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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xilin He Jingyu Hu Qinliang Lin Cheng Luo Weicheng Xie Siyang Song Muhammad Haris Khan Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and Guangdong Provincial Key Laboratory of Intelligent Information Processing University of Exeter Mohamed bin Zayed University of Artificial Intelligence

ISBN: (纸本)9798331314385

Domain generalization methods aim to learn transferable knowledge from source domains that can generalize well to unseen target domains. Recent studies show that neural networks frequently suffer from a simplicity-biased learning behavior which leads to over-reliance on specific frequency sets, namely as frequency shortcuts, instead of semantic information, resulting in poor generalization performance. Despite previous data augmentation techniques successfully enhancing generalization performances, they intend to apply more frequency shortcuts, thereby causing hallucinations of generalization improvement. In this paper, we aim to prevent such learning behavior of applying frequency shortcuts from a data-driven perspective. Given the theoretical justification of models' biased learning behavior on different spatial frequency components, which is based on the dataset frequency properties, we argue that the learning behavior on various frequency components could be manipulated by changing the dataset statistical structure in the Fourier domain. Intuitively, as frequency shortcuts are hidden in the dominant and highly dependent frequencies of dataset structure, dynamically perturbating the over-reliance frequency components could prevent the application of frequency shortcuts. To this end, we propose two effective data augmentation modules designed to collaboratively and adaptively adjust the frequency characteristic of the dataset, aiming to dynamically influence the learning behavior of the model and ultimately serving as a strategy to mitigate shortcut learning. Code is available at https://***/C0notSilly/AdvFrequency.

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An Enhanced Authentication Protocol Suitable for Constrained RFID Systems

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IEEE Access 2024年 12卷 61610-61628页

作者： Hosseinzadeh, Mehdi Servati, Mohammad Reza Rahmani, Amir Masoud Safkhani, Masoumeh Lansky, Jan Janoscova, Renata Ahmed, Omed Hassan Tanveer, Jawad Lee, Sang-Woong Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Duy Tan University School of Medicine and Pharmacy Da Nang550000 Viet Nam Shahid Rajaee Teacher Training University Department of Computer Engineering Tehran16788-15811 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliou Yunlin64002 Taiwan School of Computer Science Tehran19395-5746 Iran University of Finance and Administration Faculty of Economic Studies Department of Computer Science and Mathematics Prague101 00 Czech Republic University of Human Development Department of Information Technology Sulaymaniyah0778-6 Iraq Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of Gachon University Pattern Recognition and Machine Learning Laboratory Department of Ai Software Seongnam-si13557 Korea Republic of

RFID technology offers an affordable and user-friendly solution for contactless identification of objects and individuals. However, the widespread adoption of RFID systems raises concerns regarding security and privacy. Vulnerabilities such as message tampering, interception, and eavesdropping pose significant risks to the integrity of the system. This study examines the effectiveness of two recently proposed ultra-lightweight RFID authentication protocols, URASP and KUAJB, in addressing these security challenges. Surprisingly, our findings reveal that both URASP and KUAJB protocols are susceptible to secret disclosure attacks, despite their claims of providing robust security. Consequently, the development of a truly ultra-lightweight protocol that ensures adequate security becomes a formidable task. As a potential solution, the enhancement of the URASP protocol through the integration of a simple cryptographic primitive is suggested to bolster its security measures. © 2013 IEEE.

关键词： Hash functions

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Application of Data Analysis to Medical Manpower Adjustment 2021

Application of Data Analysis to Medical Manpower Adjustment

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5th International Conference on E-Society, E-Education and E-Technology, ICSET 2021

作者： Peng, Shih Jung School of Computer Science and Software Engineering Zhaoqing University 86-*** China

ISBN: (纸本)9781450390156

In today's environment of longer human life and higher average age, the general family does not have enough ability and economic conditions to apply for professional nursing staff to assist in the patient of the elderly in need of long-term care in the home. To maintain the family's income, they have to continue the existing work and send the elderly to the care center. However, there is still a shortage problem of nursing staff, even in nursing centers, because each nurse needs to take care of several patients with dealing the related medical matters at the same time. We found that the physical condition of each patient is different. In this paper, the data function and algorithm propose as the basis of data analysis. We can turn the physical condition of each patient into relevant data and analyze the corresponding data. In this way, we can find out the abnormal condition of the patient's body early, which is helpful for the prevention of accidents. After data analysis, if the patient's BFI continues in the stable area in the past month, some detection of Body Function can be omitted. This approach can not only appropriately improve work efficiency but also reduce the workload of nursing staff. Finally, we establish the management mode of medical manpower scheduling then the medical manpower-shortage problem would adjust appropriately. © 2021 ACM.

