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arXiv 2024年

作者： Tang, Kunming Jiang, Zhiguo Shi, Jun Wang, Wei Wu, Haibo Zheng, Yushan Beijing Advanced Innovation Center on Biomedical Engineering School of Engineering Medicine Beihang University China Image Processing Center School of Astronautics Beihang University China Tianmushan Laboratory China School of Software Hefei University of Technology China Department of Pathology The First Affiliated Hospital of USTC China Intelligent Pathology Institute Division of Life Sciences and Medicine University of Science and Technology of China China

Gigapixel image analysis, particularly for whole slide images (WSIs), often relies on multiple instance learning (MIL). Under the paradigm of MIL, patch image representations are extracted and then fixed during the training of the MIL classifiers for efficiency consideration. However, the invariance of representations makes it difficult to perform data augmentation for WSI-level model training, which significantly limits the performance of the downstream WSI analysis. The current data augmentation methods for gigapixel images either introduce additional computational costs or result in a loss of semantic information, which is hard to meet the requirements for efficiency and stability needed for WSI model training. In this paper, we propose a Promptable Representation Distribution Learning framework (PRDL) for both patch-level representation learning and WSI-level data augmentation. Meanwhile, we explore the use of prompts to guide data augmentation in feature space, which achieves promptable data augmentation for training robust WSI-level models. The experimental results have demonstrated that the proposed method stably outperforms state-of-the-art methods. © 2024, CC BY.

关键词： WSI circuits

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Countering Eavesdroppers with Meta-learning-based Cooperative Ambient Backscatter Communications

arXiv

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arXiv 2023年

作者： Chu, Nam H. Van Huynh, Nguyen Nguyen, Diep N. Hoang, Dinh Thai Gong, Shimin Shu, Tao Dutkiewicz, Eryk Phan, Khoa T. School of Electrical and Data Engineering University of Technology Sydney Australia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China Department of Computer Science and Software Engineering Auburn University AuburnAL36849 United States School of Engineering and Mathematical Sciences Department of Computer Science and Information Technology La Trobe University Melbourne Australia

This article introduces a novel lightweight framework using ambient backscattering communications to counter eavesdroppers. In particular, our framework divides an original message into two parts. The first part, i.e., the active-transmit message, is transmitted by the transmitter using conventional RF signals. Simultaneously, the second part, i.e., the backscatter message, is transmitted by an ambient backscatter tag that backscatters upon the active signals emitted by the transmitter. Notably, the backscatter tag does not generate its own signal, making it difficult for an eavesdropper to detect the backscattered signals unless they have prior knowledge of the system. Here, we assume that without decoding/knowing the backscatter message, the eavesdropper is unable to decode the original message. Even in scenarios where the eavesdropper can capture both messages, reconstructing the original message is a complex task without understanding the intricacies of the message-splitting mechanism. A challenge in our proposed framework is to effectively decode the backscattered signals at the receiver, often accomplished using the maximum likelihood (MLK) approach. However, such a method may require a complex mathematical model together with perfect channel state information (CSI). To address this issue, we develop a novel deep meta-learning-based signal detector that can not only effectively decode the weak backscattered signals without requiring perfect CSI but also quickly adapt to a new wireless environment with very little knowledge. Simulation results show that our proposed learning approach, without requiring perfect CSI and complex mathematical model, can achieve a bit error ratio close to that of the MLK-based approach. They also clearly show the efficiency of the proposed approach in dealing with eavesdropping attacks and the lack of training data for deep learning models in practical scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Backscattering

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Agile Meets Quantum: A Novel Genetic Algorithm Model for Predicting the Success of Quantum software Development Project

arXiv

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arXiv 2024年

作者： Khan, Arif Ali Akbar, Muhammad Azeem Lahtinen, Valtteri Paavola, Marko Niazi, Mahmood Alatawi, Mohammed Naif Alotaibi, Shoayee Dlaim M3S Empirical Software Engineering Research Unit University of Oulu Oulu90014 Finland Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta53851 Finland Quanscient Oy Tampere Finland VTT Technical Research Ctr of Finland Ltd Oulu Finland Department of Information and Computer Science King Fahd University of Petroleum and Minerals Saudi Arabia Interdisciplinary Research Centre for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Saudi Arabia Information Technology Department Faculty of Computers and Information Technology University of Tabuk Saudi Arabia Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Hail Hail Saudi Arabia

Context: Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve the complex problems more efficiently than classical counterparts. Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored. Objective: This study investigates key causes of challenges that could hinders the adoption of traditional agile approaches in quantum software projects and develop an Agile-Quantum software Project Success Prediction Model (AQSSPM). Methodology: Firstly, we identified 19 causes of challenging factors discussed in our previous study, which are potentially impacting agile-quantum project success. Secondly, a survey was conducted to collect expert opinions on these causes and applied Genetic Algorithm (GA) with Naive Bayes Classifier (NBC) and Logistic Regression (LR) to develop the AQSSPM. Results: Utilizing GA with NBC, project success probability improved from 53.17% to 99.68%, with cost reductions from 0.463% to 0.403%. Similarly, GA with LR increased success rates from 55.52% to 98.99%, and costs decreased from 0.496% to 0.409% after 100 iterations. Both methods result showed a strong positive correlation (rs=0.955) in causes ranking, with no significant difference between them (t=1.195, p=0.240>0.05). Conclusion: The AQSSPM highlights critical focus areas for efficiently and successfully implementing agile-quantum projects considering the cost factor of a particular project. © 2024, CC BY.

