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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Chinnasamy, P. Elumalai, Anupriya Ayyasamy, Ramesh Kumar Kavya, S.P. Dhanasekaran, S. Kiran, Ajmeera MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India VIT - AP University School of Computer Science and Engineering India Faculty of Information and Communication Technology Kampar Malaysia KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Deemed to Be University Kalasalingam Academy of Research and Education Department of Information Technology Tamilnadu Srivilliputtur India

ISBN: (纸本)9798350348217

By exploiting wireless operators, contemporary bookcrossing allows people to share books conveniently and hastens the spread of knowledge. Nevertheless, the widespread use of smartphone bookcrossing has been seriously hampered by the lack of tracking, resulting in missing and disappearance of textbooks, additionally, it leads to organizational inefficiency. Hence, we suggest using the emerging blockchain technology to exploit the advantages of its immutability and encrypted smart contract in order to overcome these difficulties. In this research, we propose a BookChain, a blockchain-based intra-campus book-sharing system that is verifiable and effective. BookChain securely records all of the book's interacting entities on blockchain because every consumer could retrace the book's renting activity, hence lowering the risk of a book being misplaced. Additionally, BookChain promotes the use of smart contracts to streamline the distribution of books with very little human interference, maximizing productivity. The experimental findings demonstrate the efficiency and affordability of the suggested system even with a large number of concurrent participants. © 2023 IEEE.

关键词： Smart contract

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Enhance Multi-domain Sentiment Analysis of Review Texts through Prompting Strategies 5

Enhance Multi-domain Sentiment Analysis of Review Texts thro...

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5th International Conference on High Performance Big data and Intelligent Systems, HDIS 2023

作者： Wang, Yajing Luo, Zongwei Hong Kong Baptist University BNU-HKBU United International College Faculty of science and technology Department of computer science Hongkong Zhuhai China BNU-UIC Institute of AI and Future Networks Artificial Intelligence and Data Science Research Hub Beijing Normal University BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798350394160

Large Language Models (LLMs) have made significant strides in both scientific research and practical applications. Existing studies have demonstrated the state-of- the-art (SOTA) performance of LLMs in various natural language processing tasks. However, the question of how to further enhance LLMs' performance in specific task using prompting strategies remains a pivotal concern. This paper explores the enhancement of LLMs' performance in sentiment analysis through the application of prompting strategies. We formulate the process of prompting for sentiment analysis tasks and introduce two novel strategies tailored for sentiment analysis: RolePlaying (RP) prompting and Chain-of-thought (CoT) prompting. Specifically, we also propose the RP-CoT prompting strategy which is a combination of RP prompting and CoT prompting. We conduct comparative experiments on three distinct domain datasets to evaluate the effectiveness of the proposed sentiment analysis strategies. The results demonstrate that the adoption of the proposed prompting strategies leads to a increasing enhancement in sentiment analysis accuracy. Further, the CoT prompting strategy exhibits a notable impact on implicit sentiment analysis, with the RP-CoT prompting strategy delivering the most superior performance among all strategies. ©2023 IEEE.

关键词： Sentiment analysis

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Mobile Application Implementation as Agriculture 4.0 Strategy for Sugarcane Yield Prediction 9

Mobile Application Implementation as Agriculture 4.0 Strateg...

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9th International HCI and UX Conference in Indonesia, CHIuXiD 2023

作者： Mawandha, Hangger Gahara Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institut Pertanian Stiper Faculty of Agricultural Technology Yogyakarta55282 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta 11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta 11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta 11480 Indonesia

ISBN: (纸本)9798350304084

Information engineering strategies play a significant and transformative role in shaping the landscape of digital agriculture. It drives innovation and efficiency in modern farming practices, with a specific focus on enhancing the value of agricultural commodities, such as sugarcane. The application of mobile technology plays a crucial part in achieving increased sugarcane productivity. Mobile applications, equipped with advanced developer features, offer rapid and user-friendly access to vital information. Traditional methods of estimating sugarcane production relied on manual data recording on paper, followed by data transfer to computer systems, typically managed by sugar factory junior plant officers. This conventional approach presents several inherent weaknesses, including time and effort intensiveness during data recording and entry. Additionally, the potential for errors in calculation and data input is a concern. The storage capacity for paper-based documents is finite, and farmers are often unable to autonomously assess their production potential. The objective of this research is to confront these obstacles by creating a sugarcane production application for Android. This application functions as an information engineering approach focused on forecasting sugarcane yields for plantation owners and their assistants. The development procedure adhered to the systematic waterfall method, following the principles of the Software Development Life Cycle (SDLC) model. data collection was carried out through observations, while interviews with junior plant officers provided valuable insights into sugarcane estimation techniques. Analysis involved the synthesis of observational and interview data to inform the design of the application's interface and algorithmic system. The resulting application significantly simplifies the process of estimating production potential for farmers, enabling them to access sugarcane productivity data during harvest. Consequently, sugar

