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Performance Analysis of Infrastructure Sharing Techniques in Cellular Networks: A Percolation Theory Approach

IEEE Transactions on Vehicular technology 2025年

作者： Lin, Hao Kishk, Mustafa A. Alouini, Mohamed-Slim King Abdullah University of Science and Technology (KAUST) Electrical and Computer Engineering Program Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE) Thuwal 23955-6900 Saudi Arabia Maynooth University Department of Electronic Engineering Maynooth W23 F2H6 Ireland King Abdullah University of Science and Technology (KAUST) CEMSE Division Thuwal 23955-6900 Saudi Arabia

In the context of 5G, infrastructure sharing has been identified as a potential solution to reduce the investment costs of cellular networks. In particular, it can help low-income regions build 5G networks more affordably and further bridge the digital divide. There are two main kinds of infrastructure sharing: passive sharing (i.e., site sharing) and active sharing (i.e., access sharing), which require mobile network operators (MNOs) to share their non-electronic elements or electronic elements, respectively. Because co-construction and sharing can achieve broader coverage with lower investment, through percolation theory, we investigate how different sharing strategies can deliver large-scale continuous services. First, we examine the percolation characteristics in signal-to-interference-plus-noise ratio (SINR) coverage graphs and the necessary conditions for percolation. Second, we propose an ‘average coverage radius’ to approximate the SINR graph with a low base station (BS) density based on the Gilbert disk model. Finally, we estimate the critical conditions of BS densities of MNOs for different sharing strategies and compare the percolation probabilities under different infrastructure sharing strategies. © 1967-2012 IEEE.

关键词： Gilbert disk model graph theory Infrastructure sharing percolation theory stochastic geometry

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Reproducing ATT&CK Techniques and Lifecycles to Train Machine Learning Classifier

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IEEE Network 2025年

作者： Yudha, Fietyata Lin, Ying-Dar Lai, Yuan-Cheng Sudyana, Didik Hwang, Ren-Hung EECS International Graduate Program National Yang Ming Chiao Tung University Hsinchu City300 Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu City300 Taiwan The Department of Information Management National Taiwan University of Science and Technology Tapei106 Taiwan The Computer and Network Center National Cheng Kung University Tainan701 Taiwan The College of Artificial Intelligence National Yang Ming Chiao Tung University Tainan711 Taiwan

he MITRE ATT&CK framework categorizes threat actor behaviors into a sequence of techniques called the attack lifecycle. Based on this, Our work introduces a dual-labeled dataset that is accurately labeled with distinct techniques and lifecycles defined by ATT&CK. The dataset offered a more thorough perspective than previous datasets that employed binary or attack classification. It encompassed 17 distinct techniques throughout five lifecycles and was generated through an automated method that guarantees reproducibility and learnability. Reproducibility guarantees the dataset’s consistency, whereas learnability signifies its use in training machine learning models. Our analysis produced a positive result. The dataset achieved a Pearson correlation coefficient of 0.7 for reproducibility. Regarding distinguishing classes, it exhibits an average AUC-ROC score of 0.92 for techniques and 0.82 for lifecycles. The model training yielded an average F1 score of 0.95 for technique classification and 0.9 for lifecycle classification, but only for the traffic dataset. © 1986-2012 IEEE.

关键词： Adversarial machine learning

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BENCHMARKING COMMUNITY DRUG RESPONSE PREDICTION MODELS: DATASETS, MODELS, TOOLS, AND METRICS FOR CROSS-DATASET GENERALIZATION ANALYSIS

arXiv

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arXiv 2025年

作者： Partin, Alexander Vasanthakumari, Priyanka Narykov, Oleksandr Wilke, Andreas Koussa, Natasha Jones, Sara E. Zhu, Yitan Overbeek, Jamie C. Jain, Rajeev Fernando, Gayara Demini Sanchez-Villalobos, Cesar Garcia-Cardona, Cristina Mohd-Yusof, Jamaludin Chia, Nicholas Wozniak, Justin M. Ghosh, Souparno Pal, Ranadip Brettin, Thomas S. Weil, M. Ryan Stevens, Rick L. Division of Data Science and Learning Argonne National Laboratory LemontIL United States Frederick National Laboratory for Cancer Research Cancer Data Science Initiatives Cancer Research Technology Program FrederickMD United States Department of Statistics University of Nebraska–Lincoln LincolnNE United States Department of Electrical & Computer Engineering Texas Tech University LubbockTX United States Division of Computer Computational and Statistical Sciences Los Alamos National Laboratory Los AlamosNM United States Department of Computer Science The University of Chicago ChicagoIL United States

Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question, raising concerns about their real-world applicability. Due to the lack of standardized benchmarking approaches, model evaluations and comparisons often rely on inconsistent datasets and evaluation criteria, making it difficult to assess true predictive capabilities. In this work, we introduce a benchmarking framework for evaluating cross-dataset prediction generalization in DRP models. Our framework incorporates five publicly available drug screening datasets, six standardized DRP models, and a scalable workflow for systematic evaluation. To assess model generalization, we introduce a set of evaluation metrics that quantify both absolute performance (e.g., predictive accuracy across datasets) and relative performance (e.g., performance drop compared to within-dataset results), enabling a more comprehensive assessment of model transferability. Our results reveal substantial performance drops when models are tested on unseen datasets, underscoring the importance of rigorous generalization assessments. While several models demonstrate relatively strong cross-dataset generalization, no single model consistently outperforms across all datasets. Furthermore, we identify CTRPv2 as the most effective source dataset for training, yielding higher generalization scores across target datasets. By sharing this standardized evaluation framework with the community, our study aims to establish a rigorous foundation for model comparison, and accelerate the development of robust DRP models for real-world applications. © 2025, CC BY.

关键词： Prediction models

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HoloFarm: Enhancing agricultural learning through immersive technology

Software Impacts

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Software Impacts 2025年 24卷

作者： Ario, Muhamad Keenan Hasani, Muhammad Fikri Balqis, Khairatul Carment, Messya Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Extended reality in education has advanced, offering safe, immersive simulations. Agriculture, a key area, lacks urban exposure. HoloFarm, a VR-based farming simulation, addresses this gap using Unity and C#. It integrates physical movement, joystick navigation, and spatial audio for crop cultivation. Evaluated with 27 urban users via the Igroup Presence Questionnaire, it showed strong spatial (M=5.59) and general presence (M=5.81), though realism (M=4.10) and involvement (M=4.77). Future updates will enhance realism and enable collaborative learning, bridging theoretical and practical agricultural knowledge. © 2025 The Author(s)

关键词： Agricultural education Educational technology Farming simulation Immersive learning Virtual Reality

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Domain-independent temporal reasoning with recurring events

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COMPUTATIONAL INTELLIGENCE 1996年第3期12卷 450-477页

作者： Morris, RA Shoaff, WD Khatib, L Program in Computer Science Florida Institute of Technology Melbourne Florida 32901

Numerous examples of temporal reasoning involve a process of abstraction from the number of times an event is to occur or the number of times events stand in a temporal relation. For example, scheduling a recurring event such as one's office hours may consider things like the relative temporal ordering of the office hours and a number of other events in a given work day. The number of times office hours will actually be held may be unknown, even irrelevant, at the time of scheduling them. The objective of this article is to formulate a domain-independent framework for reasoning about recurring events and their relations. To achieve this end, we propose an ontology of recurrence based on the model-theoretic structure underlying collective predication using plural noun phrases. We offer a calculus of binary temporal relations for temporal collections based on a well-defined transformation of interval temporal relations into recurrence relations. Finally, we describe a reasoning framework based on manipulating knowledge stored in temporal relation networks, which is in turn a specialization of the CSP (constraint satisfaction problem) framework. The reasoner manipulates recurrence relations in the network to determine the network's consistency or to generate scenarios.

