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IEEE Symposium on High-Performance Computer Architecture

作者： Zhehua Zhang Suzhen Wu Wenyan You Chunfeng Du Bo Mao Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen Fujian China

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Memory deduplication is widely used to effectively reduce the memory footprint in operating systems, while huge pages are employed to enhance access performance by increasing TLB hit rates. Unfortunately, duplicate huge pages are very rare in memory, leading to most huge pages being split into base pages during deduplication, which degrades access performance by up to $\mathbf{4 3 \%}$. Although asynchronous huge page promotion attempts to elevate multiple base pages to huge pages to maximize performance, deduplication disrupts the uniformity of page attributes, limiting the effectiveness of such promotions. To address this problem, we designed Gemina, a new memory deduplication scheme that coordinates huge page management. Gemina employs a fine-grained detector to distinguish pages, redundancy and hotness, a distributor to process pages conservatively and avoid frequent switches, and an adapter to organize memory for fast conversion. This approach balances the trade-off between performance and space efficiency in memory management. Experimental evaluations show that, compared to the naive KSM-based deduplication scheme, Gemina can achieve $96.1 \%$ of the memory savings of KSM while also improving memory access performance by $\mathbf{4 3. 1 \%}$ in random access.

关键词： Limiting Operating systems Memory management Redundancy Detectors Protection Engines

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M3-Net: A Multi-Scale Nuclei Segmentation Model for Breast Cancer Histopathology Using Contextual Patches and Attention Mechanism

M3-Net: A Multi-Scale Nuclei Segmentation Model for Breast C...

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IEEE International Symposium on Biomedical Imaging

作者： Arbab Sufyan Mohammad Faizal Ahmad Fauzi Wong Lai Kuan Faculty of Engineering Multimedia University Cyberjaya Malaysia Faculty of Computing & Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Histopathological analysis, particularly of nuclear morphology, is critical for identifying malignancies. Accurate nuclei segmentation plays a pivotal role in this process, as it enables detailed assessment of nuclear size, shape, and distribution patterns. Traditional segmentation methods, however, often fail to capture fine details, lack broader context, and struggle with overlapping nuclei of varying sizes and shapes, especially when relying on single-scale approaches. To address these challenges, we propose a multi-scale context-based encoder-decoder model named M3-Net (Multi-Scale, Multi-Level, Multi-Stream Network) that integrates both global and local tissue features. Evaluations demonstrate that M3-Net effectively segments overlapping nuclei and diverse structures, providing a robust solution for automated nuclei segmentation in breast cancer pathology.

关键词： Image segmentation Accuracy Image analysis Histopathology Shape Image color analysis Morphology Breast cancer Context modeling Biomedical imaging

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Multi-granularity feature intersection learning for visible-infrared person re-identification

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Complex and Intelligent Systems 2025年第7期11卷 1-14页

作者： Chan, Sixian Wang, Jie Cui, Jiaao Hu, Jie Li, Zhuorong Mao, Jiafa College of Computer Science and Technology Zhejiang University of Technology Hangzhou 310023 China Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University Wenzhou 325035 China School of Computer and Computing Science Hangzhou City University Hangzhou 310015 China

This paper proposes a multi-granularity feature intersection network (MGFINet) for visible-infrared person re-identification (VI-ReID). VI-ReID aims to retrieve images of the same pedestrian from different spectral cameras. The key challenge is to extract pedestrian descriptions with both inter-class discriminability and intra-class similarity. Previous methods ignore the potential loss of details during representation extraction and the presence of data bias in the metric function, limiting further improvements in retrieval performance. Meanwhile, the discrepancy regarding how to calculate the loss for representation learning and metric learning also affects the model’s training. To address the above issues, MGFINet consists of three components: a hierarchical part pooling method (HPP), a hierarchical part restriction method (HPC), and a feature intersection (FI) loss. HPP adopts a hierarchical framework to extract multi-granularity pedestrian representations, and it performs an inter-layer fusion operation to exploit the high-resolution information from shallow layers and the semantic representability from deep layers. Meanwhile, HPP employs part pooling with different step sizes to capture pedestrian details in each layer. Next, HPC spreads the identity loss across all layers to reduce the distance for gradient backpropagation and further optimize fine-grained features in shallow layers. Besides, FI loss combines representation and metric learning by incorporating hyperparameters of classifiers into metric learning, mitigating data bias and reducing the gap between the two learning processes. Finally, extensive experiments evaluated on two public datasets, SYSU-MM01 and RegDB demonstrate the effectiveness of the proposed method. © The Author(s) 2025.

