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Ethereum smart contract security research:survey and future research opportunities

Frontiers of Computer science 2021年第2期15卷 143-160页

作者： Zeli WANG Hai JIN Weiqi DAI Kim-Kwang Raymond CHOO Deqing ZOU National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabHubei Engineering Research Center on Big Data SecurityWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Blockchain has recently emerged as a research trend,with potential applications in a broad range of industries and *** particular successful Blockchain technology is smart contract,which is widely used in commercial settings(e.g.,high value financial transactions).This,however,has security implications due to the potential to financially benefit from a security incident(e.g.,identification and exploitation of a vulnerability in the smart contract or its implementation).Among,Ethereum is the most active and ***,in this paper,we systematically review existing research efforts on Ethereum smart contract security,published between 2015 and ***,we focus on how smart contracts can be maliciously exploited and targeted,such as security issues of contract program model,vulnerabilities in the program and safety consideration introduced by program execution *** also identify potential research opportunities and future research agenda.

关键词： smart contract security blockchain vulnerability unreliable data

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HerbSimNet: Deep Learning -Based Classification of Indian Medicinal Plants with High Inter-Class Similarities

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Procedia Computer science 2025年 258卷 765-774页

作者： N. Shobha Rani Bhavya K R Pushpa B.R. Ragavendra M. Devadas Department of Artificial Intelligence and Data Science GITAM School of Technology Bengaluru GITAM (Deemed to be) University India Department of Computer Science and Engineering GITAM School of Technology Bengaluru GITAM (Deemed to be) University India Department of Computer Science School of Computing Mysuru Amrita Vishwa Vidyapeetham India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Medicinal plant species recognition is important across diverse sectors such as Ayurveda, agriculture, environment conservation and botanical research. Specific groups of plants in Indian medicinal plant ecosystem exhibit significant inter-class similarities due to varying abundance and ecological factors. To address the challenges involved in the process of classifying these species in this work a deep learning model Herb-SimNet is proposed. The Herb-SimNet analyzes similarity of plant species over other plant species using vision based deep learning and machine learning techniques. The proposed model works based on the combination of wavelet features and convolutional features extracted using three sequential convolution layers to extract the prominent features that distinguish variations among the inter class similarity plant species. To perform experiments, a dataset is created by capturing medicinal plant leaf images using box model in plain background and uniform lighting. A smart phone captured twelve Indian medicinal plant species comprising of about 1400+ samples that belongs different plant species but similar morphological structure is collected. Baseline experiments are carried out between Herb-SimNet and other state-of-the-art deep learning models for classification based on the proposed dataset. The outcomes demonstrate that Herb_SimNet provides clear interpretation one plant variety with others and achieves superior accuracy in prediction than that of state-of-the-art approaches. Furthermore, the model demonstrates better generalization towards the other inter-class similarity groups considered for testing. In conclusion, the proposed dataset and Herb-SimNet plays a a crucial role in advancement of research concerning Indian medicinal plant species classification resulting into enhancement of AI-based technology for biodiversity conservation and ethnobotanical studies.

关键词： Classification inter-class similarities confusing plant species medicinal plants wavelet image features

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Deep Learning-Based Approach for Calorie Estimation in Indian Foods

Deep Learning-Based Approach for Calorie Estimation in India...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Naman Chauhan Aman Kumar Gudi Vishnu Teja Bharati Mohan G Prasanna Kumar R Yamani Kakarla Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Business Data Scientist Kinaxis Halifax Nova Scotia Canada

People in the modern era are increasingly concerned with their diet and food choices to prevent developing chronic diseases like high blood pressure and diabetes. With the increasing usage of smartphones and technology, an application to automatically monitor their diet would be a great boon, especially for the younger generation. This research work proposes automatic food calorie estimation from the food images using deep learning technology. The proposed work has three main phases. In the first phase, the food image is categorised as one of the types, for example, boiled, fried in oil or junk. A novel deep neural network namely MobileNetV2 which is a 15-layer model architecture is used for this purpose. In the second phase, the quantity of the labelled food is estimated. The third and final stage involves computing the calories present in the food using the Google search API. The details of ingredients present in each type of food are used for this purpose. Classification accuracy, food amount calculation precision, and calorie estimation accuracy are used assessing the effectiveness of the proposed approach. The proposed model is producing results with an overall accuracy of 95.21 percent which is better than the existing architecture.

