The rapid proliferation of computing domains relying on Internet of Things (IoT) devices has created a pressing need for efficient and accurate deep-learning (DL) models that can run on low-power devices. However, traditional DL models tend to be too complex and computationally intensive for typical IoT end-nodes. To address this challenge, Neural Architecture Search (NAS) has emerged as a popular design automation technique for co-optimizing the accuracy and complexity of deep neural networks. Nevertheless, existing NAS techniques require many iterations to produce a network that adheres to specific hardware constraints, such as the maximum memory available on the hardware or the maximum latency allowed by the target application. In this work, we propose a novel approach to incorporate multiple constraints into so-called Differentiable NAS optimization methods, which allows the generation, in a single shot, of a model that respects user-defined constraints on both memory and latency in a time comparable to a single standard training. The proposed approach is evaluated on five IoT-relevant benchmarks, including the MLPerf Tiny suite and Tiny ImageNet, demonstrating that, with a single search, it is possible to reduce memory and latency by 87.4% and 54.2%, respectively (as defined by our targets), while ensuring non-inferior accuracy on state-of-the-art hand-tuned deep neural networks for TinyML.
依赖于物联网（IoT）设备的计算领域迅速激增，因此迫切需要能够在低功耗设备上运行的高效、准确的深度学习（DL）模型。然而，对于典型的物联网终端节点来说，传统的深度学习模型往往过于复杂和计算密集。为了应对这一挑战，神经架构搜索（NAS）已成为一种流行的设计自动化技术，用于共同优化深度神经网络的准确性和复杂性。然而，现有的 NAS 技术需要多次迭代才能生成符合特定硬件约束条件的网络，例如硬件可用的最大内存或目标应用允许的最大延迟。在这项工作中，我们提出了一种新方法，将多个约束条件纳入所谓的可微分 NAS 优化方法中，这样就能在与单次标准训练相当的时间内，一次性生成一个遵守用户定义的内存和延迟约束条件的模型。我们在五个物联网相关基准（包括 MLPerf Tiny 套件和 Tiny ImageNet）上对所提出的方法进行了评估，结果表明，只需一次搜索，就能将内存和延迟分别减少 87.4% 和 54.2%（根据我们的目标定义），同时确保 TinyML 的最先进手工调谐深度神经网络的准确性毫不逊色。
{"title":"Enhancing Neural Architecture Search With Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devices","authors":"Alessio Burrello;Matteo Risso;Beatrice Alessandra Motetti;Enrico Macii;Luca Benini;Daniele Jahier Pagliari","doi":"10.1109/TETC.2023.3322033","DOIUrl":"10.1109/TETC.2023.3322033","url":null,"abstract":"The rapid proliferation of computing domains relying on Internet of Things (IoT) devices has created a pressing need for efficient and accurate deep-learning (DL) models that can run on low-power devices. However, traditional DL models tend to be too complex and computationally intensive for typical IoT end-nodes. To address this challenge, Neural Architecture Search (NAS) has emerged as a popular design automation technique for co-optimizing the accuracy and complexity of deep neural networks. Nevertheless, existing NAS techniques require many iterations to produce a network that adheres to specific hardware constraints, such as the maximum memory available on the hardware or the maximum latency allowed by the target application. In this work, we propose a novel approach to incorporate multiple constraints into so-called Differentiable NAS optimization methods, which allows the generation, in a single shot, of a model that respects user-defined constraints on both memory and latency in a time comparable to a single standard training. The proposed approach is evaluated on five IoT-relevant benchmarks, including the MLPerf Tiny suite and Tiny ImageNet, demonstrating that, with a single search, it is possible to reduce memory and latency by 87.4% and 54.2%, respectively (as defined by our targets), while ensuring non-inferior accuracy on state-of-the-art hand-tuned deep neural networks for TinyML.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 3","pages":"780-794"},"PeriodicalIF":5.1,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136207718","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 : 2023-10-05DOI: 10.1109/TETC.2023.3320758
Irina Arévalo;Jose L. Salmeron
Federated Learning is a machine learning approach that enables the training of a deep learning model among several participants with sensitive data that wish to share their own knowledge without compromising the privacy of their data. In this research, the authors employ a secured Federated Learning method with an additional layer of privacy and proposes a method for addressing the non-IID challenge. Moreover, differential privacy is compared with chaotic-based encryption as layer of privacy. The experimental approach assesses the performance of the federated deep learning model with differential privacy using both IID and non-IID data. In each experiment, the Federated Learning process improves the average performance metrics of the deep neural network, even in the case of non-IID data.
