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Understanding and Predicting the Burst of Burnout via Social Media

Proceedings of the ACM on Human-Computer Interaction 2021年第CSCW3期4卷 1-27页

作者： Wu, Jue Ma, Junyi Wang, Yasha Wang, Jiangtao Key Laboratory of High Confidence Software Technologies Ministry of Education China School of Electronics Engineering and Computer Science Peking University China National Engineering Research Center of Software Engineering Peking University China Center for Intelligent Healthcare Coventry University United Kingdom

Job burnout is a special type of work-related stress that is prevalent in our modern society, and constant burnout is extremely harmful for people's physical health and emotional wellbeing. Traditional studies for burnout mainly rely on surveys/questionnaires, which have revealed several interesting findings but are of high cost and very time consuming. With the prevalence of social networking applications, we aim to re-investigate the burnout phenomenon in a novel perspective. In this paper, we collected a dataset consisting of 1532 burnout Weibo users with their postings. Based on the previous literature, we propose a number of hypotheses about what might be the burst signal of the burnout from the perspective of language, time and interaction. Furthermore, extensive correlation analysis is conducted to investigate if these hypotheses are supported, which leads to a number of interesting findings. Finally, we develop machine learning models to predict the burst of burnout based on extracted features and achieve a relatively high accuracy, which reveals potential implications in early-stage intervention. © 2021 ACM.

关键词： Machine learning

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Xiang, Tian Meng, Qiwei Zhang, Ji Zhang, Beibei Song, Wei Xie, Anhuan Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China The Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Research Institute of Intelligent Computing Zhejiang Laboratory Research Center for Intelligent Computing Software Hangzhou311100 China Dalhousie University Electrical and Computer Engineering Halifax Canada

ISBN: (纸本)9798350325706

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model's partitioning strategy. It also serves as an important metric influencing the model's suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future. © 2023 IEEE.

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HybridFL: Hybrid Approach Toward Privacy-Preserving Federated Learning 6th

HybridFL: Hybrid Approach Toward Privacy-Preserving Federate...

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6th International Conference on Security and Privacy in New Computing Environments, SPNCE 2023

作者： Ali, Sheraz Mamoon, Saqib Usman, Areeba Abidin, Zain ul Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan250022 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China University of Sargodha Sargodha Pakistan Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Software Engineering University of Jinan Jinan250022 China

ISBN: (纸本)9783031736988

In this study, we introduce a novel Hybrid Federated Learning (HybridFL) approach aimed at enhancing privacy and accuracy in collaborative machine learning scenarios. Our methodology integrates Differential Privacy (DP) and secret sharing techniques to address inference risks during training and protect against information leakage in the output model. Drawing inspiration from recent advances, we present a HybridFL framework that combines the strengths of Homomorphic Encryption (HE) and Multi-Party Computation (MPC) to achieve secure computation without the computational overhead of pure HE methods. Our contributions include a privacy-preserving design for Federated Learning (FL) that ensures local data privacy through secret sharing while leveraging DP mechanisms for noise addition. The system offers resilience against unreliable participants and is evaluated using various machine learning models, including Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), and linear regression. Furthermore, we address potential external threats by deploying predictive model outputs as robust services against inference attacks. Experimental results demonstrate improved accuracy and convergence speed, establishing the viability of HybridFL as an effective solution for collaborative machine learning with enhanced privacy guarantees. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Federated learning

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Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on Retentive Network 7

Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on ...

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7th International Conference on Computer Information Science and Application Technology, CISAT 2024

作者： Su, Ruilin Zhang, Wanshan Yu, Dunhui Hubei University Wuhan China Wuhan430062 China Hubei University School of Computer Science and Information Engineering Wuhan430062 China Hubei University Hubei Key Laboratory of Big Data Intelligent Analysis and Application Wuhan430062 China Engineering and Technical Research Center of Hubei Province in Software Engineering Wuhan430062 China