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Answering from Sure to Uncertain: Uncertainty-Aware Curriculum Learning for Video Question Answering

arXiv

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arXiv 2024年

作者： Li, Haopeng Ke, Qiuhong Gong, Mingming Drummond, Tom School of Computing and Information Systems University of Melbourne Australia Department of Data Science & AI Monash University Australia School of Mathematics and Statistics University of Melbourne Australia

While significant advancements have been made in video question answering (VideoQA), the potential benefits of enhancing model generalization through tailored difficulty scheduling have been largely overlooked in existing research. This paper seeks to bridge that gap by incorporating VideoQA into a curriculum learning (CL) framework that progressively trains models from simpler to more complex data. Recognizing that conventional self-paced CL methods rely on training loss for difficulty measurement, which might not accurately reflect the intricacies of video-question pairs, we introduce the concept of uncertainty-aware CL. Here, uncertainty serves as the guiding principle for dynamically adjusting the difficulty. Furthermore, we address the challenge posed by uncertainty by presenting a probabilistic modeling approach for VideoQA. Specifically, we conceptualize VideoQA as a stochastic computation graph, where the hidden representations are treated as stochastic variables. This yields two distinct types of uncertainty: one related to the inherent uncertainty in the data and another pertaining to the model’s confidence. In practice, we seamlessly integrate the VideoQA model into our framework and conduct comprehensive experiments. The findings affirm that our approach not only achieves enhanced performance but also effectively quantifies uncertainty in the context of VideoQA. Copyright © 2024, The Authors. All rights reserved.

关键词： Curricula

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Failures in the Loop: Human Leadership in AI-Based Decision-Making

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2024年第1期5卷 2-13页

作者： Michael, Katina Schoenherr, Jordan Richard Vogel, Kathleen M. Arizona State University School for the Future of Innovation in Society TempeAZ85287 United States Arizona State University School of Computing and Augmented Intelligence TempeAZ85287 United States Applied Ai Institute Concordia University Department of Psychology MontrealQCH3G 1M8 Canada Institute for Data Science Carleton University Department of Psychology OttawaONK1S 5B6 Canada

The dark side of AI has been a persistent focus in discussions of popular science and academia (Appendix A), with some claiming that AI is 'evil' [1]. Many commentators make compelling arguments for their concerns. Techno-elites have also contributed to the polarization of these discussions, with ultimatums that in this new era of industrialized AI, citizens will need to '[join] with the AI or risk being left behind' [2]. With such polarizing language, debates about AI adoption run the risk of being oversimplified. Discussion of technological trust frequently takes an all-or-nothing approach. All technologies - cognitive, social, material, or digital - introduce tradeoffs when they are adopted, and contain both 'light and dark' features [3]. But descriptions of these features can take on deceptively (or unintentionally) anthropomorphic tones, especially when stakeholders refer to the features as 'agents' [4], [5]. When used as an analogical heuristic, this can inform the design of AI, provide knowledge for AI operations, and potentially even predict its outcomes [6]. However, if AI agency is accepted at face value, we run the risk of having unrealistic expectations for the capabilities of these systems. © 2020 IEEE.

关键词： Decision making

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Bayesian Over-the-Air FedAvg via Channel Driven Stochastic Gradient Langevin Dynamics

Bayesian Over-the-Air FedAvg via Channel Driven Stochastic G...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Boning Zhang Dongzhu Liu Osvaldo Simeone Guangxu Zhu School of Computing Science University of Glasgow Department of Engineering King's College London Shenzhen Research Institute of Big Data

The recent development of scalable Bayesian inference methods has renewed interest in the adoption of Bayesian learning as an alternative to conventional frequentist learning that offers improved model calibration via uncertainty quantification. Recently, federated averaging Langevin dynamics (FALD) was introduced as a variant of federated averaging that can efficiently implement distributed Bayesian learning in the presence of noiseless communications. In this paper, we propose wireless FALD (WFALD), a novel protocol that realizes FALD in wireless systems by integrating over-the-air computation and channel-driven sampling for Monte Carlo updates. Unlike prior work on wireless Bayesian learning, WFALD enables (i) multiple local updates between communication rounds; and (ii) stochastic gradients computed by mini-batch. A convergence analysis is presented in terms of the 2- Wasserstein distance between the samples produced by WFALD and the targeted global posterior distribution. Analysis and experiments show that, when the signal-to-noise ratio is sufficiently large, channel noise can be fully repurposed for Monte Carlo sampling, thus entailing no loss in performance.

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Real-time Stream Processing in IoT Environments 9

Real-time Stream Processing in IoT Environments

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9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Rajasekar, P. Bhosale, Rupali Shekar Indhumathi, C. Sandeep, K.V. Prasanthi Kumari, N. Rajendiran, M. School of Computing Srm Institute of Science And Technology Department of Data Science And Business Systems Tamil Nadu Kattankulathur603203 India Bharati Vidyapeeth Institute of Technology Department of Electronics And Telecommunication Engineering Maharashtra Navi Mumbai400614 India Rmk College of Engineering And Technology Department of Computer Science And Business Systems Tamil Nadu Puduvoyal601206 India Dayananda Sagar College of Engineering Department of Electronics & Telecommunication Engineering Karnataka Bengaluru560111 India School of Computer Sciences University of Petroleum And Energy Studies Uttarakhand 248007 Dehradun India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

