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Recent Advances in science and engineering Technology (ICRASET), International Conference on

作者： K. Sashi Rekha V.M. Sivagami R. Usharani T Amutha S. Pushparani Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Information Technology Sri Venkateswara College of Engineering Chennai Tamil Nadu India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu India Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The cloud services which are now the most common data transmission and endanger organizations’ confidential information, it’s more and more visible that security of any data should be a main priority for companies. MFA, which consists of adopting multi factor approach to authentication and thereby provides multiple types of verification thus ensures effective security, is leveraged to ensure resource access. This paper proposes a full-fledged way of implementing MFA for cloud services in order to strengthen the security given and manage the constatoing risks. The proposed methodology consists of main stages like requirements determining, technology reviewing, risks assessment, technology design, integration with cloud services, users training, pilot deployment and testing, implementation and configuration, monitoring and maintenance as well as constant improvement. Through a systemic approach, the organizations can be equipped with the capability to personalize the MFA solutions according to their security goals, to bring the cloud environments on board, as well as to ensure that the organizations comply with the security standards as set by various governing bodies. This research adds a grain of salt to the development of cloud security good practices through offering a disciplined approach to MFA implementation, which allows partners to harden their cloud assets and protect against growing cyber threats.

关键词： Training Cloud computing Multi-factor authentication Standards organizations Companies Regulation Maintenance Security Protection Monitoring

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Active Geospatial Search for Efficient Tenant Eviction Outreach 39

Active Geospatial Search for Efficient Tenant Eviction Outre...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Sarkar, Anindya DiChristofano, Alex Das, Sanmay Fowler, Patrick J. Jacobs, Nathan Vorobeychik, Yevgeniy Department of Computer Science and Engineering Washington University St. Louis United States Department of Computer Science and Engineering George Mason University United States Division of Computational & Data Sciences Washington University St. Louis United States Brown School at Washington University St. Louis United States

ISBN: (纸本)157735897X

Tenant evictions threaten housing stability and are a major concern for many cities. An open question concerns whether data-driven methods enhance outreach programs that target at-risk tenants to mitigate their risk of eviction. We propose a novel active geospatial search (AGS) modeling framework for this problem. AGS integrates property-level information in a search policy that identifies a sequence of rental units to canvas to both determine their eviction risk and provide support if needed. We propose a hierarchical reinforcement learning approach to learn a search policy for AGS that scales to large urban areas containing thousands of parcels, balancing exploration and exploitation and accounting for travel costs and a budget constraint. Crucially, the search policy adapts online to newly discovered information about evictions. Evaluation using eviction data for a large urban area demonstrates that the proposed framework and algorithmic approach are considerably more effective at sequentially identifying eviction cases than baseline methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Reinforcement learning

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Time Series Analysis and Forecasting of Air Quality Index of Dhaka City of Bangladesh

Time Series Analysis and Forecasting of Air Quality Index of...

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AI IoT Congress (AIIoT), World

作者： Sheikh Rahmatulla Sakib Kamarun Nahar Sara Md. Tahmid Hossain Rasel Md. Masudul Islam Asif Md. Aynul Hasan Nahid Md. Saifur Rahman M. F. Mridha Ashraful Islam Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science American International University-Bangladesh Dhaka Bangladesh Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh

In Dhaka, the capital city of Bangladesh, various sources including vehicle emissions, industrial activities, brick kilns, building sites, and open rubbish burning contribute to the air pollution problem. To assess the air quality, the Air Quality Index (AQI) is utilized, which categorizes air quality based on pollutant concentration. In this study, we have built ARIMA, Auto-ARIMA, SARIMAX, and VAR models to predict the air quality of Dhaka. Unlike previous studies, we have utilized hourly air pollutants factors such as PM 2.5 , PM 10 , SO 2 , CO, NO 2 , and O 3 to forecast air quality. Our novel approach enables us to predict the monthly and weekly air quality of Dhaka city. Our analysis reveals that the SARIMAX model, which takes into account seasonal patterns, trends, and external factors, is the most accurate in predicting Dhaka city’s air quality. The model’s prediction performance is assessed using statistical indicators such as mean absolute percentage error and root mean square error. The study highlights that the SARIMAX model could aid policymakers in evaluating the efficacy of air pollution control measures.

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"It Is Hard to Remove from My Eye": Design Makeup Residue Visualization System for Chinese Traditional Opera (Xiqu) Performers

arXiv

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arXiv 2024年

作者： Xiong, Zeyu Fu, Shihan Zhu, Yanying Zhu, Chenqing Ma, Xiaojuan Fan, Mingming Computational Media and Arts Thrust The Hong Kong University of Science and Technology Guangzhou China Data Science and Analysis Thrust The Hong Kong University of Science and Technology Guangzhou China Internet of Things Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Division of Integrative Systems and Design Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Chinese traditional opera (Xiqu) performers often experience skin problems due to the long-term use of heavy-metal-laden face paints. To explore the current skincare challenges encountered by Xiqu performers, we conducted an online survey (N=136) and semi-structured interviews (N=15) as a formative study. We found that incomplete makeup removal is the leading cause of human-induced skin problems, especially the difficulty in removing eye makeup. Therefore, we proposed EyeVis, a prototype that can visualize the residual eye makeup and record the time make-up was worn by Xiqu performers. We conducted a 7-day deployment study (N=12) to evaluate EyeVis. Results indicate that EyeVis helps to increase Xiqu performers' awareness about removing makeup, as well as boosting their confidence and security in skincare. Overall, this work also provides implications for studying the work of people who wear makeup on a daily basis, and helps to promote and preserve the intangible cultural heritage of practitioners. © 2024, CC BY-NC-ND.

