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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Baiting Ren, Zhimei Cheng, Lu Department of Statistics and Data Science UCLA United States Department of Statistics and Data Science University of Pennsylvania United States Department of Computer Science University of Illinois Chicago United States

Conformal prediction is a powerful tool for uncertainty quantification, but its application to time-series data is constrained by the violation of the exchangeability assumption. Current solutions for time-series prediction typically operate in the output space and rely on manually selected weights to address distribution drift, leading to overly conservative predictions. To enable dynamic weight learning in the semantically rich latent space, we introduce a novel approach called Conformalized Time Series with Semantic Features (CT-SSF). CT-SSF utilizes the inductive bias in deep representation learning to dynamically adjust weights, prioritizing semantic features relevant to the current prediction. Theoretically, we show that CT-SSF surpasses previous methods defined in the output space. Experiments on synthetic and benchmark datasets demonstrate that CT-SSF significantly outperforms existing state-of-the-art (SOTA) conformal prediction techniques in terms of prediction efficiency while maintaining a valid coverage guarantee. © 2024 Neural information processing systems foundation. All rights reserved.

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Peer Collaboration in DBLP Using Graph Convolutional Network

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SN computer science 2025年第1期6卷 1-7页

作者： Kumar, Chintoo Dar, Showkat Ahmad Batchu, Charishma Singamaneni, Kundan Sai Panguluri, Venkata Lakshmi Alekhya Department of Artificial Intelligence and Data Science GITAM Karnataka Bengaluru 562163 India Department of Computer Science and Engineering GITAM Karnataka Bengaluru 562163 India

The increasing popularity of Graph-based neural network architectures plays a pivotal role in providing promising results in applications, viz., Friendship networks, Co-authorship networks, Product recommendations, etc. Many machine learning tasks can be addressed when dealing with graph representation, i.e., graph classification, node classification, link prediction, graph embedding, etc. The traditional rule-based methods have many drawbacks in comparison to graph representation learning, such as it requires manual feature engineering and struggles with unseen cases (generalizability), etc., in the case of rule-based methods. At the same time, graph representation learning overcomes those issues profoundly. The advent of graph-based deep neural network architectures inspired us to complete a graph classification problem to explore clusters of researchers who worked together in academic publications. We built the model by using a graph convolutional network to solve a multi-classification problem. We conducted a comparative study to check the efficacy of graph representation learning by GCNs on the DBLP dataset. A comparative study shows that the built model outperforms the baseline models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： DBLP Graph convolutional network Graph mining Social networks

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CyberGuardian 2: Integrating LLMs and Agentic AI Assistants for Securing Distributed Networks 20

CyberGuardian 2: Integrating LLMs and Agentic AI Assistants ...

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20th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2025

作者： Paduraru, Ciprian Patilea, Catalina Camelia Stefanescu, Alin Department of Computer Science University of Bucharest Romania Institute for Logic and Data Science Romania

ISBN: (纸本)9789897587429

Robust cybersecurity measures are essential to protect complex information systems from a variety of cyber threats, which requires sophisticated security solutions. This paper explores the integration of Large Language Models (LLMs) to improve cybersecurity operations within Security Operations Centers (SOCs). The proposed framework has a modular plugin architecture where Agentic AI controls the information flow, includes Retrieval Augmented Generation (RAG), protection methods for human-chatbot interactions and tools for managing tasks such as database interactions, code generation and execution. By utilizing these techniques, the framework aims to streamline the workflows of SOC analysts, allowing them to focus on critical tasks rather than redundant activities. The study also explores the dynamic customization of LLMs based on client data, user experience, potential risks and language preferences to ensure a user-centric approach. The results show improvements in efficiency and effectiveness and highlight the potential of LLMs in cybersecurity applications. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Cyber attacks

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Enhancing Automated COVID Diagnosis from Chest X-rays using Convolutional Neural Networks and Transfer Learning 5

Enhancing Automated COVID Diagnosis from Chest X-rays using ...