{"title":"A Chaotic Maps-Based Privacy-Preserving Distributed Deep Learning for Incomplete and Non-IID Datasets","authors":"Irina Arévalo;Jose L. Salmeron","doi":"10.1109/TETC.2023.3320758","DOIUrl":"10.1109/TETC.2023.3320758","url":null,"abstract":"Federated Learning is a machine learning approach that enables the training of a deep learning model among several participants with sensitive data that wish to share their own knowledge without compromising the privacy of their data. In this research, the authors employ a secured Federated Learning method with an additional layer of privacy and proposes a method for addressing the non-IID challenge. Moreover, differential privacy is compared with chaotic-based encryption as layer of privacy. The experimental approach assesses the performance of the federated deep learning model with differential privacy using both IID and non-IID data. In each experiment, the Federated Learning process improves the average performance metrics of the deep neural network, even in the case of non-IID data.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 1","pages":"357-367"},"PeriodicalIF":5.9,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136002626","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 : 2023-10-03DOI: 10.1109/TETC.2023.3319659
Bijay Raj Paudel;Spyros Tragoudas
This article shows that Memristive Crossbar Array (MCA)-based neuromorphic architectures provide a robust defense against adversarial attacks due to the stochastic behavior of memristors. Furthermore, it shows that adversarial robustness can be further improved by compression-based preprocessing steps that can be implemented on MCAs. It also evaluates the effect of inter-chip process variations on adversarial robustness using the proposed MCA implementation and studies the effect of on-chip training. It shows that adversarial attacks do not uniformly affect the classification accuracy of different chips. Experimental evidence using a variety of datasets and attack models supports the impact of MCA-based neuromorphic architectures and compression-based preprocessing implemented using MCA on defending against adversarial attacks. It is also experimentally shown that the on-chip training results in high resiliency to adversarial attacks in all chips.
{"title":"Memristive Crossbar Array-Based Adversarial Defense Using Compression","authors":"Bijay Raj Paudel;Spyros Tragoudas","doi":"10.1109/TETC.2023.3319659","DOIUrl":"10.1109/TETC.2023.3319659","url":null,"abstract":"This article shows that Memristive Crossbar Array (MCA)-based neuromorphic architectures provide a robust defense against adversarial attacks due to the stochastic behavior of memristors. Furthermore, it shows that adversarial robustness can be further improved by compression-based preprocessing steps that can be implemented on MCAs. It also evaluates the effect of inter-chip process variations on adversarial robustness using the proposed MCA implementation and studies the effect of on-chip training. It shows that adversarial attacks do not uniformly affect the classification accuracy of different chips. Experimental evidence using a variety of datasets and attack models supports the impact of MCA-based neuromorphic architectures and compression-based preprocessing implemented using MCA on defending against adversarial attacks. It is also experimentally shown that the on-chip training results in high resiliency to adversarial attacks in all chips.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 3","pages":"864-877"},"PeriodicalIF":5.1,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135914085","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 : 2023-09-29DOI: 10.1109/TETC.2023.3315512
Chang Ruan;Jianxin Wang;Wanchun Jiang;Tao Zhang
Recently, many scheduling schemes leverage coflows to improve the communication performance of jobs in distributed application frameworks deployed in data center networks, such as MapReduce and Spark. Most of them require application modification to obtain the coflow information such as the coflow ID. The latest work CODA suggests non-intrusively extracting coflow information via an identification method. However, the method depends on the historical traffic information, which may cause the identification accuracy to decrease a lot when traffic varies. To tackle the problem, we present SOCI for Scheduling coflows by the Online Coflow Identification. By observing that flows in a coflow typically communicate with a master process for starting and ending in the up-to-date distributed application frameworks, SOCI uses this characteristic for the online coflow identification. Given identification errors are inevitable, the coflow scheduler in SOCI adopts a Selectively Late Binding (SLB) mechanism, which associates the misclassified flows with coflows according to the estimation on the impact of this association on the average Coflow Completion Time (CCT). The trace-driven simulations show that SOCI can reduce CCT by up to $1.23times$ compared to CODA when the identification accuracy decreases and is comparable to schemes without coflow identification.
