Reviewerly/exHarmony huggingface dataset:
ANALISIS GEROMBOL PADA DATA DERET WAKTU PENDERITA COVID-19 PROVINSI JAWA BARAT

This study aims to apply clustering techniques to time series data. Time series models can be formed for all research data objects, so many research objects need to be grouped so that the resulting model becomes more efficient. The object used in this study was data on Covid-19 sufferers from 27 regencies and cities in West Java Province. All objects were analyzed by time series to produce 27 models. All objects' data patterns and models have many similarities, so clustering can be done. Clustering models using the Ward method and the Piccolo dissimilarity measure. The optimum cluster uses the Hartigan and Ball indices to obtain 3 clusters
Exploring Students’ Understanding Of English Tenses Through Writing

English grammar and Indonesian grammar are differentin some ways. One of differences can be seen in constructing sentences based on referring time . The difference of grammatical structure between English and Indonesian which will be learnt especially is tenses for verbal sentences because in verbal sentences the rewill be shown the transformation of verb which does not occur in Indonesian structure. Therefore the researchers using Report Text material in spesifically about students’ experience, then There are four students which will be observed by the researchers. the method- research using is the descriptive method or what is usually called the qualitative method.And qualitative data will submitted by observing a hand-writting from the students.
Improving Network Architectures for Semantic Segmentation with Transformers

In this paper, we present SST, a simple and efficient framework for semantic segmentation that combines a Transformer-based encoder with a context-fusion decoder. Our framework uses a Transformer-based encoder to output features at multiple scales. We design a new decoder structure based on context fusion, which utilizes contextual information to fuse multiple feature maps at each pixel location and improve the semantic segmentation accuracy. Additionally, we achieve competitive results on several standard semantic segmentation benchmarks.
Safe Robot Navigation in Indoor Healthcare Workspaces

Healthcare workspaces would greatly benefit from the employment of robotic assistants in both clinical and non-clinical tasks. However, despite their advantages, a major shortcoming for the deployment of robots limiting their widespread acceptance by the market is the fact that existing robotic solutions were originally designed for large industrial and warehouse spaces. These are characterized by structured spaces and predictable environments, where robots move along predefined paths and interaction with humans is typically not required. Herein, we examine state-of-the-art computer vision methods that enable robots to detect the presence and identify the type of dynamic obstacles inside their visual field and adapt their navigation accordingly. To achieve this goal, we trained our robots using contemporary deep learning methods (namely YOLO-You Only Look Once architecture and its variations) and obtained promising results in both human and robot detection. For that purpose, a newly constructed dataset consisting of robot images was used, complementing the well-known COCO dataset. Overall, the present study contributes towards the key objective of safe robot navigation in healthcare spaces and underpins the wider application of studies on Human-Robot Interaction in less structured environments.
A new Monte Carlo sampling method based on Gaussian Mixture Model for imbalanced data classification

&lt;abstract&gt;&lt;p&gt;Imbalanced data classification has been a major topic in the machine learning community. Different approaches can be taken to solve the issue in recent years, and researchers have given a lot of attention to data level techniques and algorithm level. However, existing methods often generate samples in specific regions without considering the complexity of imbalanced distributions. This can lead to learning models overemphasizing certain difficult factors in the minority data. In this paper, a Monte Carlo sampling algorithm based on Gaussian Mixture Model (MCS-GMM) is proposed. In MCS-GMM, we utilize the Gaussian mixed model to fit the distribution of the imbalanced data and apply the Monte Carlo algorithm to generate new data. Then, in order to reduce the impact of data overlap, the three sigma rule is used to divide data into four types, and the weight of each minority class instance based on its neighbor and probability density function. Based on experiments conducted on Knowledge Extraction based on Evolutionary Learning datasets, our method has been proven to be effective and outperforms existing approaches such as Synthetic Minority Over-sampling TEchnique.&lt;/p&gt;&lt;/abstract&gt;
Dynamic Graph Learning for Feature Projection

High-dimensional features have gained widespread usage in various research fields such as multimedia computing, data mining, pattern recognition, and machine learning. However, the presence of high-dimensional features often gives rise to the “curse of dimensionality" problem and places significant computational burdens on machine learning models. To alleviate these issues, dimensionality reduction techniques are employed to identify low-dimensional latent subspaces that retain the data similarities observed in the original high-dimensional space. Two common paradigms used for dimensionality reduction are feature selection and feature projection. Feature selection involves identifying a subset of the original features as low-dimensional representations by discarding irrelevant and noisy features. On the other hand, feature projection utilizes a specific transformation matrix to generate projected dimensions that preserve the intrinsic data characteristics. Based on their reliance on semantic labels, feature projection can be further categorized into two families: unsupervised and supervised feature projection.
Research Frontiers

