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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5844-5854页

作者： Patel, Manali Jariwala, Krupa Chattopadhyay, Chiranjoy Sardar Vallabhbhai National Institute of Technology Department of Computer Science and Engineering Surat395007 India FLAME University School of Computing and Data Sciences Pune412115 India

An accurate estimation of future stock prices can help investors maximize their profits. The current advancements in the area of artificial intelligence (AI) have proven prevalent in the financial sector. Besides, stock market prediction is difficult owing to the considerable volatility and unpredictability induced by numerous factors. Recent approaches have considered fundamental, technical, or macroeconomic variables to find hidden complex patterns in financial data. At the macro level, there exists a spillover effect between stock pairs that can explain the variance present in the data and boost the prediction performance. To address this interconnectedness defined by intrasector stocks, we propose a hybrid relational approach to predict the future price of stocks in the American, Indian, and Korean economies. We collected market data of large-, mid-, and small-capitalization peer companies in the same industry as the target firm, considering them as relational features. To ensure efficient feature selection, we have utilized a data-driven approach, i.e., random forest feature permutation (RF2P), to remove noise and instability. A hybrid prediction module consisting of temporal convolution and linear model (TCLM) is proposed that considers irregularities and linear trend components of the financial data. We found that RF2P-TCLM gave the superior performance. To demonstrate the real-world applicability of our approach in terms of profitability, we created a trading method based on the predicted results. This technique generates a higher profit than the existing approaches. © 2024 IEEE.

关键词： Profitability

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Position-aware Hypergraph Message-Passing Neural Network

Position-aware Hypergraph Message-Passing Neural Network

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Xinyu Jiang, Qize Zhang, Hanyuan Sun, Weiwei School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China

ISBN: (纸本)9798350368741

Hypergraph neural networks can model more flexible connectivity relationships, are used to model higher-order interactions, and have produced strong results in many real-world applications. However, the currently existing hypergraph neural networks need more exploration in capturing the global positional information of nodes in hypergraphs. Although there have been many explorations of the problem in graph neural networks, extending these approaches to hypergraphs is fraught with challenges. The major challenge is that hyperedges in hypergraphs are the other dimensional element of the incidence structure, have more flexible definitions than edges in graphs, and require more attention when learning global positional information. We propose a novel position-aware hypergraph message-passing neural network framework to address the above challenges. Specifically, we propose a global positional embedding learning approach that can separately model global positional information for nodes and hyperedges. At the same time, we also optimize the learning of local structures with hyperedges. Experiments on several publicly available benchmark datasets find that our proposed method outperforms many state-of-the-art methods. © 2025 IEEE.

关键词： Hypergraph Learning Hypergraph Neural Network Message Passing Neural Network

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MSR: A Multifaceted Self-Retrieval Framework for Microscopic Cascade Prediction 39

MSR: A Multifaceted Self-Retrieval Framework for Microscopic...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Hong, Dongsheng Chen, Chao Li, Xujia Wang, Shuhui Lin, Wen Liao, Xiangwen College of Computer and Data Science Fuzhou University China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Hong Kong University of Science and Technology Hong Kong College of Computer and Control Engineering Minjiang University China

ISBN: (纸本)157735897X

The microscopic cascade prediction task has wide applications in downstream areas like "rumor detection". Its goal is to forecast the diffusion routines of information cascade within networks. Existing works typically formulate it as a classification task, which fails to well align with the Social Homophily assumption, as it just use the features of "infected" users while neglecting those of "uninfected" users in representation learning. Moreover, these methods focus primarily on social relationships, thereby dismissing other vital dimensions like users' historical behavior and the underlying preferences behind it. To address these challenges, we introduce the MSR (Multifaceted Self-Retrieval) framework. During encoding, in addition to the existing social graph, we construct a preference graph to represent "behavioral preferences" and further propose a modified multi-channel GRAU for multi-view analysis of cascade phenomenon. For decoding, our approach diverges from classification-based methods by reformulating the task as an information retrieval problem that predicts the target user with similarity measures. Empirical evaluations on public datasets demonstrate that this framework significantly outperforms baselines on Hits@κ and MAP@κ, affirming its enhanced ability. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Taxonomies

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Obstacle-Avoiding Rectilinear Steiner Minimal Tree Algorithm Based on Deep Reinforcement Learning

Obstacle-Avoiding Rectilinear Steiner Minimal Tree Algorithm...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Lin, Zhenkun Zhu, Yuhan Huang, Xing Yang, Liliang Liu, Genggeng Fuzhou University College of Computer and Data Science Fuzhou China Northwestern Polytechnical University School of Computer Science Xi'an China

ISBN: (纸本)9798350312270

With the rapid development of the Industrial Internet of Things (IIoT), higher requirements have been put forward for hardware devices, and as the core part of hardware devices, the performance of chips is critical. The Obstacle-Avoiding Rectilinear Steiner Minimal Tree (OARSMT) problem is a classic issue in VLSI physical design, and having a shorter OARSMT can help reduce chip delays and energy consumption. This paper proposes a framework based on Deep Reinforcement Learning (DRL) to automatically learn and generate heuristic algorithms for solving the OARSMT problem. The core of our proposed method combines Graph Convolution Network (GCN) with DRL, where GCN extracts the graph-state feature information of Obstacle-Avoiding Steiner Tree (OAST) generated throughout the process, and DRL learns policy decisions under different graph states. Experimental results have confirmed our proposed framework outperforms existing manually designed work, indicating that our framework is a promising tool for solving the OARSMT problem. © 2023 IEEE.

