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6th IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2024

作者： Sorensen, Derek University of Cambridge Dept. of Computer Science and Technology Cambridge United Kingdom

ISBN: (纸本)9798350316742

Poorly specified smart contracts can be vulnerable to attacks on faulty design. Formal methods are currently unable to address these vulnerabilities because they are not vulnerabilities of incorrect code, but of incorrect specification. We are thus in need of a paradigm shift in how we specify and verify smart contracts which allows for a rigorous and accurate notion of a contract specification's correctness. We submit that correctness includes appropriate cryptoeconomic behaviors, which are generally out of scope of a contract's specification. We advocate for an approach to contract specification consisting of contract axiomatization and metaspecification, and illustrate with an example. © 2024 IEEE.

关键词： Smart contract

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Rumor detection from online social media through feature selection and machine learning

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Multimedia Tools and Applications 2025年 1-25页

作者： Gurrapu, Neelima Baydeti, Nagaraju Department of Computer Science and Engineering National Institute of Technology Nagaland Nagaland Chumukedima797 103 India

Recently, Rumor Spreading over Online Social Media is found as one of the serious issue, which causes severe damage to society, organization and individuals. To control the rumor spread, rumor detection is found as one of the probable solution and gained huge interest in the research community. However, most of the past methods characterize the rumormonger behavior based on their own profiles and completely neglected the reputation features. A hybrid method for rumor identification by combining the user reputation feature with other features like user features, meta-content based features and linguistic features is proposed. The major novelty lies at the representation of users through their interaction with his/her followers in different communities. The own profile based features can be evaded easily but the reputation of follows cannot be evaded easily. Totally 44 features are used to characterize each tweet and trained through different classifiers. Four classifiers namely K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are used to train the system and the performance is analyzed through two standard datasets namely Zubiaga and Kwon. The obtained results show that the proposed method can recognize rumor with higher F1-score. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature Selection

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Multi-view stereo algorithms based on deep learning: a survey

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Multimedia Tools and Applications 2025年第6期84卷 2877-2908页

作者： Huang, Hongbo Yan, Xiaoxu Zheng, Yaolin He, Jiayu Xu, Longfei Qin, Dechun Computer School Beijing Information Science & Technology University Beijing China Institute of Computing Intelligence Beijing Information Science & Technology University Beijing China Foreign Languages School Beijing Information Science & Technology University Beijing China

Multi-View Stereo (MVS) is a long-standing and fundamental task in computer vision, which aims to reconstruct the 3D geometry of a scene from a set of overlapping images. With known camera parameters, MVS matches pixels across images to compute dense correspondences and recover 3D points. Traditional MVS methods often encounter difficulties in addressing complex scenes due to limitations in robustness. However, recent advancements in deep-learning-based MVS have demonstrated remarkable performance gains, attributed to their enhanced feature extraction and cost volume processing capabilities. This comprehensive survey delves into deep-learning-based MVS algorithms, highlighting key challenges and avenues for future exploration. Our focus is on studies aimed at mitigating algorithmic memory requirements and elevating processing speeds. To better understand the developments in this field, we propose a new taxonomy. The deep-learning-based MVS algorithms are categorized into two categories: scene-oriented and view-oriented methods. Scene-oriented methods typically employ voxels or neural implicit representations as the representative form of the scene and infer the complete scene directly. View-oriented methods focus on estimating the depth map of a single view. Detailed classifications and reviews have been conducted for each category. Furthermore, we introduce several widely used datasets and metrics and empirical evaluation of representative algorithms across three popular datasets. This article concludes by discussing prevalent challenges in the MVS domain and promising research directions for the future. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Stereo image processing

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The highest melting point material:Searched by Bayesian global optimization with deep potential molecular dynamics

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Journal of Advanced Ceramics 2023年第4期12卷 803-814页

作者： Yinan Wang Bo Wen Xingjian Jiao Ya Li Lei Chen Yujin Wang Fu-Zhi Dai Artificial Intelligence for Science Institute Beijing 100084China Science and Technology on Advanced Functional Composite Laboratory Aerospace Research Institute of Materials&Processing TechnologyBeijing 100076China College of Computer Science and Technology Heilongjiang Institute of TechnologyHarbin 150001China Institute for Advanced Ceramics School of Materials Science and EngineeringHarbin Institute of TechnologyHarbin 150001China Key Laboratory of Advanced Structural-Functional Integration Materials&Green Manufacturing Technology Harbin Institute of TechnologyHarbin 150001China DP Technology Bejing 100080China

The interest in refractory materials is increasing rapidly in recent decades due to the development of hypersonic ***,the substance that has the highest melting point(Tm)keeps a secret,since precise measurements in extreme conditions are overwhelmingly *** the present work,an accurate deep potential(DP)model of a Hf-Ta-C-N system was first trained,and then applied to search for the highest melting point material by molecular dynamics(MD)simulation and Bayesian global optimization(BGO).The predicted melting points agree well with the experiments and confirm that carbon site vacancies can enhance the melting point of rock-saltstructure *** solid solution with N is verified as another new and more effective melting point enhancing approach for HfC,while a conventional routing of the solid solution with Ta(e.g.,HfTa_(4)C_(5))is not suggested to result in a maximum melting *** highest melting point(~4236 K)is achieved with the composition of HfCo.638No.271,which is~80 K higher than the highest value in a Hf-C binary *** mechanism of the N addition is believed to be unstable C-N and N-N bonds in liquid phase,which reduces liquid phase entropy and renders the liquid phase less *** improved melting point and less gas generation during oxidation by the addition of N provide a new routing to modify thermal protection materials for the hypersonic vehicles.

