Pub Date : 2025-04-01DOI: 10.1109/TETC.2025.3546244
Yi-Cheng Chen;Mu-Ping Chang;Wang-Chien Lee
Recent research has focused on the integration of smart manufacturing and deep learning owing to the widespread application of neural computation. For deep learning, how to construct the architecture of a neural network is a critical issue. Especially on defect prediction or detection, a proper neural architecture could effectively extract features from the given manufacturing data to accomplish the targeted task. In this paper, we introduce a Virtual Space concept to effectively shrink the search space of potential neural network structures, with the aim of downgrading the computation complexity for learning and accuracy derivation. In addition, a novel reinforcement learning model, namely, Virtual Proximal Policy Optimization (Virtu-PPO), is developed to efficiently and effectively discover the optimal neural network structure. We also propose an optimization strategy to enhance the searching process of neural architecture for defect prediction. In addition, the proposed model is applied on several real-world manufacturing datasets to show the performance and practicability of defect prediction.
{"title":"Virtual Reinforcement Learning for Defect Prediction in Smart Manufacturing","authors":"Yi-Cheng Chen;Mu-Ping Chang;Wang-Chien Lee","doi":"10.1109/TETC.2025.3546244","DOIUrl":"https://doi.org/10.1109/TETC.2025.3546244","url":null,"abstract":"Recent research has focused on the integration of smart manufacturing and deep learning owing to the widespread application of neural computation. For deep learning, how to construct the architecture of a neural network is a critical issue. Especially on defect prediction or detection, a proper neural architecture could effectively extract features from the given manufacturing data to accomplish the targeted task. In this paper, we introduce a <italic>Virtual Space</i> concept to effectively shrink the search space of potential neural network structures, with the aim of downgrading the computation complexity for learning and accuracy derivation. In addition, a novel reinforcement learning model, namely, <italic>Virtual Proximal Policy Optimization (Virtu-PPO)</i>, is developed to efficiently and effectively discover the optimal neural network structure. We also propose an optimization strategy to enhance the searching process of neural architecture for defect prediction. In addition, the proposed model is applied on several real-world manufacturing datasets to show the performance and practicability of defect prediction.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"990-1002"},"PeriodicalIF":5.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quantum circuit simulation is playing a critical role in the current era of quantum computing. However, quantum circuit simulation suffers from huge memory requirements that scale exponentially according to the number of qubits. Our observation reveals that the conventional complex number representation using real and imaginary values adds to the memory overhead beyond the intrinsic cost of simulating quantum states. Instead, using the radius and phase value of a complex number better reflects the properties of the complex values used in the quantum circuit simulation providing better memory efficiency. This paper proposes q-Point, a compact numeric format for quantum circuit simulation that utilizes polar form representation instead of rectangular form representation to store complex numbers. The proposed q-Point format consists of three fields: i) exponent bits for radius value ii) mantissa bits for radius value iii) mantissa bits for phase value. However, a naive application of the q-Point format has the potential to cause issues with both simulation accuracy and simulation speed. To preserve simulation accuracy with fewer bits, we use a multi-level encoding scheme that employs different mantissa bits depending on the exponent range. Additionally, to prevent possible slowdown due to the add operation in polar form complex numbers, we use a technique that adaptively applies both polar and rectangular forms. Equipped with these optimizations, the proposed q-Point format demonstrates reasonable simulation accuracy while using only half of the memory requirement using the baseline format. Additionally, the q-Point format enables an average of 1.37× and 1.16× faster simulation for QAOA and VQE benchmark circuits.
