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Quality of Work-Life During Pandemic 1

丛书名： Studies in Big data

1000年

作者： Gitanjali Rahul Shinde Soumi Majumder Haribhau R. Bhapkar Parikshit N. Mahalle

ISBN: (数字)9789811675232

ISBN: (纸本)9789811675225;9789811675256

This book is focused on the impact of the COVID-19 pandemic on different sectors, i.e., education, real estate, health, and agriculture. The lockdown has been announced to control the spread of COVID-19 infections, however people/industries/organizations were not ready for lockdown and it has greatly affected their growth. The front workers in the healthcare sector suffered a lot as major responsibilities they needed to carry on. The education sector is also hampered due to the pandemic as schools, colleges were closed and teaching, examinations were carried out on online platforms. These platforms were new to teachers as well as students. The real estate sector faced tremendous loss in this pandemic as people were scared and no one ready to invest their money in such an uncertain time. The agriculture filed is also suffered as raw materials required for agriculture were not available readily due to pandemic.

关键词： Computational intelligence Simulation and Modeling Mathematical Models of Cognitive Processes and Neural Networks Statistics, general

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Interpreting Deep Forest through Feature Contribution and MDI Feature Importance

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ACM Transactions on Knowledge Discovery from data 1000年

作者： Yi-Xiao He Shen-Huan Lyu Yuan Jiang National Key Laboratory for Novel Software Technology and School of Artificial Intelligence Nanjing University China Key Laboratory of Water Big Data Technology of Ministry of Water Resources and College of Computer Science and Software Engineering Hohai University China

Deep forest is a non-differentiable deep model that has achieved impressive empirical success across a wide variety of applications, especially on categorical/symbolic or mixed modeling tasks. Many of the application fields prefer explainable models, such as random forests with feature contributions that can provide a local explanation for each prediction, and Mean Decrease Impurity (MDI) that can provide global feature importance. However, deep forest, as a cascade of random forests, possesses interpretability only at the first layer. From the second layer on, many of the tree splits occur on the new features generated by the previous layer, which makes existing explaining tools for random forests inapplicable. To disclose the impact of the original features in the deep layers, we design a calculation method with an estimation step followed by a calibration step for each layer, and propose our feature contribution and MDI feature importance calculation tools for deep forest. Experimental results on both simulated data and real-world data verify the effectiveness of our methods.

关键词： deep forest feature importance interpretability

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Feature Disentanglement Based Heterogeneous Defect Prediction

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ACM Transactions on Software engineering and Methodology 1000年

作者： Xu Yu Jiaqi Yan Qinqin Gao Qinglong Peng Bin Yu Junwei Du Ying Xing Dunwei Gong Qingdao Institute of Software China University of Petroleum East China) Qingdao China College of Computer Science and Technology China University of Petroleum East China) Qingdao China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software China University of Petroleum East China) Qingdao China School of Data Science Qingdao University of Science and Technology Qingdao China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun China School of Data Science Qingdao University of Science and Technology Qingdao China School of Computer and Information Technology Beijing Jiaotong University Beijing China School of Data Science Qingdao University of Science and TechnologyQingdao China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Yunnan Key Laboratory of Software Engineering Kunming China College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao China

Cross-project defect prediction (CPDP) utilizes the existing labeled data in the source project to assist with the prediction of unlabeled projects in the target dataset, which effectively improves the prediction performance and has become a research hotspot in software engineering. At present, CPDP can be categorized into homogeneous cross-project defect prediction and heterogeneous cross-project defect prediction (HDP), in which HDP doesn’t require that the source project and the target project have the same feature space, thus, it is more widely used in the actual CPDP. Most of current HDP methods map the original features to the latent feature space and reduce the inter-project variation by transferring domain-independent features, but the transferring process ignores the use of domain-related features, which affects the prediction performance of the model. Moreover, the mapped latent features are not conducive to the model’s interpretability. Based on these, this paper proposes a heterogeneous defect prediction method based on feature disentanglement (FD-HDP). We disentangle the features using domain-related and domain-independent feature extractors, respectively, to improve the interpretability of the model by maximizing the domain adversarial loss during training and guiding the feature extractors to produce accurate domain-related and domain-independent features. The weighted sum of the prediction results from domain-related and domain-independent predictors is used as the final prediction result of the project during the prediction process, which realizes the combination of domain-independent and domain-related features and effectively improves the prediction performance. In this paper, we conducted experiments using four publicly available defect datasets to construct heterogeneous scenarios. The results demonstrate that the FD-HDP model shows significant advantages over state-of-the-art methods in six metrics.