关键词： Genetic algorithms

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Adaptive Student Inference Network for Efficient Single Image Super-Resolution

Adaptive Student Inference Network for Efficient Single Imag...

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IEEE International Conference on Data Mining (ICDM)

作者： Kang Miao Zhao Zhang Jiahuan Ren Mingbo Zhao Haijun Zhang Richang Hong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China School of Information Science and Technology Donghua University Shanghai China Department of Computer Science Harbin Institute of Technology (Shenzhen) Shenzhen China

Recent advances in single image super-resolution (SISR) have achieved remarkable performance through deep learning. However, the high computational cost hinders the deployment of SISR models on edge devices. Instead of proposing new SISR models, a new trend is emerging to improve network efficiency by reducing parameters, FLOPs, and inference time through slight modifications to the original models. However, recent methods usually focus on reducing only one of three metrics, i.e., FLOPs, parameters and inference time, which inevitably increases the other two metrics. In this paper, we propose a novel Adaptive Student Inference Network (ASIN) on popular SISR models, which aims at reducing FLOPs and inference time while maintaining the number of parameters and restoring clearer high-resolution images. Specifically, our ASIN divides a SISR model into three components (head, body and tail) and adopts various strategies for each part. For head and tail parts, to ensure the restored images contain more detailed information, a novel auxiliary Enhanced Teacher Network (ETNet) is designed, which is trained with the ground-truth images to obtain more prior knowledge to guide student network to extract more accurate textures using a new knowledge distillation method. For the body part, owing to the varying difficulties of the reconstructions in different regions, we propose an Adaptive Depth Predicted Module (ADPM) to dynamically shorten average depth of network to reduce the computational cost of overall network. Extensive experiments on two datasets demonstrate the effectiveness and state-of-the-art performance of our ASIN compared to its counterparts.

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Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

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IEEE Transactions on Mobile Computing 2025年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J. P. C. University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Zhengzhou University School of Electrical and Information Engineering Henan International Joint Laboratory of Intelligent Health Information System National Telemedicine Center National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Nottingham Trent University Department of Engineering NottinghamNG1 4FQ United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Nanjing University of Information Science and Technology School of Software Nanjing210044 China Amazonas State University Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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Mitigating Insecure Outputs in Large Language Models(LLMs): A Practical Educational Module

Mitigating Insecure Outputs in Large Language Models(LLMs): ...

引用

IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： MD Abdul Barek Md Mostafizur Rahman Shapna Akter A.B.M Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Intelligent Systems and Robotics University of West Florida USA Dept. of Cybersecurity and Information Technology University of West Florida USA Center for Cybersecurity University of West Florida USA Computer Science and Software Engineering Auburn University USA Dept. of Computer Science Tuskegee University USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, hands-on, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output.

关键词： Training Navigation Large language models Computational modeling software Security software development management

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An Efficient Detection Approach of Content Aware Image Resizing

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Computers, Materials & Continua 2020年第8期64卷 887-907页

作者： Ming Lu Shaozhang Niu Zhenguang Gao Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and TelecommunicationsBeijing100876China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China Department of Computer Science Framingham State UniversityMassachusettsMA 01772USA

Content aware image resizing(CAIR)is an excellent technology used widely for image *** can also be used to tamper with images and bring the trust crisis of image content to the *** an image is processed by CAIR,the correlation of local neighborhood pixels will be *** local binary patterns(LBP)can effectively describe the local texture,it however cannot describe the magnitude information of local neighborhood pixels and is also vulnerable to ***,to deal with the detection of CAIR,a novel forensic method based on improved local ternary patterns(ILTP)feature and gradient energy feature(GEF)is proposed in this ***,the adaptive threshold of the original local ternary patterns(LTP)operator is improved,and the ILTP operator is used to describe the change of correlation among local neighborhood pixels caused by ***,the histogram features of ILTP and the gradient energy features are extracted from the candidate image for CAIR forgery ***,the ILTP features and the gradient energy features are concatenated into the combined features,and the combined features are used to train *** support vector machine(SVM)is exploited as a classifier to be trained and tested by the above features in order to distinguish whether an image is subjected to CAIR or *** candidate images are extracted from uncompressed color image database(UCID),then the training and testing sets are *** experimental results with many test images show that the proposed method can detect CAIR tampering effectively,and that its performance is improved compared with other *** can achieve a better performance than the state-of-the-art approaches.