{"title":"Scheduling Coflows by Online Identification in Data Center Network","authors":"Chang Ruan;Jianxin Wang;Wanchun Jiang;Tao Zhang","doi":"10.1109/TETC.2023.3315512","DOIUrl":"10.1109/TETC.2023.3315512","url":null,"abstract":"Recently, many scheduling schemes leverage coflows to improve the communication performance of jobs in distributed application frameworks deployed in data center networks, such as MapReduce and Spark. Most of them require application modification to obtain the coflow information such as the coflow ID. The latest work CODA suggests non-intrusively extracting coflow information via an identification method. However, the method depends on the historical traffic information, which may cause the identification accuracy to decrease a lot when traffic varies. To tackle the problem, we present SOCI for Scheduling coflows by the Online Coflow Identification. By observing that flows in a coflow typically communicate with a master process for starting and ending in the up-to-date distributed application frameworks, SOCI uses this characteristic for the online coflow identification. Given identification errors are inevitable, the coflow scheduler in SOCI adopts a Selectively Late Binding (SLB) mechanism, which associates the misclassified flows with coflows according to the estimation on the impact of this association on the average Coflow Completion Time (CCT). The trace-driven simulations show that SOCI can reduce CCT by up to \u0000<inline-formula><tex-math>$1.23times$</tex-math></inline-formula>\u0000 compared to CODA when the identification accuracy decreases and is comparable to schemes without coflow identification.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"11 4","pages":"1057-1069"},"PeriodicalIF":5.9,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135843617","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}
Here, we report the fabrication of Y 2 O 3 -based memristive crossbar array (MCA) by utilizing dual ion beam sputtering system, which shows high cyclic stability in the resistive switching behavior. Further, the obtained experimental results are validated with an analytical MCA based model, which exhibits extremely well fitting with the corresponding experimental data. Moreover, the experimentally validated analytical model is further used for biomedical image analysis, specifically computed tomography (CT) scan and magnetic resonance imaging (MRI) images by utilizing the 2-dimensional image decomposition technique. The different levels of decomposition are used for different threshold values which help to analyze the quality of the reconstructed image in terms of peak signal-to-noise ratio, structural similarity index and mean square error. For the MRI and CT scan images, at the first decomposition level, the data compression ratio of 21.01%, and 47.81% with Haar and 18.82%, and 46.05% with biorthogonal wavelet are obtained. Furthermore, the impact of brightness is also analyzed which shows a sufficient increment in the quality of output image by 103.72% and 18.59% for CT scan and MRI image, respectively for Haar wavelet. The proposed MCA based model for image processing is a novel approach to reduce the computation time and storage for biomedical engineering.
{"title":"Memristive Crossbar Array-Based Computing Framework via DWT for Biomedical Image Enhancement","authors":"Kumari Jyoti;Mohit Kumar Gautam;Sanjay Kumar;Sai Sushma;Ram Bilas Pachori;Shaibal Mukherjee","doi":"10.1109/TETC.2023.3318303","DOIUrl":"10.1109/TETC.2023.3318303","url":null,"abstract":"Here, we report the fabrication of Y\u0000<sub>2</sub>\u0000O\u0000<sub>3</sub>\u0000-based memristive crossbar array (MCA) by utilizing dual ion beam sputtering system, which shows high cyclic stability in the resistive switching behavior. Further, the obtained experimental results are validated with an analytical MCA based model, which exhibits extremely well fitting with the corresponding experimental data. Moreover, the experimentally validated analytical model is further used for biomedical image analysis, specifically computed tomography (CT) scan and magnetic resonance imaging (MRI) images by utilizing the 2-dimensional image decomposition technique. The different levels of decomposition are used for different threshold values which help to analyze the quality of the reconstructed image in terms of peak signal-to-noise ratio, structural similarity index and mean square error. For the MRI and CT scan images, at the first decomposition level, the data compression ratio of 21.01%, and 47.81% with Haar and 18.82%, and 46.05% with biorthogonal wavelet are obtained. Furthermore, the impact of brightness is also analyzed which shows a sufficient increment in the quality of output image by 103.72% and 18.59% for CT scan and MRI image, respectively for Haar wavelet. The proposed MCA based model for image processing is a novel approach to reduce the computation time and storage for biomedical engineering.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 3","pages":"766-779"},"PeriodicalIF":5.1,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135800896","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 : 2023-09-27DOI: 10.1109/TETC.2023.3315131
Boris Sedlak;Ilir Murturi;Praveen Kumar Donta;Schahram Dustdar
Recent developments in machine learning (ML) allow for efficient data stream processing and also help in meeting various privacy requirements. Traditionally, predefined privacy policies are enforced in resource-rich and homogeneous environments such as in the cloud to protect sensitive information from being exposed. However, large amounts of data streams generated from heterogeneous IoT devices often result in high computational costs, cause network latency, and increase the chance of data interruption as data travels away from the source. Therefore, this article proposes a novel privacy-enforcing framework for transforming data streams by executing various privacy policies close to the data source. To achieve our proposed framework, we enable domain experts to specify high-level privacy policies in a human-readable form. Then, the edge-based runtime system analyzes data streams (i.e., generated from nearby IoT devices), interprets privacy policies (i.e., deployed on edge devices), and transforms data streams if privacy violations occur. Our proposed runtime mechanism uses a Deep Neural Networks (DNN) technique to detect privacy violations within the streamed data. Furthermore, we discuss the framework, processes of the approach, and the experiments carried out on a real-world testbed to validate its feasibility and applicability.
