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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Rafif, Musyaffa Ayman Sanjaya Indrajaya, Gede Al-Ghazi, Muhammad Kent Johnny, Johnny Sagala, Noviyanti T M Bina Nusantara University School of Computer Sciences Data Science Program Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

The people in the capital city of the Republic of Indonesia, Jakarta, have been living for years with air pollution. Air quality has been a concern for a long time due to the health risks it has on people, especially those at risk. Using classification algorithms, we would like to implement data mining for the air quality index and make predictions from our capital city, DKI Jakarta. This research aims to compare Decision Tree and Support Vector Machine in predicting Air Quality Index in DKI Jakarta in 2020. The dataset was collected from the Jakarta Open data Web, and the results of this research highlight the effectiveness of data mining techniques in predicting air quality index levels in Jakarta and emphasize the importance of selecting the appropriate classification algorithm for data mining applications. This research shows that SVM, compared to the Decision tree, has a better result with an accuracy score of 87.86%. On the other hand, Decision Tree has a score of 90.56%. © 2023 IEEE.

关键词： Decision trees

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data Mining and Bioinformatics Approaches to Elucidate Bacterial Communities in the Extreme Environment

Data Mining and Bioinformatics Approaches to Elucidate Bacte...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Nirwantono, Rudi Asadi, Faisal Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Bacteria are microscopic organisms that can be found in many environments. They are abundant and have many roles in our life. Studying bacteria is essential so that we can identify the bacteria that are needed for many industrial applications. However, the main problem is that majority of the bacteria are unculturable, hampering the exploration of bacteria from different environments. Metagenomics approach which employs Next Generation Sequencing technology could help study bacteria by utilizing 16S rRNA marker gene. This study aims to demonstrate data mining and bioinformatics approaches to analyze 16S rRNA sequencing data. The raw sequencing data of 16S rRNA was collected from biological database. Then, the data were trimmed, denoised, and clustered to generate Amplicon Sequence Variants (ASVs). Diversity (alpha and beta) and taxonomic analyses were then conducted to elucidate the bacterial diversity and taxonomic profile of ASVs. The results of this work showed that Shannon and PD indices in China's hot spring were higher than Singapore and the USA. Furthermore, a significant difference was observed in the PD index. The unweighted UniFrac distance also showed there was a significant difference of the bacterial communities between the three locations. In addition, the taxonomic investigation unveiled prevalent bacterial groups in the ecosystem, namely Proteobacteria, Chloroflexi, Cyanobacteria, and Crenarchaeota. The research outcomes have the potential to serve as a foundational resource for subsequent bacterial metagenomic research, particularly the hot spring environment. © 2023 IEEE.

关键词： Bacteria

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Evaluating Cryptocurrency Market Risk on the Blockchain: An Empirical Study Using the ARMA-GARCH-VaR Model

IEEE Open Journal of the Computer Society

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IEEE Open Journal of the computer Society 2024年 5卷 83-94页

作者： Huang, Yongrong Wang, Huiqing Chen, Zhide Feng, Chen Zhu, Kexin Yang, Xu Yang, Wencheng Fujian Normal University School of Computer and Cyberspace Security Fuzhou350117 China Department of Information Engineering Fuzhou Polytechnic Fuzhou350108 China Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan College of Computer and Data Science Minjiang University Fuzhou350108 China School of Mathematics Physics and Computing University of Southern Queensland ToowoombaQLD4350 Australia

Cryptocurrency, a novel digital asset within the blockchain technology ecosystem, has recently garnered significant attention in the investment world. Despite its growing popularity, the inherent volatility and instability of cryptocurrency investments necessitate a thorough risk evaluation. This study utilizes the Autoregressive Moving Average (ARMA) model combined with the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model to analyze the volatility of three major cryptocurrencies-Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)-over a period from January 1, 2017, to October 29, 2022. The dataset comprises daily closing prices, offering a comprehensive view of the market's fluctuations. Our analysis revealed that the value-at-risk (VaR) curves for these cryptocurrencies demonstrate significant volatility, encompassing a broad spectrum of returns. The overall risk profile is relatively high, with ETH exhibiting the highest risk, followed by BTC and BNB. The ARMA-GARCH-VaR model has proven effective in quantifying and assessing the market risks associated with cryptocurrencies, providing valuable insights for investors and policymakers in navigating the complex landscape of digital assets. © 2020 IEEE.