In this book, we have delved into the realm of dynamic learning for dimension reduction and data clustering. Our exploration begins by highlighting the pressing need for a dynamic graph learning framework in the context of big data environments. We then delve into the challenges faced by dynamic graph learning when applied to dimension reduction and clustering tasks, including joint optimization, accurate modeling of data correlations with graphs, multi-view fusion, and out-of-sample extension.
Computer-aided diagnosis of polyp classification using scale invariant features and extreme gradient boosting

Analysis of colonoscopy images is an important diagnostic procedure in the identification of colorectal cancer. It has been observed that owing to advancements in technology, numerous machine-learning models now excel in the analysis of colorectal polyps classification. This work focused on developing a framework that can classify polyps using images during colonoscopy.First, the images were corrected by removing their spectral reflection. Second, feature pools were obtained by applying Radon transform (θ=45, 90, 135, and 180). From the Radon transform, fractal dimension was calculated as a feature vector combined with Zernike moment obtained from the Zernike features. Finally, Extreme Gradient Boosting (XGBoost) algorithm was applied for the classification and to compare it with state-of-the-art methods.The experimental results obtained with the proposed framework have been reported, cross-validated, and discussed. The proposed method gives a classification accuracy of 93% for light XGBoost and 92% for XGBoost.This study shows that by applying scale invariant features over a small dataset, XGBoost outperforms state-of-the-art methods when it comes to polyp classification.
Early Prediction of Breast Cancer Risks Using Convolutional Neural Network

Breast Cancer is one of the deadliest diseases in the world, among cancer categories, early detection is essential to better treatment outcomes and survival rates. Delay in diagnosis due to medical mistakes or incorrect interpretation of patient reports might have a negative effect on patient outcomes.Artificial intelligence (AI), a component of contemporary technology, has demonstrated encouraging potential for helping medical practitioners to increase the precision and effectiveness of cancer diagnosis. AI programs can be trained to examine several kinds of medical imaging data, including MRI and CT scans, and help spot anomalies or malignant cells. The study aims to establish a model for estimating the risk of breast cancer based on a combination of medical and behavior-related risk factors. The goal is to identify high-risk groups of patients who have undergone breast cancer surgery so that they can receive closer monitoring and more accurate screening.In this proposed work,we try to use CNN Model for classifying the risk of breast cancer.
The application of Artificial Intelligence (AI) in Mobile Learning (M-learning)

This paper study the application of artificial intelligence (AI) in mobile learning (m-learning). In the this report study we discuss the meaning of Artificial intelligent AI and Mobile Learning Materials, make them clear to people and students. then, explain the significance of the application of Artificial Intelligence in mobile learning materials. The use of “artificial intelligence” in mobile learning Materials a opportunity to breakdown the traditional borders of learning in the classroom to make students-instructors regarding teaching classes fixed to the future m-learning market. This report research introduce five problems in mobile learning.These problems make it necessary to apply artificial Intelligence in mobile learning materials. Furthermore, the development of mobile learning materials as part of ubiquitous learning in Open Learning Universities. Furthermore, how are students utilising Mobile Learning for open learning? Smartphone devices have evolved from a means of communication to tools for M-learning, open learning, work, socialisation, and entertainment as smart mobile technology has advanced and their affordability has increased. Mobile learning Materials are the most important thing for open learning students to make learning as simple as possible. Additionally, the impact of applying artificial intelligence to mobile learning materials in open learning faculty and students was investigated. Finally, the study discusses the possibility of creating Mobile-learning Materials for use in open learning. The data were gathered through the use of an E-survey administered by faculty, as well as interviews with students and a sample of each study group. The findings indicate that college students' perceptions of using mobile Learning Materials are improving, and they are willing to use mobile learning.
Experiences of Novice Design Facilitators in a Remote Participatory Workshop

In this work, we explore the perspectives of novice design facilitators, or early career designers, as they lead participatory design sessions in a Digital Wellbeing workshop. In our study, five undergraduate and recently graduated students acted as design facilitators in small groups with middle school students to design positive online social experiences. Through a pre-workshop training sessions, observations during the workshop, and individual post-workshop interviews, we describe facilitators’ experiences, challenges, and learnings from their first experience as design facilitators. We found that novice facilitators employed many strategies to overcome initial discomfort. In particular, they attempted to incite conversation through side channels and cold-calling, and leveraged improvisation strategies to deal with unexpected situations. They also relied on support structures to make in-the-moment decisions. Our findings suggest that the design facilitators found their experiences meaningful for both their personal growth and for advancing their career aspirations.
A Privacy-Aware Provably Secure Smart Card Authentication Protocol Based on Physically Unclonable Functions