关键词： Digital image forensics content aware image resizing local ternary patterns gradient energy feature

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A Secure Authentication Framework for Consumer Mobile Crowdsourcing Networks

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IEEE Transactions on Consumer Electronics 2024年

作者： Aldosary, Abdallah Tanveer, Muhammad Ahmad, Musheer Maghrabi, Louai A. Ahmed, Emad A. Hussain, Amir El-Latif, Ahmed A. Abd Department of Computer Engineering Prince Sattam bin Abdulaziz University Riyadh Wadi Addwasir11991 Saudi Arabia University of Management and Technology Department of Computer Science Lahore54770 Pakistan Jamia Millia Islamia Department of Computer Engineering New Delhi110025 India University of Business and Technology College of Engineering Department of Software Engineering Jeddah Saudi Arabia South Valley University Faculty of Computers and Information Department of Computer Science Qena Egypt Edinburgh Napier University United Kingdom Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt

The Mobile crowdsourcing network (MCN) leverages collaborative intelligence to solve complex tasks through group cooperation. It comprises three main components: the end-user, the service provider, and the mobile user. The end-user requests crowd-sensing and computing services from the service provider, who handles task decomposition, distribution, re-composition, and recommendation. Mobile users perform the crowd sensing and computing tasks. Despite its benefits, MCN faces significant security and privacy challenges. Communications between the service provider and the end-user involve sensitive data, and mobile users and service providers communicate over open channels, making them vulnerable to security threats. To address these concerns, we propose a secure authentication framework for MCN, called SAF-MCN. This framework combines the lightweight cryptographic primitive ' ASCON' with elliptic curve cryptography. SAF-MCN allows the service provider to authenticate mobile users before they join the MCN. Once authenticated, a secure channel is established for encrypted communication over the public Internet. The security of SAFMCN is validated using both formal and informal methods, demonstrating its resilience against potential attacks. The use of Scyther further ensures the security of SAF-MCN. Comparative analysis shows that SAF-MCN is superior in terms of security features, computational efficiency, and communication cost. © 1975-2011 IEEE.

关键词： ASCON Authenticated Encryption Authentication Mobile Crowdsourcing Network Scyther Security

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INTEGRATION OF BUSINESS PROCESS AND INVENTORY MANAGEMENT MODELS FOR SUPPLY CHAIN SIMULATION

TechRxiv

引用

TechRxiv 2022年

作者： Kopp, Andrii Orlovskyi, Dmytro Syzonova, Kateryna The Department of Software Engineering and Management Intelligent Technology National Technical University Kharkiv Polytechnic Institute Ukraine The Educational and Scientific Institute of Computer Science and Information Technology National Technical University Kharkiv Polytechnic Institute Ukraine

This paper proposes an approach to the integration of the Business Process Model and Notation (BPMN) and Economic Order Quantity (EOQ) models for supply chain business process simulation. This approach considers the synergy of Business Process Management (BPM) and Supply Chain Management (SCM) concepts both strive to reduce costs and satisfy customers. Therefore, we consider the extension of BPMN-based models with EOQ-driven parameters to simulate supply chain business processes and support decision-making in the context of supply chain management. © 2022, CC BY.

关键词： Supply chain management

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Solubility Measurement, Correlation and Thermodynamics Properties of Oxibendazole in 14 Pure Solvents and 2 Binary Solvents from 283.15 K to 328.15 K

SSRN

引用

SSRN 2023年

作者： Yu, Chao Zhang, Hao Tian, Ailing Zhou, Yiyu Li, Haohuan Xu, Funeng Fu, Hualin Wang, Xianxiang Shu, Gang Lin, Juchun Tang, Huaqiao Zhang, Wei Department of Pharmacy College of Veterinary Medicine Sichuan Agricultural University Sichuan Chengdu611130 China Intelligent Science and Technology School of Computer and Software Engineering Sichuan Yibin644005 China College of Science Sichuan Agricultural University Sichuan Chengdu611130 China

Oxibendazole (OXIB) is an oral anthelmintic heterocyclic compound used in humans and veterinary, whose solubility is closely related to its formulation. In this study, the molar fraction solubility of OXIB in 14 pure solvents and two binary mixed solvents were determined at standard atmospheric pressure and in the temperature range of 283.15K to 328.15K, and the results showed that the solubility of OXIB in all solvents increased with increasing temperature. OXIB has the highest molar fraction solubility in N,N-Dimethylacetamide (DMA) among single solvents. In binary solvents, the molar fractional solubility of OXIB increases with the increase of the ratio of N,N-Dimethylformamide (DMF) and DMA. The Apelblat equation and Van’t Hoff model were employed to correlate solubility data in 14 single solvents. The modified Apelblat equation, Van’t Hoff model, GSM model, and Jouyban-Acree model were employed to correlate solubility data in binary solvents. At the same time, the Hansen solubility parameters (HSPs) were used to analyze the solubility order of OXIB in 14 pure solvents and 2 binary solvent systems. Meanwhile, ideal solubility and activity coefficients indicate solute-solvent molecular interactions. Furthermore, from the results of thermodynamic parameters such as enthalpy, entropy, and apparent standard Gibbs free energy, the dissolution of OXIB is an endothermic and entropy-friendly process. © 2023, The Authors. All rights reserved.

关键词： Solubility

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