Reviewerly/exHarmony huggingface dataset:
The Research of Smart Planning and Method in Chengyu Metropolitan Area from the Perspective “Great Health”

As the all-around gateway area of "The Belt and Road", Chengyu Metropolitan Area should constantly improve the construction of various functional Spaces and promote the integration of industry and city.With the improvement of people's material living standards in recent years, as well as the impact of the epidemic, food safety, obesity and other problems, the concept of "Great Health" has been gradually introduced into people's lives.The research on the intelligent planning and utilization of the integration of urban space in Chengyu Metropolitan Area from the perspective of "Great Health" can help promote and deepen the research theory related to the integration of industry and city, and help promote the improvement of the functions of the metropolitan area of Chengdu and Chongqing.Based on the spatial intelligent planning and management of Chengyu Metropolitan Area from the perspective of "Great Health", this paper hopes to make some discussions and thoughts on the spatial construction strategies and methods of Chengyu Metropolitan Area.
GENDER DIFFERENCES IN VERBAL AND NONVERBAL AGGRESSION

Gender differences permeate every aspect of human personality and appearance, and dictate how men and women should act, think and behave. Gender embodies a pattern of relations that evolves over time to define male and female, masculinity and femininity, concurrently structuring and regulating people’s relation to society. Gender decides what is expected, permitted and valued in a woman or a man in a given context. This paper discusses male and female aggression in political debates with a special focus on the recent debate held on 9th September 2021 in Canada. The analyses carried out through the methods of content, discourse and pragmalinguistic analyses, show that aggression is frequently categorized as a social behavior, and thus falls within a set of criteria depending on the roles that people occupy. In the world today, there is an increase in the use of communicative aggression, both verbal and non-verbal in the political arena. Aggression is widely applied in political communication where the main purpose is to fight the opponent and get the attention of the audience and voters. Male politicians are expected to be verbally and non-verbally more aggressive than women while female politicians perceived as less aggressive and considered to be better performers. However, this assumption remains an area of contention.
Application of Data Mining and Imputation Algorithms for Missing Value Handling: A Study Case Car Evaluation Dataset

Data mining is a data analysis process using software to find certain patterns or rules in a large amount of data, which is expected to provide knowledge to support decisions. However, missing value in data mining often leads to a loss of information. The purpose of this study is to improve the performance of data classification with missing values, ​​precisely and accurately. The test method is carried out using the Car Evaluation dataset from the UCI Machine Learning Repository. RStudio and RapidMiner tools were used for testing the algorithm. This study will result in a data analysis of the tested parameters to measure the performance of the algorithm. Using test variations: performance at C5.0, C4.5, and k-NN at 0% missing rate, performance at C5.0, C4.5, and k-NN at 5–50% missing rate, performance at C5.0 + k-NNI, C4.5 + k-NNI, and k-NN + k-NNI classifier at 5–50% missing rate, and performance at C5.0 + CMI, C4.5 + CMI, and k-NN + CMI classifier at 5–50% missing rate, The results show that C5.0 with k-NNI produces better classification accuracy than other tested imputation and classification algorithms. For example, with 35% of the dataset missing, this method obtains 93.40% validation accuracy and 92% test accuracy. C5.0 with k-NNI also offers fast processing times compared with other methods.
Spatio-temporal influence of solar activity on global air temperature

Previous studies on the impact and influence of solar activity on terrestrial weather has yielded contradictory results in literatures.Present study presents, on a global scale, the correlation between surface air temperature and two solar activity indices (Sunspot number, 'Rz', and solar radio flux at 10.7, 'F10.7' ) at different time scales during solar cycle 23.Global air temperature has higher correlation values of ±0.8 with F10.7 compared to Rz (±0.3).Our results showed hemispheric delineation of the correlation between air temperature and solar activity with negative correlation in the southern hemisphere and positive correlation in the northern hemisphere.At the onset of the solar cycle, this hemispheric delineation pattern was prevalent, however, an inverse hemispheric delineation was observed at the recession of the solar cycle.
System aspects of the implementation of experiments to improve the reliability of software and information technologies

The article discusses the system aspects of the implementation of experiments to improve the reliability of software and information technologies. As research methods, system analysis, prototyping of software and information technologies, analysis of business processes, multiversion methodology for developing software tools for information and control systems are used. It is shown that at present, based on open computer networks, it is possible to build reliable systems from universal components. It is noted that the software and information technologies of corporate integrated structures, organized according to this principle, must have an adequate level of fault tolerance within the allocated budgets. Therefore, system experiments in software development are becoming more common in the implementation of such software and information technologies. The basic system aspects and factors that developers and experimenters should take into account when conducting experiments with research prototypes are formulated
The Ecosystem of Shared Value Model for the Community Development Program in Saguling Hydroelectric Power Station area (West Java)