关键词： Reinforcement learning

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Surveying the Impact of National Identity Card data Leak to the Security and Serenity in Indonesia

Surveying the Impact of National Identity Card Data Leak to ...

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2023 International Conference on Information Technology and Computing, ICITCOM 2023

作者： Aldifansa, Radithya Wicaksono, Nugroho Damar Nurfadilah, Adiguna Hirzi Santoso Gunawan, Alexander Agung Edbert, Ivan Sebastian School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Departement Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350359633

data is a crucial component that holds a lot of information. Whether it's information that is public or private. The data is obtained through a long process until in the end the data has been proven valid. Valid data is a high value asset owned by its users. National Identity Card (KTP) is an ID card that is useful for storing valid data on its holder. This card must be owned by everyone who lives in Indonesia. Because this card contains a lot of information on its holder, this card is a high value asset and many are targeted for taking and selling its data by unauthorized parties. As in the case of KTP data leaks that occurred in the RI Ministry of Social database which was carried out by a group of hackers and sold them in online forums. This case occurred for several reasons, including Human error, Malware, insiders with bad intentions, etc. The impact of data leaks can be misused by criminals, especially those related to fraud and social engineering. data leaks can cause huge losses. Therefore, the purpose of this study was to find out how much people know about the value of KTP and the impact that can be generated if their data is successfully retrieved by someone else. In this study, the author uses survey research whereas we are going to distribute some questionnaires to the public who have KTP according to the topic to be discussed and draw a conclusion out of it. In general, we concluded that most respondents still do not know the impact of leaking KTP data and the level of danger that occurs when it occurs. However, if the government can develop a high security service to secure customer personal data and provide knowledge of the importance of the value in KTP. So, the data can be maintained properly. © 2023 IEEE.

关键词： Malware

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Study of Cross-Site Request Forgery on Web-Based Application: Exploitations and Preventions 8

Study of Cross-Site Request Forgery on Web-Based Application...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Siahaan, Crisdeo Nuel Rufisanto, Mario Nolasco, Raymond Achmad, Said Siahaan, Chrisando Ryan Pardomuan Cyber Security Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Cross-Site Request Forgery (CSRF) is a prominent web exploit that continues to pose significant security risks, even on highly ranked websites. This research focuses on identifying the underlying vulnerability, understanding the techniques employed, and proposing effective preventive measures. The hybrid method which is systematic report review method and lab-based scenarios method is conducted to identify CSRF exploitation and prevention mechanisms. Using a scenario-based testing approach, this study investigated the cause and effect of CSRF attacks, uncovering numerous techniques for exploiting this vulnerability. Based on the report review, 35 CSRF vulnerability reports from the Hacker one Bug Bounty Platform are analyzed. The result shows there is a lack of awareness and implementation of primary defenses, resulting in a high violation of end-user data integrity. The result also extensively describes some of the profound causes and effects of CSRF attacks and uncovers a broad range of techniques for exploiting this vulnerability. Furthermore, this study provides actionable recommendations to enhance system security and raise awareness among developers and users. The findings from this research serve as a valuable resource for improving security practices and mitigating CSRF attacks. © 2023 The Authors. Published by Elsevier B.V.

关键词： cross-site request forgery vulnerability web exploitation

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Identification and Grouping of Skin Sickness by Means of Deep Learning

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SN computer science 2023年第3期4卷 236页

作者： Udupa, Pradeep Naidu, Aishwarya V. K. Shettigar, Suraj Kumar Maurya, Jayender Bahadur School of Computer Science and Applications REVA University Bangalore India School of Computer Science and Applications Reva University Bangalore India School of CSA II MSc in Data Science Reva University Bangalore India

The individuals’ health is important than any other situations. Skin situations are significantly produced by fungoid contagion, microbes, or infections, etc. The spotlights progression and Photonics stranded medical technology is used in view of the skin conditions impatiently and directly. The medical accoutrements for alike opinion is limited and most valuable. Therefore, Deep learning customs assist in discovery of skin grievance at an exclusive phase. The point birth plays a vital part in bracket of skin circumstances. The process of Deep Knowledge procedures has condensed the need for mortal labour, alike as household point birth and data reconstruction for bracket purpose. A dataset of 938 images has been taken for the Bracket of Skin illnesses they include Melanoma, Nevus, and Sebborheic Keratosis. Using CNN procedures, 70 delicacy is attained in bracket of skin complaint. AlexNet gives 83% delicacy as in Table 1, in proposed research three skin disease images were taken into consideration, in proposed method features are extracted using AlexNet then classified using support vector machine and compared with images in the dermnet dataset from Kaggle as mentioned in Table 1 and Figs. 2 and 3. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence Artificial neural networks Civil production Expert systems Pattern recognition Structural engineering

