Reviewerly/exHarmony huggingface dataset:
SIFDriveNet: Speed and Image Fusion for Driving Behavior Classification Network

Authors:
raw_author_name: Yan Gong
raw_affiliation_string: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China
raw_author_name: Jianli Lu
raw_affiliation_string: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, China
raw_author_name: Wenzhuo Liu
raw_affiliation_string: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, China
raw_author_name: Zhiwei Li
raw_affiliation_string: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China
raw_author_name: Xin Gao
raw_affiliation_string: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, China
raw_author_name: Xingang Wu
raw_affiliation_string: School of Vehicle and Mobility, Tsinghua University, Beijing, China

Abstract: Driving behavior classification is an important direction in the field of social transportation systems and advanced driving assistance system (ADAS), which has attracted more and more attention in recent years. An accurate driving behavior classification algorithm plays a great role in traffic safety, energy saving, and other fields. In this article, we propose a novel vehicle speed and image fusion for driving behavior classification network (SIFDriveNet), which classifies driver behaviors into normal driving, aggressive driving, and drowsy driving. Our method has the following key advantages. First, in the research of driving behavior classification, we are the first to introduce a 2-D image with rich roadside information and convert speeds into a 2-D spectrogram expressing the time–frequency characteristics of speeds through short-time Fourier transform (STFT) while unifying the data space of image information and speed information. Second, we propose a tensor fusion method based on weight decomposition to fully fuse the vectors of the two modalities. This method maps the tensor outer product results to the low-dimensional space through weight decomposition and has a low computational cost while maintaining the fusion effect of the tensor outer product. In addition, we evaluated our model on the public UAH-DriveSet and compared it with the most advanced model. Experimental results show that our model has a better performance, and F1-score is 97.9% on all roads. Especially on the secondary road, our F1-score is 99.4%. Also, our model has strong generalization, and we have reached 99.3% F1 in distracted driving multimodal dataset. In addition, the inference speed reaches 411 FPS, enabling real-time needs. The code is available on https://github.com/alu222/SIFDriveNet.
Modeling Long- and Short-Term Service Recommendations with a Deep Multi-Interest Network for Edge Computing

Authors:
raw_author_name: Rui Yuan
raw_affiliation_string: Nanjing University of Science and Technology,Department of Computer Science and Engineering,Nanjing,China,210056
raw_author_name: Shunmei Meng
raw_affiliation_string: Nanjing University of Science and Technology,Department of Computer Science and Engineering,Nanjing,China,210056
raw_author_name: Ruihan Dou
raw_affiliation_string: Faculty of Mathematics, University of Waterloo, Waterloo, Canada; Nanjing University of Science and Technology,Department of Computer Science and Engineering,Nanjing,China,210056
raw_author_name: Xinna Wang
raw_affiliation_string: Nanjing University of Science and Technology,Department of Computer Science and Engineering,Nanjing,China,210056

Abstract: Edge computing platforms enable application developers and content providers to provide context-aware services (such as service recommendations) using real-time wireless access network information. How to recommend the most suitable candidate from these numerous available services is an urgent task. Click-through rate (CTR) prediction is a core task of traditional service recommendation. However, many existing service recommender systems do not exploit user mobility for prediction, particularly in an edge computing environment. In this paper, we propose a model named long and short-term user preferences modeling with a multi-interest network based on user behavior. It uses a logarithmic network to capture multiple interests in different fields, enriching the representations of user short-term preferences. In terms of long-term preferences, users' comprehensive preferences are extracted in different periods and are fused using a nonlocal network. Extensive experiments on three datasets demonstrate that our model relying on user mobility can substantially improve the accuracy of service recommendation in edge computing compared with the state-of-the-art models.
A Server Placement Algorithm for Reducing Risk and Improving Power Utilization in Data Centers

Authors:
raw_author_name: Rui Chen
raw_affiliation_string: School of Computer Science and Engineering, South China University of Technology,Guangzhou,China,510006
raw_author_name: Huikang Huang
raw_affiliation_string: School of Computer Science and Engineering, South China University of Technology,Guangzhou,China,510006
raw_author_name: Xiaoxuan Luo
raw_affiliation_string: School of Computer Science and Engineering, South China University of Technology,Guangzhou,China,510006
raw_author_name: Weiwei Lin
raw_affiliation_string: Peng Cheng Laboratory, Shenzhen, China; School of Computer Science and Engineering, South China University of Technology,Guangzhou,China,510006

