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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Yufei Wu Baojian Hua School of Software Engineering University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China

Despite the fact that Rust is designed to be a secure programming language for system programming, it is still vulnerable and exploitable due to its inclusion of an unsafe sub-language. However, existing studies on Rust security only focus on static detection or rectification of vulnerability, but ignore the problem of timely and effective rectifications of vulnerabilities dynamically. In this paper, to fill this gap, we present RUSPATCH, the first infrastructure to timely and effectively patch vulnerable Rust applications. RUSPATCH consists of two main phases: static partitioning and dynamic patching. In the static partitioning phase, RUSPATCH divides the candidate program into target code and patch candidates via a customized compiler. During the patching phase, RUSPATCH dynamically validates and applies the security patch once a vulnerability is detected. To realize the whole process, we tackled three technical challenges of language discrepancy, efficiency issues, and security threats. We have designed and implemented a software prototype for RUSPATCH, and have conducted extensive experiments to evaluate its effectiveness, performance, overhead, and usefulness. Experimental results demonstrated that RusPATCH is effective in patching off-the-shelf Rust applications including real-world Rust CVEs, and the extra overhead RusPATCH introduced is less than 3.28% and thus insignificant. Furthermore, RUSPATCH is easy to incorporate into existing Rust applications without any manual interventions.

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Scalable fabrication of magnetic soft microfiberbots for robotic embolization

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National Science Open 2024年第4期3卷 142-144页

作者： Tengrui Wang Xianhu Liu Chuntai Liu Changyu Shen State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450002China

Intracranial tumors and aneurysms rank among the foremost causes of mortality globally,presenting a significant public health challenge[1,2].Traditional embolization techniques,which involve the manual navigation of slender catheters and guidewires through complex vascular networks,are highly dependent on the clinician's expertise and the mechanical capabilities of the instruments used.

关键词： embolization mortality globally

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Research and Improvement of Multi-granularity Method for Pedestrian Tracing System 6

Research and Improvement of Multi-granularity Method for Ped...

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2021 6th International Symposium on Advances in Electrical, Electronics and Computer engineering, ISAEECE 2021

作者： Ni, Ruixuan Beijing Engineering Research Center for IOT Software and Systems Beijing University of Technology Beijing100124 China

In the field of video surveillance, with the continuous development of the Internet and artificial intelligence, the related technology for single device has been very mature, but how to accurately realize pedestrian tracing under cross-devices is still an active research field. In this paper, a multi-granularity pedestrian traceability method is proposed, which is based on the attribute extraction of pedestrian image and the person re-identification algorithm based on attention mechanism retrieves the same pedestrian image cross a lot of devices. The results show that the video surveillance system has good performance, which reduces the occurrence of tracking loss, and the track is visualized at the platform. © Published under licence by IOP Publishing Ltd.

关键词： Security systems

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Predicting software Functional Size Using Natural Language Processing: An Exploratory Case Study

Predicting Software Functional Size Using Natural Language P...

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Euromicro Conference on software engineering and advanced Applications (SEAA)

作者： Hüseyin Ünlü Samet Tenekeci Can Çiftçi İbrahim Baran Oral Tunahan Atalay Tuna Hacaloğlu Burcu Musaoğlu Onur DemirǶrs Department of Computer Engineering Izmir Institute of Technology Izmir Turkiye Bilgi Grubu Izmir Turkiye Department of Information Systems Engineering Atilim University Ankara Turkiye Department of Software and IT Engineering Ecole de Technologie Superieure Montreal Canada Siskon Software & Automation Izmir Turkiye

ISBN: (数字)9798350380262

ISBN: (纸本)9798350380279

software Size Measurement (SSM) plays an essential role in software project management as it enables the acquisition of software size, which is the primary input for development effort and schedule estimation. However, many small and medium-sized companies cannot perform objective SSM and software Effort Estimation (SEE) due to the lack of resources and an expert workforce. This results in inadequate estimates and projects exceeding the planned time and budget. Therefore, organizations need to perform objective SSM and SEE using minimal resources without an expert workforce. In this research, we conducted an exploratory case study to predict the functional size of software project requirements using state-of-the-art large language models (LLMs). For this aim, we fine-tuned BERT and BERT_SE with a set of user stories and their respective functional size in COSMIC Function Points (CFP). We gathered the user stories included in different project requirement documents. In total size prediction, we achieved 72.8% accuracy with BERT and 74.4% accuracy with BERT_SE. In data movement-based size prediction, we achieved 87.5% average accuracy with BERT and 88.1% average accuracy with BERT_SE. Although we use relatively small datasets in model training, these results are promising and hold significant value as they demonstrate the practical utility of language models in SSM.

关键词： Training Schedules Accuracy Estimation Project management Companies Predictive models Size measurement software Natural language processing

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An Intelligent and Interpretable Intrusion Detection System for Unmanned Aerial Vehicles

An Intelligent and Interpretable Intrusion Detection System ...