关键词： Metric learning Multi-granularity Representation learning VI-ReID

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Teaching parallel and distributed computing using data-intensive computing modules

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Journal of Parallel and Distributed computing 2025年 202卷

作者： Gowanlock, Michael School of Informatics Computing & Cyber Systems Northern Arizona University FlagstaffAZ86011 United States

Parallel and distributed computing (PDC) courses are useful for computer science (CS) and domain science students. For CS students, PDC is a fundamental field that examines concepts relating to a range of CS subfields, such as algorithms, architecture, simulation, software, systems, among others. Students with domain science backgrounds also require PDC to carry out their research objectives, and the ongoing data revolution has exacerbated this necessity. Given the rise of data science and other data-enabled computational fields, we propose several data-intensive pedagogic modules that are used to teach PDC using message-passing programming with the Message Passing Interface (MPI). These modules employ activities that are interesting, relevant, and accessible to both computer and domain science students enrolled in graduate level programs. Using pre- and post-module completion quizzes and anonymous free response surveys, we evaluated the efficacy of the pedagogic modules across four cohorts of students enrolled in a graduate level High Performance computing (HPC) course at Northern Arizona University. The students have diverse educational backgrounds as some students were enrolled in programs outside of CS. These programs include electrical and computer engineering, mechanical engineering, astronomy & planetary science, bioinformatics, and ecoinformatics. Despite the multi-disciplinary backgrounds of the students, we find that the hands-on application-driven approach to teaching PDC was successful at helping students learn core PDC concepts, and that the modules are useful for facilitating online learning which was required during the COVID-19 pandemic. © 2025 Elsevier Inc.

关键词： Message passing

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FarExStance: Explainable Stance Detection for Farsi 31

FarExStance: Explainable Stance Detection for Farsi

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zarharan, Majid Hashemi, Maryam Behroozrazegh, Malika Eetemadi, Sauleh Pilehvar, Mohammad Taher Foster, Jennifer School of Computing Dublin City University Ireland Iran University of Science and Technology Iran Dadmatech Tehran Iran School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (纸本)9798891761964

We introduce FarExStance, a new dataset for explainable stance detection in Farsi. Each instance in this dataset contains a claim, the stance of an article or social media post towards that claim, and an extractive explanation which provides evidence for the stance label. We compare the performance of a fine-tuned multilingual RoBERTa model to several large language models in zero-shot, few-shot, and parameter-efficient fine-tuned settings on our new dataset. On stance detection, the most accurate models are the fine-tuned RoBERTa model, the LLM Aya-23-8B which has been fine-tuned using parameter-efficient fine-tuning, and few-shot Claude-3.5-Sonnet. Regarding the quality of the explanations, our automatic evaluation metrics indicate that few-shot GPT-4o generates the most coherent explanations, while our human evaluation reveals that the best Overall Explanation Score (OES) belongs to few-shot Claude-3.5-Sonnet. The fine-tuned Aya-32-8B model produced explanations most closely aligned with the reference explanations. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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RT-PPS: Real-time privacy-preserving scheme for cloud-hosted IoT data

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Journal of High Speed Networks 2025年第1期31卷 71-89页

作者： Elhoseny, Mohamed Riad, Khaled College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates Faculty of Computers and Information Mansoura University Mansoura35516 Egypt Computer Science Department College of Computer Sciences & Information Technology King Faisal University Al-Ahsa 31982 Saudi Arabia Mathematics Department Faculty of Science Zagazig University Zagazig44519 Egypt