New IoT devices generate massive real-time data. Fast, real-time stream processing is needed for IoT data insight extraction. There are pros and downsides to real-time stream processing for the Internet of Things (IoT). This article analyzes present methods and technologies. The inquiry begins with large-scale, real-time IoT data analysis. Real-time analytics and bulk processing limits are important for IoT decision-making. We then investigate IoT stream processing frameworks, methodologies, and systems' complexity. We explore Internet of Things technology selection and tradeoffs between scalability, accuracy, latency, and real-time processing system scalability. It covers real-time stream processing system design and deployment and risk mitigation. Case studies and use cases highlight real-time IoT stream processing. Healthcare, smart city, and industrial IoT operational efficiency and proactive decision-making improve with real-time data. Finally, the report suggests the Internet of Things real-time stream processing research. New IoT data analytics protocols, methods, and technologies are highlighted. We teach real-time stream processing to IoT academics, practitioners, and decision-makers. They could then develop more efficient and scalable IoT data stream optimization solutions. © 2024 IEEE.

关键词： Edge computing

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SleepCVT: Combination of Convolution Neural Network and Vision Transformer for Automatic Sleep Scoring

SleepCVT: Combination of Convolution Neural Network and Visi...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chih-En Kuo Hao-Hsiang Wang Tsung-Hua Lu Institute of Data Science and Information Computing National Chung Hsing University Taichung Taiwan Department of Psychiatry National Cheng Kung University Hospital Tainan Taiwan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Sleep scoring is a subjective and time-consuming job. In this paper, SleepCVT that combination of convolution neural network and vision transformer for automatic sleep scoring was proposed. All-night sleep physiological signals from 994 patients with sleep disorders were used. First, the all-night polysomnography signals including electroencephalogram, electrooculogram, and electromyogram were segmented into 60-sec segments with 30-sec overlapping. Subsequently, each segment was transformed into spectrograms by continuous wavelet transform and a simple merged processing was applied to generate the merged spectrograms. Next, the merged spectrogram images were utilized as an input of our proposed SleepCVT with horizontal attention. The experimental results showed that the accuracy, kappa coefficient, and F1 score of the proposed method were 82.1%, 0.754, and 79.65%, respectively. The results proved that the proposed SleepCVT had highly accuracy for patients with sleep disorder.

关键词： Image segmentation Computer vision Accuracy Continuous wavelet transforms Sleep Convolution Neural networks

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Advancing Automated Knowledge Transfer in Evolutionary Multitasking via Large Language Models

arXiv

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arXiv 2024年

作者： Huang, Yuxiao Lv, Xuebin Wu, Shenghao Wu, Jibin Feng, Liang Tan, Kay Chen The Department of Computing The Hong Kong Polytechnic University Hong Kong College of Computer Science Chongqing University Chongqing400044 China The Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong

Evolutionary Multi-task Optimization (EMTO) is a paradigm that leverages knowledge transfer across simultaneously optimized tasks for enhanced search performance. To facilitate EMTO’s performance, various knowledge transfer models have been developed for specific optimization tasks. However, designing these models often requires substantial expert knowledge. Recently, large language models (LLMs) have achieved remarkable success in autonomous programming, aiming to produce effective solvers for specific problems. In this work, a LLM-based optimization paradigm is introduced to establish an autonomous model factory for generating knowledge transfer models, ensuring effective and efficient knowledge transfer across various optimization tasks. To evaluate the performance of the proposed method, we conducted comprehensive empirical studies comparing the knowledge transfer model generated by the LLM with existing state-of-the-art knowledge transfer methods. The results demonstrate that the generated model is able to achieve superior or competitive performance against hand-crafted knowledge transfer models in terms of both efficiency and effectiveness. © 2024, CC BY.

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Personalized Federated Learning with Enhanced Implicit Generalization

Personalized Federated Learning with Enhanced Implicit Gener...

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International Joint Conference on Neural Networks (IJCNN)

作者： Heping Liu Songbai Liu Junkai Ji Qiuzhen Lin Jianyong Chen Kay Chen Tan College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Integrating personalization into federated learning is crucial for addressing data heterogeneity and surpassing the limitations of a single aggregated model. Personalized federated learning excels at capturing inter-client similarities and meeting diverse client needs through custom-made models. However, even with personalized approaches, it’s essential to aggregate knowledge among clients to ensure universal benefits. This paper proposes Federated Dual Objectives and Dual Models (FedDodm), a novel approach that employs two independent models to separately address explicit personalization and implicit generalization objectives in personalized federated learning. By treating these objectives as distinct loss functions and training models accordingly, we achieve a balance between the two through a fusion method. Extensive experiments across various models and learning tasks demonstrate that FedDodm outperforms state-of-the-art federated learning approaches, marking a significant advancement in effectively integrating personalized and generalized knowledge.

关键词： Training Federated learning Aggregates Neural networks data models

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