Abstract: As the power demand in data centers is increasing, the power capacity of the power supply system has become an essential resource to be optimized. Although many data centers use power oversubscription to make full use of the power capacity, there are unavoidable power supply risks associated with it. Therefore, how to improve the data center power capacity utilization while ensuring power supply security has become an important issue. To solve this problem, we first define it and propose a risk evaluation metric called Weighted Power Supply Risk (WPSRisk). Then, a method, named Hybrid Genetic Algorithm with Ant Colony System (HGAACS), is proposed to improve power capacity utilization and reduce power supply risks by optimizing the server placement in the power supply system. HGAACS uses historical power data of each server to find a better placement solution by population iteration. HGAACS possesses not only the remarkable local search ability of Ant Colony System (ACS), but also enhances the global search capability by incorporating genetic operators from Genetic Algorithm (GA). To verify the performance of HGAACS, we experimentally compare it with five other placement algorithms. The experimental results show that HGAACS can perform better than other algorithms in both improving power utilization and reducing the risk of power supply system.
ISFB-GAN: Interpretable semantic face beautification with generative adversarial network

Authors:
raw_author_name: Tianhao Peng
raw_affiliation_string: Guizhou University, Guiyang, 550025, Guizhou, China; Moutai Institute, Renhuai, 564507, Guizhou, China
raw_author_name: Mu Li
raw_affiliation_string: Harbin Institute of Technology(Shenzhen), Shenzhen, 518055, Guangdong, China
raw_author_name: Fangmei Chen
raw_affiliation_string: Dalian Minzu University, Dalian, 116600, Liaoning, China
raw_author_name: Yong Xu
raw_affiliation_string: Harbin Institute of Technology(Shenzhen), Shenzhen, 518055, Guangdong, China
raw_author_name: Yuan Xie
raw_affiliation_string: The Chinese University of Hongkong(Shenzhen), Shenzhen, 518172, Guangdong, China
raw_author_name: David Zhang
raw_affiliation_string: The Chinese University of Hongkong(Shenzhen), Shenzhen, 518172, Guangdong, China

Abstract: Face beautification is an emerging application widely used in the entertainment industry. Previous studies have proved that Generative Adversarial Networks (GANs) can successfully build the mapping from a general face to a target face. However, their facial style code in the latent space is taken as a black box with a lack of semantics interpretation. They usually fail to consider the geometric semantic information, and cannot focus on the facial beauty-related regions. Moreover, most of them change the background information. To overcome these problems, we propose a novel Interpretable Semantic Generative Adversarial Network (ISFB-GAN) for face beautification. First, we design a new dual-branch facial style encoder to factorize the abundant semantic information of the reference face into the facial geometry style codes and the appearance style codes in the latent space. Then, a novelty facial attention generator is proposed to preserve more background information in the generated images. In the attention generator, the facial style attention is designed to focus on the most discriminative facial region; the background attention is designed to retain the background information. Finally, we propose a hybrid loss function including geometric loss, orthogonality loss, segmentation loss, style reconstruction loss, perceptual loss, etc., to enhance the learning capacity and improve the visual quality. The combination of the geometric loss and the orthogonality loss is deployed to guarantee the disentanglement between the semantic information. Extensive experiments conducted on the CelebAMask-HQ dataset and the SCUT-FBP5500 dataset, demonstrate that ISFB-GAN performs better than the state-of-the-art methods in facial attractiveness enhancement.
Adaptive stepsize estimation based accelerated gradient descent algorithm for fully complex-valued neural networks

Authors:
raw_author_name: He Huang
raw_affiliation_string: School of Electronics and Information Engineering, Soochow University, Suzhou 215006, PR China

Abstract: Nesterov accelerated gradient (NAG) method is an efficient first-order algorithm for optimization problems. To ensure the convergence, it usually takes a relatively conservative constant as the stepsize. However, the choice of stepsize has a great impact on the optimization process. In this paper, two adaptive stepsize estimation methods are proposed for complex-valued NAG algorithm for efficient training of fully complex-valued neural networks. The basic idea of the first one is to adaptively determine suitable stepsize by estimating the local smoothness constant of the loss function with the norm of approximate complex Hessian matrix. Its validity is theoretically analyzed by means of the decomposition of complex matrix. Furthermore, by introducing a new parameter design method for multi-step quasi-Newton condition, an improved stepsize estimation is presented. Experimental results on pattern recognition, channel equalization, wind forecasting and synthetic aperture radar (SAR) target classification demonstrate the effectiveness of the proposed methods.
Graph isomorphism U-Net

Authors:
raw_author_name: Zahra Dehghanian
raw_affiliation_string: Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran
raw_author_name: Saeed Saravani
raw_affiliation_string: Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran
raw_author_name: Maryam Amirmazlaghani
raw_affiliation_string: Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran
raw_author_name: Behnam Roshanfekr
raw_affiliation_string: Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran

Abstract: Graph embedding learning is a fundamental task when dealing with diverse datasets. While encoder–decoder architectures, such as U-Nets, have shown great success in image pixel-wise prediction tasks, applying similar methods to graph data poses challenges due to the lack of natural pooling and up-sampling operations for graphs. Recent methods leverage learnable parameters to extract structural information from neural networks and extend pooling and unpooling to graphs using node features and graph structural information. This paper proposes a novel model called GIUNet (Graph Isomorphism U-Net) for the graph classification task. The proposed Graph U-Net structure is based on graph isomorphism convolution while using a comprehensive pqPooling layer. The pqPooling layer in our approach effectively combines node features and graph structure information during the graph down-sampling stage. To incorporate graph structure information, we utilize both the spectral representation and node centrality measurements. Node centrality measurements capture various structural aspects of nodes in the graph, while the spectral representation helps us focus on the informative low-frequency components of the graph structure. Through ablation studies, we have demonstrated that leveraging the GIUNet model leads to significant improvements compared to state-of-the-art methods across multiple benchmark datasets.
Collaborative Learning of Different Types of Healthcare Data From Heterogeneous IoT Devices

Authors:
raw_author_name: Lei Zhao
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Victoria, Victoria, Canada
raw_author_name: Lin Cai
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Victoria, Victoria, Canada
raw_author_name: Wu-Sheng Lu
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Victoria, Victoria, Canada

Abstract: In the realm of healthcare data analysis, privacy concerns have been tackled by the Federated Learning (FL) framework. However, in the situation that heterogeneous healthcare Internet of Things (IoT) devices collect different types of data, applying FL becomes difficult. To train a model leveraging diverse healthcare IoT devices, we propose an advanced collaborative learning framework to fill the gap. With the proposed collaborative learning framework, individual IoT devices project their sensed features into a carefully developed latent space, which are transmitted to a central server. For privacy preservation, the latent local features are encoded within this space, while the samples’ labels remain securely stored in the individual IoT devices. Collaboratively, the deep neural network model is trained by both the central server and the diverse IoT devices. The central server handles the computationally intensive training processes, while the individual IoT devices evaluate the model’s performance and initiate back-propagation based on their locally stored labels. Experimental results demonstrate that the proposed collaborative learning framework achieves performance similar to centralized training and significantly outperforms individual training while preserving data privacy.
FDTR: Weakening feature disparity transformer for accurate multicategory computed tomography image segmentation

Authors:
raw_author_name: Xupeng Kou
raw_affiliation_string: College of Information and Electrical Engineering, China Agricultural University, Haidian, 100083, Beijing, China; School of Intelligent Manufacturing, Sichuan University of Arts and Science, 635000, Dazhou, China
raw_author_name: Zhan Tang
raw_affiliation_string: College of Information and Electrical Engineering, China Agricultural University, Haidian, 100083, Beijing, China; School of Intelligent Manufacturing, Sichuan University of Arts and Science, 635000, Dazhou, China
raw_author_name: Houwei Feng
raw_affiliation_string: College of Information and Electrical Engineering, China Agricultural University, Haidian, 100083, Beijing, China; School of Intelligent Manufacturing, Sichuan University of Arts and Science, 635000, Dazhou, China
raw_author_name: Lin Li
raw_affiliation_string: College of Information and Electrical Engineering, China Agricultural University, Haidian, 100083, Beijing, China; School of Intelligent Manufacturing, Sichuan University of Arts and Science, 635000, Dazhou, China

Abstract: Accurately segmenting object structures in computed tomography (CT) images is crucial for computer-aided surgery, diagnosis, and other interdisciplinary applications. Most state-of-the-art CT segmentation methods employ a classical jump structure to integrate shallow and deep feature information. However, the effectiveness of the current CT image segmentation and related subtasks must still be improved due to feature disparity between different layers and information transfer loss. To alleviate the problem, we propose a novel three-dimensional multicategory segmentation model, the weakening feature disparity transformer (FDTR), based on the transformer structure for CT imaging. Firstly, to effectively capture global features, we design a vision transformer-based encoder. Secondly, to enhance the model's information representation in a lightweight manner, we devise a nested structure of dense compression connections. Lastly, to mitigate the disparity in semantic features at shallow layers, we propose supervised semantic signals. By employing the vision transformer-based encoder and the dense connection structure, we enhance the detailed information of deep features. Additionally, the semantic supervisory signal introduces deep semantic information to the shallow features, enriching the overall feature representation. We conducted extensive experiments on the KIPA2022 dataset for multiorgan segmentation and the YC2022 dataset for volumetric trait segmentation in broiler breeding. The proposed method, FDTR, effectively enhances feature transfer between shallow and deep features, achieving optimal results compared to other advanced models and demonstrating broad prospects for application.
Consistent penalizing field loss for zero-shot image retrieval