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IEEE International Conference on Communications (ICC)

作者： Danish Javeed Tianhan Gao Prabhat Kumar Shifa Shoukat Ijaz Ahmad Randhir Kumar Software College Northeastern University Shenyang China Department of Software Engineering School of Engineering Science LUT University Lappeenranta Finland Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China Department of Computer Science and Engineering SRM University AP AP India

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The increasing adoption of Unmanned Aerial Ve-hicles (UAV s) in various critical applications necessitates robust security measures to protect these systems from cyber threats. In response, this research introduces an innovative Intrusion Detection System (IDS) specifically tailored for UAV s. The proposed IDS leverages Hierarchical Attention-based Long Short-Term Memory (H-LSTM) networks to effectively model the intricate temporal dependencies in UAV data. This architecture allows for comprehensive surveillance of UAV behavior, capturing both short-term anomalies and long-term deviations from expected patterns. The hierarchical attention mechanism enables the system to focus on salient features within the data, enhancing detection accuracy and robustness. To address the critical need for interpretable AI in cybersecurity, we incorporate Shapley Ad-ditive Explanations (SHAP) into our IDS. SHAP values provide a coherent and intuitive explanation of the IDS's decisions by emphasizing the specific features and their contributions to the intrusion detection process. The performance of the proposed system is rigorously evaluated using the N-BaIoT dataset. Our experiments demonstrate that the H-LSTM-based IDS outper-forms traditional methods, achieving a higher detection rate while minimizing false positives. Moreover, the incorporation of SHAP explanations facilitates rapid incident analysis, allowing security professionals to discern between genuine threats and benign anomalies effectively.

关键词： Industries Federated learning Surveillance Intrusion detection Autonomous aerial vehicles Feature extraction Robustness

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Predictive Hiring System: Information technology Consultants Soft Skills

Predictive Hiring System: Information Technology Consultants...

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International Conference on advanced Intelligent systems for Sustainable Development, AI2SD 2022

作者： Lamjid, Asmaa El Bouchti, Karim Ziti, Soumia Mohamed, Reda Oussama Labrim, Hicham Riadsolh, Anouar Belkacemi, Mourad Intelligence Processing System and Security Mohammed V University in Rabat Rabat Morocco Algorithms Networks Intelligent Systems and Software Engineering Research Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Advanced Systems Engineering Laboratory National School of Applied Sciences Ibn Tofail University Kenitra Morocco Faculty of Sciences Mohammed V University in Rabat Rabat Morocco

ISBN: (纸本)9783031263835

Recruiting candidates who will perform well within any organization has become a challenge in the Human Resources sector. Generally, poor recruitment can negatively reflect the progression and evolution of the organism. Soft skills are personality traits and behaviors wanted by recruiters to provide high performance, and they are the skills that will help candidates succeed in their jobs. This work proposes a new predictive hiring model based on soft skills used in the human resources area for Information technology Consultants. This model helps organizations’ recruiters choose the best candidates and avoid lousy recruitment. The implementation of the proposed model has been running under a machine learning algorithm, the Support Vector Machine algorithm using different functions to choose which one of the implementations is best displayed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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Realization and Performance Tests of a Dual-Polarization Fiber Optic Gyroscope 8

Realization and Performance Tests of a Dual-Polarization Fib...

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8th Symposium on Novel Photoelectronic Detection technology and Applications

作者： Zhou, Tong Zhu, Lanxin Cao, Yuwen Chen, Yanjun Huang, Huimin Li, Zhengbin School of Software and Microelectronics Peking University Beijing102600 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing100871 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Interferometric fiber optical gyroscopes (IFOGs) have become one of the widely used sensors of inertial technology and rotational seismology, owing to their high precision and stability. In recent years, the dual-polarization IFOG, which allows two orthogonal polarization to work simultaneously, has been proved to achieve better measurement performance than the conventional minimum configuration through mutual compensation. In this work, a prototype of dual-polarization fiber-optic gyroscope is designed and realized. Laboratory tests show the self-noise has reached 9 nrad/s/sqrt(Hz) over the frequency from 0.01 to 125 Hz and the angular random walk has achieved 2.6×10-5 deg/sqrt(hr). For an Open-loop IFOG, which does not need a confiscated feedback control system, it avoids the dead-zone of closed-loop IFOG. However, limited by the measurement range, as a result of the periodic and nonlinear response of open-loop demodulation, the scale factor of open-loop IFOG varies around 100~1000 parts per million (ppm) conventionally, which hinders its detection accuracy. In this work, with new measurement extension and linearity compensation methods, the prototype achieves an outstanding precision with scale factor nonlinearity up to 5 ppm within ±10 deg/s, and shows the potential of dual-polarization IFOGs for rotational seismology. © 2022 SPIE

关键词： Seismology

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Sleep Monitoring with Wearable Sensor Data in an eCoach Recommendation System: A Conceptual Study with Machine Learning Approach 7th

Sleep Monitoring with Wearable Sensor Data in an eCoach Reco...