关键词： temporal logic constraint solving ontologies for time

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Edit Propagation via Edge-Aware Filtering

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Journal of computer science & technology 2012年第4期27卷 830-840页

作者：胡伟董朝袁国栋 Department of Computer Science and Technology Beijing University of Chemical Technology Program of Computer Graphics Cornell University Department of Computer Science and Technology Tsinghua University

This paper presents a novel framework for efficiently propagating the stroke-based user edits to the regions with similar colors and locations in high resolution images and videos. Our framework is based on the key observation that the edit propagation intrinsically can also be achieved by utilizing recently proposed edge-preserving filters. Therefore, instead of adopting the traditional global optimization which may involve a time-consuming solution, our algorithm propagates edits with the aid of the edge-preserve filters. Such a propagation scheme has low computational complexity and supports multiple kinds of strokes for more flexible user interactions. Further, our method can be easily and efficiently implemented in GPU. The experimental results demonstrate the efficiency and user-friendliness of our approach.

关键词： edit propagation edge-aware domain transform guided filter

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Innovative Integration of Machine Learning Predictive Models Within Blockchain Frameworks for Supply Chain Fault Tolerance 1st

Innovative Integration of Machine Learning Predictive Models...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Kaur, Jatinder Rusho, Maher Ali Yogi, Kottala Sri Soni, Mukesh Raparthi, Mohan Garg, Yakshit Department of Computer Science and Engineering Lovely Professional University Jalandhar Phagwara India Degree Program University of Colorado Boulder Boulder United States Telangana Hyderabad India Dr. D. Y. Patil Vidyapeeth Pune India Dr. D. Y. Patil School of Science and Technology Tathawade Pune India Alphabet Life Science Texas Dallas75063 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9783031860683

The creative incorporation of machine learning predictive models into blockchain frameworks appears as a game-changing approach for augmenting fault tolerance and increasing operational resilience in the dynamic environment of supply chain management. The potential, results, and implications of the integration are examined in this paper, providing a holistic perspective on its effect on contemporary supply networks. The essay starts by discussing the ever-changing difficulties encountered by supply chain networks operating in the modern global economy. Fault tolerance in the supply chain is becoming a pressing issue, calling for creative solutions to minimize interruptions and maximize efficiency. The importance of using machine learning predictive models into blockchain frameworks is outlined in the introduction, which also provides context for the rest of the paper. Methods used to test the integration's usefulness are described in the research paper. Logical regression, random forests, CNNs, SVMs, and LSTM models are all part of this category of cutting-edge machine learning methods. Machine learning algorithms in blockchain frameworks may protect supply networks from catastrophic breakdowns. The suggested strategy beats conventional models in accuracy (92%), precision (88%), recall (93%), F1-Score (90%), MAE (0.12), and MSE (0.15). The recommended method has a higher MSE than traditional models. The findings suggest that supply chain networks anticipate future occurrences and identify issues *** a result, businesses will be able to prevent and correct problems like shipment delays and stock-outs. The danger of data tampering and fraudulent activities has also been reduced thanks to the use of blockchain technology, which has improved data integrity and trust among supply chain players. This impact is shown by a 30% decrease in data tampering events inside the supply chain. The revolutionary potential of the novel integration of machine learning prediction

关键词： Supply chain management

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“Is It Future or Is It Past?”: From Self-contained Microtasks to AI-driven Collaborative Crowdsourcing

“Is It Future or Is It Past?”: From Self-contained Microta...

引用

Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Daniel Schneider Marcos Antonio De Almeida Ramon Chaves Benjamim Fonseca Hesam Mohseni António Correia Postgraduate Program in Informatics (PPGI) Federal University of Rio de Janeiro Rio de Janeiro Brazil Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Interest in artificial intelligence (AI)-driven crowd work has increased during the last few years as a line of inquiry that expands upon prior research on microtasking to represent a means of scaling up complex tasks through AI mediation. Despite the increasing attention to the macrotask phenomenon in crowdsourcing, there is a need to understand the processes, elements, and constraints underlying the infrastructural and behavioral aspects in such form of crowd work when involving collaboration. To this end, this paper provides a first attempt to characterize some of the research conducted in this direction to identify important paths for an agenda comprising key drivers, challenges, and prospects for integrating human-centered AI in collaborative crowdsourcing environments.

