检索结果-内蒙古大学图书馆

2nd International Conference on Intelligent Systems and data science, ISDS 2024

作者： Dinh, Dao Lan Vy Tran, Ngoc Hang May, Vo Huyen Khanh Tran, Hung Minh, Tran Duc Lan, Dang Thu Vo, Van Nhan DATCOM Lab Faculty of Data Science and Artificial Intelligence College of Technology National Economics University Hanoi Viet Nam Faculty of Business and Economics Phenikaa University Yen Nghia Ha Dong Hanoi Viet Nam Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam

ISBN: (纸本)9789819796151

Recently algorithmic trading has revolutionized financial markets. This study investigates the application of the Kelly criterion and technical indicators pairs (relative strength index RSI, moving average convergence divergence (MACD) on-balance volume (OBV), Bollinger bands (BB)) to optimize trading strategies within the Vietnamese VN30 index. Historical data from 2021–2024 was split into 2 parts, one is to find parameters for Kelly Criteria, another is used to analyze and backtest the proposed models. Results indicate that combining RSI and MACD is effective during downtrends, while RSI with OBV and BB performs better in uptrends. The Kelly Criterion consistently enhances risk management during periods, especially in downtrends. This research fills a gap in the literature and offers a foundation for future studies on algorithmic trading in emerging markets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Risk management

来源：评论

学校读者我要写书评

暂无评论

2D-DOA for a Monostatic ULA EMVS-MIMO Radar Based on RC-PARAFAC

引用

IEEE Sensors Journal 2025年

作者： Wang, Jianlong Shi, Junpeng Wen, Fangqing China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hvdroelectric Engineering College of Computer and Information Technology Yichang443002 China National University of Defense Technology College of Electronic Countermeasure Hefei230037 China City University of Macau College of Computer and Information Technology China Three Gorges University Faculty of Data Science 999078 China

The electromagnetic vector sensor (EMVS) embedded multiple-input multiple-output (MIMO) radar is an emerging technology capable of two-dimensional (2D) direction of arrival (DOA) estimation. In this paper, we propose a low-complexity algorithm for monostatic uniform linear array (ULA) EMVS-MIMO radar to rapidly estimate 2D-DOA. First, we decompose the received signal into a third-order tensor using parallel factor analysis (PARAFAC). Next, we eliminate data redundancy through a reduced complexity (RC) process. Then, we use the least squares method to extract ambiguous elevation angle information from the spatial response matrix and apply the vector cross-product (VCP) technique to the electromagnetic vector to achieve unambiguous 2D-DOA estimation. Finally, we combine these estimates to achieve high-resolution, unambiguous elevation angle estimation. The proposed algorithm is suitable for large-scale and sparse EMVS-MIMO radar systems. © 2001-2012 IEEE.

关键词： Direction of arrival

来源：评论

学校读者我要写书评

暂无评论

“Weibing” in traditional Chinese medicine—biological basis and mathematical representation of disease-susceptible state

引用

Acta Pharmaceutica Sinica B 2025年第5期15卷 2363-2371页

作者： Sun, Wan-Yang Wang, Rong Ouyang, Shu-Hua Liang, Wan-Li Duan, Junwei Gong, Wen-Yong Hu, Lian-Ting Chen, Xiu-Juan Li, Yi-Fang Kurihara, Hiroshi Yao, Xin-Sheng Gao, Hao He, Rong-Rong Institute of Traditional Chinese Medicine and Natural Products College of Pharmacy/Guangdong Engineering Research Center of Traditional Chinese Medicine & Disease Susceptibility/Guangdong Engineering Research Center of Traditional Chinese Medicine & Health Products/International Cooperative Laboratory of TCM Modernization and Innovative Drug Development of Chinese Ministry of Education (MOE)/Guangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research/State Key Laboratory of Bioactive Molecules and Druggability Assessment Jinan University Guangzhou 510632 China State Key Laboratory of Quality Research in Chinese Medicine Macau University of Science and Technology Macau 999078 China College of Information Science and Technology Jinan University Guangzhou 510632 China Faculty of Data Science City University of Macau Macau 999078 China Medical Big Data Center Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou 510632 China The Data Center Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital) Tongji Medical College Huazhong University of Science and Technology Wuhan 430016 China