{"title":"q-Point: A Numeric Format for Quantum Circuit Simulation Using Polar Form Complex Numbers","authors":"Seungwoo Choi;Enhyeok Jang;Youngmin Kim;Sungwoo Ahn;Won Woo Ro","doi":"10.1109/TETC.2025.3572935","DOIUrl":"https://doi.org/10.1109/TETC.2025.3572935","url":null,"abstract":"Quantum circuit simulation is playing a critical role in the current era of quantum computing. However, quantum circuit simulation suffers from huge memory requirements that scale exponentially according to the number of qubits. Our observation reveals that the conventional complex number representation using real and imaginary values adds to the memory overhead beyond the intrinsic cost of simulating quantum states. Instead, using the radius and phase value of a complex number better reflects the properties of the complex values used in the quantum circuit simulation providing better memory efficiency. This paper proposes q-Point, a compact numeric format for quantum circuit simulation that utilizes polar form representation instead of rectangular form representation to store complex numbers. The proposed q-Point format consists of three fields: i) exponent bits for radius value ii) mantissa bits for radius value iii) mantissa bits for phase value. However, a naive application of the q-Point format has the potential to cause issues with both simulation accuracy and simulation speed. To preserve simulation accuracy with fewer bits, we use a multi-level encoding scheme that employs different mantissa bits depending on the exponent range. Additionally, to prevent possible slowdown due to the add operation in polar form complex numbers, we use a technique that adaptively applies both polar and rectangular forms. Equipped with these optimizations, the proposed q-Point format demonstrates reasonable simulation accuracy while using only half of the memory requirement using the baseline format. Additionally, the q-Point format enables an average of 1.37× and 1.16× faster simulation for QAOA and VQE benchmark circuits.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"1142-1155"},"PeriodicalIF":5.4,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In today’s digital age, the proliferation of social networks and advanced camera technology has led to countless images being shared on online social platforms daily, potentially resulting in significant breaches of personal privacy. In recent years, many methods have been proposed to protect image privacy, allowing users to be notified of potential privacy leaks before publishing their photos. However, most existing research primarily addresses the privacy protection of image owners or co-owners, while neglecting the privacy of people who appear in the background of others’ images or who are co-occurring with others in the same image. In this paper, we propose a system capable of conducting real-time access control for protecting privacy of every individual appearing in a photo, as well as the privacy of people who co-occur in the same image. Specifically, we first detect all the faces in the image, then use a facial recognition algorithm to identify the corresponding users’ privacy policies, and finally determine whether the image violates any user’s privacy policy. In order to provide real-time access control, we have designed a facial attribute index tree to speed up the process of user identification. The experimental results show that compared with the method without our proposed index tree, our approach improves the time efficiency by almost two orders of magnitude while maintaining the accuracy of more than 97%.
{"title":"Real-Time Access Control for Background and Co-Occurrence Image Privacy Protection","authors":"Chaoquan Cai;Dan Lin;Kannappan Palaniappan;Chris Clifton","doi":"10.1109/TETC.2025.3572396","DOIUrl":"https://doi.org/10.1109/TETC.2025.3572396","url":null,"abstract":"In today’s digital age, the proliferation of social networks and advanced camera technology has led to countless images being shared on online social platforms daily, potentially resulting in significant breaches of personal privacy. In recent years, many methods have been proposed to protect image privacy, allowing users to be notified of potential privacy leaks before publishing their photos. However, most existing research primarily addresses the privacy protection of image owners or co-owners, while neglecting the privacy of people who appear in the background of others’ images or who are co-occurring with others in the same image. In this paper, we propose a system capable of conducting real-time access control for protecting privacy of every individual appearing in a photo, as well as the privacy of people who co-occur in the same image. Specifically, we first detect all the faces in the image, then use a facial recognition algorithm to identify the corresponding users’ privacy policies, and finally determine whether the image violates any user’s privacy policy. In order to provide real-time access control, we have designed a facial attribute index tree to speed up the process of user identification. The experimental results show that compared with the method without our proposed index tree, our approach improves the time efficiency by almost two orders of magnitude while maintaining the accuracy of more than 97%.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"1130-1141"},"PeriodicalIF":5.4,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-28DOI: 10.1109/TETC.2025.3572277
Hossein Hosseini;Mohsen Ansari;Jörg Henkel
Task replication is a common technique for achieving fault tolerance. However, its effectiveness is limited by the accuracy of the fault detection mechanism; imperfect detection imposes a ceiling on achievable reliability. While perfect fault detection mechanisms offer higher reliability, they introduce significant overhead. To address this, we introduce Dynamic Task Replication, a fault tolerance technique that dynamically determines the number of replicas at runtime to overcome the limitations of imperfect fault detection. Our primary contribution, Reliability-Aware Replica-Efficient Dynamic Task Replication, optimizes this approach by minimizing the expected number of replicas while achieving the desired reliability target. We incorporate actual execution times into the reliability assessment. Additionally, we propose the Energy-Aware Reliability-Guaranteeing scheduling technique, which integrates our optimized replication method into hard real-time systems and leverages Dynamic Voltage and Frequency Scaling to minimize energy consumption while ensuring reliability and system schedulability. Experimental results demonstrate that our method requires 24% fewer replicas on average than the N-Modular Redundancy technique, with the advantage increasing to 58% for tasks with low base reliabilities. Furthermore, our scheduling technique significantly conserves energy and enhances feasibility compared to existing methods across diverse system workloads.
