IEEE Transactions on Emerging Topics in Computing最新文献_第4页

Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics 减轻学生预测分析偏差的多层个性化联合学习

IF 5.9 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-06-07 DOI: 10.1109/tetc.2024.3407716

Yun-Wei Chu, Seyyedali Hosseinalipour, Elizabeth Tenorio, Laura Cruz, Kerrie Douglas, Andrew S. Lan, Christopher G. Brinton

引用次数: 0

Balancing Throughput and Fair Execution of Multi-DNN Workloads on Heterogeneous Embedded Devices 在异构嵌入式设备上平衡吞吐量和多 DNN 工作负载的公平执行

IF 5.9 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-06-06 DOI: 10.1109/tetc.2024.3407055

Andreas Karatzas, Iraklis Anagnostopoulos

引用次数: 0

BCIM: Efficient Implementation of Binary Neural Network Based on Computation in Memory BCIM：基于内存计算的二元神经网络的高效实现

IF 5.9 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-06-04 DOI: 10.1109/tetc.2024.3406628

Mahdi Zahedi, Taha Shahroodi, Carlos Escuin, Georgi Gaydadjiev, Stephan Wong, Said Hamdioui

引用次数: 0

Investigating the Resilience Source of Classification Systems for Approximate Computing Techniques 探究近似计算技术分类系统的复原力来源

IF 5.9 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-05-30 DOI: 10.1109/tetc.2024.3403757

Mario Barbareschi, Salvatore Barone

引用次数: 0

Janus: A Trusted Execution Environment Approach for Attack Detection in Industrial Robot Controllers Janus：用于工业机器人控制器攻击检测的可信执行环境方法

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-04-24 DOI: 10.1109/TETC.2024.3390435

Stefano Longari;Jacopo Jannone;Mario Polino;Michele Carminati;Andrea Zanchettin;Mara Tanelli;Stefano Zanero

{"title":"Janus: A Trusted Execution Environment Approach for Attack Detection in Industrial Robot Controllers","authors":"Stefano Longari;Jacopo Jannone;Mario Polino;Michele Carminati;Andrea Zanchettin;Mara Tanelli;Stefano Zanero","doi":"10.1109/TETC.2024.3390435","DOIUrl":"10.1109/TETC.2024.3390435","url":null,"abstract":"In the last few decades, technological progress has led to a spike in the adoption of robots by the manufacturing industry. With the new “Industry 4.0” paradigm, companies strive to automate their production processes by interconnecting and integrating different industrial systems. The resulting increase in complexity contributes to a larger attack surface and paves the way for novel attacks. In the context of cyber-physical systems, consequences include economic and physical damage, as well as harm to human workers. In this article, we present Janus, a novel monitoring mechanism for industrial robot controllers that exploits the trusted execution environment (TEE) to guarantee the integrity of the attack detection algorithm even in case the controller's software is compromised, while not requiring external hardware for its detection process. In particular, we use the state observers strategy for detecting low-level controller (LLC) attacks. We assess our approach by testing it against various attacks, identifying those that are simpler to detect and pinpointing the more elusive ones, which are mostly detected nonetheless. Finally, we demonstrate that our approach does not add significant computation overheads.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"185-195"},"PeriodicalIF":5.1,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10508318","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140801415","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}

引用次数: 0

HARPOCRATES: An Approach Towards Efficient Encryption of Data-at-Rest HARPOCRATES：高效加密静态数据的方法

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-04-17 DOI: 10.1109/TETC.2024.3387558