“Weibing” is a fundamental concept in traditional Chinese medicine (TCM), representing a transitional state characterized by diminished self-regulatory abilities without overt physiological or social dysfunction. This perspective delves into the biological foundations and quantifiable markers of Weibing, aiming to establish a research framework for early disease intervention. Here, we propose the “Health Quadrant Classification” system, which divides the state of human body into health, sub-health, disease-susceptible state, and disease. We suggest the disease-susceptible stage emerges as a pivotal point for TCM interventions. To understand the intrinsic dynamics of this state, we propose laboratory and clinical studies utilizing time-series experiments and stress-induced disease susceptibility models. At the molecular level, bio-omics technologies and bioinformatics approaches are highlighted for uncovering intricate changes during disease progression. Furthermore, we discuss the application of mathematical models and artificial intelligence in developing early warning systems to anticipate and avert the transition from health to disease. This approach resonates with TCM's preventive philosophy, emphasizing proactive health maintenance and disease prevention. Ultimately, our perspective underscores the significance of integrating modern scientific methodologies with TCM principles to propel Weibing research and early intervention strategies forward. © 2025 The Authors

关键词： Disease-susceptible state Mathematical modeling Preventive medicine Traditional Chinese medicine Weibing

来源：评论

学校读者我要写书评

暂无评论

Path optimization method for the sign problem caused by fermion determinant

arXiv

引用

arXiv 2025年

作者： Hisayoshi, Kazuki Kashiwa, Kouji Namekawa, Yusuke Takase, Hayato Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka811-0295 Japan Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima730-0053 Japan

The path optimization method with machine learning is applied to the one-dimensional massive lattice Thirring model, which has the sign problem caused by the fermion determinant. This study aims to investigate how the path optimization method works for the sign problem. We show that the path optimization method successfully reduces statistical errors and reproduces the analytic results. We also examine an approximation of the Jacobian calculation in the learning process and show that it gives consistent results with those without an approximation. Copyright © 2025, The Authors. All rights reserved.

关键词： Fermions

来源：评论

学校读者我要写书评

暂无评论

Towards A Hybrid Quantum Differential Privacy

arXiv

引用

arXiv 2025年

作者： Song, Baobao Pokhrel, Shiva Raj Vasilakos, Athanasios V. Zhu, Tianqing Li, Gang The Faculty of Engineering and IT University of Technology Sydney Sydney Australia The Centre for Cyber Security Research and Innovation Deakin University Geelong Australia The Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia University of Agder Grimstad Norway The Faculty of Data Science City University of Macau China

Quantum computing offers unparalleled processing power but raises significant data privacy challenges. Quantum Differential Privacy (QDP) leverages inherent quantum noise to safeguard privacy, surpassing traditional DP. This paper develops comprehensive noise profiles, identifies noise types beneficial for QDP, and highlights the need for practical implementations beyond theoretical models. Existing QDP mechanisms, limited to single noise sources, fail to reflect the multi-source noise reality of quantum systems. We propose a resilient hybrid QDP mechanism utilizing channel and measurement noise, optimizing privacy budgets to balance privacy and utility. Additionally, we introduce Lifted Quantum Differential Privacy, offering enhanced randomness for improved privacy audits and quantum algorithm evaluation. © 2025, CC BY.

关键词： Quantum noise

来源：评论

学校读者我要写书评

暂无评论

GPT-Based Wasm Instruction Analysis for Program Language Processing 5th

GPT-Based Wasm Instruction Analysis for Program Language Pro...