{"title":"Dynamic Task Replication With Imperfect Fault Detection in Multicore Cyber-Physical Systems","authors":"Hossein Hosseini;Mohsen Ansari;Jörg Henkel","doi":"10.1109/TETC.2025.3572277","DOIUrl":"https://doi.org/10.1109/TETC.2025.3572277","url":null,"abstract":"Task replication is a common technique for achieving fault tolerance. However, its effectiveness is limited by the accuracy of the fault detection mechanism; imperfect detection imposes a ceiling on achievable reliability. While perfect fault detection mechanisms offer higher reliability, they introduce significant overhead. To address this, we introduce Dynamic Task Replication, a fault tolerance technique that dynamically determines the number of replicas at runtime to overcome the limitations of imperfect fault detection. Our primary contribution, Reliability-Aware Replica-Efficient Dynamic Task Replication, optimizes this approach by minimizing the expected number of replicas while achieving the desired reliability target. We incorporate actual execution times into the reliability assessment. Additionally, we propose the Energy-Aware Reliability-Guaranteeing scheduling technique, which integrates our optimized replication method into hard real-time systems and leverages Dynamic Voltage and Frequency Scaling to minimize energy consumption while ensuring reliability and system schedulability. Experimental results demonstrate that our method requires 24% fewer replicas on average than the N-Modular Redundancy technique, with the advantage increasing to 58% for tasks with low base reliabilities. Furthermore, our scheduling technique significantly conserves energy and enhances feasibility compared to existing methods across diverse system workloads.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"1113-1129"},"PeriodicalIF":5.4,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-27DOI: 10.1109/TETC.2025.3552941
Yijun Cui;Jiatong Tian;Chuanchao Lu;Yang Li;Ziying Ni;Chenghua Wang;Weiqiang Liu
Lattice-based cryptography is considered secure against quantum computing attacks. However, naive implementations on embedded devices are vulnerable to side-channel attacks (SCAs) with full key recovery possible through power and electromagnetic leakage analysis. This article presents two protection schemes, masking and shuffling, for the baseline Radix-2 multi-path delay commutator (R2MDC) number theoretic transform (NTT) architecture. The proposed masking NTT scheme introduces a random number to protect the secret key during the decryption phase and leverages the linear property of arithmetic transform in NTT polynomial multiplication. By adjusting the comparing decoding threshold, the masking method greatly reduces the ratio of $t$-$test$ value exceeding the threshold of unprotected NTT scheme from 77.38% to 3.91%. An ingenious shuffling transform process is also proposed to disturb the calculation sequence of butterfly transformation, adapting to the high-throughput architecture of R2MDC-NTT. This shuffling NTT scheme does not require operations to remove shuffle or additional operation cycles, reducing the leakage ratio to 13.49% with minimal extra hardware resources and wide applicability. The proposed masking and shuffling techniques effectively suppress side-channel leakage, improving the security of hardware architecture while maintaining a balance between overall performance and additional hardware resources.
