Cognitive Computation最新文献_第4页

Prescribed-Time Sampled-Data Control for the Bipartite Consensus of Linear Multi-Agent Systems in Singed Networks 针对单一网络中线性多代理系统的两方共识的规定时间采样数据控制

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-07-06 DOI: 10.1007/s12559-024-10319-8

Mengke Liu, Wenbing Zhang, Guanglei Wu

引用次数: 0

Twin Bounded Support Vector Machine with Capped Pinball Loss 带有弹球损失上限的孪生有界支持向量机

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-07-06 DOI: 10.1007/s12559-024-10307-y

Huiru Wang, Xiaoqing Hong, Siyuan Zhang

引用次数: 0

Pruning Deep Neural Networks for Green Energy-Efficient Models: A Survey 修剪深度神经网络，建立绿色节能模型：调查

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-07-05 DOI: 10.1007/s12559-024-10313-0

Jihene Tmamna, Emna Ben Ayed, Rahma Fourati, Mandar Gogate, Tughrul Arslan, Amir Hussain, Mounir Ben Ayed

{"title":"Pruning Deep Neural Networks for Green Energy-Efficient Models: A Survey","authors":"Jihene Tmamna, Emna Ben Ayed, Rahma Fourati, Mandar Gogate, Tughrul Arslan, Amir Hussain, Mounir Ben Ayed","doi":"10.1007/s12559-024-10313-0","DOIUrl":"https://doi.org/10.1007/s12559-024-10313-0","url":null,"abstract":"<p>Over the past few years, larger and deeper neural network models, particularly convolutional neural networks (CNNs), have consistently advanced state-of-the-art performance across various disciplines. Yet, the computational demands of these models have escalated exponentially. Intensive computations hinder not only research inclusiveness and deployment on resource-constrained devices, such as Edge Internet of Things (IoT) devices, but also result in a substantial carbon footprint. Green deep learning has emerged as a research field that emphasizes energy consumption and carbon emissions during model training and inference, aiming to innovate with light and energy-efficient neural networks. Various techniques are available to achieve this goal. Studies show that conventional deep models often contain redundant parameters that do not alter outcomes significantly, underpinning the theoretical basis for model pruning. Consequently, this timely review paper seeks to systematically summarize recent breakthroughs in CNN pruning methods, offering necessary background knowledge for researchers in this interdisciplinary domain. Secondly, we spotlight the challenges of current model pruning methods to inform future avenues of research. Additionally, the survey highlights the pressing need for the development of innovative metrics to effectively balance diverse pruning objectives. Lastly, it investigates pruning techniques oriented towards sophisticated deep learning models, including hybrid feedforward CNNs and long short-term memory (LSTM) recurrent neural networks, a field ripe for exploration within green deep learning research.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"67 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141547232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Unmasking GAN-Generated Faces with Optimal Deep Learning and Cognitive Computing-Based Cutting-Edge Detection System 利用基于优化深度学习和认知计算的尖端检测系统揭开 GAN 生成的人脸的面纱

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-07-02 DOI: 10.1007/s12559-024-10318-9

Rana Alabdan, Jamal Alsamri, Siwar Ben Haj Hassine, Faiz Abdullah Alotaibi, Saud S. Alotaibi, Ayman Yafoz, Mrim M. Alnfiai, Mesfer Al Duhayyim