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7th International Conference on Emerging Applications of Information technology, EAIT 2022

作者： Chatterjee, Ayan Prinz, Andreas Pahari, Nibedita Das, Jishnu Riegler, Michael Alexander Department of Information Technology Center for eHealth University of Agder Kristiansand Norway Department of Software Engineering Knowit AS Oslo Norway Department of Library and Information Science University of Calcutta Calcutta India Department of Holistic Systems Simula Research Laboratory Oslo Norway

ISBN: (纸本)9789811951909

The collective effects of sleep loss and sleep disorders are correlated with many adverse health consequences, including increased risk of high blood pressure, obesity, diabetes, depressive state, and cardiovascular symptoms. Research in eHealth can provide methods to enrich personal health care with information and communication technologies (ICTs). An eCoach system may allow people to manage a healthy lifestyle with extended health state monitoring (e.g., sleep) and tailored recommendation generation. Using supervised machine learning (ML) techniques, this study investigated the possibility of classifying sleep stages at night for adults on hourly and daily basis. The daily total sleep minutes and hourly total sleep minutes for defined sleeping period served as input for the classification models. We first used publicly available Fitbit dataset to build the initial classification models. Second, using the transfer learning approach, we re-used the top five best-performing models on a real dataset as collected from the MOX2-5 wearable medical-grade activity device. We found that support vector classifier (SVC) with "linear" kernel outdated other classifiers with a mean accuracy score of 99.92% for hourly sleep classification and a K-nearest neighbor (KNN) outpaced other classifiers with a mean accuracy score of 99.47% for daily sleep classification, for the public Fitbit datasets. Moreover, to determine the practical efficacy of the classifier models, we conceptualized to use the classifier models in an eCoach prototype system to attain tailored sleep goals (e.g., a weekly goal of 49–63 h of sleeping). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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INNSE: Invertible neural network-based DNA image storage with self-correction encoding

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Computational and Structural Biotechnology Journal 2025年 27卷 2492-2502页

作者： Zheng, Yanfen Zhang, Xiaokang Sun, Lijun Cao, Ben Wang, Bin Zhang, Qiang Zheng, Pan School of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Liaoning Dalian116622 China Department of Accounting and Information Systems University of Canterbury Upper Riccarton Christchurch8140 New Zealand

DNA storage has become an alternative to silicon-based storage of media. However, when used to store multimodal data such as images, it fails to take full advantage of the characteristics of high correlation and variability present in such data, and currently remains inferior in terms of encoding density and time complexity. It also has the shortcoming of weak performance in terms of error correction methods to address insertion and deletion errors caused by error crosstalk. Therefore, we proposed an invertible neural network-based DNA image storage self-correction encoding method (INNSE) that uses the invertibility of network to perform up-down sampling processing on image and video data, thereby greatly reducing the number of DNA sequences required for data storage. A codebook generator was then designed to encode low-dimensional vectors into DNA sequences that meet various constraints. Finally, a self-correction method was constructed to achieve DNA sequence error correction at the base level without adding any redundant information. Our experimental results showed that, compared to the Yin-Yang algorithm, the encoding density of INNSE was increased by 3×, encoding time complexity was reduced by 95.26%. Under a simulated error rate of 2%, PSNR and SSIM were raised by 80.88% and 63.82%, respectively, proving that INNSE has significant advantages in encoding performance and error correction ability. © 2025

关键词： Network coding

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A Hybrid Genetic Algorithm with Integer Coding for Task Offloading in Edge-Cloud Cooperative Computing

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第2期49卷 503页

作者： Wang, Bo Lv, Bin Song, Ying Software Engineering College Zhengzhou University of Light Industry China Linyi Branch of China Mobile Communications Group Shandong Co. Ltd China Beijing Advanced Innovation Center for Materials Genome Engineering and Beijing Key Laboratory of Internet Culture and Digital Dissemination Beijing Information Science and Technology University China

Edge-Cloud Cooperative Computing (E3C) is an efficient way to address the resource scarcity issue of mobile devices by edge servers with low network delays and the cloud with abundant computing resources. To improve the user satisfaction which directly affects the profit and the reputation of service providers, we focus on the task offloading problem for E3C in this paper. We design an integer genetic algorithm incorporating earliest deadline first (EDF) to map each chromosome as a task offloading solution. Our method uses each gene of a chromosome to represent the computing core that the corresponding task assigned to, and decides the task execution order by EDF in each computing core. Experiment results show that our method achieves better accepted ratio and resource efficiency than several offloading methods based on heuristics, the particle swarm optimization, and the binary genetic algorithm © 2022. IAENG International Journal of Computer *** Rights Reserved.

关键词： Genetic algorithms

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