关键词： Heterogeneous Defect Prediction Feature Disentanglement Domain-related Features Domain-independent Features

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Internet of Things and Its Applications 1

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丛书名： Lecture Notes in Electrical engineering

1000年

作者： Keshav Dahal Debasis Giri Sarmistha Neogy Subrata Dutta Sanjay Kumar

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Vehicle Dynamics and Interaction for Trajectory Prediction and Traffic Control

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Jian Chen Shaorui Zhou Wei Wang Yuzhu Hu Jianqing Li Ben-Guo He Junxin Chen Marwan Omar Ali Kashif Bashir Xiping Hu School of Intelligent Systems Engineering Sun Yat-Sen University China Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macao SAR China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern University China the School of Software Dalian University of Technology China Information Technology and Management Illinois Institute of Technology USA Department of Computing and Mathematics Manchester Metropolitan University UK Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India

Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. To address this issue, this paper proposes a novel approach to extracting vehicle velocity and acceleration, enabling the learning of vehicle dynamics and encoding them as auxiliary information. The VDI-LSTM model is designed, incorporating graph convolution and attention mechanisms to capture vehicle interactions using trajectory data and dynamic information. Specifically, a dynamics encoder is designed to capture the dynamic information, a dynamic graph is employed to represent vehicle interactions, and an attention mechanism is introduced to enhance the performance of LSTM and graph convolution. To demonstrate the effectiveness of our model, extensive experiments are conducted, including comparisons with several baselines and ablation studies on real-world highway datasets. Experimental results show that VDI-LSTM outperforms other baselines compared, which obtains a 3% improvement on the average RMSE indicator over the five prediction steps.

关键词： Trajectory Prediction Vehicle Dynamics Interaction Perception Graph Convolution Attention Mechanism

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Multimodal Large Language Model with LoRA Fine-Tuning for Multimodal Sentiment Analysis

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ACM Transactions on Intelligent Systems and Technology 1000年

作者： Jie Mu Wei Wang Wenqi Liu Tiantian Yan Guanglu Wang School of Data Science and Artificial Intelligence Dongbei University of Finance and Economics China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University China the National and Local Joint Engineering Laboratory of Computer Aided Design Dalian University China School of Software Dalian University of Technology China

Multimodal sentiment analysis has become a popular research topic in recent years. However, existing methods have two unaddressed limitations: (1) they use limited supervised labels to train models, which makes it impossible for model to fully learn sentiments in different modal data; (2) they employ text and image pre-trained models trained in different unimodal tasks to extract different modal features, so that the extracted features cannot take into account the interactive information between image and text. To solve these problems, in this paper we propose a Vision-Language Contrastive Learning network (VLCLNet). First, we introduce a pre-trained Large Language Model (LLM), which is trained from vast quantities of multimodal data, has better understanding ability for image and text contents, thus being effectively applied to different tasks while requiring few amount of labelled training data. Second, we adapt a Multimodal Large Language Model (MLLM), BLIP-2 (Bootstrapping Language-Image Pre-training) network, to extract multimodal fusion feature. Such MLLM can fully consider the correlation between images and texts when extracting features. In addition, due to the discrepancy between the pre-training task and the sentiment analysis task, the pre-trained model will output the suboptimal prediction results. We use LoRA (Low-Rank Adaptation) fine-tuning strategy to update the model parameters on sentiment analysis task, which avoids the issue of inconsistent task between pre-training task and downstream task. Experiments verify that the proposed VLCLNet is superior to other strong baselines.