For many industrial applications, the smart card is a necessary safety component in user authentication. Smart cards provided to the users are used in open and public places, making them susceptible to physical and cloning attacks. Thus, the opponent can break the authentication process without the smart card if the information is exposed. In addition, many existing authentication systems employ challenge-response pairs (CRPs) to identify users by creating large numbers of data on the server and spending much time looking for and comparing responses. To address these concerns, we propose a lightweight privacy-preserving authentication protocol in which the physically unclonable function is considered a necessary tool. The suggested technique avoids creating a significant number of CRPs on the server to identify users uniquely. Under formal security models, the proposed protocol is resistant to user impersonation attacks and session key disclosure attacks and achieves robust mutual authentication. Nonetheless, it is immune to other essential security vulnerabilities. Empirical performance analysis demonstrates its viability in comparison to prior works.
Task-Oriented Communication for Edge Video Analytics

With the development of artificial intelligence (AI) techniques and the increasing popularity of camera-equipped devices, many edge video analytics applications are emerging, calling for the deployment of computation-intensive AI models at the network edge. Edge inference is a promising solution to move computation-intensive workloads from low-end devices to a powerful edge server for video analytics, but device-server communications will remain a bottleneck due to limited bandwidth. This paper proposes a task-oriented communication framework for edge video analytics, where multiple devices collect the visual sensory data and transmit the informative features to an edge server for processing. To enable low-latency inference, this framework removes video redundancy in spatial and temporal domains and transmits minimal information that is essential for the downstream task, rather than reconstructing the videos on the edge server. Specifically, it extracts compact task-relevant features based on the deterministic information bottleneck (IB) principle, which characterizes a tradeoff between the informativeness of the features and the communication cost. As the features of consecutive frames are temporally correlated, we propose a temporal entropy model (TEM) to reduce the bitrate by taking the previous features as side information in feature encoding. To further improve the inference performance, we build a spatial-temporal fusion module on the server to integrate features of the current and previous frames for joint inference. Extensive experiments on video analytics tasks evidence that the proposed framework effectively encodes task-relevant information of video data and achieves a better rate-performance tradeoff than existing methods.
From Click To Cash Exploring The Latest Trends Fueling E-Commerce Success

E-Business, short for electronic business, indeed refers to commercial transactions and activities conducted through telecommunications, particularly over the Internet. It represents a fundamental shift in the way businesses operate and interact with customers. it has disrupted traditional business models and opened up new opportunities for businesses to reach a global audience and operate more efficiently. The acceptance of digital shopping has played a significant role in the growth of E-Business. The growth of E-Business is closely tied to advancements in telecommunications services, broadband internet access, and the proliferation of smartphones and other connected devices. These technologies have enabled businesses to reach customers more easily and efficiently. The country has a large and diverse consumer base, making it an attractive market for E-Businesses. The research makes use of secondary material, such as previously published studies papers, articles, journals, and reports
Towards analysis on textual inference at ASSIN-2 dataset

In this article, we conduct a preliminary analysis of different methods to address the Textual Entailment Recognition (RTE) task in Portuguese. We use the ASSIN-2 dataset as a benchmark to evaluate our models. Our work combines various textual representation approaches, including bag of words and word embeddings, with machine learning models. Additionally, we present a rule-based approach. Our highest performance was achieved by the BERTimbau-large model fine-tuned on ASSIN-2, which attained an F1 score of 0.89%, positioning it just 1% below the current state-of-the-art. Our ongoing experiment aims to combine our different approaches to leverage their full potential.
Appendix: Commonwealth laws on moral rights - August 2022

This comprehensive Research Handbook examines moral rights since their establishment in the 19th century and considers the roles they play in the 21st century in relation to the technological environment in which copyright exists. Drawing together rich perspectives on intellectual property law around the world, this Research Handbook provides new insights on the traditional issues of moral rights and analyses more recent challenges in copyright law, patent law, and trademark law.
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 : Single Anonymous • Conference submission management system : Morressier • Number of submissions received : 72 • Number of submissions sent for review : 40 • Number of submissions accepted : 39 • Acceptance Rate (Submissions Accepted / Submissions Received × 100) : 54.2 • Average number of reviews per paper : 2 • Total number of reviewers involved : 60 • Contact person for queries : Name : Josh Sheng Email : aoprconf@163.com Affiliation : Hubei Zhongke Institute of Geology and Environment Technology, China
НЕЙРОННИЙ МАШИННИЙ ПЕРЕКЛАД: ОСНОВНІ ПРИНЦИПИ, СИЛЬНІ ТА СЛАБКІ СТОРОНИ

