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International Symposium on Digital Forensic and Security (ISDFS)

作者： Semih Cal Ramazan Ozgur Dogan Hulya Dogan Department of Engineering Computer Science & Physics Otterbein University Westerville OH USA Department of Artificial Intelligence Engineering Faculty of Computer and Information Sciences Trabzon University Trabzon Türkiye Department of Software Engineering Faculty of Engineering Karadeniz Technical University Trabzon Türkiye

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

The usage of various drugs has increased significantly in recent years, leading to a higher possibility of drug-drug interactions (DDIs). The concurrent usage of multiple drugs can result in potentially hazardous interactions, making it crucial to foresee DDIs to prevent adverse effects and enhance patient safety. Traditional DDI prediction methods often require extensive examinations, which can be time consuming and resource intensive. As a result, automatic DDI prediction methods have gained attention in the literature, offering clinicians support for making more accurate decisions and designing effective treatment plans. Despite progress in DDI prediction studies, substantial challenges remain in the field. Our study addresses these challenges by proposing a deep learning-based model leveraging drug features. Specifically, this study introduces an enhanced multi-head self-attention transformer-based method, which incorporates pharmacological features to achieve improved performance. The proposed method consists of two primary stages: feature extraction and model design. To evaluate the efficacy of the proposed method, performance evaluation procedures -Accuracy (ACC), Precision (PRE), Recall (REC), and F -Score-are utilized. Comparative experiments are conducted with several state-of-the-art methods on a data set specifically created for this study. Out of all, the proposed method achieves mean values of ACC, PRE, REC, and F-Score as 87.49%, 87.19%, 82.76%, and 84.56 %, respectively, surpassing the performance of other methods. The results unequivocally demonstrate the effectiveness and superiority of the proposed method in predicting DDIs.

关键词： Drugs Learning systems Accuracy Predictive models Feature extraction Transformers Robustness Safety Matrix decomposition Security

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ContraPonzi: Smart Ponzi Scheme Detection for Ethereum via Contrastive Learning 23

ContraPonzi: Smart Ponzi Scheme Detection for Ethereum via C...

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Proceedings of the 2023 4th Asia Service sciences and software engineering Conference

作者： Jiajing Wu Jieli Liu Jinze Chen Ting Chen Jingwei Li Shuwei Song Jiahao He School of Software Engineering Sun Yat-sen University China School of Computer Science and Engineering Sun Yat-sen University China School of Cyber Security University of Electronic Science and Technology of China China

ISBN: (纸本)9798400708534

In recent years, blockchain technology has witnessed rapid development and received considerable attention. However, its decentralized and pseudonymous nature has also attracted many criminal activities. Among them, Ponzi schemes, a classic form of financial fraud, also hide their true face in smart contracts, causing huge losses to blockchain users. Although numerous methods have been proposed to detect Ponzi contracts, these methods still have limitations in terms of generalization and feature learning. To address this issue, we conduct research on Ethereum, the currently largest blockchain platform enabling smart contracts, and propose a novel contrastive learning-based smart Ponzi scheme detection method named ContraPonzi. This method first extracts control flow graph information from bytecodes and models it as attribute graphs that preserve both semantic and structural information. Next, by augmenting the bytecode data of multi-version compilers and maximizing the graph representation similarity of multi-version bytecodes of the same contract, a pre-training graph encoder is obtained and then can be used in Ponzi contract detection. Experimental results on real-world data demonstrate that ContraPonzi is significantly superior to the state-of-the-art in Ethereum Ponzi scheme detection.

关键词： Contrastive Learning Ethereum Ponzi Schemes Smart contract

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Optimal Multi-Bitrate Video Caching and Processing in Edge Computing: A Stackelberg Game Approach

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IEEE Internet of Things Journal 2025年

作者： Guo, Min Zhang, Di Xing, Weiwei Shao, Xun Liu, Zhi Zhang, Yaoxue Beijing Jiaotong University School of Software Engineering Beijing100044 China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Aichi441-8580 Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan Tsinghua University Department of Computer Science and Technology Beijing100084 China

By caching and transcoding video files on edge servers, video edge caching (VEC) can alleviate network congestion and improve user experience. To achieve this, VEC needs to address resource allocation and pricing problems and develop caching and transcoding strategies. However, existing works have not effectively coordinated and resolved these issues. Towards this end, we propose a game-theoretic multi-bitrate video caching and processing method for VEC. Specifically, to address these interdependent issues concurrently, we first incorporate multidimensional strategies into the system model and formalize the profit maximization problems of the network service provider and multiple video providers (VPs) as a constrained multi-objective optimization problem. Second, we transform the problem into a Stackelberg game and identify a Nash equilibrium as the solution. To solve the game problem, we decompose the game process into two stages focusing on resource competition and price determination, respectively. Then, we address the problems of transcoding and caching strategies development, and resource allocation in parallel across varying resource price conditions. The transcoding strategy is determined based on the constraints imposed by delay, value, and the source video. For the caching strategy development and resource allocation, we transform them into two grouped knapsack problems and apply the "dominant strategy» from game theory to identify the "feasible strategy» of VPs. Finally, we determine the resource prices in a traversal manner. Theoretical analysis shows that our proposed method achieves the optimal solution. Experimental results show that our algorithm achieve a 6.04% increase in overall profit compared to the greedy strategy algorithm. © 2025 IEEE.