关键词： Forecasting

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Incivility and Contentiousness Spillover between COVID-19 and Climate science Engagement∗

arXiv

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arXiv 2025年

作者： Narimanzadeh, Hasti Badie-Modiri, Arash Smirnova, Iuliia Chen, Ted Hsuan Yun Department of Computer Science Aalto University Finland Department of Network and Data Science Central European University Austria Faculty of Social Sciences University of Helsinki Finland Department of Environmental Science and Policy George Mason University United States

Affective polarization and its accompanying cleavage-based sorting drives incivility and contentiousness around climate change and other science-related issues. Looking at the COVID-19 period, we study cross-domain spillover of incivility and contentiousness in public engagements with climate change and climate science on Twitter and Reddit. We find strong evidence of the signatures of affective polarization surrounding COVID-19 spilling into the climate change domain. Across different social media systems, COVID-19 content is associated with incivility and contentiousness in climate discussions. These patterns of increased antagonism were responsive to pandemic events that made the link between science and public policy more salient. We also show that the observed spillover activated along pre-pandemic political cleavages, specifically anti-internationalist populist beliefs, that linked climate policy opposition to vaccine hesitancy. Our findings highlight the dangers of entrenched cross-domain polarization manifesting as spillover of antagonistic behavior. © 2025, CC BY.

关键词： COVID-19

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Graph Compression for Adjacency-Matrix Multiplication

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SN computer science 2022年第3期3卷 193页

作者： Francisco, Alexandre P. Gagie, Travis Köppl, Dominik Ladra, Susana Navarro, Gonzalo INESC-ID / IST Universidade de Lisboa Lisbon Portugal Faculty of Computer Science Dalhousie University Halifax NS Canada M & D Data Science Center Tokyo Medical and Dental University Tokyo Japan CITIC Universidade da Coruña Coruña Spain Millennium Institute for Foundational Research on Data Santiago Chile Department of Computer Science University of Chile Santiago Chile

Computing the product of the (binary) adjacency matrix of a large graph with a real-valued vector is an important operation that lies at the heart of various graph analysis tasks, such as computing PageRank. In this paper, we show that some well-known webgraph and social graph compression formats are computation-friendly, in the sense that they allow boosting the computation. We focus on the compressed representations of (a) Boldi and Vigna and (b) Hernández and Navarro, and show that the product computation can be conducted in time proportional to the compressed graph size. Our experimental results show speedups of at least 2 on graphs that were compressed at least 5 times with respect to the original. © 2022, The Author(s).

关键词： Bicliques data compression Matrix multiplication Webgraph

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Time series for rail passengers flow prediction: a survey

Time series for rail passengers flow prediction: a survey

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Maciej Szymkowski Włodzimierz Woźniak Bartosz Jura Faculty of Computer Science Białystok University of Technology Białystok Poland Computer Science Research Group Artificial Intelligence Development division Łukasiewicz Research Network – Poznań Institute of Technology Poznań Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

Nowadays, rails are one of the most popular forms of transportation. It is related to the relatively lower costs of the tickets in comparison to airplanes or even costs connected with travelling by car. It needs to be pointed out that this type of transportation has also one important issue that must be addressed. It is connected to the selection of the appropriate train type for all interested passengers. It is extremely hard especially when passengers’ pileups are observed. In this work, we would like to propose an idea to consume time-series data for prediction of the passengers’ flow in one, selected train station that is Łόdź, Poland. Diversified algorithms that can be used directly in these data were presented and our own ideas are given. We would like to also propose our own pipeline built on the basis of all analyzed approaches.

关键词： Rails Surveys Costs Time series analysis Pipelines Transportation Predictive models

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HaWANet: Road Scene Understanding with Multi-modal Sensor data Using Height-Width-Driven Attention network 23rd

HaWANet: Road Scene Understanding with Multi-modal Sensor D...

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23rd International Conference of the Italian Association for Artificial Intelligence, AIxIA 2024

作者： Chatterjee, Soumick Xu, Jiahua Kuzhipathalil, Adarsh Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Hertie Institute for Clinical Brain Research Tuebingen Germany XenomatiX NV Leuven Belgium

ISBN: (纸本)9783031806063

In recent years, the field of autonomous vehicles and driverless technology has seen remarkable advancements, driven by contributions from mainstream automotive manufacturers and open-source projects. This research aims to develop a pipeline for road scene understanding through semantic segmentation. The proposed pipeline utilises a multi-modal segmentation model, incorporating greyscale images and point cloud data from Xenolidar, specifically designed to capture the structural priors of highway road scenes. The fusion of input modalities and the design of an encoder-decoder architecture with a novel attention scheme called HaWANet is introduced, which focuses on the height and width contextual information to improve the accuracy of road segmentation, are the primary aspects explored for the proposed model. The output of the encoder is a two-dimensional point cloud, which effectively represents the road’s planar nature, and is crucial for improving the accuracy of road segmentation, particularly in edge cases, addressing current challenges in autonomous driving research. This research, aimed at addressing the segmentation problem for multimodal sensor data, has presented significant performance improvement over single-modal approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Artificial Intelligence models for prediction of passengers’ flow in rail transport