{"title":"A Privacy Enforcing Framework for Data Streams on the Edge","authors":"Boris Sedlak;Ilir Murturi;Praveen Kumar Donta;Schahram Dustdar","doi":"10.1109/TETC.2023.3315131","DOIUrl":"10.1109/TETC.2023.3315131","url":null,"abstract":"Recent developments in machine learning (ML) allow for efficient data stream processing and also help in meeting various privacy requirements. Traditionally, predefined privacy policies are enforced in resource-rich and homogeneous environments such as in the cloud to protect sensitive information from being exposed. However, large amounts of data streams generated from heterogeneous IoT devices often result in high computational costs, cause network latency, and increase the chance of data interruption as data travels away from the source. Therefore, this article proposes a novel privacy-enforcing framework for transforming data streams by executing various privacy policies close to the data source. To achieve our proposed framework, we enable domain experts to specify high-level privacy policies in a human-readable form. Then, the edge-based runtime system analyzes data streams (i.e., generated from nearby IoT devices), interprets privacy policies (i.e., deployed on edge devices), and transforms data streams if privacy violations occur. Our proposed runtime mechanism uses a Deep Neural Networks (DNN) technique to detect privacy violations within the streamed data. Furthermore, we discuss the framework, processes of the approach, and the experiments carried out on a real-world testbed to validate its feasibility and applicability.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 3","pages":"852-863"},"PeriodicalIF":5.1,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135793727","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 : 2023-09-26DOI: 10.1109/TETC.2023.3317393
Huiwei Wang;Yaqian Huang;Huaqing Li
Nowadays, researchers have shown significant interest in geographic location-based spatial data analysis due to its wide range of application scenarios. However, the accuracy of the grid-based quadtree range query (GT-R) algorithm, which utilizes the uniform grid method to divide the data space, is compromised by the excessive noise introduced in the divided area. In addition, the private adaptive grid (PrivAG) algorithm does not adopt any index structure, which leads to inefficient query. To address above issues, this paper presents the Quadtree-based Adaptive Spatial Decomposition (ASDQT) algorithm. ASDQT leverages reservoir sampling technology under local differential privacy (LDP) to extract spatial data as the segmentation object. By setting a reasonable threshold, ASDQT dynamically constructs the tree structure, enabling coarse-grained division of sparse regions and fine-grained division of dense regions. Extensive experiments conducted on two real-world datasets demonstrate the efficacy of ASDQT in handling large-scale spatial datasets with different distributions. The results indicate that ASDQT outperforms existing methods in terms of both accuracy and running efficiency.