关键词： Costs

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Adversarial Contrastive Graph Augmentation with Counterfactual Regularization 39

Adversarial Contrastive Graph Augmentation with Counterfactu...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Long, Tao Zhang, Lei Zhang, Liang Cui, Laizhong College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China Shenzhen Research Institute of Big Data Guangdong Shenzhen China

ISBN: (纸本)157735897X

With the advancement of graph representation learning, self-supervised graph contrastive learning (GCL) has emerged as a key technique in the field. In GCL, positive and negative samples are generated through data augmentation. While recent works have introduced model-based methods to enhance positive graph augmentations, they often overlook the importance of negative samples, relying instead on rule-based methods that can fail to capture meaningful graph patterns. To address this issue, we propose a novel model-based adversarial contrastive graph augmentation (ACGA) method that automatically generates both positive graph samples with minimal sufficient information and hard negative graph samples. Additionally, we provide a theoretical framework to analyze the process of positive and negative graph augmentation in self-supervised GCL. We evaluate our ACGA method through extensive experiments on representative benchmark datasets, and the results demonstrate that ACGA outperforms state-of-the-art baselines. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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A data science approach to Predict the university Students at risk of semester dropout: Bangladeshi university Perspective 5

A data science approach to Predict the University Students a...

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5th International Conference on Trends in Electronics and Informatics, ICOEI 2021

作者： Rafiq, Md Abdur Rabbi, Abir Mahamud Ahammad, Rasel University of Asia Pacific Computer Science and Engineering Dhaka Bangladesh Daffodil International University Software Engineering Dhaka Bangladesh

ISBN: (纸本)9781665415712

In Bangladeshi institutions, the likelihood of student semester dropout has increased in recent years. A large number of university students, particularly in science background disciplines, are enrolled in a variety of undergraduate courses. Nevertheless, the perfection rate is poor. In general, students drop out for a variety of reasons, including academic, family, personal, and political concerns. The main focus of this study is to predict the risk of semester dropout in Bangladesh so that the massive dropout can be stopped. In this research, the current student information is preprocessed to discover the major reason as well as students whoever at the threat of semester dropout will help to grow a new structure in the area of higher education. To predict the dropout risk, random forest and logistic regression were practiced for obtaining the detection model. © 2021 IEEE.

关键词： Students

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Adaptive IoT Decision Making in Uncertain Environments

Adaptive IoT Decision Making in Uncertain Environments

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IEEE International Conference on Smart Internet of Things (SmartIoT)

作者： Yan Wang Liwei Zheng Jingjing He Zhanqi Cui Computer School Software Engineering Research Center Beijing Information Science and Technology University Beijing China

The rapid growth of the Internet of Things (IoT) has led to widespread deployment of IoT systems in domains such as smart homes, healthcare, and transportation. However, IoT systems often operate under uncertainty, making it difficult to predict and control their behavior. In this paper, we propose an adaptive decision making approach for IoT systems in uncertain, dynamic environments. We present a framework with perception, decision and execution layers to handle uncertainty in IoT systems. The perception layer senses the environment and system state. The decision layer employs an optimized deep Q-network algorithm (Ad-DQN) specifically designed to handle uncertain environments, enabling it to make informed decisions based on learned experiences. The execution layer implements the actions. We demonstrate the framework on an intelligent air conditioning system as a case study of an IoT system operating under uncertainty. The Ad-DQN based decision layer adapts the air conditioning control policy to maximize comfort while minimizing energy usage. Experiments show our method outperforms traditional DQN method in uncertain environments.

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Detection of Deepfakes: Protecting Images and Vedios Against Deepfake

Detection of Deepfakes: Protecting Images and Vedios Against...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Ying Tian Wang Zhou Amin Ul Haq Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China School of Computer and Software Engineering Xihua University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

In recent years, with the rapid development of deep learning and computer vision technology, the forgery technology of images and videos has become increasingly mature, posing new challenges to information security and social stability. Behind the re-evolution of deepfake lies the rampant proliferation of fake content, which is used for election tampering, identity fraud, fraud, spreading fake news, and so on. To address these challenges, researchers are constantly exploring and developing image-based deepfake detection techniques, which aim to effectively identify and prevent deepfake content in images and videos. This article will introduce the current development status of deepfake detection technology, present its principles and methods in detail, and look ahead to its future development directions.