引用

5th International Conference on Emerging information Security and Applications, EISA 2024

作者： Deng, Liangjun Zhong, Qi Lei, Hang Qiu, Yao Chen, Jingxue School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Faculty of Data Science City University of Macau China Ningbo Polytechnic College Zhejiang Ningbo315800 China School of Mathematical and Physical Sciences University of Technology Sydney SydneyNSW2007 Australia

ISBN: (纸本)9783031804182

Capturing the logical structure of programming languages poses a significant challenge for program analysis. Given the complex syntax rules, subjective code vulnerabilities, irrelevant statements, code annotations, and intricate structural information, related studies have explored various semantic comprehension and intermediate representation approaches to extract precise information for program analysis. However, most research in the generic domain ignores defective and non-defective program code, putting them in the same category. In this paper, we introduce a new program analysis method that combines WebAssembly (Wasm) instructions with a 20-billion-parameter transformer model and natural language processing. This approach aims to advance the capabilities of program analysis tools in computer science by jointly embedding Wasm instructions and natural language for more effective program analysis. Our experiments demonstrate that this fused embedding approach achieves state-of-the-art performance, and the accuracy reaches approximately 98 percent, better than traditional small-scale weight models based on intricate conversion tasks such as abstract syntax trees(AST). Moreover, it is more valuable to classify potential vulnerable and non-vulnerable programs in the formal verification special field. Our exploration enhances traditional program classification methods in software security and introduces the application of GPT to offer a more straightforward, convenient, and high-performance approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Formal verification

来源：评论

学校读者我要写书评

暂无评论

Enhancing lung cancer detection through integrated deep learning and transformer models

引用

Scientific Reports 2025年第1期15卷 1-28页

作者： Durgam, Revathi Panduri, Bharathi Balaji, V. Khadidos, Adil O. Khadidos, Alaa O. Selvarajan, Shitharth Department of Data Science AVN Institute of Engineering and Technology Hyderabad India Department of Information Technology Gokaraju Rangaraju Institute of Engineering & Technology Hyderabad India Department of CSE-AIML Vardhaman College of Engineering Hyderabad India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Department of Computer Science and Engineering Kebri Dehar University Kebri Dehar Ethiopia Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia

Lung cancer has been stated as one of the prevalent killers of cancer up to this present time and this clearly underlines the rationale for early diagnosis to enhance life expectancy of patients afflicted with the condition. The reasons behind the usage of the transformer and deep learning classifiers for the detection of lung cancer include accuracy, robustness along with the capability to handle and evaluate large data sets and much more. Such models can be more complex and can help to utilize multiple modalities of data to give extensive information that will be critical in ascertaining the right diagnosis at the right time. However, the existing works encounter several limitations including reliance on large annotated data, overfitting, high computation complexity, and interpretability. Third, the issue of the stability of these models’ performance when applied to actual clinical datasets is still an open question;this is an even bigger issue that will greatly reduce the actual utilization of these models in clinical practice. To tackle these, we develop a novel Cancer Nexus Synergy (CanNS), which applies of A. Swin-Transformer UNet (SwiNet) Model for segmentation, Xception-LSTM GAN (XLG) CancerNet for classification, and Devilish Levy Optimization (DevLO) for fine-tuning parameters. This paper breaks new ground in that the presented elements are incorporated in a manner that co-operatively elevates the diagnostic capabilities while at the same time being computationally light and resilient. These are SwiNet for segmented analysis, XLG CancerNet for precise classification of the cases, and DevLO that optimizes the parameters of the lung cancer detection system, making the system more sensible and efficient. The performance outcomes indicate that the CanNS framework enhances the detection’s accuracy, sensitivity, and specificity compared to the previous approaches. © The Author(s) 2025.