ISBN: (纸本)9798350375107

Multi-hop Knowledge Reasoning is a task that involves generating an answer given a query and a knowledge graph. Existing sequence-to-sequence reasoning models use the Transformer to encode and decode sequences, but these models have some flaws, such as the inability to effectively handle long-sequence reasoning and susceptibility to exposure bias. To address these issues, we provides a sequence-to-sequence reasoning model named STSR, which is based on Retentive Network. Aiming at improving training efficiency through parallel training, this model leverages the advantages in Retentive Network, reduce time overhead, and enhance reasoning efficiency through iterative reasoning. It effectively mitigates the problems of high spatial overhead and low efficiency in long-sequence reasoning faced by the Transformer. Moreover, it retains and updates historical information during the encoding and decoding process, enhancing the model's memory and generalization capabilities. Additionally, the Scheduled Sampling method is adopted to alleviate the exposure bias introduced during reasoning, which involves using the model's own output as the input for the next moment with a certain probability during training, instead of using the true label. We conduct the experiment on six public datasets, with the results showing that the proposed model outperforms existing baseline models in terms of precision and generation quality. This paper provides a new solution for the task of sequence-to-sequence multihop knowledge reasoning and also demonstrates the potential application of the Retentive Network in natural language processing. © 2024 IEEE.

关键词： Iterative decoding

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Small Object Orientated Detection Model Optimization Based on Lightweight UAV Platform 1

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3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Zhang, Zhonglei Deng, Baosong Gui, Jianjun Yang, Benyi Tan, Hao School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu611731 China Defense Innovation Institute Chinese Academy of Military Sciences Beijing100071 China Intelligent Game and Decision Laboratory Chinese Academy of Military Sciences Beijing100071 China

ISBN: (数字)9789819711079

ISBN: (纸本)9789819711062

In light of limited hardware resources and the inherent challenge in detecting small objects, existing object detection models often struggle to strike an optimal balance between accuracy and delay when deployed on UAV platforms. To address this issue, this paper presents a novel approach involving the enhancement and optimization of the YOLOv8n model. According to the need of UAV object detection tasks, we optimize the original detection head to improve reasoning speed while maintaining accuracy. Additionally, a novel efficient feature fusion network structure is designed to enhance the level of detail in the feature map. Through these enhancements, our proposed model aims to address the accuracy-delay trade-off conundrum encountered in object detection on UAV platforms. Experiments on the VisDrone2019 dataset demonstrate that compared with the original benchmark model, this optimized YOLOv8n detection model improves the accuracy with the APval value increasing by 0.2% to 18.2%, while simultaneously increasing the FPS by 17, thereby achieving a good balance between accuracy and delay. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Object detection

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Study on the influence of environmentally friendly plasticizers on the properties of polyvinyl chloride 3

Study on the influence of environmentally friendly plasticiz...

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2023 3rd International Conference on Advanced Materials and Chemical engineering, AMCE 2023

作者： Wu, Hailong Zhou, Biyun Cen, Chuanfu Cao, Yu School of Data Science and Software Engineering Wu Zhou University Guangxi Wuzhou543002 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wu Zhou University Guangxi Wuzhou543002 China School of Mechanical and Resource Engineering Wu Zhou University Guangxi Wuzhou543002 China

In this work, the torque rheology, mechanical properties, migration resistance, thermal deformation, and Vicat softening point temperature of PVC plasticized by four environmentally friendly plasticizers are investigated. The experimental results reveal that the plasticizer's heavy metal content meets production requirements while falling below the limiting standard. Plasticizer melting points differ, as do the enthalpy and torque of the entire melting process, resulting in different energy consumption when melting plasticizers. Also, the mechanical properties of PVC are not different, but the mechanical properties of PVC plasticized by epoxy soybean oil are the most prominent. Besides, the Vicat softening point temperature of PVC plasticized by four environmentally friendly plasticizers is within the normal standard range, and the migration amount in different solvents is changed in a small range, which will not affect human health. © Published under licence by IOP Publishing Ltd.

关键词： Plasticizers

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SysML static model conversion based on QVT-O 24

SysML static model conversion based on QVT-O

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8th International Conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Ni, Yongkang Deng, Yuandan Mao, Yanling Chen, Weiqin School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Sichuan Provincial Key Laboratory of Intelligent Terminal jointly built by Hall and City Sichuan Yibin China University of Electronic Science and Technology of China Yibin Park Sichuan Yibin China

ISBN: (纸本)9798400710094

For the conversion from SysML (System Modeling Language) static model to dynamic model, the mapping between the two models is not direct or automated. A method to implement SysML static model conversion based on QVT-O (Query/Views/Transformation - Operational) is proposed. Extract necessary information from the BDD (Block Definition Diagram) of the SysML static model, and generate a sequence diagram corresponding to the dynamic model based on predefined rules and mapping between models. The effectiveness of this method is verified through a simple system. This automated conversion not only improves modeling efficiency, but also enhances the consistency and accuracy of the model, which has important practical value for system engineers in system design and analysis. © 2024 Copyright held by the owner/author(s).