Purpose: This study aims to integrate CSR programs and stakeholders in the Saguling Reservoir area (West Java) by proposing recommendations in the form of an ecosystem of shared value model that can be implemented by all stakeholders in related ecosystems to restore the function of the Citarum River and increase regional competitiveness, to increase the standard of living of the people in the area. Theoretical framework: The theoretical framework used is The Ecosystem of Shared Value as a New Model of CSR, Pentahelix Collaboration, and Nature-Based Solution Approach. Design/methodology/approach: This study uses a qualitative research approach with an action research design. There are two types of data used in this study, namely Primary Data (Group Discussion, Observation, and Depth Interview) and Secondary Data (Literature and Document Review). This study also uses the data analysis technique of the Miles and Huberman model which consists of three stages that must be carried out, namely data reduction, data display, and data verification. Findings: The results show that the CSR approach in the Saguling Hydropower area is quite good, but the shared value generated is still low, especially for the Saguling Reservoir and the hydropower itself, because the current community development programs are only aimed at the community. The community development program in the Saguling Reservoir has been running for a long time, but these programs have not been integrated, so an ecosystem of shared value model is needed that can integrate several of these programs. The mapping of community development programs along the Citarum river basin (West Java) from upstream to downstream (Citarum River ring 1 area) must be carried out through intense collaboration and coordination with stakeholders. Research, Practical &amp; Social implications: The practical implication is that this ecosystem of shared value model can help stakeholders carry out a common agenda, namely "Restoring the Functions of the Citarum River". In addition, the application of the model, which is also accompanied by the ongoing collaboration between stakeholders, can transform the Saguling Reservoir area into a tourist village area as a form of support for the development agenda in West Java and Indonesia. Originality/value: The research carried out is very significant in the study of corporate social responsibility, especially in the implementation of the ecosystem of shared value model, the output of which is a change towards a tourism village area. This research is also research oriented to social change.
Activities of National Institute of Informatics in Japan

research-article Free Access Share on Activities of National Institute of Informatics in Japan Authors: Masaru Kitsuregawa University of Tokyo, Japan University of Tokyo, JapanView Profile , Shigeo Urushidani National Institute of Informatics, Tokyo, Japan National Institute of Informatics, Tokyo, JapanView Profile , Kazutsuna Yamaji National Institute of Informatics, Tokyo, Japan National Institute of Informatics, Tokyo, JapanView Profile , Hiroki Takakura National Institute of Informatics, Tokyo, Japan National Institute of Informatics, Tokyo, JapanView Profile , Ichiro Hasuo National Institute of Informatics, Tokyo, Japan National Institute of Informatics, Tokyo, JapanView Profile , Imari Sato National Institute of Informatics, Tokyo, Japan National Institute of Informatics, Tokyo, JapanView Profile , Fuyuki Ishikawa National Institute of Informatics, Tokyo, Japan National Institute of Informatics, Tokyo, JapanView Profile , Isao Echizen National Institute of Informatics, Tokyo, Japan National Institute of Informatics, Tokyo, JapanView Profile , Kensaku Mori Nagoya University, and Director of NII Research Center for Medical Bigdata, Tokyo, Japan Nagoya University, and Director of NII Research Center for Medical Bigdata, Tokyo, JapanView Profile Authors Info & Claims Communications of the ACMVolume 66Issue 7July 2023pp 58–63https://doi.org/10.1145/3589736Published:22 June 2023Publication History 0citation1,922DownloadsMetricsTotal Citations0Total Downloads1,922Last 12 Months1,922Last 6 weeks1,554 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Desktop Based Personal Assistance Using Python Programming Language

Chat bots, conversational interfaces, and personal assistants all reimagine how people engage with computers.A virtual personal assistant allows users to ask questions in the same way they would talk to a person, and some basic actions, like opening apps, reading news, taking notes, etc., may be performed with just a voice command.Personal assistants like Google Assistant, Alexa, and Siri operate using textto-speech and speech recognition.For instance, those who are unable to walk utilize the Internet of Things function to operate and maintain household items.So, we created a voice assistant that help users in carrying out day to day task with help of internet.Also, it does certain activities when the internet is absent.So, a voice assistant that will be practical for users and comparable to other voice assistants that are now popular has been created.
Multi-Attribute Decision Making Method for Car Selection by Individuals