关键词： Investments

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Outliers are Real: Detecting VLM-Generated Images via One-Class Classification 24th

Outliers are Real: Detecting VLM-Generated Images via One-C...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Liu, Baoping Liu, Bo Ding, Ming Australian Artificial Intelligence Institute School of Computer Science University of Technology Sydney Ultimo Australia Data61 CSIRO Eveleigh Australia

ISBN: (纸本)9789819615247

Recent generative artificial intelligence (AI) advancements have enabled high-quality images with cross-modal controllability, e.g., text-to-image generation. Vision-language models (VLMs) play an indispensable role in such cross-modal generation tasks by aligning the representations of conceptions in different modalities, which inspires us to detect VLM-generated images with traces and fingerprints of VLMs. Therefore, in this paper, we propose a one-class classification (OOC) framework, namely Outliers are Real (OaReal) to recognize VLM-generated images. In sight of the rarity of VLMs due to the high cost of training, we regard VLM-generated images as normal samples and explore their distributions in CLIP latent space. During the testing phase, samples far from the explored distribution (outliers) are regarded as real images, while samples in the explored distribution are classified as VLM-generated samples. Compared with binary classifiers, the OOC design of our OaReal significantly relieves training from learning complex patterns with diverse real and fake images. Furthermore, we propose a hard sample aware contrastive loss (Harsacol) that takes edited samples into consideration and improves the inclusiveness of the explored space for both VLM-synthetic and VLM-edited samples. We conducted comprehensive experiments to test the performance of our OaReal. The results suggested the superiority of our methods in terms of both effectiveness and efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Visual languages

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Multi-Granularity History and Entity Similarity Learning for Temporal Knowledge Graph Reasoning

Multi-Granularity History and Entity Similarity Learning for...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Mingcong, Shi Zhu, Chunjiang Zhang, Detian Wen, Shiting Li, Qing School of Computer Science and Technology Soochow University Suzhou China Department of Computer Science University of North Carolina Greensboro United States School of Computer and Data Engineering NingboTech University Ningbo China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798891761643

Temporal Knowledge Graph (TKG) reasoning, aiming to predict future unknown facts based on historical information, has attracted considerable attention due to its great practical value. Insight into history is the key to predict the future. However, most existing TKG reasoning models singly capture repetitive history, ignoring the entity's multi-hop neighbour history which can provide valuable background knowledge for TKG reasoning. In this paper, we propose Multi-Granularity History and Entity Similarity Learning (MGESL) model for Temporal Knowledge Graph Reasoning, which models historical information from both coarse-grained and fine-grained history. Since similar entities tend to exhibit similar behavioural patterns, we also design a hypergraph convolution aggregator to capture the similarity between entities. Furthermore, we introduce a more realistic setting for the TKG reasoning, where candidate entities are already known at the timestamp to be predicted. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed model. © 2024 Association for Computational Linguistics.

关键词： Knowledge graph

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A multilayer network diffusion-based model for reviewer recommendation

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Chinese Physics B 2024年第3期33卷 700-717页

作者：黄羿炜徐舒琪蔡世民吕琳媛 Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 611731China Yangtze Delta Region Institute (Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Dataspace Hefei Comprehensive National Science CenterHefei 230088China Big Data Research Center University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Cyber Science and Technology University of Science and Technology of ChinaHefei 230026China

With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy *** systems are powerful tools developed in computer science and information science to deal with this ***,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform *** this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual *** network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among *** results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking *** work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.

关键词： reviewer recommendation multilayer network network diffusion model recommender systems complex networks

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Comparison of Various Computational Models for Accurate Stroke Prediction and Interpretation Using LIME and SHAP Kernels 1

Comparison of Various Computational Models for Accurate Stro...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Arun, Chalana B Anusha, M. Kodipalli, Ashwini Gargi, N. Harish, L. Devamane, Shridhar B Global Academy of Technology Dept. of Artificial Intelligence and Data Science Bangalore India School of Computing Amrita Vishwavidhyapeetam Department of Computer Science Mysuru India

ISBN: (纸本)9798331518943

Stroke is one dangerous illness that kills people. Stroke is so called because of the way it strikes people down. Across the globe, it is among the main causes of mortality and disability. It requires early identification and precise measures to be taken in order to prevent stroke and further damages to the brain and other parts of body. Our study investigates the use of machine learning techniques that includes Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Free Classifier, Random Forest Classifier, Naive Bares and multiple Boosting Classifiers. The 'Healthcare dataset Stroke data' is the dataset used, which is preprocessed and then trained upon by Ml. models. For Random Forest Classifier, the maximum accuracy observed was 94.91%, and upon Hyper parameter Tuning- there was rise in accuracy to 95.64%, Evaluation criteria like F1-score, precision, and recall provides additional evidence for the effectiveness of these models: Further in order to explain how each feature contributes differently to each prediction we have made use of two popular techniques for XAT, LIME and SHAP. LIVE and SHIP provides local, interpretable explanations and global explanations respectively. © 2024 IEEE.

