, and the test results show that its performance meets the use needs.
Challenges brought by and in response to algorithms: the perspective of Chinas Anti-Monopoly Law

This chapter discusses challenges brought by and in responses to algorithms from the perspective of Mainland China’s anti-monopoly law. By April 2022, the Chinese competition authority has gained some experience in analysing algorithms in two abuse of dominance cases. Since 2018, big data ‘shashu’ has become a buzz phrase in China, which may constitute price discrimination, a type of abuse of dominance. In addition to price discrimination, algorithms might also facilitate collusion. Chinese competition authorities that generally practiced discrimination and collusion in the past may provide some references for the future anti-monopoly regulation of algorithmic price discrimination and algorithmic collusion. Relevant legislative studies have explicitly listed algorithmic collusion as a form of anti-competitive agreement. The existing Chinese anti-monopoly law does not provide the legal basis for imposing comprehensive and effective remedies. Some challenges also exist in supervising the implementation of the proposed remedies, such as requiring algorithms to be more transparent. Thus, we put forward several proposals accordingly.
Synopsis on Cyberethics Behaviour: A Literature Review

This paper provides an overview of the empirical literature on cyberethics issues within an academic environment, specifically young adults’ behaviours in using cyber technology. While digital media is a part of the institutional and societal drive for informational inclusion and knowledge development, the ethical practices and behaviours among the users of cyber technology have raised questions on users’ awareness and understanding of the implications of ethical violations in cyberspace. Using technology might provide significant theoretical paradigms in understanding how digital media adoption and diffusion, driven by information technology, can vary globally. The study reviews the literature on the emergence of cyber technology ethics, individual characteristics, awareness level, challenges to undergraduates’ cyberethics behaviour, and the central role policy plays in strengthening or promoting ethical conduct in cyberspace. This paper provides current information for awareness of CE, teaching and research on information ethics and related domains.
Physical Activity Monitoring for Older Adults through IoT and Wearable Devices: Leveraging Data Fusion Techniques

The emergence of low-cost individual sensing devices has facilitated the application of data fusion methods to yield insights useful for score-level, rank-level, or hybrid-level fusion. Intelligent tools for fusion processing, such as fuzzy methods and optimization algorithms, may be used to the deluge of raw data generated by these devices. The use of numerous sensors allows for multi-levelhybrid-level fusion, and the combination of several models for intelligent systems allows for fusion system design optimized for score improvement. Multimedia data fusion applications and machine learning methods can be used to accomplish data fusion in cloud settings. For older people in independent living conditions, a physical activity assessment framework (PAAF) that uses deep learning models for fusion to identify activity and evaluate progress based on the spectral domain of each window is needed. This study highlights the significance of data fusion in outlining the needs for IoT devices in networked computers for distant patient monitoring. In order to provide for the health of the elderly without compromising their comfort or freedom of choice, we need a seniors network based on the Internet of Things and wearable health technology. The sensors' functionality was investigated by analyzing data gathered from the environment and the organisms within it. The proposed PAAF-IoT architecture has many layers, each one connected to a different device, with the most important part being the integration of data from all of them to classify types of physical activity. Cloud services geographically close to the customer are used to process the resulting mountain of data, reducing end-to-end delay and facilitating prompt responses from healthcare professionals. Data fusion in healthcare and remote patient monitoring are demonstrated through the deployment of an app that allows doctors to remotely administer prescriptions and maintain track of patients' medical histories.
Perbandingan Algoritma K-Means dan Fuzzy C-Means untuk Clustering Citra Daun Melon

Melon plants are plants that are susceptible to disease, both diseases caused by viruses and those caused by bacteria. One part of the plant that can be affected by the disease is the leaves. Leaves on diseased plants generally change color which will then affect other leaves and inhibit the development and growth of these plants. This study aims to classify melon plant diseases from melon leaf images. The data used in this study are 160 images of melon leaves which will be grouped into several groups from the healthy group to the unhealthy group. The method used is the Clustering method, namely: K-Means algorithm and Fuzzy C-Means algorithm. Clustering results using K-Means and Fuzzy C-Means can be compared to get the best clustering results. The comparison results show the Fuzzy C-Means Clustering method with a validation value of 0.8359 and the K-Means Clustering method with a validation value of 0.5793. The final result shows that the Fuzzy C-Means Clustering method is better than the K-Means Clustering method because the validation value is close to 1.
Mental Representations as The Students’ Academic Success Predictor