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Reliable Entropy-Induced Anchor Learning for Incomplete Multi-View Subspace Clustering

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5293-5306页

作者： Shen, Qiangqiang Guo, Zihou Wang, Hanzhang Xu, Yanhui Chen, Yongyong Wang, Shiqi Liang, Yongsheng City University of Hong Kong Department of Computer Science Hong Kong Harbin Institute of Technology School of Science Shenzhen518055 China Nanjing University of Posts and Telecommunications College of Electronic and Optical Engineering Nanjing210023 China School of Computer Science and Technology Shenzhen518055 China School of Electronics and Information Engineering Shenzhen518055 China Shenzhen Technology University College of Big Data and Internet Shenzhen518118 China

Under large-scale data with missing views, fast incomplete multi-view clustering (IMVC) with anchor learning is of critical importance due to its linear complexity O(n). However, existing anchor-based methods only explore the column orthogonality of anchor points, where their arbitrary column orthogonal basis vectors have weak constraint relationships with real samples and significant deviations from more representative anchors, thereby impeding the precise representation of sample similarities. To solve this issue, we propose a Reliable Entropy-induced anchor learning for incomplete Multi-view subspace Clustering (REMC), which performs an entropy approximation term to learn more representative anchors, and we prove that the information entropy minimization can be relaxed into the 2,1-norm paradigm. Specifically, the proposed REMC first integrates anchor learning and subspace clustering to produce multiple view-specific bipartite graphs and capture the high-order correlations by imposing these bipartite graphs with the tensor nuclear norm. Then, we fuse all the view-specific bipartite graphs to build a consensus bipartite graph with entropy approximation regularization, and hence the proposed REMC can produce a more discriminative similarity graph, preserving each non-zero element in its column close to 1, while the other elements are approaching 0. Besides, an efficient algorithm is designed to solve the proposed REMC. Numerous results show the superior performance of our method on both the complete and incomplete data. © 1991-2012 IEEE.

关键词： Graph theory

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data-Efficient Radiology Report Generation via Similar Report Features Enhancement 3rd

Data-Efficient Radiology Report Generation via Similar Repo...

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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Li, Yanfeng Sun, Jinghan Wang, Liansheng Department of Computer Science at School of Informatics Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China

ISBN: (纸本)9783031820069

The utilization of Artificial Intelligence in automatically generating radiology reports presents a promising solution for enhancing the efficiency of the diagnostic process and reducing human error. However, existing methods require training on large datasets of image-report pairs, which are often scarce. Moreover, the accuracy of reports generated with limited paired data significantly diminishes. To address these challenges, this study introduces a data-efficient method that integrates the retrieval of similar reports with text fusion enhancements to tackle the scarcity of image-report pairs and generate accurate radiology reports. Our method is compared with several state-of-the-art approaches, showing advancements on the MIMIC-CXR and IU X-ray benchmarks with the same limited data pairs. It achieves near-optimal results on MIMIC-CXR and comparable results on IU-Xray, highlighting not only its effectiveness and potential to improve radiological diagnosis with fewer image reports but also its ability to generate more accurate reports. By enhancing cross-modal feature interaction and demonstrating higher diagnostic accuracy, this work contributes to the fields of clinical medicine and artificial intelligence. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radiology

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Performance Analysis on Web Development programming Language (Javascript, Golang, PHP)

Performance Analysis on Web Development Programming Language...

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2023 International Conference on Information Technology and Computing, ICITCOM 2023

作者： Nabiil, Arkaan Makmur, Bintang Hermawan Wijaya, Reynard Wiratama Santoso Gunawan, Alexander Agung Edbert, Ivan Sebastian School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350359633

This paper aims to use an experimental approach to determine the best performing and most stable web programming languages for building a website. Through a comprehensive comparison of different languages such as Golang, JavaScript, and PHP, our research aims to provide valuable insights for developers when it comes to choosing the right web programming language for their next web development project. The objective is to identify the web programming language that offers the best performance and stability in terms of key metrics such as CPU utilization, response time, and scalability. Additionally, the study will also consider the unique set of advantages and disadvantages of each language, to provide a comprehensive assessment of their suitability for different web development projects. Ultimately, the goal of this research is to make it easier for developers to choose the best web programming language for their needs, and to ensure that the resulting website is fast, stable, and easy to maintain. Based on the research and comparison of different languages, it is found that Golang is the best choice for web development projects as it offers the best performance and stability in terms of key metrics such as CPU utilization, response time, and scalability. Additionally, Golang also has a unique set of advantages that make it suitable for different web development projects. © 2023 IEEE.

关键词： Websites

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