{"title":"Two Low-Cost and Security-Enhanced Implementations Against Side-Channel Attacks of NTT for Lattice-Based Cryptography","authors":"Yijun Cui;Jiatong Tian;Chuanchao Lu;Yang Li;Ziying Ni;Chenghua Wang;Weiqiang Liu","doi":"10.1109/TETC.2025.3552941","DOIUrl":"https://doi.org/10.1109/TETC.2025.3552941","url":null,"abstract":"Lattice-based cryptography is considered secure against quantum computing attacks. However, naive implementations on embedded devices are vulnerable to side-channel attacks (SCAs) with full key recovery possible through power and electromagnetic leakage analysis. This article presents two protection schemes, masking and shuffling, for the baseline Radix-2 multi-path delay commutator (R2MDC) number theoretic transform (NTT) architecture. The proposed masking NTT scheme introduces a random number to protect the secret key during the decryption phase and leverages the linear property of arithmetic transform in NTT polynomial multiplication. By adjusting the comparing decoding threshold, the masking method greatly reduces the ratio of <inline-formula><tex-math>$t$</tex-math></inline-formula>-<inline-formula><tex-math>$test$</tex-math></inline-formula> value exceeding the threshold of unprotected NTT scheme from 77.38% to 3.91%. An ingenious shuffling transform process is also proposed to disturb the calculation sequence of butterfly transformation, adapting to the high-throughput architecture of R2MDC-NTT. This shuffling NTT scheme does not require operations to remove shuffle or additional operation cycles, reducing the leakage ratio to 13.49% with minimal extra hardware resources and wide applicability. The proposed masking and shuffling techniques effectively suppress side-channel leakage, improving the security of hardware architecture while maintaining a balance between overall performance and additional hardware resources.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"977-989"},"PeriodicalIF":5.4,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-17DOI: 10.1109/TETC.2025.3546602
Chengjie Wang;Yuejun Zhang;Ziyu Zhou
Porcelain, as a significant cultural heritage, embodies the wisdom of human civilization. However, existing anti-counterfeiting and authentication technologies for porcelain are often unreliable and costly. This paper proposes a physical unclonable functions (PUF) design based on crack physical feature extraction for the anti-counterfeiting and authentication of Gold-Wire porcelain. The proposed method generates PUF information by extracting inherent physical deviations in the surface cracks of Gold-Wire porcelain. First, a standard crack extraction process is established using digital image processing to obtain crack information from the porcelain surface. Then, a physical feature extraction model based on the chain code encoding technique and the Delaunay triangulation technique is used to derive the physical feature values from the cracks. Subsequently, a PUF encoding algorithm is designed to convert these physical feature values into a PUF response. Finally, the security and reliability of the designed PUF are evaluated, and a PUF-based porcelain authentication protocol is developed. Experimental results show that the proposed PUF exhibits 50.16% uniqueness and 98.85% reliability, and the PUF data successfully passed the NIST randomness test, demonstrating that the proposed technology can effectively achieve low-cost, high-reliability anti-counterfeiting for commercial porcelain.
{"title":"A Novel Porcelain Fingerprinting Technique","authors":"Chengjie Wang;Yuejun Zhang;Ziyu Zhou","doi":"10.1109/TETC.2025.3546602","DOIUrl":"https://doi.org/10.1109/TETC.2025.3546602","url":null,"abstract":"Porcelain, as a significant cultural heritage, embodies the wisdom of human civilization. However, existing anti-counterfeiting and authentication technologies for porcelain are often unreliable and costly. This paper proposes a physical unclonable functions (PUF) design based on crack physical feature extraction for the anti-counterfeiting and authentication of Gold-Wire porcelain. The proposed method generates PUF information by extracting inherent physical deviations in the surface cracks of Gold-Wire porcelain. First, a standard crack extraction process is established using digital image processing to obtain crack information from the porcelain surface. Then, a physical feature extraction model based on the chain code encoding technique and the Delaunay triangulation technique is used to derive the physical feature values from the cracks. Subsequently, a PUF encoding algorithm is designed to convert these physical feature values into a PUF response. Finally, the security and reliability of the designed PUF are evaluated, and a PUF-based porcelain authentication protocol is developed. Experimental results show that the proposed PUF exhibits 50.16% uniqueness and 98.85% reliability, and the PUF data successfully passed the NIST randomness test, demonstrating that the proposed technology can effectively achieve low-cost, high-reliability anti-counterfeiting for commercial porcelain.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"964-976"},"PeriodicalIF":5.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-13DOI: 10.1109/TETC.2025.3548672