关键词： Nursing

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Frontiers of computer science 2020年第1期14卷 191-207页

作者： Huiping LIU Cheqing JIN Aoying ZHOU School of Data Science and Engineering School of Computer Science and Software EngineeringEast China Normal UniversityShanghai 200062China

With the increasing number of GPS-equipped vehicles,more and more trajectories are generated continuously,based on which some urban applications become feasible,such as route *** general,popular route that has been travelled frequently is a good choice,especially for people who are not familiar with the road ***,accurate estimation of the travel cost(such as travel time,travel fee and fuel consumption)will benefit a wellscheduled trip *** this paper,we address this issue by finding the popular route with travel cost *** this end,we design a system consists of three main ***,we propose a novel structure,called popular traverse graph where each node is a popular location and each edge is a popular route between locations,to summarize historical trajectories without road network ***,we propose a self-adaptive method to model the travel cost on each popular route at different time interval,so that each time interval has a stable travel ***,based on the graph,given a query consists of source,destination and leaving time,we devise an efficient route planning algorithmwhich considers optimal route concatenation to search the popular route from source to destination at the leaving time with accurate travel cost ***,we conduct comprehensive experiments and implement our system by a mobile App,the results show that our method is both effective and efficient.

关键词： location-based services route planning travel cost estimation minimum description length optimal road concatenation

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Towards Understanding Third-party Library Dependency in C/C++ Ecosystem 7

Towards Understanding Third-party Library Dependency in C/C+...

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37th IEEE/ACM International Conference on Automated software engineering, ASE 2022

作者： Tang, Wei Xu, Zhengzi Liu, Chengwei Wu, Jiahui Yang, Shouguo Li, Yi Luo, Ping Liu, Yang School of Software Tsinghua University Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781450396240

Third-party libraries (TPLs) are frequently reused in software to reduce development cost and the time to market. However, external library dependencies may introduce vulnerabilities into host applications. The issue of library dependency has received considerable critical attention. Many package managers, such as Maven, Pip, and NPM, are proposed to manage TPLs. Moreover, a significant amount of effort has been put into studying dependencies in language ecosystems like Java, Python, and JavaScript except C/C++. Due to the lack of a unified package manager for C/C++, existing research has only few understanding of TPL dependencies in the C/C++ ecosystem, especially at large scale. Towards understanding TPL dependencies in the C/C++ ecosystem, we collect existing TPL databases, package management tools, and dependency detection tools, summarize the dependency patterns of C/C++ projects, and construct a comprehensive and precise C/C++ dependency detector. Using our detector, we extract dependencies from a large-scale database containing 24K C/C++ repositories from GitHub. Based on the extracted dependencies, we provide the results and findings of an empirical study, which aims at understanding the characteristics of the TPL dependencies. We further discuss the implications to manage dependency for C/C++ and the future research directions for software engineering researchers and developers in fields of library development, software composition analysis, and C/C++ package manager. © 2022 ACM.

关键词： Ecosystems

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Seismic multiple attenuation based on improved U-Net

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Applied Geophysics 2024年第4期21卷 680-696,879页

作者： Quan Zhang Xiao-yu Lv Qin Lei Bo Peng Yan Li Yao-wen Zhang State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(Southwest Petroleum University) Chengdu 610500China School of Computer Science and Software Engineering Southwest Petroleum UniversityChengdu 610500China

Eff ective attenuation of seismic multiples is a crucial step in the seismic data processing workfl *** the existence of various methods for multiple attenuation,challenges persist,such as incomplete attenuation and high computational requirements,particularly in complex geological *** multiple attenuation methods rely on prior geological information and involve extensive *** deep neural networks for multiple attenuation can effectively reduce manual labor costs while improving the efficiency of multiple *** study proposes an improved U-net-based method for multiple *** conventional U-net serves as the primary network,incorporating an attentional local contrast module to effectively process detailed information in seismic *** is placed on distinguishing between seismic multiples and *** improved network is trained using seismic data containing both multiples and primaries as input and seismic data containing only primaries as *** eff ectiveness and stability of the proposed method in multiple attenuation are validated using two horizontal layered velocity models and the Sigsbee2B velocity *** learning is employed to endow the trained model with the capability to suppress multiples across seismic exploration areas,eff ectively improving multiple attenuation efficiency.