Md Rasid Ali;Debranjan Pal;Abhijit Das;Dipanwita Roy Chowdhury

{"title":"HARPOCRATES: An Approach Towards Efficient Encryption of Data-at-Rest","authors":"Md Rasid Ali;Debranjan Pal;Abhijit Das;Dipanwita Roy Chowdhury","doi":"10.1109/TETC.2024.3387558","DOIUrl":"10.1109/TETC.2024.3387558","url":null,"abstract":"This paper proposes a new block cipher called HARPOCRATES, which is different from traditional SPN, Feistel, or ARX designs. The new design structure that we use is called the substitution convolution network. The novelty of the approach lies in that the substitution function does not use fixed S-boxes. Instead, it uses a key-driven lookup table storing a permutation of all 8-bit values. If the lookup table is sufficiently randomly shuffled, the round sub-operations achieve good confusion and diffusion to the cipher. While designing the cipher, the security, cost, and performances are balanced, keeping the requirements of encryption of data-at-rest in mind. The round sub-operations are massively parallelizable and designed such that a single active bit may make the entire state (an <inline-formula><tex-math>$8 times 16$</tex-math></inline-formula> binary matrix) active in one round. We analyze the security of the cipher against linear, differential, and impossible differential cryptanalysis. The cipher's resistance against many other attacks like algebraic attacks, structural attacks, and weak keys are also shown. We implemented the cipher in software and hardware; found that the software implementation of the cipher results in better throughput than many well-known ciphers. Although HARPOCRATES is appropriate for the encryption of data-at-rest, it is also well-suited in data-in-transit environments.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"173-184"},"PeriodicalIF":5.1,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140612836","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}

引用次数: 0

LP-Star: Embedding Longest Paths into Star Networks With Large-Scale Missing Edges Under an Emerging Assessment Model LP-Star：在新兴评估模型下将最长路径嵌入具有大规模缺边的星形网络

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-04-16 DOI: 10.1109/TETC.2024.3387119

Xiao-Yan Li;Jou-Ming Chang

{"title":"LP-Star: Embedding Longest Paths into Star Networks With Large-Scale Missing Edges Under an Emerging Assessment Model","authors":"Xiao-Yan Li;Jou-Ming Chang","doi":"10.1109/TETC.2024.3387119","DOIUrl":"10.1109/TETC.2024.3387119","url":null,"abstract":"Star networks play an essential role in designing parallel and distributed systems. With the massive growth of faulty edges and the widespread applications of the longest paths and cycles, it is crucial to embed the longest fault-free paths and cycles in edge-faulty networks. However, the traditional fault model allows a concentrated distribution of faulty edges and thus can only tolerate faults that depend on the minimum degree of the network vertices. This article introduces an improved fault model called the partitioned fault model, which is an emerging assessment model for fault tolerance. Based on this model, we first explore the longest fault-free paths and cycles by proving the edge fault-tolerant Hamiltonian laceability, edge fault-tolerant strongly Hamiltonian laceability, and edge fault-tolerant Hamiltonicity in the <inline-formula><tex-math>$n$</tex-math></inline-formula>-dimensional star network <inline-formula><tex-math>$S_{n}$</tex-math></inline-formula>. Furthermore, based on the theoretical proof, we give an <inline-formula><tex-math>$O(nN)$</tex-math></inline-formula> algorithm to construct the longest fault-free paths in star networks based on the partitioned fault model, where <inline-formula><tex-math>$N$</tex-math></inline-formula> is the number of vertices in <inline-formula><tex-math>$S_{n}$</tex-math></inline-formula>. We also make comparisons to show that our result of edge fault tolerance has exponentially improved other known results.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"147-161"},"PeriodicalIF":5.1,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140612684","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}

引用次数: 0

A Bio-Inspired Implementation of a Sparse-Learning Spike-Based Hippocampus Memory Model 基于稀疏学习尖峰的海马记忆模型的生物启发实现

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-04-16 DOI: 10.1109/TETC.2024.3387026

Daniel Casanueva-Morato;Alvaro Ayuso-Martinez;Juan P. Dominguez-Morales;Angel Jimenez-Fernandez;Gabriel Jimenez-Moreno

{"title":"A Bio-Inspired Implementation of a Sparse-Learning Spike-Based Hippocampus Memory Model","authors":"Daniel Casanueva-Morato;Alvaro Ayuso-Martinez;Juan P. Dominguez-Morales;Angel Jimenez-Fernandez;Gabriel Jimenez-Moreno","doi":"10.1109/TETC.2024.3387026","DOIUrl":"10.1109/TETC.2024.3387026","url":null,"abstract":"The brain is capable of solving complex problems simply and efficiently, far surpassing modern computers. In this regard, neuromorphic engineering focuses on mimicking the basic principles that govern the brain in order to develop systems that achieve such computational capabilities. Within this field, bio-inspired learning and memory systems are still a challenge to be solved, and this is where the hippocampus is involved. It is the region of the brain that acts as a short-term memory, allowing the learning and storage of information from all the sensory nuclei of the cerebral cortex and its subsequent recall. In this work, we propose a novel bio-inspired hippocampal memory model with the ability to learn memories, recall them from a fragment of itself (cue) and even forget memories when trying to learn others with the same cue. This model has been implemented on SpiNNaker using Spiking Neural Networks, and a set of experiments were performed to demonstrate its correct operation. This work presents the first simulation implemented on a special-purpose hardware platform for Spiking Neural Networks of a fully functional bio-inspired spike-based hippocampus memory model, paving the road for the development of future more complex neuromorphic systems.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"119-133"},"PeriodicalIF":5.1,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10502330","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140612832","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}