Riya Samanta;Soumya K. Ghosh;Sajal K. Das
Traditional task assignment approaches in crowdsourcing platforms have focused on optimizing utility for workers or tasks, often neglecting the general utility of the platform and the influence of mutual preference considering skill availability and budget restrictions. This oversight can destabilize task allocation outcomes, diminishing user experience, and, ultimately, the platform’s long-term utility and gives rise to the Worker Task Stable Matching (WTSM) problem. To solve WTSM, we propose the Skill-oriented Stable Task Assignment with a Bi-directional Preference (SoSTA) method based on deferred acceptance strategy. SoSTA aims to generate stable allocations between tasks and workers considering mutually their preferences, optimizing overall utility while following skill and budget constraints. Our study redefines the general utility of the platform as an amalgamation of utilities on both the workers’ and tasks’ sides, incorporating the preference lists of each worker or task based on their respective utility scores for the other party. SoSTA incorporates Multi Skill-oriented Stable Worker Task Mapping (Multi-SoS-WTM) algorithm for contributions with multiple skills per worker. SoSTA is rational, non-wasteful, fair, and hence stable. SoSTA outperformed other approaches in the simulations of the MeetUp dataset. SoSTA improves execution speed by 80%, task completion rate by 60%, and user happiness by 8%.
在众包平台中，传统的任务分配方法侧重于优化工人或任务的效用，往往忽略了平台的一般效用以及考虑技能可用性和预算限制的相互偏好的影响。这种疏忽会破坏任务分配结果的稳定性，降低用户体验，最终影响平台的长期效用，并导致工作任务稳定匹配（Worker task stability Matching， WTSM）问题。为了解决WTSM问题，我们提出了基于延迟接受策略的双向偏好（SoSTA）的技能导向稳定任务分配方法。SoSTA的目标是在任务和工人之间产生稳定的分配，考虑他们的相互偏好，在遵循技能和预算约束的同时优化整体效用。我们的研究将平台的一般效用重新定义为工人和任务双方效用的合并，结合每个工人或任务的偏好列表，基于他们各自对另一方的效用得分。SoSTA结合了面向多技能的稳定工人任务映射（Multi- sos - wtm）算法，用于每个工人的多技能贡献。SoSTA是理性的、不浪费的、公平的，因此是稳定的。在MeetUp数据集的模拟中，SoSTA优于其他方法。SoSTA将执行速度提高了80%，任务完成率提高了60%，用户满意度提高了8%。
{"title":"SoSTA: Skill-Oriented Stable Task Assignment With Bidirectional Preferences in Crowdsourcing","authors":"Riya Samanta;Soumya K. Ghosh;Sajal K. Das","doi":"10.1109/TETC.2025.3548672","DOIUrl":"https://doi.org/10.1109/TETC.2025.3548672","url":null,"abstract":"Traditional task assignment approaches in crowdsourcing platforms have focused on optimizing utility for workers or tasks, often neglecting the general utility of the platform and the influence of mutual preference considering skill availability and budget restrictions. This oversight can destabilize task allocation outcomes, diminishing user experience, and, ultimately, the platform’s long-term utility and gives rise to the Worker Task Stable Matching (WTSM) problem. To solve WTSM, we propose the Skill-oriented Stable Task Assignment with a Bi-directional Preference (SoSTA) method based on deferred acceptance strategy. SoSTA aims to generate stable allocations between tasks and workers considering mutually their preferences, optimizing overall utility while following skill and budget constraints. Our study redefines the general utility of the platform as an amalgamation of utilities on both the workers’ and tasks’ sides, incorporating the preference lists of each worker or task based on their respective utility scores for the other party. SoSTA incorporates Multi Skill-oriented Stable Worker Task Mapping (Multi-SoS-WTM) algorithm for contributions with multiple skills per worker. SoSTA is rational, non-wasteful, fair, and hence stable. SoSTA outperformed other approaches in the simulations of the MeetUp dataset. SoSTA improves execution speed by 80%, task completion rate by 60%, and user happiness by 8%.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"947-963"},"PeriodicalIF":5.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10925570","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-11DOI: 10.1109/TETC.2025.3547612
Alberto Avritzer;Andrea Janes;Andrea Marin;Catia Trubiani;Andre van Hoorn;Matteo Camilli;Daniel S. Menasché;André B. Bondi