关键词： melting point(T_(m)) carbides carbonitrides deep potential(DP) Bayesiannglobal optimization(BGO)

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Video based CLBP rehabilitation exercise classification using machine learning and deep learning

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Multimedia Tools and Applications 2025年 1-24页

作者： Singh, Manvendra Ansari, Md. Sarfaraj Alam Govil, Mahesh Chandra Department of Computer Science National Institute of Technology Sikkim Ravangla Sikkim South Sikkim737139 India

According to WHO data, Chronic Low Back Pain (CLBP) is one of the leading causes of disability. Exercise plays a significant role in CLBP rehabilitation. Proper ordering of exercise is further important for the rehabilitation of patients because it affects the effectiveness of exercise training. Unfortunately, patients fail to do the correct sequence when performing the exercises. The challenge of exercise recognition stems from data quality issues and noise. Effectively recognizing different poses necessitates appropriate datasets and classification architecture. To pursue the problem, a novel method is proposed for video-based exercise recognition and pose estimation that does not require wearable devices. The proposed model formulates with 2D coordinates obtained from the 2D poses. The model has considered 2D coordinates of 18 human body joints as the primary feature to classify different exercises. Using these coordinates, a dataset is constructed for eight different exercises. Each exercise represents a class in the dataset for training and evaluating the Machine learning (ML) and Deep learning (DL) models for exercise recognition and tracking. The performance of the models is evaluated and demonstrated its effectiveness in detecting and classifying various exercises with high accuracy for various ML/DL models. Random Forest (RF) and K-Nearest Neighbors (KNN) have an accuracy of 99.97% and 99.92%, respectively, which outperforms other ML models. Convolutional Neural Network (CNN) and MobilenetV3 have better accuracy of 97.23% and 97.45% in comparison to other state-of-art DL models using eight exercise class datasets. Furthermore, the proposed models provide better accuracy compared to the earlier reported similar approaches in the literature. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

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Machine Learning Based Intelligent Management System for Energy Storage Using Computing Application

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2024年 11卷 1-6页

作者： Panigrahi, Bhawani Sankar Kanna, R. Kishore Das, Pragyan Paramita Sahoo, Susanta Kumar Dutta, Tanusree Department of Computer Science & Engineering GITAM Institute of Technology GITAM University Vishakhapatnam India Chennai India Department of Computer Science & Engineering Silicon Institute of Technology Odisha Bhubaneswar India Odisha Sarang India Hyderabad India

INTRODUCTION: Cloud computing, a still emerging technology, allows customers to pay for services based on usage. It provides internet-based services, whilst virtualization optimizes a PC’s available resources. OBJECTIVES: The foundation of cloud computing is the data center, comprising networked computers, cables, electricity components, and various other elements that host and store corporate data. In cloud data centers, high performance has always been a critical concern, but this often comes at the cost of increased energy consumption. METHODS: The most problematic factor is reducing power consumption while maintaining service quality and performance to balance system efficiency and energy use. Our proposed approach requires a comprehensive understanding of energy usage patterns within the cloud environment. RESULTS: We examined power consumption trends to demonstrate that with the application of the right optimization principles based on energy consumption models, significant energy savings can be made in cloud data centers. During the prediction phase, tablet optimization, with its 97 % accuracy rate, enables more accurate future cost forecasts. CONCLUSION: Energy consumption is a major concern for cloud data centers. To handle incoming requests with the fewest resources possible, given the increasing demand and widespread adoption of cloud computing, it is essential to maintain effective and efficient data center strategies. © (2024) Panigrahi, B.S. et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

关键词： Machine learning

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Searching, Navigating, and Querying Arguments and Debates: Tools, Languages, and Methodologies

Journal of Applied Logics

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Journal of Applied Logics 2025年第3期12卷 295-322页

作者： Flouris, Giorgos Lawrence, John Zografistou, Dimitra Institute of Computer Science FORTH Heraklion Greece Centre for Argument Technology University of Dundee United Kingdom

Recent developments in Web technologies have transformed Web users from passive consumers to active creators of digital content. As users see the Web as a means to enable dialogical exchange, debating, and commenting on products, services or events, a significant portion of web content is of argumentative form. This content can be unstructured, e.g., free-text, or (semi-)structured, both at the debate level, e.g., through a reply structure, and/or at the argument level, e.g., by requiring a specific argument format. The plethora of arguments online is useful only with the support of appropriate tools for identifying relevant arguments for any given information need. Depending on the form of the debate, the context, and the application at hand, this identification could require capabilities for simple keyword-based searching, navigational or explorational capabilities, the ability to perform analytical queries, as well as the ability to perform more complex searches and queries that involve the arguments’ (or debates’) structure and interrelationships. In this article, we provide a short survey of various tools and languages for searching, exploring and querying arguments, aiming to highlight the main advances in this area. © 2025, College Publications. All rights reserved.