关键词： Deep learning Deepfakes computer vision Voting Neural networks Information security Information processing Forgery Fraud computer security

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On EDA-Driven Learning for SAT Solving 23

On EDA-Driven Learning for SAT Solving

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Min Li Zhengyuan Shi Qiuxia Lai Sadaf Khan Shaowei Cai Qiang Xu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong S.A.R Communication University of China China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China

ISBN: (纸本)9798350323481

We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability (SAT) problem. Unlike existing solutions trained on random SAT instances with relatively weak supervision, we propose applying the knowledge of the well-developed electronic design automation (EDA) field for SAT solving. Specifically, we first resort to logic synthesis algorithms to pre-process SAT instances into optimized and-inverter graphs (AIGs). By doing so, the distribution diversity among various SAT instances can be dramatically reduced, which facilitates improving the generalization capability of the learned model. Next, we regard the distribution of SAT solutions being a product of conditional Bernoulli distributions. Based on this observation, we approximate the SAT solving procedure with a conditional generative model, leveraging a novel directed acyclic graph neural network (DAGNN) with two polarity prototypes for conditional SAT modeling. To effectively train the generative model, with the help of logic simulation tools, we obtain the probabilities of nodes in the AIG being logic '1' as rich supervision. We conduct comprehensive experiments on various SAT problems. Our results show that, DeepSAT achieves significant accuracy improvements over state-of-the-art learning-based SAT solutions, especially when generalized to SAT instances that are relatively large or with diverse distributions.

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An Intelligent Machine Learning Approach for Prediction of Road Accident Severity 3

An Intelligent Machine Learning Approach for Prediction of R...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Ragam, Prashanth Rahul, Namana Sri Balaji, Pandi Jyothi, Sankati Danaveni, Pavithra School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India VIT-AP University Software Development Cell Andhra Pradesh Amaravati522237 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad500090 India

ISBN: (纸本)9798331533205

Road accidents are a primary global concern for public safety, with India having a very high death toll. This study presents an intelligent machine learning approach to predict the severity of road accidents, contributing to the development of more effective and preventive measures. We utilized a comprehensive dataset of road accidents in India, employing advanced machine learning algorithms, including Histogram-based Gradient Boosting (HGB), Random Forest (RF), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN). Our methodology involved extensive data pre-processing, feature engineering, and model optimization. The Random Forest algorithm demonstrated superior performance, achieving an accuracy of 94.7% and an F1-score of 0.947. This research provides a robust predictive model and information on the key factors influencing the accident's severity. The novelty of our approach lies in the comprehensive feature engineering tailored to the Indian context and the application of ensemble learning techniques to capture complex, nonlinear relationships in accident data. The findings of this study have significant implications for policymakers, urban planners, and law enforcement agencies in their efforts to improve road safety. © 2025 IEEE.

关键词： Highway accidents

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Overlapped Pedestrian Detection Based on YOLOv5 in Crowded Scenes 3

Overlapped Pedestrian Detection Based on YOLOv5 in Crowded S...

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3rd International Conference on computer Vision, Image and Deep Learning and International Conference on computer engineering and Applications, CVIDL and ICCEA 2022

作者： Guo, Weijie Shen, Nanbo Zhang, Tianze Hunan University of Technology Department of Computer Science and Technology Zhuzhou China China University of Geosciences School of Information Engineering Beijing China Beijing University of Technology School of Software Beijing China

ISBN: (纸本)9781665459112

Pedestrian detection in a crowded environment is challenging for vehicle intelligent driving systems. At present, pedestrian detection algorithms have achieved great performance in detecting well-separated figures. However, pedestrians are generally overlapped in crowded scenes, resulting in slow detection speed, low detection accuracy, and poor robustness in pedestrian detection technology. In this paper, the YOLOv5 algorithm is used for pedestrian detection. In the aspect of data pretreatment, Mosaic data enhancement, unified image size, adaptive anchor frame calculation, and other processing are carried out for ***5 can detect targets at multiple scales, and CIOU_Loss and DIOU_nms are applied to the YOLOv5 algorithm. It can improve the recognition ability of the occlusion target and has a good detection effect on the detection of the occlusion pedestrian target through the training network of amplified data set. The verification experiment shows that the pedestrian detection model based on YOLOv5 has great detection accuracy and recall rate in detecting covered pedestrians. © 2022 IEEE.

关键词： Feature extraction

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