Purchasing management is most essential in todays competitive world, especially the most useful and essential purchase of the car by individuals.With the development of management and economics, real-world decision-making problems are becoming diversified and complicated to an increasing extent, especially within a changeable and unpredictable environment.Multi-attribute decision making is a decision-making technique that explicitly evaluates numerous contradictory criteria.There are many characteristics possessed by car like engine displacement, mileage in city and highway, max power, comfort, attractiveness, cost etc.and a customer has to choose the best car among several brand cars.In this paper, TOPSIS are used to find best car to be purchased.TOPSIS is a wellknown multi-criteria decision-making process.The distance between two Pythagorean fuzzy numbers are utilised to create the model using the spherical distance measure.To construct a ranking order of alternatives and determine the best one, the revised index approach is utilised.Finally, demonstrate proposed method to solve a car selection problems from different brands with different attributes for customer.In addition, it shows comparative data from the relative closeness and updated index methods.
Трансформации геофизических полей и геолого-геофизические признаки золото-кварцевого оруденения на примере Средне-Урканской перспективной площади (Амурская область)

In this paper, the results of the analysis of geophysical fields and their transformants (statistical, filtration) of gold mineralization areas are presented and some of their features are established. A set of geophysical studies and methodological processing techniques that can solve the problems of forecasting gold mineralization are presented. Based on the interpretation of a set of geophysical data and available geological materials, prospecting geological and geophysical signs of raremetal low-sulfide gold-quartz mineralization in the hierarchical sequence ore field - ore zone are offered.
USE OF PROJECT ACTIVITY IN THE PROFESSIONAL TRAINING OF FUTURE TEACHERS

The article reveals the problem of preparing future teachers for the organization of project activities of pupils/students, the readiness of the teacher to teach pupils/students project activities as a component of his professional and pedagogical work, which allows to ensure competent, professional and creative implementation of it in the educational process of educational institutions. In order to prepare the future teacher for a new type of pedagogical activity, it is necessary to build a training system that is focused on becoming ready to use project activities. A research-experimental program was developed on the use of students' project work and on the basis of this formation of pedagogical knowledge. Stages: 1 stage. Formation of students' positive attitude towards the implementation of project activities. II stage. Involvement of students in the process of solving creative project problems. III stage. Control by the experimenter over the impact of project work on solving problems on the growth of the level of student activation and the growth of pedagogical knowledge. Control by the experimenter over the impact of project work on solving problems on the growth of the level of student activation and the growth of pedagogical knowledge. The essence of the project activity is to stimulate the interest of both students and teachers in certain problems, which leads to the possession of a certain amount of knowledge, and through the project technology, which involves the solution of one or a whole set of problems, allows them to be practically applied, going from theory to practice , maintaining their appropriate balance at each stage of training. Project activity in the educational process can be considered as a highly motivated independent, searching and cognitive labor activity, focused on the creation of an educational project, which is carried out under the guidance of a teacher. Project activities, unlike traditional activities, contribute to the development and self-development of students as subjects of culture, creators and designers of their lives. Project activity of students ensures the priority of socially significant knowledge and skills, which most closely corresponds to the paradigm of person-oriented education, because it is these knowledge and skills that allow young people to be successfully realized in professional activities throughout their lives.
Value-Aware Active Learning

In many practical applications, machine learning models are embedded into a pipeline involving a human actor that decides whether to trust the machine prediction or take a default route (e.g., classify the example herself). Selective classifiers have the option to abstain from making a prediction on an example they do not feel confident about. Recently, the notion of the value of a machine learning model has been introduced as a way to jointly consider the benefit of a correct prediction, the cost of an error, and that of abstaining. In this paper, we study how active learning of selective classifiers is affected by the focus on value. We show that the performance of the state-of-the-art active learning strategies drops significantly when we evaluate them based on value rather than accuracy. Finally, we propose a novel value-aware active learning strategy that outperforms the state-of-the-art ones when the cost of incorrect predictions substantially outweighs that of abstaining.
Human Factors in Interactive Online Machine Learning

Interactive machine learning (ML) adds a human-in-the-loop aspect to a ML system. Even though the input from human users to the system is a central part of the concept, the uncertainty caused by the human feedback is often not considered in interactive ML. The assumption that the human user is expected to always provide correct feedback, typically does not hold in real-world scenarios. This is especially important for when the cognitive workload of the human is high, for instance in online learning from streaming data where there are time constraints for providing the feedback. We present experiments of interactive online ML with human participants, and compare the results to simulated experiments where humans are always correct. We found combining the two interactive learning paradigms, active learning and machine teaching, resulted in better performance compared to machine teaching alone. The results also showed an increased discrepancy between the experiments with human participants and the simulated experiments when the cognitive workload was increased. The findings suggest the importance of taking uncertainty caused by human factors into consideration in interactive ML, especially in situations which requires a high cognitive workload for the human.
A Comprehensive Performance Analysis of Stream Processing with Kafka in Cloud Native Deployments for IoT Use-cases