The analysis is aimed at defining peculiarities of mental representations in homogeneous cluster groups, represented by the students characterized by different (high, average or low) academic progress levels. Deductive and correlation design of the research is defined here as a general method. Statistic design: the research is held taking into account a mixed method, including several qualitative and quantitative techniques of data processing and interpreting; more specifically cluster analysis (used for relatively homogeneous groups defining), multiple regression analysis (used to determine the impact of mental representations characteristics on academic success) as well as Kruskal-Wallis rank sum test. The average value of the regression model significance reflects the veracity of the research results. The attained statistic results prove that mental representations have peculiar impact on students’ academic success and there is an effect of the students’ academic activity progress splitting. On the one hand, the research helps to identify cluster groups of students characterized by success exteriorization indices growth and academic success indices reduction. On the other hand, non-linear impact of mental representations characteristics on students’ academic success was identified (the effect of inversion); i.e. some mental representations indices are highly developed, but the quality of education is low and individual resource is hard to implement in everyday life. Academic progress index, high level of studying and professional competences acquirement orientation as well as the activity factor are defined as measures of academic success mental representations splitting in groups. These results and conclusions prove the scientific fact that mental representations have an influence on the students’ academic success. This influence is multidimensional and vague. These facts offer a challenge of the research in considering highly-effective psychotechnologies for developing mental representations in general and students’ academic success in particular.
Penerapan Metode Teorema Bayes Dalam Mendiagnosa Penyakit Tubercolosis

In human life, health is the most valuable asset. a healthy body and soul gives happiness to everyone. But sometimes circumstances make human health decline. One of the causes of declining human health is due to viruses or bacteria that spread without people knowing it. One of the infectious diseases is tuberculosis (TB). Tuberculosis is a disease that occurs due to an infection caused by a bacterium called Mycobacterium tuberculosis. TB is a disease that spreads very easily, namely by air. The patient will spread germs through the air if the patient coughs or sneezes. Death is not uncommon because those with symptoms are afraid to go to the hospital for medical check-ups because they are hampered by a lack of funds. This situation is very concerning. Because of these circumstances, technological developments can be used as an alternative solution in replacing or assisting in diagnosing diseases experienced by people based on their symptoms. One of the technologies that develop about diagnosing disease is by using an expert system. An expert system is a system created by experts by developing technology based on science. In expert systems, their use must be combined with a method so that they can work properly and optimally. In this study the method used is the Bayes Theorem method. The Bayes Theorem method was developed by a priest named Thomas Bayes. The pastor comes from England who is a Presbyterian pastor. The Rev. Thomas Bayes developed Bayes' Theorem in 1763 and was perfected and redeveloped by an expert named Laplace. Based on the results of the above process, information was obtained that 87% of patients who consulted had pulmonary tuberculosis
Multi Kelas Speaker Recognition Menggunakan Deep Learning dengan CN-Celeb Dataset

Speaker recognition has been widely applied in various fields of human life such as Siri from Apple, Cortana from Microsoft, and Voice Assistant by Google. One of the problems when creating speaker recognition is related to the dataset used for the modeling process. The dataset used for creating the speaker recognition model is mostly data that cannot represent real-world conditions. The result is when implemented in the real-world conditions are not optimal. This study develops a speaker recognition model using deep learning (LSTM) with the CN-Celeb dataset. The CN-Celeb dataset is data taken directly from the real world so there is a lot of noise. The hope of using this dataset is that it can represent real world conditions. Model development uses 2 stacked LSTM for multi-class speaker recognition tasks. In addition, this study performs tuning hyperparameters with a grid search method to obtain the most optimal model configuration. The results showed that the EER value of the LSTM model was 10.13% better than the reference baseline paper of 15.52%. In addition, when compared with other studies that also used the CN-Celeb dataset but using different models, it was found that the LSTM model had promising results. From the results of study that has been carried out and also compared with other people's research, it was found that the LSTM model gave promising performance. The LSTM model is compared with the x-vectors, PLDA, TDNN, and transformers models
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Double Anonymous • Conference submission management system: Morressier • Number of submissions received: 138 • Number of submissions sent for review: 97 • Number of submissions accepted: 49 • Acceptance Rate (Submissions Accepted / Submissions Received × 100): 35.5% • Average number of reviews per paper: 3 • Total number of reviewers involved: 10 • Contact person for queries: Name: Komariah, PhD. Email: komariah@staff.uns.ac.id Affiliation: Sebelas Maret University
Learning Writing Skill During Covid-19 Pandemic through E-Learning in Indonesian English Classroom

The objectives of this research were to find out the kinds of E-learning used during the learning writing skill in the Fourth Semester of IAIN Samarinda and to find out the students’ perception of English Department Students in the Fourth Semester of IAIN Samarinda in learning writing skill through E-learning. The design of this research was quantitative research. The researcher analyzed the data by using rate percentage and mean score. The instrument used in this research was a questionnaire. The researcher used a close-ended questionnaire to gather the data. The researcher used “Yes/No” questions for 11 questions and “5-Likert Scales” for seven questions. In this research, the questionnaire’s link was distributed via WhatsApp group. The population and sample of this research were English Department Students in the Fourth Semester of IAIN Samarinda. There were 94 students in this research. Based on the results of this research, the researcher found that the most used kind of E-learning utilized by English Department Students in the Fourth Semester of IAIN Samarinda in learning writing skills during the COVID-19 Pandemic was Mobile Learning. The data showed that in Mobile Learning, 94 students, or 100% used smartphones, 81 students, and 86,2% used laptops. Based on the findings, the mean of students’ perception was 23,11. It can be concluded that students’ perception of learning writing skills through E-learning was categorized into a very bad category.