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Bukhari, Muhammad Abdullah Shah Siddiqui, Muhammad Ahmad Khalique, Sarmad Bukhari, Faisal Iqbal, Waheed University of the Punjab Department of Computer Science Lahore Pakistan University of the Punjab Department of Data Science Lahore Pakistan

ISBN: (纸本)9798350394764

We propose an approach for the early detection of COVID-19 and other related lung diseases using artificial intelligence (AI) and deep learning-based methods. The proposed approach involves utilizing transfer learning over convolutional neural networks (CNNs) for the classification of chest X-ray images as normal or COVID-19 positive. To address the limited availability of X-ray images, we employed data augmentation techniques to expand the dataset size and enhance our model's performance on unseen data. We used variations of four pre-trained models: DenseNet, Inception, ResNet, and VGG, and found that DenseNet169 demonstrated the best performance with a validation accuracy of 100%. © 2024 IEEE.

关键词： COVID-19

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Empowering Biomedical Named Entity Recognition Through Multi-Tagger Collaboration 27

Empowering Biomedical Named Entity Recognition Through Multi...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhao, Jin Xie, Jian Zhu, Tinghui Guo, Qian Li, Zhixu Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Department of Computer Science and Engineering East China University of Science and Technology China

ISBN: (纸本)9781643685489

Biomedical Named Entity Recognition (BioNER) plays a crucial role in automatically identifying specific categories of entities from biomedical texts. Currently, region-based methods have shown promising performance in BioNER. However, existing paradigms in the region-based methods suffer from inherent limitations, including the generation of negative samples, and the ignorance of token dependencies. To overcome these limitations, we propose a new paradigm, implemented as Token Cascade Tagger (TCT), which combines span identification and category classification. The TCT utilizes category information to enhance the correlation between the heads and tails of entities, effectively reducing the generation of negative samples. Additionally, we introduce a Token Dependency Tagger (TDT) that captures token dependencies within entity spans by identifying the longest span in a sentence. The TDT filters out incorrect spans and further improves the accuracy of span detection obtained from the TCT. Furthermore, we employ a multi-task learning framework to optimize both the TCT and TDT, leading to superior performance in BioNER. Extensive experiments on publicly available biomedical datasets demonstrate our method outperforms the previous state-of-the-art methods, achieving 92.44%, 92.54%, and 81.26% on NCBI-Disease, BC5CDR, and GENIA, respectively, in terms of F1 score. © 2024 The Authors.

关键词： Multi-task learning

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A Language Model's Guide Through Latent Space 41

A Language Model's Guide Through Latent Space

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41st International Conference on Machine Learning, ICML 2024

作者： von Rütte, Dimitri Anagnostidis, Sotiris Bachmann, Gregor Hofmann, Thomas Data Analytics Lab Department of Computer Science ETH Zurich Switzerland

Concept guidance has emerged as a cheap and simple way to control the behavior of language models by probing their hidden representations for concept vectors and using them to perturb activations at inference time. While the focus of previous work has largely been on truthfulness, in this paper we extend this framework to a richer set of concepts such as appropriateness, humor, creativity and quality, and explore to what degree current detection and guidance strategies work in these challenging settings. To facilitate evaluation, we develop a novel metric for concept guidance that takes into account both the success of concept elicitation as well as the potential degradation in fluency of the guided model. Our extensive experiments reveal that while some concepts such as truthfulness more easily allow for guidance with current techniques, novel concepts such as appropriateness or humor either remain difficult to elicit, need extensive tuning to work, or even experience confusion. Moreover, we find that probes with optimal detection accuracies do not necessarily make for the optimal guides, contradicting previous observations for truthfulness. Our work warrants a deeper investigation into the interplay between detectability, guidability, and the nature of the concept, and we hope that our rich experimental test-bed for guidance research inspires stronger follow-up approaches. Copyright 2024 by the author(s)

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Bridging the Gap: Evaluating the Symbiotic Relationship of Cloud Computing with data science and data Engineering 3

Bridging the Gap: Evaluating the Symbiotic Relationship of C...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Rajesh Sharma, R. Naikwade, Imamhussain Sungheetha, Akey Adere, Ketema Gandasala, G.S. Pradeep Banshal, Sumit Kumar Alliance University Department of Computer Science and Engineering Bangalore India Alliance University Department of Data Science Bangalore India Adama Science and Technology University Department of Computer Science & Engineering Adama Ethiopia

ISBN: (纸本)9798331518592

This paper examines the convergence of cloud computing, facts science, and facts engineering, providing a primer for college kids getting into those fields. The examine highlights the synergistic courting among those domains, displaying how cloud infrastructure complements collaboration and efficiency. By illuminating those interconnections, the paper presents college students with a holistic view of the current facts landscape, making ready them to leverage current equipment withinside the cloud era. © 2024 IEEE.

