Person re-identification with intra-domain similarity grouping based on semantic fusion by Qiqi KOU, Ji HUANG, Deqiang CHENG, Yunlong LI, Jianying ZHANG

“…Unsupervised cross-domain person re-identification aims to adapt a model trained on a labeled source-domain dataset to a target-domain dataset.However, the cluster-based unsupervised cross-domain pedestrian re-identification algorithm often generates noise due to the different input pedestrian pictures during the network feature learning process, which affects the clustering results.To solve this problem, An intra-domain similarity grouping pedestrian re-identification network based on semantic fusion was proposed.Firstly, a semantic fusion layer was added on the basis of the Baseline network, and the semantic fusion of similar features was performed on the intermediate feature maps from the two aspects of space and channel in turn, so as to improve the adaptive perception ability of the network.In addition, by making full use of the fine-grained information of intra-domain similarity, the network’s clustering accuracy of global and local features was improved.Experiments were carried out on three public datasets, DukeMTMC-ReID, Market1501, MSMT17, and the results demonstrate that the mAP and Rank recognition accuracy are significantly improved compared with recent unsupervised cross-domain person re-identification algorithms.…”
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Trustworthy and efficient project scheduling in IIoT based on smart contracts and edge computing by Peng Liu, Xinglong Wu, Yanjun Peng, Hangguan Shan, Saïd Mahmoudi, Bong Jun Choi, Haksrun Lao

“…The results indicate that the suggested CA-RS approach significantly enhances efficiency and security in resource arrangement within the industrial Internet of Things compared to baseline algorithms.…”
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Improved Extended Multiplicative Scatter Correction Algorithm Applied in Blood Glucose Noninvasive Measurement with FT-IR Spectroscopy by Qingbo Li, Qishuo Gao, Guangjun Zhang

“…Im-EMSC can efficiently remove most of the pathlength difference and baseline shift influences. Firstly, Im-EMSC is used as a preprocessing method, and then partial least squares (PLS) regression method is adopted to establish a quantitative analysis model. …”
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p-Norm Broad Learning for Negative Emotion Classification in Social Networks by Guanghao Chen, Sancheng Peng, Rong Zeng, Zhongwang Hu, Lihong Cao, Yongmei Zhou, Zhouhao Ouyang, Xiangyu Nie

“…Specifically, there are mainly three contributions in this paper. Firstly, we fine-tune the RoBERTa to adapt it to the task of negative emotion classification. …”
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The Fruit Recognition and Evaluation Method Based on Multi-Model Collaboration by Mingzheng Huang, Dejin Chen, Dewang Feng

“…The improved YOLOv8 model improves the P, R, mAP50, and MAP50-95 indicators by 2.4%, 2.1%, 1%, and 1.3%, respectively, compared with the baseline model on only one generalized “fruit” label dataset. …”
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Dynamic QoS-aware intelligent edge computing resource management algorithm for body area networks by MU Siqi, WEN Shuo, LU Yang, AI Bo

“…The simulation results show that the proposed optimization scheme outperforms existing baseline methods, effectively meeting the dynamic priority requirements of tasks in BAN and reducing the energy consumption as well as the average delay required for task completion.…”
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Student knowledge tracking based multi-indicator exercise recommendation algorithm by Bin ZHUGE, Zhenghu YIN, Wenxue SI, Lei YAN, Ligang DONG, Xian JIANG

“…Personalized exercise recommendation was an important topic in the era of education informatization, the forgetting laws of students in the learning process were ignored by the traditional problem recommendation algorithm, which failed to fully tap the students’ knowledge mastery level and the common characteristics of similar students, insufficient, could not reasonably promote students’ learning of new knowledge or help students find and fill omissions.In view of the above defects, a multi-index exercise recommendation method based on student knowledge tracking was proposed, which was divided into two modules: preliminary screening and re-filtering of exercises, focusing on the novelty, difficulty and diversity of exercise recommendation.Firstly, a knowledge probability prediction (SF-KCCP) model combined with students’ forgetting law was constructed to ensure the novelty of the recommended exercises.Then, students’ knowledge and concept mastery level was accurately excavated based on the dynamic key-value knowledge tracking (DKVMN) model to ensure that exercises of appropriate difficulty were recommended.Finally, the user-based collaborative filtering (UserCF) algorithm was integrated into the re-filtering module, and the similarity between student groups was used to achieve the diversity of recommendation results.The proposed method is demonstrated by extensive experiments to achieve better performance than some existing baseline models.…”
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Seroprevalence and Risk Factors Associated with Hepatitis E Virus Infection in Three Species of Pet Birds in Northwest China by Wei Cong, Qing-Feng Meng, Xiao-Feng Shan, Wu-Wen Sun, Si-Yuan Qin, Xiao-Xuan Zhang, Si-Yang Huang, Ai-Dong Qian

“…Age and collecting region of pet birds were the main risk factors associated with HEV infection. The present study firstly revealed the seroprevalence of HEV infection in three species of pet birds in northwest China, which provided the baseline data for taking comprehensive countermeasures and measures for effectively preventing and controlling HEV infection in birds.…”
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Detection of cervical cell based on multi-scale spatial information by Gang Li, Xinyu Fan, Chuanyun Xu, Pengfei Lv, Ru Wang, Zihan Ruan, Zheng Zhou, Yang Zhang