The Internet of Things (IoT) is a rapidly growing network of devices that can communicate with each other and with cloud-based services. These devices generate vast amounts of data that can be used to provide valuable insights into user behavior, environmental conditions, and other important factors. However, as this data is collected and processed by cloud-hosted services, there is a growing concern about privacy and security. Without adequate protection, sensitive information could be exposed to hackers or other malicious actors, putting both individuals and organizations at risk. To address this challenge, real-time privacy-preserving techniques can be used to protect IoT data without compromising its value. This paper introduces an efficient Real-time privacy-preserving scheme (RT-PPS) for cloud-hosted IoT data. RT-PPS employs multi-authority attribute-based encryption on a hybrid cloud environment to keep data secure and private, while still allowing it to be processed and analyzed by cloud-hosted services. RT-PPS has efficient response time and resource consumption, which gives it the ability to handle a huge number of concurrent users at the same time without notable delay. The proposed RT-PPS has been validated through extensive experimental evaluation on a variety of configurations. Moreover, the proposed scheme has been computationally compared with the state-of-the-artwork. RT-PPS has shown excellent performance, effectiveness, and efficiency. The RT-PPS encryption time for a 1 GB dataset while considering 1024 slices is approximately 1000 ms. Also, the RT-PPS decryption time for a 1 GB ciphertext while considering 1024 slices are approximately 235 ms. Finally, RT-PPS is proven secure against any polynomial-time attacks and their variations that have at most a negligible advantage in the introduced security model. Moreover compared to most of the state-of-the-artwork, RT-PPS reduced the ciphertext size and lowered the computations in the encryption, key g

关键词： Ciphertext

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Evaluating and Improving Graph to Text Generation with Large Language Models

arXiv

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arXiv 2025年

作者： He, Jie Yang, Yijun Long, Wanqiu Xiong, Deyi Basulto, Victor Gutierrez Pan, Jeff Z. School of Informatics University of Edinburgh United Kingdom College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Informatics Cardiff University United Kingdom

Large language models (LLMs) have demonstrated immense potential across various tasks. However, research for exploring and improving the capabilities of LLMs in interpreting graph structures remains limited. To address this gap, we conduct a comprehensive evaluation of prompting current open-source LLMs on graph-to-text generation tasks. Although we explored the optimal prompting strategies and proposed a novel and effective diversity-difficulty-based few-shot sample selection method, we found that the improvements from tuning-free approaches were incremental, as LLMs struggle with planning on complex graphs, particularly those with a larger number of triplets. To further improve LLMs in planning with graph sequences and grounding in truth, we introduce a new graph-to-text dataset, PlanGTG, annotated with two sub-tasks: reordering and attribution. Through extensive automatic and human evaluations, we demonstrate significant improvements in the quality of generated text from both few-shot learning and fine-tuning perspectives using the PlanGTG dataset. Our study paves the way for new research directions in graph-to-text generation1 © 2025, CC BY-NC-SA.

关键词： Zero-shot learning

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N2C2: Nearest Neighbor Enhanced Confidence Calibration for Cross-Lingual In-Context Learning

arXiv

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arXiv 2025年

作者： He, Jie Yu, Simon Xiong, Deyi Basulto, Víctor Gutiérrez Pan, Jeff Z. School of Informatics University of Edinburgh United Kingdom School of Computer Science and Informatics Cardiff University United Kingdom College of Intelligence and Computing Tianjin University Tianjin China

Recent advancements of in-context learning (ICL) show language models can significantly improve their performance when demonstrations are provided. However, little attention has been paid to model calibration and prediction confidence of ICL in cross-lingual scenarios. To bridge this gap, we conduct a thorough analysis of ICL for cross-lingual sentiment classification. Our findings suggest that ICL performs poorly in cross-lingual scenarios, exhibiting low accuracy and presenting high calibration errors. In response, we propose a novel approach, N2C2, which employs a k-nearest neighbors augmented classifier for prediction confidence calibration. N2C2 narrows the prediction gap by leveraging a datastore of cached few-shot instances. Specifically, N2C2 integrates the predictions from the datastore and incorporates confidence-aware distribution, semantically consistent retrieval representation, and adaptive neighbor combination modules to effectively utilize the limited number of supporting instances. Evaluation on two multilingual sentiment classification datasets demonstrates that N2C2 outperforms traditional ICL. It surpasses fine tuning, prompt tuning and recent state-of-the-art methods in terms of accuracy and calibration errors. © 2025, CC BY-NC-SA.