{"title":"Unmasking GAN-Generated Faces with Optimal Deep Learning and Cognitive Computing-Based Cutting-Edge Detection System","authors":"Rana Alabdan, Jamal Alsamri, Siwar Ben Haj Hassine, Faiz Abdullah Alotaibi, Saud S. Alotaibi, Ayman Yafoz, Mrim M. Alnfiai, Mesfer Al Duhayyim","doi":"10.1007/s12559-024-10318-9","DOIUrl":"https://doi.org/10.1007/s12559-024-10318-9","url":null,"abstract":"<p>The emergence of deep learning (DL) has improved the excellence of generated media. However, with the enlarged level of photorealism, synthetic media is becoming very similar to tangible media, increasing severe problems regarding transmitting fake or deployed data over the Internet. In this situation, it is significant to improve automatic tools to constantly and early identify synthetic media. Generative Adversarial Network (GAN)-based models can create realistic faces that cause deep social and security issues. Existing techniques for identifying GAN-generated faces can execute well on restricted public datasets. Nevertheless, images from existing datasets must signify real situations sufficient for view variants and data distributions, where real faces mainly outnumber artificial ones. Therefore, this study develops an optimal DL-based GAN-generated face detection and classification (ODL-GANFDC) technique. The ODL-GANFDC technique aims to examine the input images properly and recognize whether GAN generates them. To accomplish this, the ODL-GANFDC technique follows the initial stage of the CLAHE-based contrast enhancement process. In addition, the deep residual network (DRN) model must be employed to learn the complex and intrinsic patterns from the preprocessed images. Besides, the hyperparameters of the DRN model can be optimally chosen using an improved sand cat swarm optimization (ISCSO) algorithm. Finally, the GAN-generated faces can be detected using a variational autoencoder (VAE). An extensive set of experimentations can be carried out to highlight the performance of the ODL-GANFDC technique. The experimental outcomes stated the promising results of the ODL-GANFDC technique over compared approaches on the GAN-generated face detection process.\u0000</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"8 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141502718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Cognitive Intelligent Decisions for Big Data and Cloud Computing in Industrial Applications using Trifold Algorithms 利用三折算法为工业应用中的大数据和云计算做出认知智能决策

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-06-28 DOI: 10.1007/s12559-024-10317-w

Shitharth Selvarajan, Hariprasath Manoharan, Rakan A. Alsowail, Achyut Shankar, Saravanan Pandiaraj, Carsten Maple, Wattana Viriyasitavat

{"title":"Cognitive Intelligent Decisions for Big Data and Cloud Computing in Industrial Applications using Trifold Algorithms","authors":"Shitharth Selvarajan, Hariprasath Manoharan, Rakan A. Alsowail, Achyut Shankar, Saravanan Pandiaraj, Carsten Maple, Wattana Viriyasitavat","doi":"10.1007/s12559-024-10317-w","DOIUrl":"https://doi.org/10.1007/s12559-024-10317-w","url":null,"abstract":"<p>In contemporary real-time applications, diminutive devices are increasingly employing a greater portion of the spectrum to transmit data despite the relatively small size of said data. The demand for big data in cloud storage networks is on the rise, as cognitive networks can enable intelligent decision-making with minimal spectrum utilization. The introduction of cognitive networks has facilitated the provision of a novel channel that enables the allocation of low power resources while minimizing path loss. The proposed method involves the integration of three algorithms to examine the process of big data. Whenever big data applications are examined then distance measurement, decisions mechanism and learning techniques from past data is much importance thus algorithms are chosen according to the requirements of big data and cloud storage networks. Further the effect of integration process is examined with three case studies that considers low resource, path loss and weight functions where optimized outcome is achieved in all defined case studies as compared to existing approach.\u0000</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"46 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141502779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Learning from Failure: Towards Developing a Disease Diagnosis Assistant That Also Learns from Unsuccessful Diagnoses 从失败中学习：开发能从失败诊断中学习的疾病诊断助手

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-06-27 DOI: 10.1007/s12559-024-10274-4

Abhisek Tiwari, Swarna S, Sriparna Saha, Pushpak Bhattacharyya, Minakshi Dhar, Sarbajeet Tiwari