“…We use Sparse R-CNN as the baseline and integrate MSA and CAE into it. Experiments on the CDetector dataset achieved an Average Precision (AP) of 65.3%, outperforming the state-of-the-art (SOTA) methods.…”
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An interpretable semi‐supervised system for detecting cyberattacks using anomaly detection in industrial scenarios by Ángel Luis Perales Gómez, Lorenzo Fernández Maimó, Alberto Huertas Celdrán, Félix J. García Clemente

“…However, two main limitations hinder using them in a real environment. Firstly, most solutions are trained using a supervised approach, which is impractical in the real industrial world. …”
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Application of a Plug-and-Play Immunogenicity Assay in Cynomolgus Monkey Serum for ADCs at Early Stages of Drug Development by Montserrat Carrasco-Triguero, Helen Davis, Yuda Zhu, Daniel Coleman, Denise Nazzal, Paul Vu, Surinder Kaur

“…Secondly, we propose a cut point methodology based on animal specific baseline variability instead of a population data approach. …”
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Lightweight detection model for safe wear at worksites using GPD-YOLOv8 algorithm by Jian Xing, Chenglong Zhan, Jiaqiang Ma, Zibo Chao, Ying Liu

“…Abstract To address the significantly elevated safety risks associated with construction workers’ improper use of helmets and reflective clothing, we propose an enhanced YOLOv8 model tailored for safety wear detection. Firstly, this study introduces the P2 detection layer within the YOLOv8 architecture, which substantially enriches semantic feature representation. …”
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Fault Diagnosis and Prediction of Continuous Industrial Processes Based on Hidden Markov Model-Bayesian Network Hybrid Model by Jiarula Yasenjiang, Chenxing Xu, Shengpeng Zhang, Xin Zhang

“…Combining the Tennessee Eastman (TE) process and the continuously stirred tank reactor (CSTR) process, the feasibility and effectiveness of the model are verified. …”
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DM-YOLO: improved YOLOv9 model for tomato leaf disease detection by Abudukelimu Abulizi, Junxiang Ye, Halidanmu Abudukelimu, Wenqiang Guo

“…When evaluated based on the tomato leaf disease dataset, the precision (P) of the model was 92.5%, and the average precision (AP) and the mean average precision (mAP) were 95.1% and 86.4%, respectively, which were 3%, 1.7%, and 1.4% higher than the P, AP, and mAP of YOLOv9, the baseline model, respectively. The proposed detection method had good detection performance and detection potential, which will provide strong support for the development of smart agriculture and disease control.…”
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A Lightweight Anti-Unmanned Aerial Vehicle Detection Method Based on Improved YOLOv11 by Yunlong Gao, Yibing Xin, Huan Yang, Yongjuan Wang

“…This paper focuses on the challenges of long-distance UAV identification and proposes a lightweight anti-UAV detection method based on improved YOLOv11. Firstly, HWD is imported as the backbone’s downsampling module, which lowers feature loss in the feature extraction procedure while using fewer parameters. …”
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IEDSFAN: information enhancement and dynamic-static fusion attention network for traffic flow forecasting by Lianfei Yu, Ziling Wang, Wenxi Yang, Zhijian Qu, Chongguang Ren

“…We conducted extensive experiments on five real-world traffic datasets, and the experimental results show that the performance of IEDSFAN is significantly better than all baselines.…”
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Dynamic Warping Network for Semantic Video Segmentation by Jiangyun Li, Yikai Zhao, Xingjian He, Xinxin Zhu, Jing Liu

“…Extensive experiments show that our DWNet can achieve consistent improvement over various strong baselines and achieves state-of-the-art accuracy on the Cityscapes and CamVid benchmark datasets.…”
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Aspect category sentiment analysis based on pre-trained BiLSTM and syntax-aware graph attention network by Guixian Xu, Zhe Chen, Zixin Zhang

“…The performance evaluation of this method is carried out on five user comment text datasets, and the comprehensive ablation experiments prove that this method performs best compared with baseline models.…”
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YOLOV9-CBM: An Improved Fire Detection Algorithm Based on YOLOV9 by Xin Geng, Xiao Han, Xianghong Cao, Yixuan Su, Dongxue Shu

“…Regarding the current problems of false alarms and missed detections in fire detection, we propose a high-precision fire detection algorithm, YOLOV9-CBM (C3-SE, BiFPN, MPDIoU), by optimizing YOLOV9. Firstly, to tackle the shortage of both quality and quantity in the existing fire datasets, we collected 2,000 fire and smoke images to establish a dataset named CBM-Fire. …”
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Task offloading optimization in mobile edge computing based on a deep reinforcement learning algorithm using density clustering and ensemble learning by Yi Qin, Junyan Chen, Lei Jin, Rui Yao, Zidan Gong

“…Simulation results demonstrate that the proposed DCEDRL method can reduce the backlog of tasks by greater than over 21% compared to the baseline algorithms.…”
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