关键词： Crowdsourcing Human computer interaction Collaboration Artificial intelligence Mediation Robots Optimization

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Pan-cancer analysis of CDC7 in human tumors: Integrative multi-omics insights and discovery of novel marine-based inhibitors through machine learning and computational approaches

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computers in Biology and Medicine 2025年 190卷 110044-110044页

作者： Saif, Ahmed Islam, Md. Tarikul Raihan, Md. Obayed Yousefi, Niloofar Rahman, Md. Ajijur Faridi, Hafeez Hasan, Al Riyad Hossain, Mirza Mahfuj Saleem, Rasha Mohammed Albadrani, Ghadeer M. Al-Ghadi, Muath Q. Ahasan Setu, Md. Ali Kamel, Mohamed Abdel-Daim, Mohamed M. Aktaruzzaman, Md. Department of Pharmacy Faculty of Science University of Rajshahi Rajshahi6205 Bangladesh Department of Genetic Engineering and Biotechnology Faculty of Biological Science and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Jashore7408 Bangladesh Department of Pharmaceutical Sciences College of Health Sciences and Pharmacy Chicago State University ChicagoIL United States Department of Industrial Engineering and Management Systems University of Central Florida USA OrlandoFL United States Department of Pharmacy Faculty of Biological Science and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Department of Computer Science and Engineering Faculty of Engineering and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Department of Laboratory Medicine Faculty of Applied Medical Sciences Al-Baha University Al-Baha65431 Saudi Arabia Department of Biology College of Science Princess Nourah bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia Department of Zoology College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia Department of Microbiology Faculty of Biological Science and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Department of Medicine and Infectious Diseases Faculty of Veterinary Medicine Cairo University Giza12211 Egypt Department of Pharmaceutical Sciences Pharmacy Program Batterjee Medical College P.O. Box 6231 Jeddah21442 Saudi Arabia Pharmacology Department Faculty of Veterinary Medicine Suez Canal University Ismailia41522 Egypt

Cancer remains a significant global health challenge, with the Cell Division Cycle 7 (CDC7) protein emerging as a potential therapeutic target due to its critical role in tumor proliferation, survival, and resistance. However, a comprehensive analysis of CDC7 across multiple cancers is lacking, and existing therapeutic options have come with limited clinical success. The aim of this is to integrate a comprehensive pan-cancer analysis of CDC7 with the identification of novel marine-derived inhibitors, bridging the understanding of CDC7's role as a prognostic biomarker and therapeutic target across diverse cancer types. In this study, we conducted a pan-cancer analysis of CDC7 across 33 tumor types using publicly available datasets to evaluate its expression, genetic alterations, immune interactions, survival, and prognostic significance. Additionally, a marine-derived compound library of 31,492 molecules was screened to identify potential CDC7 inhibitors using chemoinformatics and machine learning. The top candidates underwent rigorous evaluations, including molecular docking, pharmacokinetics, toxicity, Density Functional Theory (DFT) calculations, and Molecular Dynamics (MD) simulations. The findings revealed that CDC7 is overexpressed in several cancers and is associated with poor survival outcomes and unfavorable prognosis. Enrichment analysis linked CDC7 to critical DNA replication pathways, while its role in modulating tumor-immune interactions highlighted its potential as a target for immunotherapy. Among all tested compounds, Tetrahydroaltersolanol D (CMNPD21999) exhibited the strongest binding affinity and stability, along with better drug-likeness and zero toxicity. These attributes highlight its potential as a promising drug candidate for CDC7 inhibition and future cancer treatment development. Furthermore, additional in vitro and in vivo studies are required to confirm the effectiveness of this drug candidate against the CDC7 protein. © 2025

关键词： Gene expression

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AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Taiwan International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei11529 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Chengchi University Department of Computer Science Taipei11605 Taiwan National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan

The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multimodal datasets of deepfake videos involving acoustic and visual manipulations, and are mostly based on convolutional neural networks with low detection accuracy. Considering that human cognition instinctively integrates multisensory information including audio and visual cues to perceive and interpret content and the success of transformer in various fields, this study introduces the audio-visual transformer-based ensemble network (AVTENet). This innovative framework tackles the complexities of deepfake technology by integrating both acoustic and visual manipulations to enhance the accuracy of video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that the proposed model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. We also compare AVTENet against humans in detecting video forgery. The results show that AVTENet significantly outperforms humans. © 2016 IEEE.

关键词： audio-visual AVTENet Deepfake detection

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