{"title":"Learning from Failure: Towards Developing a Disease Diagnosis Assistant That Also Learns from Unsuccessful Diagnoses","authors":"Abhisek Tiwari, Swarna S, Sriparna Saha, Pushpak Bhattacharyya, Minakshi Dhar, Sarbajeet Tiwari","doi":"10.1007/s12559-024-10274-4","DOIUrl":"https://doi.org/10.1007/s12559-024-10274-4","url":null,"abstract":"<p>In recent years, automatic disease diagnosis has gained immense popularity in research and industry communities. Humans learn a task through both successful and unsuccessful attempts in real life, and physicians are not different. When doctors fail to diagnose disease correctly, they re-assess the extracted symptoms and re-diagnose the patient by inspecting a few more symptoms guided by their previous experience and current context. Motivated by the experience gained from failure assessment, we propose a novel end-to-end automatic disease diagnosis dialogue system called Failure Assessment incorporated Symptom Investigation and Disease Diagnosis (FA-SIDD) Assistant. The proposed FA-SIDD model includes a knowledge-guided, incorrect disease projection-aware failure assessment module that analyzes unsuccessful diagnosis attempts and reinforces the assessment for further investigation and re-diagnosis. We formulate a novel Markov decision process for the proposed failure assessment, incorporating symptom investigation and disease diagnosis frameworks, and optimize the policy using deep reinforcement learning. The proposed model has outperformed several baselines and the existing symptom investigation and diagnosis methods by a significant margin (1–3%) in all evaluation metrics (including human evaluation). The improvements over the multiple datasets and across multiple algorithms firmly establish the efficacy of learning gained from unsuccessful diagnoses. The work is the first attempt that investigate the importance of learning gained from unsuccessful diagnoses. The developed assistant learns diagnosis task more efficiently than traditional assistants and shows robust behavior. Furthermore, the code is available at https://github.com/AbhisekTiwari/FA-SIDA.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"49 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141502721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Multi-Modal Generative DeepFake Detection via Visual-Language Pretraining with Gate Fusion for Cognitive Computation 通过视觉语言预训练与认知计算门融合进行多模态生成式 DeepFake 检测

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-06-25 DOI: 10.1007/s12559-024-10316-x

Guisheng Zhang, Mingliang Gao, Qilei Li, Wenzhe Zhai, Gwanggil Jeon

{"title":"Multi-Modal Generative DeepFake Detection via Visual-Language Pretraining with Gate Fusion for Cognitive Computation","authors":"Guisheng Zhang, Mingliang Gao, Qilei Li, Wenzhe Zhai, Gwanggil Jeon","doi":"10.1007/s12559-024-10316-x","DOIUrl":"https://doi.org/10.1007/s12559-024-10316-x","url":null,"abstract":"<p>With the widespread adoption of deep learning, there has been a notable increase in the prevalence of multimodal deepfake content. These deepfakes pose a substantial risk to both individual privacy and the security of their assets. In response to this pressing issue, researchers have undertaken substantial endeavors in utilizing generative AI and cognitive computation to leverage multimodal data to detect deepfakes. However, the efforts thus far have fallen short of fully exploiting the extensive reservoir of multimodal feature information, which leads to a deficiency in leveraging spatial information across multiple dimensions. In this study, we introduce a framework called Visual-Language Pretraining with Gate Fusion (VLP-GF), designed to identify multimodal deceptive content and enhance the accurate localization of manipulated regions within both images and textual annotations. Specifically, we introduce an adaptive fusion module tailored to integrate local and global information simultaneously. This module captures global context and local details concurrently, thereby improving the performance of image bounding-box grounding within the system. Additionally, to maximize the utilization of semantic information from diverse modalities, we incorporate a gating mechanism to strengthen the interaction of multimodal information further. Through a series of ablation experiments and comprehensive comparisons with state-of-the-art approaches on extensive benchmark datasets, we empirically demonstrate the superior efficacy of VLP-GF.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"145 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141502719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Towards Long-Term Remembering in Federated Continual Learning 在联合持续学习中实现长期记忆

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-06-21 DOI: 10.1007/s12559-024-10314-z

Ziqin Zhao, Fan Lyu, Linyan Li, Fuyuan Hu, Minming Gu, Li Sun

{"title":"Towards Long-Term Remembering in Federated Continual Learning","authors":"Ziqin Zhao, Fan Lyu, Linyan Li, Fuyuan Hu, Minming Gu, Li Sun","doi":"10.1007/s12559-024-10314-z","DOIUrl":"https://doi.org/10.1007/s12559-024-10314-z","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>Federated Continual Learning (FCL) involves learning from distributed data on edge devices with incremental knowledge. However, current FCL methods struggle to retain long-term memories on the server.</p><h3 data-test=\"abstract-sub-heading\">Method</h3><p>In this paper, we introduce a method called Fisher INformation Accumulation Learning (FINAL) to address catastrophic forgetting in FCL. First, we accumulate a global Fisher with a federated Fisher information matrix formed from clients task by task to remember long-term knowledge. Second, we present a novel multi-node collaborative integration strategy to assemble the federated Fisher, which reveals the task-specific co-importance of parameters among clients. Finally, we raise a Fisher balancing method to combine the global Fisher and federated Fisher, avoiding neglecting new learning or causing catastrophic forgetting.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>We conducted evaluations on four FCL datasets, and the findings demonstrate that the proposed FINAL effectively maintains long-term knowledge on the server.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>The exceptional performance of this method indicates its significant value for future FCL research.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"86 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141502734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