{"title":"Quadtree-Based Adaptive Spatial Decomposition for Range Queries Under Local Differential Privacy","authors":"Huiwei Wang;Yaqian Huang;Huaqing Li","doi":"10.1109/TETC.2023.3317393","DOIUrl":"10.1109/TETC.2023.3317393","url":null,"abstract":"Nowadays, researchers have shown significant interest in geographic location-based spatial data analysis due to its wide range of application scenarios. However, the accuracy of the grid-based quadtree range query (GT-R) algorithm, which utilizes the uniform grid method to divide the data space, is compromised by the excessive noise introduced in the divided area. In addition, the private adaptive grid (PrivAG) algorithm does not adopt any index structure, which leads to inefficient query. To address above issues, this paper presents the Quadtree-based Adaptive Spatial Decomposition (ASDQT) algorithm. ASDQT leverages reservoir sampling technology under local differential privacy (LDP) to extract spatial data as the segmentation object. By setting a reasonable threshold, ASDQT dynamically constructs the tree structure, enabling coarse-grained division of sparse regions and fine-grained division of dense regions. Extensive experiments conducted on two real-world datasets demonstrate the efficacy of ASDQT in handling large-scale spatial datasets with different distributions. The results indicate that ASDQT outperforms existing methods in terms of both accuracy and running efficiency.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"11 4","pages":"1045-1056"},"PeriodicalIF":5.9,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135755255","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}
The continuous advancement of complementary metal-oxide-semiconductor technologies makes flip-flops (FFs) vulnerable to soft errors. Single-node upsets (SNUs), as well as double-node upsets (DNUs), are typical soft errors. This article proposes two radiation-hardened FF designs, namely DNU-tolerant FF (DUT-FF) and DNU-recoverable FF (DUR-FF). First, the DUT-FF which mainly consists of four dual-interlocked-storage-cells (DICEs) and three 2-input C-elements, is proposed. Then, to provide complete self-recovery from DNUs, the DUR-FF which mainly uses six interlocked DICEs is proposed. They have the following advantages: 1) They can completely protect against SNUs as well as DNUs; 2) the DUT-FF is cost-effective but the DUR-FF can provide complete self-recovery from any DNU. Simulations show the complete SNU/DNU tolerance of DUT-FF and the complete SNU/DNU self-recovery of DUR-FF but at the cost of indispensable area overhead when compared to the SNU hardened FFs. Besides, compared to the FFs of the same-type, the proposed FFs achieve a low delay making them suitable for high-performance applications.
{"title":"Two Double-Node-Upset-Hardened Flip-Flop Designs for High-Performance Applications","authors":"Aibin Yan;Aoran Cao;Zhengfeng Huang;Jie Cui;Tianming Ni;Patrick Girard;Xiaoqing Wen;Jiliang Zhang","doi":"10.1109/TETC.2023.3317070","DOIUrl":"10.1109/TETC.2023.3317070","url":null,"abstract":"The continuous advancement of complementary metal-oxide-semiconductor technologies makes flip-flops (FFs) vulnerable to soft errors. Single-node upsets (SNUs), as well as double-node upsets (DNUs), are typical soft errors. This article proposes two radiation-hardened FF designs, namely DNU-tolerant FF (DUT-FF) and DNU-recoverable FF (DUR-FF). First, the DUT-FF which mainly consists of four dual-interlocked-storage-cells (DICEs) and three 2-input C-elements, is proposed. Then, to provide complete self-recovery from DNUs, the DUR-FF which mainly uses six interlocked DICEs is proposed. They have the following advantages: 1) They can completely protect against SNUs as well as DNUs; 2) the DUT-FF is cost-effective but the DUR-FF can provide complete self-recovery from any DNU. Simulations show the complete SNU/DNU tolerance of DUT-FF and the complete SNU/DNU self-recovery of DUR-FF but at the cost of indispensable area overhead when compared to the SNU hardened FFs. Besides, compared to the FFs of the same-type, the proposed FFs achieve a low delay making them suitable for high-performance applications.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"11 4","pages":"1070-1081"},"PeriodicalIF":5.9,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135699691","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}
This paper examines how geometric deep learning techniques may be employed to analyze academic collaboration networks (ACNs) and how using textual information drawn from publications improves the overall performance of the system. The proposed experimental pipeline was used to analyze the collaboration network of the Machine Learning Genoa Center (MaLGa) research group. First, we find the optimal method for embedding the input data graph and extracting meaningful keywords for the available publications. We then use Graph Neural Networks (GNN) for node type and research topic classification. Finally, we explore how the resulting corpus can be used to create a recommender system for optimal navigation of the ACN. Our results show that the GNN-based recommender system achieved high accuracy in suggesting unexplored nodes to users. Overall, this study demonstrates the potential for using geometric deep learning and Natural Language Processing (NLP) to best represent the scientific production of ACNs. In the future, we plan to incorporate the temporal nature of the data and navigation statistics of users exploring the graph as additional input for the recommender system.