关键词： And classification Deep learning Disease detection Lung Cancer Optimization Segmentation Transformer models

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Based Revocable Key-Aggregate Searchable Encryption for Group data Sharing in Cloud-Assisted Industrial IoT

引用

IEEE Internet of Things Journal 2025年

作者： Zhang, Kai Hu, Xinyi Zhao, Jian Wei, Lifei Ning, Jianting Shanghai University of Electric Power College of Computer Science and Technology Shanghai China Shanghai University of Electric Power College of Electrical Engineering Shanghai China Shanghai Maritime University College of Information Engineering Shanghai China Wuhan University School of Cyber Science and Engineering Wuhan430072 China City University of Macau Faculty of Data Science China

In the cloud-assisted Industrial Internet of Things (IIoT), flexible and secure data sharing promotes industry processes optimization and new products-making. To enable selective data retrieval over categorized data collected by IoT devices in the encryption domain, key-aggregate searchable encryption (KASE) is adopted for group data sharing in Industrial IoT due to efficient management of encryption keys. Nonetheless, existing solutions rarely consider user revocation property and suffered from the following limitations: (i) the user revocation needs interaction process between data owner and cloud;(ii) the non-revoked users may be accidentally revoked. Therefore, we propose a blockchain-based revocable KASE system (BC-RKASE) that achieves non-interactive user revocation and trusted revoked user management. Technically, the result is non-trivially obtained from revisiting the generation of aggregate keys and key updates via secret sharing, along with re-designing trapdoor adjustment and providing public verifiable proof for key updates by public blockchain. Besides formal security analysis, we conduct extensive experiments in a real cloud environment and thin IoT devices (Raspberry Pi) using a public IIoT dataset to confirm the practical performance of BC-RKASE. In particular, BC-RKASE outperforms state-of-the-art schemes with 13.4×~15× time efficiency accelerating for data retrieval and runs more than 16× faster for user revocation. © 2025 IEEE.

关键词： blockchain data sharing Industrial IoT searchable encryption user revocation

来源：评论

学校读者我要写书评

暂无评论

Multimodal Dialogue Emotion Recognition Based on Label Optimization and Coarse-Grained Assisted Fine-Grained

Multimodal Dialogue Emotion Recognition Based on Label Optim...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant information from other utterances, reducing the accuracy of emotion recognition. Existing one-hot labels often fail to capture the full range of emotional expressions, leading to biased results. To address these issues, we propose a model that fuses different modalities within the same utterance to avoid redundancy. It employs a progressive classification process, refining emotion recognition from coarse to fine granularity. Additionally, we use emotion polarity probabilities as weights for fine-grained classification and introduce a multimodal information-rich label that considers both the data and their interactions. Experiments on IEMOCAP and MELD datasets demonstrate the model’s effectiveness, significantly improving dialog emotion recognition accuracy. Our code is available at https://***/r/LOCG-188E.

关键词： Emotion recognition Accuracy Fuses Refining Termination of employment Speech recognition Signal processing Speech processing Optimization Faces

来源：评论

学校读者我要写书评

暂无评论

On the -differential spectra of two classes of power functions over finite fields

引用

Applicable Algebra in Engineering, Communication and Computing 2025年 1-30页

作者： Liu, Qian Huang, Zhiwei Chen, Zhixiong Jiang, Rong Zhang, Liupiao Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Fujian Key Laboratory of Financial Information Processing Putian University Putian China School of Computer Science and Technology Dongguan University of Technology Dongguan China

Recently, a new concept called multiplicative differential was introduced by Ellingsen et al. [7]. As an extension of the differential uniformity, it is theoretically appealing to determine the properties of c-differential uniformity and the corresponding c-differential spectrum. In this paper, based on certain quadratic character sums and two special elliptic curves over $$\mathbb {F}_p$$ , the $$(-1)$$ -differential spectra of the following two classes of power functions over $$\mathbb {F}_{p^n}$$ is completely determined: (1) $$f_1(x)=x^{\frac{p^n+3}{2}}$$ , where $$p>3$$ and $$p\equiv 3\pmod 4$$ ; (2) $$f_2(x)=x^{p^n-3}$$ , where $$p>3$$ . The obtained result shows that the $$(-1)$$ -differential spectra of $$f_1(x)$$ and $$f_2(x)$$ can be expressed explicitly in terms of n. Moreover, an upper bound of the c-differential uniformity of $$f_2(x)$$ is given.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：