Authors:
raw_author_name: Minjie Chen
raw_affiliation_string: Faculty of Science and Technology, University of Macau, Macau, PR China
raw_author_name: Simon X. Yang
raw_affiliation_string: School of Engineering, University of Guelph, Guelph, Ontario, Canada

Abstract: Zero-shot image retrieval involves retrieving images of unseen classes using a query image of the same class. To determine whether a given image is of the same class as the query image, a universal threshold of similarity measures is needed, as class-specific thresholds are not feasible for unseen classes. However, existing methods for zero-shot image retrieval focus on pushing a margin between intra-class and inter-class similarities for each class during the training phase. This approach can result in varying decision boundaries between intra- and inter-class similarities across classes, which could compromise performance when a universal threshold is used in the inference stage. Additionally, for classes with low intra-class variances or inter-class correlations, the pushing force of the margin-pushing approach might be too weak to learn high-quality embeddings. To address these issues, we propose a novel Consistent Penalizing Field (CPF) Loss for zero-shot image retrieval. The proposed method has a single consistent penalizing field for all classes, resulting in similar decision boundaries across classes. By penalizing samples outside the penalizing field, CPF Loss can better utilize the information of samples with highly unbalanced intra-class and inter-class correlations, and improve the discriminative power of DML learning for zero-shot image retrieval. Extensive experiments are conducted on the challenging Shopee Product Matching dataset and other established benchmarks, and the results demonstrate that the proposed method consistently outperforms the state-of-the-art methods. The code is available at https://github.com/cloudlc/CPF.
Explainable subgradient tree boosting for prescriptive analytics in operations management

Authors:
raw_author_name: Pascal M. Notz
raw_affiliation_string: Chair of Logistics and Quantitative Methods, Julius-Maximilians-Universität Würzburg, Sanderring 2, 97070 Würzburg, Germany

Abstract: Motivated by the success of gradient boosting approaches in machine learning and driven by the need for explainable prescriptive analytics approaches in operations management (OM), we propose subgradient tree boosting (STB) as an explainable prescriptive analytics approach to solving convex stochastic optimization problems that frequently arise in OM. The STB approach combines the well-known method of subgradient descent in function space with sample average approximation, and prescribes decisions from a problem-specific loss function, historical demand observations, and prescriptive features. The approach provides a decision-maker with detailed explanations for the prescribed decisions, such as a breakdown of individual features' impact. These explanations are particularly valuable in practice when the decision-maker has the discretion to adjust the recommendations made by a decision support system. We show how subgradients can be derived for common single-stage and two-stage stochastic optimization problems; demonstrate the STB approach's applicability to two real-world, complex capacity-planning problems in the service industry; benchmark the STB approach's performance against those of two prescriptive approaches—weighted sample average approximation (wSAA) and kernelized empirical risk minimization (kERM); and show how the STB approach's prescriptions can be explained by estimating the impact of individual features. The results suggest that the quality of the STB approach's prescriptions is comparable to that of wSAA's and kERM's prescriptions while also providing explanations.
Multi-Level Medical Image Segmentation Network Based on Multi-Scale and Context Information Fusion Strategy

Authors:
raw_author_name: Dayu Tan
raw_affiliation_string: Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, Hefei, China
raw_author_name: Zhiyuan Yao
raw_affiliation_string: Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, Hefei, China
raw_author_name: Xin Peng
raw_affiliation_string: Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
raw_author_name: Haiping Ma
raw_affiliation_string: Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, Hefei, China
raw_author_name: Yansen Su
raw_affiliation_string: Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, Hefei, China
raw_author_name: Weimin Zhong
raw_affiliation_string: Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

Abstract: Accurate segmentation of human tissue structure from medical images is one of the critical links in medical image diagnosis. However, due to the medical image scale of different tissues varying significantly and being structurally complex, the low contrast between tissues and background in some medical imaging makes it challenging to identify. The existing models are difficult to extract representative multi-scale features of medical images that cannot accurately segment the organizational structure from the background in low-contrast medical images. To solve these problems, this study presents a scale and context information fusion network structure based on multi-type medical image segmentation (SCIF-Net), which contains three modules: feature pyramid encoder (FPE), multi-scale feature dynamic aggregation (MFDA), and adaptive spatial information fusion (ASIF). We build the FPE module to further enhance the representational ability of the network encoder output feature map at each stage. The MFDA module is used to effectively extract multi-scale information from the encoder output feature map and aggregate multi-scale features. The constructed ASIF module enables the network to selectively concentrate on the vital spatial information in the encoder feature map and merge the decoder feature map semantic information, minimizing background noise influence. Extensive experiments on the retinal segmentation task, gland segmentation task, and femur segmentation task, show that the SCIF-Net network outperforms other advanced methods.