{"title":"Geometric Deep Learning Strategies for the Characterization of Academic Collaboration Networks","authors":"Daniele Pretolesi;Davide Garbarino;Daniele Giampaoli;Andrea Vian;Annalisa Barla","doi":"10.1109/TETC.2023.3315954","DOIUrl":"10.1109/TETC.2023.3315954","url":null,"abstract":"This paper examines how geometric deep learning techniques may be employed to analyze academic collaboration networks (ACNs) and how using textual information drawn from publications improves the overall performance of the system. The proposed experimental pipeline was used to analyze the collaboration network of the Machine Learning Genoa Center (MaLGa) research group. First, we find the optimal method for embedding the input data graph and extracting meaningful keywords for the available publications. We then use Graph Neural Networks (GNN) for node type and research topic classification. Finally, we explore how the resulting corpus can be used to create a recommender system for optimal navigation of the ACN. Our results show that the GNN-based recommender system achieved high accuracy in suggesting unexplored nodes to users. Overall, this study demonstrates the potential for using geometric deep learning and Natural Language Processing (NLP) to best represent the scientific production of ACNs. In the future, we plan to incorporate the temporal nature of the data and navigation statistics of users exploring the graph as additional input for the recommender system.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 3","pages":"840-851"},"PeriodicalIF":5.1,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135599193","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}
Machine learning is at the center of mainstream technology and outperforms classical approaches to handcrafted feature design. Aside from its learning process for artificial feature extraction, it has an end-to-end paradigm from input to output, reaching outstandingly accurate results. However, security concerns about its robustness to malicious and imperceptible perturbations have drawn attention since its prediction can be changed entirely. Salient object detection is a research area where deep convolutional neural networks have proven effective but whose trustworthiness represents a significant issue requiring analysis and solutions to hackers’ attacks. Brain programming is a kind of symbolic learning in the vein of good old-fashioned artificial intelligence. This work provides evidence that symbolic learning robustness is crucial in designing reliable visual attention systems since it can withstand even the most intense perturbations. We test this evolutionary computation methodology against several adversarial attacks and noise perturbations using standard databases and a real-world problem of a shorebird called the Snowy Plover portraying a visual attention task. We compare our methodology with five different deep learning approaches, proving that they do not match the symbolic paradigm regarding robustness. All neural networks suffer significant performance losses, while brain programming stands its ground and remains unaffected. Also, by studying the Snowy Plover, we remark on the importance of security in surveillance activities regarding wildlife protection and conservation.
{"title":"Adversarial Attacks Assessment of Salient Object Detection via Symbolic Learning","authors":"Gustavo Olague;Roberto Pineda;Gerardo Ibarra-Vazquez;Matthieu Olague;Axel Martinez;Sambit Bakshi;Jonathan Vargas;Isnardo Reducindo","doi":"10.1109/TETC.2023.3316549","DOIUrl":"10.1109/TETC.2023.3316549","url":null,"abstract":"Machine learning is at the center of mainstream technology and outperforms classical approaches to handcrafted feature design. Aside from its learning process for artificial feature extraction, it has an end-to-end paradigm from input to output, reaching outstandingly accurate results. However, security concerns about its robustness to malicious and imperceptible perturbations have drawn attention since its prediction can be changed entirely. Salient object detection is a research area where deep convolutional neural networks have proven effective but whose trustworthiness represents a significant issue requiring analysis and solutions to hackers’ attacks. Brain programming is a kind of symbolic learning in the vein of good old-fashioned artificial intelligence. This work provides evidence that symbolic learning robustness is crucial in designing reliable visual attention systems since it can withstand even the most intense perturbations. We test this evolutionary computation methodology against several adversarial attacks and noise perturbations using standard databases and a real-world problem of a shorebird called the Snowy Plover portraying a visual attention task. We compare our methodology with five different deep learning approaches, proving that they do not match the symbolic paradigm regarding robustness. All neural networks suffer significant performance losses, while brain programming stands its ground and remains unaffected. Also, by studying the Snowy Plover, we remark on the importance of security in surveillance activities regarding wildlife protection and conservation.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"11 4","pages":"1018-1030"},"PeriodicalIF":5.9,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135650408","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}
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