关键词： computer vision

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Conceptual structure of federated learning research field

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Procedia computer science 2022年 214卷 1374-1381页

作者： A. Velez-Estevez P. Ducange I.J. Perez M.J. Cobo Department of Computer Science and Engineering University of Cadiz Cádiz Spain Department of Information Engineering University of Pisa Italy Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Granada Spain

Nowadays there are a great amount of data that can be used to train artificial intelligent systems for classification, or prediction purposes. Although there are tons of publicly available data, there are also very valuable data that is private, and therefore, it can not be shared without breaking the data protections laws. For example, hospital data has great value, but it involves persons, so we must try to preserve their privacy rights. Furthermore, although it could be interesting to train a model with the data of only one entity (i.e. a hospital), it could have more value to train the model with the data of several entities. But, since the data of each entity might not be shared, it is not possible to train a global model. In that sense, Federated Learning has emerged as a research field that deals with the training of complex models, without the necessity to share data, and therefore, keeping the data private. In this contribution, we present a global conceptual analysis based on co-words networks of the Federated Learning research field. To do that, the field was delimited using an advance query in Web of science. The corpus contain a total of 2444 documents. As the main result, it should be highlighted that the Federated Learning research field is focused on six main global areas: telecommunications, privacy and security, computer architecture and data modeling, machine learning, and applications.

关键词： Federated Learning bibliometric analysis science mapping analysis co-words SciMAT

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Development and Comparative Analysis of Event Relation Extraction Methods 2

Development and Comparative Analysis of Event Relation Extra...

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2nd IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2022

作者： Ni, Shangyuan Ng, Taishing Xue, Ling Zhang, Jiawen Data Science New York University Shanghai Shanghai China Paul G. Allen School of Computer Science & Engineering University of Washington Seattle United States School of Mathematics and Statistics Henan University of Science and Technology Luoyang China Applied & Computational Mathematical Sciences University of Washington Seattle United States

ISBN: (数字)9781728181158

ISBN: (纸本)9781728181158

Event relation extraction is an important research direction in the field of information extraction. Compared with named entity recognition, entity relation extraction, and event extraction. Event relation extraction can extract common-sense knowledge that is more in line with human intuition from unstructured texts and can be organized together in the form of logical interaction. In this paper, the current event relation extraction works are reviewed in detail. This paper is organized according to different types of event relations. In addition, this paper also introduces two kinds of event relation data sets. Finally, we discuss the challenges and prospects of event relation extraction. © 2022 IEEE.

关键词： Deep learning

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Pre-trained language models in Spanish for health insurance coverage 5

Pre-trained language models in Spanish for health insurance ...

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5th Workshop on Clinical Natural Language Processing, ClinicalNLP 2023. held at ACL 2023

作者： Aracena, Claudio Rodríguez, Nicolás Rocco, Victor Dunstan, Jocelyn Faculty of Physical and Mathematical Sciences University of Chile Chile Millennium Institute Foundational Research on Data Chile Chilean Safety Association Chile Department of Computer Science Catholic University of Chile Chile Institute for Mathematical and Computational Engineering Catholic University of Chile Chile Center for Mathematical Modeling University of Chile Chile

ISBN: (纸本)9781959429883

The field of clinical natural language processing (NLP) can extract useful information from clinical text. Since 2017, the NLP field has shifted towards using pre-trained language models (PLMs), improving performance in several tasks. Most of the research in this field has focused on English text, but there are some available PLMs in Spanish. In this work, we use clinical PLMs to analyze text from admission and medical reports in Spanish for an insurance and health provider to give a probability of no coverage in a labor insurance process. Our results show that fine-tuning a PLM pretrained with the provider's data leads to better results, but this process is time-consuming and computationally expensive. At least for this task, fine-tuning publicly available clinical PLM leads to comparable results to a custom PLM, but in less time and with fewer resources. Analyzing large volumes of insurance requests is burdensome for employers, and models can ease this task by pre-classifying reports that are likely not to have coverage. Our approach of entirely using clinical-related text improves the current models while reinforcing the idea of clinical support systems that simplify human labor but do not replace it. To our knowledge, the clinical corpus collected for this study is the largest one reported for the Spanish language. © 2023 Association for computational Linguistics.

关键词： Health insurance

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Development of Predictive Mathematical Model for Millimeter Wave Degradation in Sandstorm Regions

Development of Predictive Mathematical Model for Millimeter ...