关键词： Deep learning Large language model Multimodal representation learning Sentiment analysis

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Wireless Sensor Networks 1

引用

1000年

作者： Elena Gaura James Brusey Michael Allen Lewis Girod Geoffrey Challen

ISBN: (数字)9781441958341

ISBN: (纸本)9781441958334;9781489990587

The twentieth century ended with the vision of smart dust: a network of wirelessly connected devices whose size would match that of a dust particle, each one a se- containedpackageequippedwithsensing,computation,communication,andpower. Smart dust held the promise to bridge the physical and digital worlds in the most unobtrusive manner, blending together realms that were previously considered well separated. Applications involved scattering hundreds, or even thousands, of smart dust devices to monitor various environmental quantities in scenarios ranging from habitat monitoring to disaster management. The devices were envisioned to se- organize to accomplish their task in the most ef?cient way. As such, smart dust would become a powerful tool, assisting the daily activities of scientists and en- neers in a wide range of disparate disciplines. Wireless sensor networks (WSNs), as we know them today, are the most no- worthy attempt at implementing the smart dust vision. In the last decade, this ?eld has seen a fast-growing investment from both academia and industry. Signi?cant ?nancial resources and manpower have gone into making the smart dust vision a reality through WSNs. Yet, we still cannot claim complete success. At present, only specialist computerscientists or computerengineershave the necessary background to walk the road from conception to a ?nal, deployed, and running WSN system.

关键词： Communications engineering, Networks Signal, Image and Speech Processing computer Communication Networks

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Autonomous Robotic Systems 1

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丛书名： Studies in Fuzziness and Soft Computing

1000年

作者： Changjiu Zhou Darío Maravall Da Ruan

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Advances in artificial intelligence 1

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丛书名： Lecture Notes in computer science

1000年

作者： Francisco Herrera Sergio Damas Rosana Montes Sergio Alonso Óscar Cordón Antonio González Alicia Troncoso

ISBN: (数字)9783030003746

ISBN: (纸本)9783030003739

This book constitutes the refereed proceedings of the 18th Conference of the Spanish Association for;artificial intelligence, CAEPIA 2018, held in Granada, Spain, in October 2018.;The 36 full papers presented were carefully selected from 240 submissions. The Conference of the Spanish Association of;artificial intelligence (CAEPIA) is a biennial forum open to researchers from all over the world to present and discuss their latest scientific and technological advances in Antificial intelligence (AI). Authors are kindly requested to submit unpublished original papers describing relevant research on AI issues from all points of view: formal, methodological, technical or applied.

关键词： artificial intelligence Algorithm Analysis and Problem Complexity Discrete Mathematics in computer science data Structures Image Processing and computer Vision Models and Principles

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Interventional Feature Generation for Few-shot Learning

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ACM Transactions on Multimedia Computing, Communications, and Applications 1000年

作者： Shuo Wang Jinda Lu Huixia Ben Yanbin Hao Xingyu Gao Meng Wang School of Data Science School of Information Science and Technology University of Science and Technology of China China School of Cyber Science and Technology University of Science and Technology of China China School of Public Security and Emergency Management Anhui University of Science and Technology China School of Computer Science and Information Engineering School of Artificial Intelligence Hefei University of Technology China Institute of Microelectronics Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing China

Few-shot learning (FSL) aims to classify a novel object into a specific category under limited training samples. This is a challenging task since (1) the features expressed by pre-trained knowledge introduce perceived bias and then constrain the classification space, and (2) the use of general hallucination techniques based on global features fails to escape the limited classification space, resulting in suboptimal improvements. To solve these issues, this paper proposes an interventional feature generation (IFG) method. Specifically, we first use the relations of the categories or instances as interventional operations to implicitly constrain the feature representations (pre-trained knowledge) into different classification subsets. Then, we employ a parameter-free feature generation strategy to enrich each subset’s training samples of the support category. In other words, IFG provides a multi-subsets learning strategy to reduce the influence of perceived bias, enrich the diversity of generated features, and improve the robustness of the few-shot classifier. We apply our method to four benchmark datasets and observe state-of-the-art performance across all experiments. Specifically, compared to the baseline on the Mini-ImageNet dataset, our approach yields accuracy improvements of 6.03% and 3.46% for 1 and 5 support training samples, respectively. Furthermore, the proposed interventional feature generation technique can improve classifier performance in other FSL methods, demonstrating its versatility and potential for broader applications. The code is available at https://***/ShuoWangCS/IFG-FSL/.

关键词： Few-Shot Learning Interventional Operation data Hallucination

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