The constant growth of the number of Internet of Things devices drives a huge increase in data that needs to be analyzed, at times in real time. Multiple platforms are available for delivering such data to analytics engines that can perform various operations on the data with low processing latency. These platforms can find their home in cloud native environments where high availability and scaling to the actual workload can be easily achieved. While the deployment environment is elastic, clusters still need to be adequately dimensioned to accommodate the components of the platforms even under high load.In this paper, we provide an analysis in this regard: we discuss key performance indicators of the popular Kafka message bus and the related Kafka Streams processing engine. Namely, we analyze latency, throughput, CPU and memory resource footprint aspects of these services under varying load and processing tasks that appear in Internet of Things applications. We find subsecond processing latency and linear but heavily task-dependent scaling behavior in the other performance indicators’ case.
Detection of COVID-19-related Malicious Domain Names Based on Feature Fusion

Since its outbreak, coronavirus disease (COVID-19) has significantly impacted people's lives worldwide, with a large number of COVID-19-related cyberattacks. Cybercriminals have created many malicious websites and spam emails named after COVID-19-related terms to engage in malicious activities, taking advantage of the rise in online activity that people experience due to COVID-19. In this paper, we propose a feature fusion-based COVID-19-related malicious domain name detection method to detect COVID-19-related malicious domain names and block the malicious behaviors of attackers in time. Firstly, the WHOIS data of domain names in publicly available sources are obtained. Secondly, the global and local features are respectively extracted with Transformer network and one-dimension convolutional neural network (1DCNN) from COVID-19 domain name strings and WHOIS registration data. Finally, both the extracted global features and local features are fused and fed into the classifier to determine whether the domain name is malicious. The experimental results show that the proposed detection method has the accuracy rate of 99.24% and the precision rate of 99.42% on the self-built dataset. Both of these indicators are over 95% on the CIC-Bell-DNS 2021 dataset, which proves our method is efficient in detecting COVID-19-related malicious domain names and it has an excellent performance in detecting other malicious domain names.
A Simplified Technique Of Design For Double Density Wavelets With Enhanced Denoising APPLICATIONS

Denoising Images is a crucial step in noise removal in any data or image communication problem. Recently two image denoising techniques were presented that depends on bivariate analysis. These two techniques were called Double Density Discrete Wavelet Transform (DD DWT) and Double Density Dual Tree Complex Wavelet Transform (DD CWT). They relayed on the decomposition of noisy images with either DD CWT or DD DWT decompositions. This decomposition was after the bivariate based shrinkage technique was applied to exploit the wavelet parent and children correlation for better denoising performance. In this paper we present a novel filter design technique for a DD DWT structure for denoising applications. It composed of 3 dimensional cascaded orthogonal sections. This proposed 3-channel Double Density Wavelet decomposition structure is simple and is fast in terms of convergence. It overcomes most problems for other filter design techniques of being slowly iterative and relays on randomness in parameter optimization. The proposed filter design DD-DWT structure guarantees the Perfect Reconstruction PR and the Alias Cancellation AC conditions and is suitable for VLSI iterative designs. Illustrative examples for the usage of the proposed DD-DWT based design in different image denoising applications are shown in comparison with other denoising structures from recent literature.
Swarm Learning for Oncology Research

The medical industry is one of the biggest victims of unavailability and lack of variety of data. Despite sizable medical data being available across the world, privacy norms prevent it from being shared. Adding variety to data is impossible without an efficient, privacy-preserving approach to enable data use from multiple sources. This lack of variety in data leads to extremely biased machine learning models. Cancer data, like data of many other diseases, is multivariate and demands personalized treatment protocols, showing importance of the variety that data from multiple sources can bring in. The research work tries to shed light on how decentralized learning can be employed to improve and strengthen clinical research by enabling researchers to analyze diverse insights from heterogeneous data sources. Along with discussing cutting-edge Deep learning architectures for Cancer diagnosis, we show experiments with “Swarm Learning” i.e. HPE’s decentralized machine-learning platform that facilitates collaboration between data owners. This experiment focuses on Breast Cancer tumor diagnosis. As a part of the study, we examined around 1300 histochemical images of breast cancer tumors as well as follow-up records for multiple cases using various deep learning techniques. The experimental results show that decentralized learning can compete with traditional machine learning and make way for more robust methods in not just cancer research, but the healthcare domain as a whole.