Neural Machine translation (NMT) is a new and advanced method of machine translation based on neural networks. NMT systems use artificial intelligence to predict words in the process of translation. This method allows to achieve high efficiency and goo
MACHINE TRANSLATION METHODS AND SOME CURRENT TRENDS

This paper provides an overview of some major machine translation methods designed to speed up the rate of multilingual text translation. Machine translation is achieved by computer software transforming text from one language to another. At present, d
PSYCHOLOGICAL FACTORS OF WOMEN'S PARENTAL BURNOUT

The purpose of the article is to present results of the study of psychological factors of women`s parental burnout (PB), in particular it`s connection with features of parental attitude and nature of the family system. The study surveyed 120 women who had at least one (47.2%), two (37%) and three (11%) minor children living with them permanently. The age of mothers ranges from 18 to 48 years, and the age of children from 2 months to 17 years. The study of PB was performed using the following methods: Parental Burnout Assessment (M.Mikolajczak, I.Roskam), FACES 3 (D.Olson), Parental Attitudes Questionnaire (A. Varga, V. Stolin), Beck Depression Inventory (A.Beck), Rosenberg's Self-Esteem Scale (M.Rosenberg). The survey was conducted using the open platform Google Forms, where the questionnaire was posted. The link was distributed via the Internet, including the social network Facebook, between April, 7 and November, 1, 2021. The results of statistical analyzes showed that the number of women with the syndrome does not exceed 10%, ie the total number of Ukrainian women with burnout is twice as high as in Europe and the United States (Roskam et al., 2018). 24% have an average level, almost 12% of respondents have a low level and more than 56% of surveyed women are characterized by a lack of PB. It is statistically established that there are significant links between parental burnout among women and the features of parental attitude towards the child, in particular, the dimensions of parental attitudes such as "symbiosis" and "infantilization" were found to be significant predictors of burnout according to regression analysis. The merger of mother and child, the absence of psychological boundaries and the promotion of infantilization of the child are defined as the factors of family upbringing, which are predictors of parental burnout of women. The results also show that the closer the relationship between family members, the lower the burnout level, as a woman in such a family receives much more support, help and understanding. The age of the mother does not play a significant role in the formation of the syndrome, but the younger the child, the higher the level of burn out in the mother, which corresponds to the results of similar cross-cultural studies (Roskam et al., 2021).
PSYCHOLOGICAL FACTORS OF SPIRITUAL DEVELOPMENT AND SELF-REALIZATION OF PSYCHOLOGY STUDENTS

The purpose of the article is to analyze the psychological factors of spiritual development and self-realization of future psychologists at different stages of their educational and professional socialization. An empirical study was conducted with the participation of students (217 people), aged 16 to 25, who studied psychology at higher educational institutions in Ukraine. The following research methods were used in the study: "The Spiritual Potential of the Individual" by Pomytkin E., "Life meaning orientations" by Leont’ev D., "Test of Emotional Intelligence" by Hall N., "Psychological Well-Being Scale" Riff K., "Personal Differential" (as adapted in The St. Petersburg Bekhterev Psychoneurological Research Institute), "The questionnaire of diagnostics of practical psychologist’s personality orientation type" by Danilova T., "The questionnaire for the study of the process of spiritual self-realization of learning youth" Pomytkin E.; mathematical methods for processing empirical data using the IBM SPSS Statistics 20 package (comparison of means, linear regression analysis). It was revealed that 1.4% of the respondents have a high level of spiritual potential, 32.3% - a medium high level, 65.4% - a medium low level, and 0.9% - a low level. 20.7% of respondents have a high level of self-realization of the spiritual "Self", 46.5% - a medium level, 32.7% - a low level. Based on the regression analysis of the results of an empirical study, it has been proved that there are statistically significant individual psychological factors that have a noticeable impact on the manifestations of the spiritual potential of future psychologists (in 40% of cases due to the influence of variables: emotional intelligence, meaningfulness of life, humanistic type of professional orientation, personality, personality growth, egocentric type of professional orientation and self-acceptance) and their capaсity for spiritual self-realization while studying at higher educational institutions (in 20% of cases: life orientation, cognition and self-knowledge, environmental and aesthetic values). The article substantiates the need to focus on the formation and development of the humanistic type of students’ professional orientation, to create conditions for reducing the influence of egocentric attitudes during their familiarization with professional activity, to pay attention to the development of the young people’s emotional intelligence, meaningfulness of life and positive self-acceptance, when organizing the processes of educational and professional socialization of students in the educational setting.