In this article, we report on the application of resiliency enforcement strategies that were applied to a microservices system running on a real-world deployment of a large cluster of heterogeneous Virtual Machines (VMs). We present the evaluation results obtained from measurement and modeling implementations. The measurement infrastructure was composed of 15 large and 15 extra-large VMs. The modeling approach used Markov Decision Processes (MDP). On the measurement testbed, we implemented three different levels of software rejuvenation granularity to achieve software resiliency. We have discovered two threats to resiliency in this environment. The first threat to resiliency was a memory leak that was part of the underlying open-source infrastructure in each VM. The second threat to resiliency was the result of the contention for resources in the physical host, which is dependent on the number and size of VMs deployed to the physical host. In the MDP modeling approach, we evaluated four strategies for assigning tasks to VMs with different configurations and different levels of parallelism. Using the large cluster under study, we compared our approach of using software aging and rejuvenation with the state-of-the-art approach of using a network of VMs deployed to a private cloud without software aging detection and rejuvenation. In summary, we show that in a private cloud with non-elastic resource allocation in the physical hosts, careful performance engineering needs to be performed to optimize the trade-offs between the number of VMs allocated and the total memory allocated to each VM.
{"title":"Software Aging Detection and Rejuvenation Assessment in Heterogeneous Virtual Networks","authors":"Alberto Avritzer;Andrea Janes;Andrea Marin;Catia Trubiani;Andre van Hoorn;Matteo Camilli;Daniel S. Menasché;André B. Bondi","doi":"10.1109/TETC.2025.3547612","DOIUrl":"https://doi.org/10.1109/TETC.2025.3547612","url":null,"abstract":"In this article, we report on the application of resiliency enforcement strategies that were applied to a microservices system running on a real-world deployment of a large cluster of heterogeneous Virtual Machines (VMs). We present the evaluation results obtained from measurement and modeling implementations. The measurement infrastructure was composed of 15 large and 15 extra-large VMs. The modeling approach used Markov Decision Processes (MDP). On the measurement testbed, we implemented three different levels of software rejuvenation granularity to achieve software resiliency. We have discovered two threats to resiliency in this environment. The first threat to resiliency was a memory leak that was part of the underlying open-source infrastructure in each VM. The second threat to resiliency was the result of the contention for resources in the physical host, which is dependent on the number and size of VMs deployed to the physical host. In the MDP modeling approach, we evaluated four strategies for assigning tasks to VMs with different configurations and different levels of parallelism. Using the large cluster under study, we compared our approach of using software aging and rejuvenation with the state-of-the-art approach of using a network of VMs deployed to a private cloud without software aging detection and rejuvenation. In summary, we show that in a private cloud with non-elastic resource allocation in the physical hosts, careful performance engineering needs to be performed to optimize the trade-offs between the number of VMs allocated and the total memory allocated to each VM.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 2","pages":"299-313"},"PeriodicalIF":5.1,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10923615","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-10DOI: 10.1109/TETC.2025.3546366
Wenming Cao;Jiewen Zeng;Qifan Liu
Few-shot classification is the task of recognizing unseen classes using a limited number of samples. In this paper, we propose a new contrastive learning method called Feature-Level Contrastive Learning (FLCL). FLCL conducts contrastive learning at the feature level and leverages the subtle relationships between positive and negative samples to achieve more effective classification. Additionally, we address the challenges of requiring a large number of negative samples and the difficulty of selecting high-quality negative samples in traditional contrastive learning methods. For feature learning, we design a Feature Enhancement Coding (FEC) module to analyze the interactions and correlations between nonlinear features, enhancing the quality of feature representations. In the metric stage, we propose a centered hypersphere projection metric to map feature vectors onto the hypersphere, improving the comparison between the support and query sets. Experimental results on four few-shot classification benchmark datasets demonstrate that our method, while simple in design, outperforms previous methods and achieves state-of-the-art performance. A detailed ablation study further confirms the effectiveness of each component of our model.