关键词： data science

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BoxCare: A Box Embedding Model for Disease Representation and Diagnosis Prediction in Healthcare data 24

BoxCare: A Box Embedding Model for Disease Representation an...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Lv, Hang Chen, Zehai Yang, Yacong Ma, Guofang Tan, Yanchao Yang, Carl College of Computer and Data Science Fuzhou University China School of Computer Science Zhejiang Gongshang University China Department of Computer Science Emory University United States

ISBN: (纸本)9798400701726

Diagnosis prediction is becoming crucial to develop healthcare plans for patients based on Electronic Health Records (EHRs). Existing works usually enhance diagnosis prediction via learning accurate disease representation, where many of them try to capture inclusive relations based on the hierarchical structures of existing disease ontologies such as those provided by ICD-9 codes. However, they overlook exclusive relations that can reflect different and complementary perspectives of the ICD-9 structures, and thus fail to accurately represent relations among diseases and ICD-9 codes. To this end, we propose to project disease embeddings and ICD-9 code embeddings into boxes, where a box is an axis-aligned hyperrectangle with a geometric region and two boxes can clearly "include" or "exclude" each other. Upon box embeddings, we further obtain patient embeddings via aggregating the disease representations for diagnosis prediction. Extensive experiments on two real-world EHR datasets show significant performance gains brought by our proposed framework, yielding average improvements of 6.04% for diagnosis prediction over state-of-the-art competitors. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Diagnosis

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Evaluating the Efficacy of Latent Variables in Mitigating data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

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computer Modeling in Engineering & sciences 2024年第5期139卷 1635-1654页

作者： Shahad Alzahrani Hatim Alsuwat Emad Alsuwat Independent Researcher Specializing in Data Security and Privacy Taif26571Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah24382Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif26571Saudi Arabia

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among ***,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data *** of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their *** this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning *** framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over *** use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian *** regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC *** doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky ***,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost *** results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning ***,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

关键词： Bayesian networks data poisoning attacks latent variables structure learning algorithms adversarial attacks

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GeoBeeDashboard: An Intelligent Geospatial Platform for Enhancing Waste Management in Tangerang City 13

GeoBeeDashboard: An Intelligent Geospatial Platform for Enha...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Muljana, Richie Deffansyah, Syauqi Akmal Andrew, Hari Gunawan, Alexander Agung Santoso Irwansyah, Edy Pramudya, Fabian Surya Ario, Muhamad Keenan Syahputra, Muhammad Edo Achmad, Said School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368086

In this study, we created an Intelligent Geospatial Platform called GeoBeeDashboard. The GeoBeeDashboard combines advanced geospatial technologies and artificial intelligence (AI), allowing real-time data visualization and optimized decision-making across multiple sectors. The system architecture consists of a ***-based front end, a FastAPI-driven back end, and PostGIS for geospatial data management. One of the GeoBeeDashboard's primary use cases is addressing waste management issues in Tangerang City, Indonesia. As rapid urbanization increases waste generation, effective management becomes critical. The dashboard identifies critical trends and inefficiencies in Tangerang's districts by K-Means clustering waste management data from 2020 to 2023. Our findings identify distinct clusters, highlighting areas with effective waste management and those that require targeted interventions. The benefits that local government can get from GeoBeeDashboard by utilising insights based on clustering analysis of data per each district in Tangerang to optimise waste collection, allocate resources efficiently, and improve the policy-making process for each district in Tangerang. The GeoBeeDashboard improves local government capabilities by providing a scalable model for other cities facing similar urban challenges. In conclusion, GeoBeeDashboard provides local governments with a powerful tool for addressing waste management inefficiencies and fostering sustainable urban development. © 2024 IEEE.

关键词： Urban planning

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