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IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE)

作者： Esmail Abuhdima Abdulmajid Mrebit Ricsheia Barr Avery Basden Jian Liu Amirhossein Nazeri Gurcan Comert Chin-Tser Huang Pierluigi Pisu Engineering Technology ECPI University Newport News VA USA Engineering and Computer Science Benedict College Columbia SC USA Computer Science and Engineering University of South Carolina Columbia SC USA International Center for Automotive Clemson University Greenville SC USA Computational Data Science and Engineering North Carolina A&T State University Greensboro NC USA International Center for Automotive Clemson University Greenville USA

ISBN: (数字)9798350351118

ISBN: (纸本)9798350351125

The advent of millimeter wave (mm-Wave) technology in modern communication systems, including 5G networks, has brought about unprecedented data transmission speeds and bandwidths. However, environmental factors highly affect mm-Wave signals, particularly in regions susceptible to dust and sand storms. Dust storms, characterized by high concentrations of suspended particles, lead to significant signal attenuation and degradation during the absorbed and scattered incident wave. This attenuation poses challenges to the reliability and performance of mm-Wave communication systems. The previous research used Mie theory to compute the specific attenuation due to dusty storms because it provides a complete analytical solution to Maxwell’s equations compared to other analytical and numerical methods. However, the Mie scattering model lacks accuracy due to consideration of only the amplitude of the attenuation factor with respect to the dust and sand environment. This paper presents the development of predictive mathematical models designed to estimate mm-Wave signal degradation in dust and sand storm conditions. The models integrate key physical parameters such as dust particle size distribution, storm intensity, signal frequency, and atmospheric *** predictive model demonstrates a significant accuracy in estimating signal attenuation by considering the phase shift in signal by introducing complex attenuation factor. We mathematically demonstrated that dust and sandstorms can cause mm-Wave signal attenuation but also cause a significant signal phase shift. This complex attenuation factor provides valuable insights for network engineers to design and optimize mm-Wave communi cation systems in dust-prone environments. Comparative analysis with existing models underscores the proposed models’ enhanced predictive capability and flexibility in adapting to diverse dust storm *** research outcomes contribute to the ongoing efforts to improve mm-Wave communicat

关键词： Degradation Adaptation models Storms Millimeter wave technology Predictive models Attenuation Mathematical models Environmental factors Numerical models Millimeter wave communication

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An Enhanced Energy Efficient Clustering for Biomedical Wireless Sensor Networks

An Enhanced Energy Efficient Clustering for Biomedical Wirel...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： G. Vennira Selvi Chandrakala Hl V. Sheeja Kumari S. Ponmaniraj K. Anbazhagan S. Nanthini School of Information Science Presidency University Rajanukunte Yelahanka Bengaluru India School of Computer Science and Engineering Presidency University Rajanukunte Yelahanka Bengaluru India Department of Computational Intelligence Saveetha School of Engineering SIMATS Chennai Department of Data Science Saveetha School of Engineering SIMATS Chennai

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

Biomedical Wireless Sensor Networks (BWSN) is major technique for Health Care applications for providing quality of life with minimum cost. To enforce the quality of medical care provided to the citizen, such networks should be integrated with existing network infrastructures. The requirement of quality of service should be concentrated on development stages and are monitored for the duration of network operation. The life time of the network is most relevant feature for ensuring the quality of medical care requirements. In order to increases the network lifetime, maintaining the remaining energy of each sensor node with reliable communication link throughout the network. An energy aware clustering for biomedical wireless sensor network (EACBWSN) is proposed for increasing the network lifetime by minimizing the energy consumption among sensor nodes and provides a reliable communication link between sensor nodes. The proposed algorithm efficiently prolong the lifetime of the network when compared with existing algorithm.

关键词： Wireless communication Wireless sensor networks Clustering algorithms Medical services Quality of service Energy efficiency Reliability

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Logging Multi-Component Supply Chain Production in Blockchain 2021

Logging Multi-Component Supply Chain Production in Blockchai...

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4th International Conference on computers in Management and Business, ICCMB 2021

作者： Madhwal, Yash Chistiakov, Ivan Yanovich, Yury Faculty of Computer Science National Research University Higher School of Economics Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Inst. of Sci. and Technol. and Lab. of Data Mining and Predictive Modelling Inst. for Info. Transmiss. Prob. Russia

ISBN: (纸本)9781450388610

The supply chain is a thriving industry where numerous parties have different interests. Subsequently, the immense volume of data produced is difficult to audit. Some information can be lost or intentionally distorted in the process. Blockchain as an open, public, borderless, neutral, and censorship-resistant architecture can significantly complement supply chains. A new supply chain architecture is proposed in this work, where the tokenized directed acyclic hypergraph (DAG) represents real-world production processes. An anti-aerosol respirator manufacturing is used as an illustration example. By tokenizing all parts of multi-component products, supply chain data is automatically timestamped and secured. Moreover, the DAG design allows one to trace-back all the elements of the final product to their origin. Blockchain can formally audit the entire supply chain without the need to go from place to place. A single incorruptible operations log creates an enabling environment for an unbiased reputation system to emerge. © 2021 ACM.

关键词： Smart contract

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