关键词： Structured Query Language

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ETiSeg-Net: edge-aware self attention to enhance tissue segmentation in histopathological images

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Multimedia Tools and Applications 2024年 1-21页

作者： Rashmi, R. Girisha, S. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Digital pathology employing Whole Slide Images (WSIs) plays a pivotal role in cancer detection. Nevertheless, the manual examination of WSIs for the identification of various tissue regions presents formidable challenges due to its labor-intensive nature and subjective interpretation. Convolutional Neural Network (CNN) based semantic segmentation algorithms have emerged as valuable tools for assisting in this task by automating ROI delineation. The incorporation of attention modules and carefully designed loss functions has shown promise in further augmenting the performance of these algorithms. However, there exists a notable gap in research regarding the utilization of attention modules specifically for tissue segmentation, thereby constraining our comprehension and application of these modules in this context. This study introduces ETiSeg-Net (Edge-aware self attention to enhance Tissue Segmentation), a CNN-based semantic segmentation model that uses a novel edge-based attention module to achieve effective delineation of class boundaries. In addition, an innovative iterative training strategy is devised to efficiently optimize the model parameters. The study also conducts a comprehensive investigation into the impact of attention modules and loss functions on the efficacy of semantic segmentation models. Qualitative and quantitative evaluations of these semantic segmentation models are conducted using publicly available datasets. The findings underscore the potential of attention modules in enhancing the accuracy and effectiveness of tissue semantic segmentation. © The Author(s) 2024.

关键词： Semantic Segmentation

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A Graph-Based Model for Leveraging Spatial and Fingerprint Similarity in RF-based Indoor Positioning

A Graph-Based Model for Leveraging Spatial and Fingerprint S...

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2024 Work-in-Progress in Hardware and Software for Location Computation, WIPHAL 2024

作者： Self, Liam Mark Wassell, Ian James Department of Computer Science and Technology University of Cambridge United Kingdom

A limitation on conventional deep learning methods for fingerprint-based indoor positioning is the difficulty in leveraging the underlying geometry of the environment. Solutions to this involving Graph Neural Networks have been proposed, however these place a dominant focus on the geometric relationships between reference points (RPs), which are often sparse in number, and ignore the relationships between the training data. In this work, we present a proof-of-concept model for RF-based indoor positioning based on Graph Neural Networks that captures both the spatial relationships between RPs and the similarity between fingerprints of the training database. We use a Graph Attention Network to train a graph composed of 31 000 power readings gathered from a large office simulation of a environment produced using a commercial ray-tracer, and a Graph Convolutional Network to model the spatial relationships between RPs. Preliminary evaluation shows a positioning accuracy of 0.78 m, a 29% improvement over benchmark models using kNN, MLP and Random Forest, and suggests that combining both graph models outperforms the use of either model independently. © 2024 Copyright for this paper by its authors.

关键词： Graph neural networks

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Scalable and High-Performance Large-Scale Dynamic Graph Storage and Processing System

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ACM Transactions on Storage 2025年第2期21卷 1-34页

作者： Wang, Rui Zong, Weixu He, Shuibing Li, Yongkun Xu, Yinlong Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China University of Science and Technology of China Hefei China Computer Science University of Science and Technology of China Heifei China

Existing in-memory graph storage systems that rely on DRAM have scalability issues because of the limited capacity and volatile nature of DRAM. The emerging persistent memory (PMEM) offers us a chance to solve these issues through its larger capacity and non-volatile characteristics. However, simply adapting existing DRAM-based graph storage systems to PMEM would result in inefficient PMEM stores and accesses, including high read and write amplification to PMEM, imbalanced work division for PMEM accesses, and costly remote PMEM access across NUMA nodes. These issues severely limit the performance of large graph processing. In this article, we aim at achieving scalable and high-performance graph processing in PMEM. We first propose an XPLine-friendly graph storage model that uses vertex-centric graph buffering, hierarchical vertex buffer managing, and in-place vertex block merging to optimize PMEM graph storage. Furthermore, we develop a scalable graph processing model that leverages multi-threaded work dividing and NUMA-friendly graph accessing to optimize PMEM graph accesses. Based on these techniques, we implement XPGraph, a PMEM-based graph storage system for large-scale evolving graphs, and several variants for different system settings. Our experiments demonstrate that XPGraph surpasses the state-of-the-art in-memory graph storage system on a PMEM-based system by 3.07× to 4.99× in update performance and up to 5.87× in query performance, and performs much better in highly parallel multi-threaded scenarios. © 2025 Copyright held by the owner/author(s).

关键词： Dynamic random access storage

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