关键词： Multiple suppression U-net Attentional local contrast

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A Comprehensive Review of Protein Language Models

arXiv

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arXiv 2025年

作者： Wang, Lei Li, Xudong Zhang, Han Wang, Jinyi Jiang, Dingkang Xue, Zhidong Wang, Yan School of Software Engineering Huazhong University of Science and Technology China School of Life Science and Technology Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

At the intersection of the rapidly growing biological data landscape and advancements in Natural Language Processing (NLP), protein language models (PLMs) have emerged as a transformative force in modern research. These models have achieved remarkable progress, highlighting the need for timely and comprehensive overviews. However, much of the existing literature focuses narrowly on specific domains, often missing a broader analysis of PLMs. This study provides a systematic review of PLMs from a macro perspective, covering key historical milestones and current mainstream trends. We focus on the models themselves and their evaluation metrics, exploring aspects such as model architectures, positional encoding, scaling laws, and datasets. In the evaluation section, we discuss benchmarks and downstream applications. To further support ongoing research, we introduce relevant mainstream tools. Lastly, we critically examine the key challenges and limitations in this rapidly evolving field. Copyright © 2025, The Authors. All rights reserved.

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Dynamic Behavior Pattern: Mining the Fraudsters in Telecom Network 24

Dynamic Behavior Pattern: Mining the Fraudsters in Telecom N...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Li, Dengshi Zeng, Lu Hu, Ruimin Huang, Zijun Liang, Xiaocong Zang, Yilong School of Artificial Intelligence Jianghan University Wuhan China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798350319934

In telecom network fraud, the fraud method of the fraudsters varies significantly in different periods. However, the traditional method of telecom fraud is to train the telecom fraud mining model through the user's long-term telephone data. So they can not detect the new fraud method of fraudsters and can not identify those with only have short-term telephone data. To increase the identification precision of telecom fraudsters, the dynamic changes in social structure and behavior pattern features of fraudsters were studied in this paper. Specifically, from the perspective of social structure change, the social structure of fraudsters changes rapidly over time. In contrast, the social structure of normal users is stable over time. From the perspective of user behavior pattern features, we found that the behavior patterns of fraudsters and normal users were very different. The behavior pattern of fraudsters in the different periods also changes significantly. Therefore, a telecom network fraud detection framework based on the social relationship evo-lution model was proposed. We designed the feature extraction method of dynamic telephone social behavior patterns based on time slices. The method combines the features of social structure and behavior patterns of users. And the Telephone Network Fraud Detection(TNFD) model is built using LSTM. We set up a sliding time window to enable the model to identify users with only short-term telephone data. This time window dynamically takes the features of the user's telephone social behavior pattern as the input of the TNFD model. Experiments on real dataset show that the precision of TNFD is 7% higher than that of the latest method. Our research may reference public security organs in telecom fraud cases. © 2022 IEEE.

关键词： Crime

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A Method Based on Genetic Programming to Automatically Construct Factors for Annual Report Scenarios

A Method Based on Genetic Programming to Automatically Const...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Yan Ma Changsheng Zhang Yan Gao Ying Guo School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

Factors have always played an important role in stock analysis, but they are only effective for specific problems in specific scenarios. Therefore, constructing factors timely and quickly for different scenarios is an urgent problem to be solved. Although some experts have constructed factors, they need to manually construct factors for each scenario, and the construction process consumes time and effort. The annual report is an important and common scenario. It is an important form of information disclosure and financial reporting. Therefore, this paper proposes a method for automatically constructing factors based on genetic programming that combines expert experience for this scenario. It incorporates the knowledge and insights of experts in the field of stock analysis into the process of automati-cally constructing factors, and continuously adjusts and improves the combination of factors through genetic programming to adapt to the needs of the scenario. The effectiveness of this method is verified through empirical analysis and its advantages in specific scenarios are demonstrated.

关键词： Industries Genetic programming Prediction algorithms Knowledge discovery Boosting Decision trees Stock markets Random forests Fuzzy systems

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Towards Planning Package Delivery Paths by Exploiting the Crowd of Private Cars 24

Towards Planning Package Delivery Paths by Exploiting the Cr...

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作者： Nie, Hejing Wen, Yiping Li, Tiancai Ouyang, Sipeng Tian, Muyang School of Computer Science and Engineering Hunan University of Science and Technology China Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology China

ISBN: (纸本)9798350319934

Crowdsourced delivery can provide cost-effective deliveries and it is becoming increasingly important in practice. This paper considers a package delivery problem which exploits relays of private cars to transport packages via their commuter routes. Specifically, we propose a method of planning package delivery paths based on the commuter routes of private cars. Using the commuter routes and time constraints as the reference, it can find optimal delivery paths and assign drivers of private cars to transport packages. The simulation experiment results show the validity and effectiveness of such method. © 2022 IEEE.

关键词： Cost effectiveness

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