关键词： Random forests

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One-Shot Sequential Federated Learning for Non-IID data by Enhancing Local Model Diversity 24

One-Shot Sequential Federated Learning for Non-IID Data by E...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Naibo Deng, Yuchen Feng, Wenjie Fan, Shichen Yin, Jianwei Ng, See-Kiong Institute of Data Science National University of Singapore Singapore Singapore School of Mathematics and Statistics Changchun University of Technology Changchun China School of Computer Science and Technology Xidian University Xi'an China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798400706868

Traditional federated learning mainly focuses on parallel settings (PFL), which can suffer significant communication and computation costs. In contrast, one-shot and sequential federated learning (SFL) have emerged as innovative paradigms to alleviate these costs. However, the issue of non-IID (Independent and Identically Distributed) data persists as a significant challenge in one-shot and SFL settings, exacerbated by the restricted communication between clients. In this paper, we improve the one-shot sequential federated learning for non-IID data by proposing a local model diversity-enhancing strategy. Specifically, to leverage the potential of local model diversity for improving model performance, we introduce a local model pool for each client that comprises diverse models generated during local training, and propose two distance measurements to further enhance the model diversity and mitigate the effect of non-IID data. Consequently, our proposed framework can improve the global model performance while maintaining low communication costs. Extensive experiments demonstrate that our method exhibits superior performance to existing one-shot PFL methods and achieves better accuracy compared with state-of-the-art one-shot SFL methods on both label-skew and domain-shift tasks (e.g., 6%+ accuracy improvement on the CIFAR-10 dataset). Our code and supplementary are available online: https://***/NaiboWang/FedELMY. © 2024 ACM.

关键词： Federated learning

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Exploring nonintrusive measurements of spatio-temporal portrait of microservices

Exploring nonintrusive measurements of spatio-temporal portr...

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作者： Zeng, Tao Xu, Zichen Wu, Dan Li, Xiaoling Liu, Biyong Hu, Haichuan Tan, Shuang Tan, Yusong Xu, Chenren Stewart, Christopher Zhou, Qihe Cao, Ye School of Mathematics and Computer Sciences Nanchang University Nanchang China College of Computer National University of Defense Technology Hunan China School of Computer Science Peking University Beijing China Computer Science and Engineering The Ohio State University ColumbusOH United States Faculty of Data Science City University of Macau China

As cloud native technology advances, the scale and complexity of applications built on microservice architecture continue to expand, leading to increasingly intricate differences between software within the same application. Microservice applications, offering high flexibility, are deployed in data centers as black boxes from the users' perspective, leaving them with no insight into the orchestration of cloud service providers. Consequently, users face challenges in promptly recognizing performance imbalances within their deployed applications. Meanwhile, cloud service providers may cut costs by offering a mix of qualified and unqualified services, potentially deceiving users. To enhance the understanding of microservice application organization, we propose a non-intrusive measurement framework, termed NMPI. NMPI facilitates rapid identification of microservice application defects, offering insights into cloud services and detecting fraudulent behavior in microservice-based applications. We model microservice applications using a queue analysis-based approach and filter the dominant frequency components of average response time signals by employing k-means on the fast fourier transform (FFT). Our model constructs a library of performance portraits for various software, with these portraits resembling human fingerprints that carry and mark the software's internal information. Utilizing a two-tier microservices-based application incorporating a database as a case study allows us to demonstrate the effectiveness of NMPI. Our experimental results show that NMPI can produce differentiable profiles of data service performance portraits across a diverse and extensive range of workloads, enabling the identification of software types and the analysis of performance conditions. © 2023 John Wiley & Sons, Ltd.

关键词： Fast Fourier transforms

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What's the Real: A Novel Design Philosophy for Robust AI-Synthesized Voice Detection 24

What's the Real: A Novel Design Philosophy for Robust AI-Syn...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Hai, Xuan Liu, Xin Tan, Yuan Liu, Gang Li, Song Niu, Weina Zhou, Rui Zhou, Xiaokang School of Information Science and Engineering Lanzhou University Lanzhou China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Business Data Science Kansai University Osaka Japan

ISBN: (纸本)9798400706868

Voice is one of the most widely used media for information transmission in human society. While high-quality synthetic voices are extensively utilized in various applications, they pose significant risks to content security and trust building. Numerous studies have concentrated on AI-synthesized voice detection to mitigate these risks, with many claiming to achieve promising performance. However, recent research has demonstrated that fake voice detectors suffer from serious overfitting to speaker-irrelative features (SiFs) and cannot be used in real-world scenarios. In this paper, we analyze the limitations of existing fake voice detectors and propose a new design philosophy, guiding the detection model to prioritize learning human voice features rather than the difference between the human voice and the synthetic voice. Based on this philosophy, we propose a novel AI-synthesized voice detection framework named SiFSafer, which uses pre-trained speech representation models to enhance the learning of feature distribution in human voices and the adapter fine-tuning to optimize the performance. The evaluation shows that the average EERs of existing fake voice detectors in the ASVspoof datasets can exceed 20% if the SiFs like silence segments are removed, while SiFSafer achieves an EER of less than 8%, indicating that SiFSafer is robust to SiFs and strongly resistant to existing attacks. © 2024 ACM.

关键词： Fake detection

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