引用次数: 0

One-Spike SNN: Single-Spike Phase Coding With Base Manipulation for ANN-to-SNN Conversion Loss Minimization 单穗 SNN：单梭子相位编码与基数操纵，实现 ANN 到 SNN 转换损失最小化

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-04-16 DOI: 10.1109/TETC.2024.3386893

Sangwoo Hwang;Jaeha Kung

{"title":"One-Spike SNN: Single-Spike Phase Coding With Base Manipulation for ANN-to-SNN Conversion Loss Minimization","authors":"Sangwoo Hwang;Jaeha Kung","doi":"10.1109/TETC.2024.3386893","DOIUrl":"10.1109/TETC.2024.3386893","url":null,"abstract":"As spiking neural networks (SNNs) are event-driven, energy efficiency is higher than conventional artificial neural networks (ANNs). Since SNN delivers data through discrete spikes, it is difficult to use gradient methods for training, limiting its accuracy. To keep the accuracy of SNNs similar to ANN counterparts, pre-trained ANNs are converted to SNNs (ANN-to-SNN conversion). During the conversion, encoding activations of ANNs to a set of spikes in SNNs is crucial for minimizing the conversion loss. In this work, we propose a single-spike phase coding as an encoding scheme that minimizes the number of spikes to transfer data between SNN layers. To minimize the encoding error due to single-spike approximation in phase coding, threshold shift and base manipulation are proposed. Without any additional retraining or architectural constraints on ANNs, the proposed conversion method does not lose inference accuracy (0.58% on average) verified on three convolutional neural networks (CNNs) with CIFAR and ImageNet datasets. In addition, graph convolutional networks (GCNs) are converted to SNNs successfully with an average accuracy loss of 0.90%. Most importantly, the energy efficiency of our SNN improves by 4.6<inline-formula><tex-math>$sim!! 17.3times$</tex-math></inline-formula> compared to the ANN baseline.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"162-172"},"PeriodicalIF":5.1,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140612834","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}

引用次数: 0

FakeTracer: Catching Face-Swap DeepFakes via Implanting Traces in Training FakeTracer：通过在训练中植入痕迹捕捉人脸交换深度假动作

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-04-16 DOI: 10.1109/TETC.2024.3386960

Pu Sun;Honggang Qi;Yuezun Li;Siwei Lyu

{"title":"FakeTracer: Catching Face-Swap DeepFakes via Implanting Traces in Training","authors":"Pu Sun;Honggang Qi;Yuezun Li;Siwei Lyu","doi":"10.1109/TETC.2024.3386960","DOIUrl":"10.1109/TETC.2024.3386960","url":null,"abstract":"Face-swap DeepFake is an emerging AI-based face forgery technique that can replace the original face in a video with a generated face of the target identity while retaining consistent facial attributes such as expression and orientation. Due to the high privacy of faces, the misuse of this technique can raise severe social concerns, drawing tremendous attention to defend against DeepFakes recently. In this article, we describe a new proactive defense method called FakeTracer to expose face-swap DeepFakes via implanting traces in training. Compared to general face-synthesis DeepFake, the face-swap DeepFake is more complex as it involves identity change, is subjected to the encoding-decoding process, and is trained unsupervised, increasing the difficulty of implanting traces into the training phase. To effectively defend against face-swap DeepFake, we design two types of traces, sustainable trace (STrace) and erasable trace (ETrace), to be added to training faces. During the training, these manipulated faces affect the learning of the face-swap DeepFake model, enabling it to generate faces that only contain sustainable traces. In light of these two traces, our method can effectively expose DeepFakes by identifying them. Extensive experiments corroborate the efficacy of our method on defending against face-swap DeepFake.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"134-146"},"PeriodicalIF":5.1,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140612835","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}

引用次数: 0