关键词： Mapping

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Inherent-attribute-aware dual-graph autoencoder for rating prediction

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Journal of Information and Intelligence 2024年第1期2卷 82-97页

作者： Yangtao Zhou Qingshan Li Hua Chu Jianan Li Lejia Yang Biaobiao Wei Luqiao Wang Wanqiang Yang School of Computer Science and Technology Xidian UniversityXi'an 710071China Intelligent Financial Software Engineering New Technology Joint Laboratory Xidian UniversityXi'an 710071China Shanghai Fairyland Software Corp. Ltd.Shanghai 200233China

Autoencoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users'***,existing methods still have two significant limitations:i)External attributes are often unavailable in the real world due to privacy issues,leading to low quality of representations;and ii)existing methods lack considering complex associations in users'rating behaviors during the encoding *** meet these challenges,this paper innovatively proposes an inherent-attribute-aware dual-graph autoencoder,named IADGAE,for rating *** address the low quality of representations due to the unavailability of external attributes,we propose an inherent attribute perception module that mines inductive user active patterns and item popularity patterns from users'rating behaviors to strengthen user and item *** exploit the complex associations hidden in users’rating behaviors,we design an encoder on the item-item co-occurrence graph to capture the co-occurrence frequency features among ***,we propose a dual-graph feature encoder framework to simultaneously encode and fuse the high-order representations learned from the user-item rating graph and item-item co-occurrence *** experiments on three real datasets demonstrate that IADGAE is effective and outperforms existing rating prediction methods,which achieves a significant improvement of 4.51%~41.63%in the RMSE metric.

关键词： Rating prediction Graph convolutional network Autoencoder Inherent attribute aware

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Based on the Conformer End-to-End Chinese Speech Recognition Method

Based on the Conformer End-to-End Chinese Speech Recognition...

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Electronic Information engineering and Computer Science (EIECS), 2021 International Conference on

作者： Haiwen Feng Yunxiao Shi Shenyang Key Laboratory of Intelligent Manufacturing of High-end Industrial Equipment School of Software Engineering Shenyang University of Technology Shenyang Liaoning China School of Software Engineering Shenyang University of Technology Shenganp Liaoning China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

Aiming at the problem that the acoustic input network of Conformer encoder is insufficient for Fbank speech feature extraction, an end-to-end speech recognition modeling method of DM-Conformer is proposed. Firstly, the Dilated convolution is utilized in the downsampling module to increase the receptive field, which captures more speech feature information without adding extra model parameters, while ensuring that the height and width of the original input feature map remain unchanged. Then, the Mish activation function is introduced to alleviate the problem of slow convergence of the ReLU activation function due to its negative value. Finally, experiments are carried out on the public dataset AISHELL-1, and the speech recognition model with the improved downsampling module proposed in this paper reduces the word error rate by 5.6% compared with the Conformer baseline model on the test set, and the model finally achieves a word error rate of 6.15%.

关键词： Training Computer science Error analysis Convolution Computational modeling Speech recognition Feature extraction Acoustics Convergence

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Humanoid Robot Collaborative Lifting Integrating Executable Judgment

Humanoid Robot Collaborative Lifting Integrating Executable ...

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2022 IEEE International Symposium on Product Compliance engineering - Asia, ISPCE-ASIA 2022

作者： Chang, Hua Yi, Pengfei Liu, Rui Dong, Jing Hou, Yaqing Zhou, Dongsheng The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China Dalian University of Technology Dalian China

ISBN: (数字)9798350332483

ISBN: (纸本)9798350332483

Humanoid robot collaborative lifting can be used in a variety of scenarios that require repetitive lifting tasks. Most existing studies of humanoid robot collaboration often assume that objects can always be lifted, which may result in damage to both the robot and the object if objects are too heavy to lift. To avoid such situations as much as possible, a collaborative lifting approach integrating executable judgment is proposed. First, a target search and localization method is constructed using monocular vision and marker points to identify the task object. Then, an executable judgment strategy is designed to determine whether the object is overweight or not according to robot force analysis. Finally, a multi-robot joint control model is proposed based on collaborative communication to perform collaborative tasks with different loads based on the judgment results. Experiments on two humanoid robots for different types and weights of targets show the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Anthropomorphic robots

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