{"title":"FLCL: Feature-Level Contrastive Learning for Few-Shot Image Classification","authors":"Wenming Cao;Jiewen Zeng;Qifan Liu","doi":"10.1109/TETC.2025.3546366","DOIUrl":"https://doi.org/10.1109/TETC.2025.3546366","url":null,"abstract":"Few-shot classification is the task of recognizing unseen classes using a limited number of samples. In this paper, we propose a new contrastive learning method called Feature-Level Contrastive Learning (FLCL). FLCL conducts contrastive learning at the feature level and leverages the subtle relationships between positive and negative samples to achieve more effective classification. Additionally, we address the challenges of requiring a large number of negative samples and the difficulty of selecting high-quality negative samples in traditional contrastive learning methods. For feature learning, we design a Feature Enhancement Coding (FEC) module to analyze the interactions and correlations between nonlinear features, enhancing the quality of feature representations. In the metric stage, we propose a centered hypersphere projection metric to map feature vectors onto the hypersphere, improving the comparison between the support and query sets. Experimental results on four few-shot classification benchmark datasets demonstrate that our method, while simple in design, outperforms previous methods and achieves state-of-the-art performance. A detailed ablation study further confirms the effectiveness of each component of our model.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"935-946"},"PeriodicalIF":5.4,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Today, malware is one of the primary cyber threats to organizations, pervading all types of computing devices, including resource constrained devices such as mobile phones, tablets and embedded devices like Internet-of-Things (IoT) devices. In recent years, researchers have leveraged machine learning based strategies for malware detection and classification. However, malware analysis approaches can only be employed in resource constrained environments if the methods are lightweight in nature. In this paper, we present MALITE, a lightweight malware analysis system, that can distinguish between benign and malicious binaries and classify various malware families. MALITE converts a binary into a grayscale or an RGB image requiring low memory and battery power consumption and uses computationally inexpensive malware analysis strategies. We have designed MALITE-MN, a lightweight neural network based architecture and MALITE-HRF, an ultra lightweight random forest based method that uses histogram features extracted by a sliding window. An extensive empirical evaluation is conducted on seven publicly available datasets (Malimg, Microsoft BIG, Dumpware10, MOTIF, Drebin, CICAndMal2017 and MalNet), and performance is compared to four state-of-the-art baselines. The results show that MALITE-MN and MALITE-HRF not only accurately identify and classify malware but also respectively consume several orders of magnitude lower resources (in terms of both memory as well as computation capabilities), making them much more suitable for resource constrained environments.
{"title":"MALITE: Lightweight Malware Detection and Classification for Constrained Devices","authors":"Sidharth Anand;Barsha Mitra;Soumyadeep Dey;Abhinav Rao;Rupsha Dhar;Jaideep Vaidya","doi":"10.1109/TETC.2025.3566370","DOIUrl":"https://doi.org/10.1109/TETC.2025.3566370","url":null,"abstract":"Today, malware is one of the primary cyber threats to organizations, pervading all types of computing devices, including resource constrained devices such as mobile phones, tablets and embedded devices like Internet-of-Things (IoT) devices. In recent years, researchers have leveraged machine learning based strategies for malware detection and classification. However, malware analysis approaches can only be employed in resource constrained environments if the methods are lightweight in nature. In this paper, we present MALITE, a lightweight malware analysis system, that can distinguish between benign and malicious binaries and classify various malware families. MALITE converts a binary into a grayscale or an RGB image requiring low memory and battery power consumption and uses computationally inexpensive malware analysis strategies. We have designed MALITE-MN, a lightweight neural network based architecture and MALITE-HRF, an ultra lightweight random forest based method that uses histogram features extracted by a sliding window. An extensive empirical evaluation is conducted on seven publicly available datasets (Malimg, Microsoft BIG, Dumpware10, MOTIF, Drebin, CICAndMal2017 and MalNet), and performance is compared to four state-of-the-art baselines. The results show that MALITE-MN and MALITE-HRF not only accurately identify and classify malware but also respectively consume several orders of magnitude lower resources (in terms of both memory as well as computation capabilities), making them much more suitable for resource constrained environments.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"1099-1112"},"PeriodicalIF":5.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：