关键词： Calibration

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Tibyan corpus: balanced and comprehensive error coverage corpus using ChatGPT for Arabic grammatical error correction

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PeerJ Computer Science 2025年 11卷 e2724-e2724页

作者： Alrehili, Ahlam Alhothali, Areej Department of Computer Sciences Faculty of Computing and Information Technology King Abdul Aziz University Jeddah Saudi Arabia Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

Natural language processing (NLP) augments text data to overcome sample size constraints. Scarce and low-quality data present particular challenges when learning from these domains. Increasing the sample size is a natural and widely used strategy for alleviating these challenges. Moreover, data-augmentation techniques are commonly used in languages with rich data resources to address problems such as exposure bias. In this study, we chose Arabic to increase the sample size and correct grammatical errors. Arabic is considered one of the languages with limited resources for grammatical error correction (GEC) despite being one of the most popular among Arabs and non-Arabs because of its close connection to Islam. Therefore, this study aims to develop an Arabic corpus called "Tibyan" for grammatical error correction using ChatGPT. ChatGPT is used as a data augmenter tool based on a pair of Arabic sentences containing grammatical errors matched with a sentence free of errors extracted from Arabic books, called guide sentences. Multiple steps were involved in establishing our corpus, including collecting and pre-processing a pair of Arabic texts from various sources, such as books and open-access corpora. We then used ChatGPT to generate a parallel corpus based on the text collected previously, as a guide for generating sentences with multiple types of errors. By engaging linguistic experts to review and validate the automatically generated sentences, we ensured they were correct and error-free. The corpus was validated and refined iteratively based on feedback provided by linguistic experts to improve its accuracy. Finally, we used the Arabic Error Type Annotation tool (ARETA) to analyze the types of errors in the Tibyan corpus. Our corpus contained 49% of errors, including seven types: orthography, morphology, syntax, semantics, punctuation, merge, and split. The Tibyan corpus contains approximately 600 K tokens. © 2025 Alrehili and Alhothali

关键词： Semantics

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Development of malware in Windows sandbox and Kali MSFvenom for hacking Android and Windows Operating Systems

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1831-1838页

作者： Pandey, Bishwajeet Shukla, Man Mohan Pandey, Pushpanjali Bakar, W.A.W.A. GL Bajaj College of Technology and Management Greater Noida India Pranveer Singh Institute of Technology Kanpur India R & D Lab Gyancity Research Consultancy Greater Noida India Faculty of Informatics and Computing Universiti Sultan Zainul Abidin (UniSZA) Kuala Terengganu Malaysia

In this research, we developed seven malware programs utilizing Windows Notepad, NJRAT, and MSFvenom malware development environment. Two BAT viruses were created using Windows Notepad, one to simulate a Matrix effect and another for app bombing. We also developed three types of spyware using MSFvenom targeting Android, macOS, and Windows systems, designed to transmit information from these victims back to the Kali machine. When the spyware was deployed on a Windows system, it was capable of extracting password hashes, taking screenshots, uploading and executing payloads, downloading files, and performing various other exploits through the meterpreter shell. When the spyware was installed on an Android device, it allowed access to call logs, contacts, and geolocation, as well as the ability to send SMS messages and delete apps on the victim's device. When we executed spyware on a Mac machine, we got the process ID of all running applications on Mac, we were able to kill those running applications. The seventh spyware, built using the NJRAT Danger Edition, enabled a range of attacks, such as opening File Explorer and deleting specific folders to gain control over the victim's computer. The entire research and development process took place at the R&D Lab of Gyancity Research Consultancy, located at latitude 28.6519 and longitude 77.2315. We utilized an IP location tracker to obtain the geoinformatics coordinates based on the IP address of our Kali machine. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Kali MSFvenom Notepad Sandbox Spyware Virus Windows

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