Cognitive Computation最新文献_第4页

Disentangling User Cognitive Intent with Causal Reasoning for Knowledge-Enhanced Recommendation 将用户认知意图与因果推理相分离，实现知识增强型推荐

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-07-18 DOI: 10.1007/s12559-024-10321-0

Hongcai xu, Junpeng Bao, Qika Lin, Lifang Hou, Feng Chen

{"title":"Disentangling User Cognitive Intent with Causal Reasoning for Knowledge-Enhanced Recommendation","authors":"Hongcai xu, Junpeng Bao, Qika Lin, Lifang Hou, Feng Chen","doi":"10.1007/s12559-024-10321-0","DOIUrl":"https://doi.org/10.1007/s12559-024-10321-0","url":null,"abstract":"The primary objective of an effective recommender system is to provide accurate, varied, and personalized recommendations that align with the user’s cognitive intents. Given their ability to represent structural and semantic information effectively, knowledge graphs (KGs) are increasingly being utilized to capture auxiliary information for recommendation systems. This trend is supported by the recent advancements in graph neural network (GNN)-based models for KG-aware recommendations. However, these models often struggle with issues such as insufficient user-item interactions and the misalignment of user intent weights during information propagation. Additionally, they face a popularity bias, which is exacerbated by the disproportionate influence of a small number of highly active users and the limited auxiliary information about items. This bias significantly curtails the effectiveness of the recommendations. To address this issue, we propose a Knowledge-Enhanced User Cognitive Intent Network (KeCAIN), which incorporates item category information to capture user intents with information aggregation and eliminate popularity bias based on causal reasoning in recommendation systems. Experiments on three real-world datasets show that KeCAIN outperforms state-of-the-art baselines.","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141744867","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

Evaluative Item-Contrastive Explanations in Rankings 排名中的评价性项目对比解释

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-07-10 DOI: 10.1007/s12559-024-10311-2

Alessandro Castelnovo, Riccardo Crupi, Nicolò Mombelli, Gabriele Nanino, Daniele Regoli

引用次数: 0

Granular Syntax Processing with Multi-Task and Curriculum Learning 利用多任务和课程学习进行细粒度语法处理

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-07-08 DOI: 10.1007/s12559-024-10320-1

Xulang Zhang, Rui Mao, Erik Cambria

{"title":"Granular Syntax Processing with Multi-Task and Curriculum Learning","authors":"Xulang Zhang, Rui Mao, Erik Cambria","doi":"10.1007/s12559-024-10320-1","DOIUrl":"https://doi.org/10.1007/s12559-024-10320-1","url":null,"abstract":"Syntactic processing techniques are the foundation of natural language processing (NLP), supporting many downstream NLP tasks. In this paper, we conduct pair-wise multi-task learning (MTL) on syntactic tasks with different granularity, namely Sentence Boundary Detection (SBD), text chunking, and Part-of-Speech (PoS) tagging, so as to investigate the extent to which they complement each other. We propose a novel soft parameter-sharing mechanism to share local and global dependency information that is learned from both target tasks. We also propose a curriculum learning (CL) mechanism to improve MTL with non-parallel labeled data. Using non-parallel labeled data in MTL is a common practice, whereas it has not received enough attention before. For example, our employed PoS tagging data do not have text chunking labels. When learning PoS tagging and text chunking together, the proposed CL mechanism aims to select complementary samples from the two tasks to update the parameters of the MTL model in the same training batch. Such a method yields better performance and learning stability. We conclude that the fine-grained tasks can provide complementary features to coarse-grained ones, while the most coarse-grained task, SBD, provides useful information for the most fine-grained one, PoS tagging. Additionally, the text chunking task achieves state-of-the-art performance when joint learning with PoS tagging. Our analytical experiments also show the effectiveness of the proposed soft parameter-sharing and CL mechanisms.","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141572133","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

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":"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.","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":null,"pages":null},"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":"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","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":null,"pages":null},"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":"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","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":null,"pages":null},"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":"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.","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":null,"pages":null},"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":"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.","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":null,"pages":null},"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