Artificial Intelligence models for prediction of passengers...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

Nowadays, one of the most popular and ecological forms of passengers’ transport is rail. In the last couple of years (especially after COVID-19 pandemic) many more passengers selected this type of transportation rather than cars or even airplanes. It is connected with much higher travel comfort as well as the possibility of effective work on the train. However, due to the higher popularity, one extremely important problem was observed. It is connected with the passengers’ flow that can lead even to traffic. Sometimes it is hard to select an appropriate type of train to effectively serve all the interested people. In this work, we would like to propose not only the survey under the latest AI-based models for prediction of passengers’ pileups but also provide information about possible ideas that will be tested on the real data, collected from one of the trains stations in Poland.

关键词： Rails Surveys COVID-19 Pandemics Biological system modeling Transportation Predictive models

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A Novel Framework for Detection of Digital Face Video Manipulation using Deep Learning 3

A Novel Framework for Detection of Digital Face Video Manipu...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Chandra, Sanskriti Saxena, Shivam Kumar, Santosh Chaube, Mithilesh Kumar Srinivas, K.G. Alsamhi, Saeed Hamood Curry, Edward Saif, Abdu IIIT-Naya Raipur Department of Data Science and Artificial Intelligence Chhattisgarh India IIIT-Naya Raipur Department of Computer Science and Engineering Chhattisgarh India IIIT-Naya Raipur Department of Mathematical Sciences Chhattisgarh India University of Galway Insight Sfi Research Centre for Data Analytics Galway Ireland Faculty of Engineering & Information Technology Taiz Yemen

ISBN: (纸本)9798350321487

Digital face manipulation and classification have recently attracted the attention of academia and industry worldwide. researchers have developed deep learning and computer vision techniques for detecting face manipulations, and it has become a challenging task to differentiate between authentic and manipulated face images manually. The challenge results in the decline of authenticity in digital media content. In this paper, we propose a framework for the classification of manipulating face images using the EfficientNet learning model. The proposed framework takes four digital facial forgeries: Face-Swap, Face-2-Face, DeepFakes, and neural textures. Multiple manipulation techniques are used to process manipulated faces, such as the Blaze-face tracking method, to determine the locations of the face images and pixel coordinates. The proposed framework is used first to identify the type of face manipulation and then to perform detection of the tampered regions in the face images. The proposed framework provided an automated benchmark that considers all four modification techniques in a realistic situation. The results show that the proposed framework outperforms existing approaches regarding accuracy and efficiency. Furthermore, the proposed framework is suitable for detecting digital face video manipulation in various applications, including forensics and security. © 2023 IEEE.

关键词： Textures

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Multi-Temporal Cloud Removal of Satellite Image for Surface Water Segmentation

Multi-Temporal Cloud Removal of Satellite Image for Surface ...

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International Conference on computer, Control, Informatics and Its Applications (IC3INA)

作者： Suhardiman Roni Salambue Andre Sihombing Ira Maryati Lia Sadita Aruni Yasmin Azizah Zul Indra Gita Sastria Department of Computer Science Universitas Riau Pekanbaru Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency (BRIN) Bandung Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

Satellite imagery utilization for water surface monitoring faces challenges in the presence of clouds that interfere with the segmentation process. This study aims to overcome the problem by implementing and developing the Multi-temporal Cloud Removal MCR method at the pre-processing stage. This research uses (MCR) approach to produce cloud-free satellite images. The difference from the current MCR method comes from using a red band in the cloud detection step, adding a dilation process on the cloud masking result, and the pixel mapping process. The best MCR results are obtained from cloud cover percentages below 20% with an NRMSE value of 0.156 and a PSNR of 29.815. In addition, this research compares the accuracy of the U-net water segmentation model between the original satellite image and the resulting MCR data. The model evaluation process uses binary cross-entropy loss, dice coefficient, and intersection over union (IoU) to check the model's performance. The model achieved a Dice coefficient of 0,842, IoU of 0.913 and a binary cross-entropy loss of 0,137 for MCR images. For images with cloud cover below 20%, the Dice coefficient and IoU slightly decreased to 0.819 and 0.892 (loss: 0.1563). As cloud cover increased to 20-40%, these values dropped to 0.687 and 0.812 (loss: 0.25) and further decreased to 0.546 and 0.706 (loss: 0.2656) for 40-60% cloud cover. The results demonstrate that the percentage of cloud cover in satellite images significantly influences the accuracy of surface water segmentation.

关键词： Digital control Image segmentation Accuracy data models Satellite images Informatics Surface treatment Monitoring Faces

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