SPEI-FL: Serverless Privacy Edge Intelligence-Enabled Federated Learning in Smart Healthcare Systems SPEI-FL：智能医疗系统中的无服务器隐私边缘智能联合学习

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-06-17 DOI: 10.1007/s12559-024-10310-3

Mahmuda Akter, Nour Moustafa, Benjamin Turnbull

{"title":"SPEI-FL: Serverless Privacy Edge Intelligence-Enabled Federated Learning in Smart Healthcare Systems","authors":"Mahmuda Akter, Nour Moustafa, Benjamin Turnbull","doi":"10.1007/s12559-024-10310-3","DOIUrl":"https://doi.org/10.1007/s12559-024-10310-3","url":null,"abstract":"<p>Smart healthcare systems promise significant benefits for fast and accurate medical decisions. However, working with personal health data presents new privacy issues and constraints that must be solved from a cybersecurity perspective. Edge intelligence-enabled federated learning is a new scheme that utilises decentralised computing that allows data analytics to be carried out at the edge of a network, enhancing data privacy. However, this scheme suffers from privacy attacks, including inference, free-riding, and man-in-the-middle attacks, especially with serverless computing for allocating resources to user needs. Edge intelligence-enabled federated learning requires client data insertion and deletion to authenticate genuine clients and a serverless computing capability to ensure the security of collaborative machine learning models. This work introduces a serverless privacy edge intelligence-based federated learning (SPEI-FL) framework to address these issues. SPEI-FL includes a federated edge aggregator and authentication method to improve the data privacy of federated learning and allow client adaptation and removal without impacting the overall learning processes. It also can classify intruders through serverless computing processes. The proposed framework was evaluated with the unstructured COVID-19 medical chest x-rays and MNIST digit datasets, and the structured BoT-IoT dataset. The performance of the framework is comparable with existing authentication methods and reported a higher accuracy than comparable methods (approximately 90% as compared with the 81% reported by peer methods). The proposed authentication method prevents the exposure of sensitive patient information during medical device authentication and would become the cornerstone of the next generation of medical security with serverless computing.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"86 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141502720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Cognitive Tracing Data Trails: Auditing Data Provenance in Discriminative Language Models Using Accumulated Discrepancy Score 认知追踪数据轨迹：使用累积差异得分审核判别语言模型中的数据出处

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-06-14 DOI: 10.1007/s12559-024-10315-y

Zhirui Zeng, Jialing He, Tao Xiang, Ning Wang, Biwen Chen, Shangwei Guo

{"title":"Cognitive Tracing Data Trails: Auditing Data Provenance in Discriminative Language Models Using Accumulated Discrepancy Score","authors":"Zhirui Zeng, Jialing He, Tao Xiang, Ning Wang, Biwen Chen, Shangwei Guo","doi":"10.1007/s12559-024-10315-y","DOIUrl":"https://doi.org/10.1007/s12559-024-10315-y","url":null,"abstract":"<p>The burgeoning practice of unauthorized acquisition and utilization of personal textual data (e.g., social media comments and search histories) by certain entities has become a discernible trend. To uphold data protection regulations such as the Asia–Pacific Privacy Initiative (APPI) and to identify instances of unpermitted exploitation of personal data, we propose a novel and efficient audit framework that helps users conduct cognitive analysis to determine if their textual data was used for data augmentation. and training a discriminative model. In particular, we focus on auditing models that use BERT as the backbone for discriminating text and are at the core of popular online services. We first propose an accumulated discrepancy score, which involves not only the response of the target model to the auditing sample but also the responses between pre-trained and finetuned models, to identify membership. We implement two types of audit methods (i.e., sample-level and user-level) according to our framework and conduct comprehensive experiments on two downstream applications to evaluate the performance. The experimental results demonstrate that our sample-level auditing achieves an AUC of 89.7% and an accuracy of 83%, whereas the user-level method can audit membership with an AUC of 89.7% and an accuracy of 88%. Additionally, we undertake an analysis of how augmentation methods impact auditing performance and expound upon the underlying reasons for these observations.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141512680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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