Reviewerly/exHarmony huggingface dataset:
TSAGNN: Temporal link predict method based on two stream adaptive graph neural network

Authors:
raw_author_name: Yuhang Zhu
raw_affiliation_string: Institute of Information Technology, PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan, China
raw_author_name: Jing Guo
raw_affiliation_string: Henan Xinda Wangyu Technology Co., Ltd, Henan Information Security Industry Demonstration Base, Zhengzhou, Henan, China
raw_author_name: Shuxin Liu
raw_affiliation_string: Institute of Information Technology, PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan, China
raw_author_name: Yingle Li
raw_affiliation_string: Institute of Information Technology, PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan, China

Abstract: Temporal link prediction based on graph neural networks has become a hot spot in the field of complex networks. To solve the problems of the existing temporal link prediction methods based on graph neural networks do not consider the future time-domain features and spatial-domain features are limited used, this paper proposes a novel temporal link prediction method based on two streams adaptive graph neural networks. Firstly, the network topology features are extracted from the micro, meso, and middle perspectives. Combined with the adaptive mechanism of convolution and self-attention, the preprocessing of the feature extraction is more effective; Secondly, an extended bi-directional long short-term memory network is proposed, which uses graph convolution to process topological features, and recursively learns the state vectors of the target snapshot by using the future time-domain information and the past historical information; Thirdly, the location coding is replaced by the time-coding for the transformer mechanism, so that past information and future information can be learned from each other, and the time-domain information of the network can be further mined; Finally, a novel two-stream network framework is proposed, which combines the processing results of point features and edge features. The experimental results on 9 data sets show that the proposed method has a better prediction effect and better robustness than the classical graph neural network methods.
Digital mammogram based robust feature extraction and selection for effective breast cancer classification in earlier stage

Authors:
raw_author_name: S. Mary Joans
raw_affiliation_string: Department of ECE, Velammal Engineering College, Chennai, Tamil Nadu, India

Abstract: Breast cancer poses a significant health risk for women, demanding early detection to mitigate its mortality impact. Leveraging the power of Deep Learning (DL) in medical imaging, this paper introduces a hybrid model that integrates YOLOv7 and Half UNet for feature extraction. YOLOv7 identifies and localizes potential cancerous regions, while Half UNet focuses on extracting pertinent features with its encoder-decoder structure. The fusion of these discriminative features, coupled with feature selection via Coati Optimization, ensures a comprehensive and optimized dataset. The selected features then feed into the CatBoost classification algorithm, refining parameters iteratively for precise predictions and minimizing the loss function. Evaluation metrics, including precision, recall, specificity, and accuracy, demonstrate the model’s superior performance. Notably, the proposed model surpasses existing methods in early-stage breast cancer detection. Beyond numerical metrics, its significance lies in the potential to positively impact patient outcomes and increase survival rates. By amalgamating cutting-edge DL techniques, the model excels in identifying intricate patterns crucial for early cancer detection. The efficient fusion of YOLOv7 and Half UNet, coupled with feature optimization through Coati Optimization, sets this model apart. This research contributes to the evolving landscape of medical imaging and DL applications, emphasizing the potential for enhanced breast cancer diagnosis and improved patient prognoses.
Exploiting classifier inter-level features for efficient out-of-distribution detection

Authors:
raw_author_name: Kashish Gupta
raw_affiliation_string: Faculty of Engineering and Computer Science, University of Victoria, 3800 Finnerty Road, Victoria V8P 5C2, BC, Canada
raw_author_name: Homayoun Najjaran
raw_affiliation_string: Faculty of Engineering and Computer Science, University of Victoria, 3800 Finnerty Road, Victoria V8P 5C2, BC, Canada

Abstract: Deep learning approaches have achieved state-of-the-art performance in a wide range of applications. Most often, however, it is falsely assumed that samples at inference follow a similar distribution as the training data. This assumption impairs models' ability to handle Out-of-Distribution (OOD) data during deployment. While several OOD detection approaches mostly focus on outputs of the last layer, we propose a novel mechanism that exploits features extracted from intermediate layers of a deep classifier. Specifically, we train an off-the-shelf auxiliary network using features of early layers to learn distinctive representations that improve OOD detection. The proposed network can be appended to any classification model without imposing any modification to its original architecture. Additionally, the mechanism does not require access to OOD data during training. We evaluate the performance of the mechanism on a variety of backbone architectures and datasets for near-OOD and far-OOD scenarios. The results demonstrate improvements in OOD detection compared to other state-of-the-art approaches. In particular, our proposed mechanism improves AUROC by 14.2% and 8.3% in comparison to the strong OOD baseline method, and by 3.2% and 3.9% in comparison to the second-best performing approach, on CIFAR-10 and CIFAR-100 datasets respectively.
Protocol choice and iteration for the free cornering

Authors:
raw_author_name: Chad Nester
raw_affiliation_string: Tallinn University of Technology, Akadeemia Tee 21/1, 12618, Tallinn, Estonia
raw_author_name: Niels Voorneveld
raw_affiliation_string: Cybernetica AS, Mäealuse 2/1, 12618, Tallinn, Estonia; Tallinn University of Technology, Akadeemia Tee 21/1, 12618, Tallinn, Estonia

Abstract: We extend the free cornering of a symmetric monoidal category, a double categorical model of concurrent interaction, to support branching communication protocols and iterated communication protocols. We validate our constructions by showing that they inherit significant categorical structure from the free cornering, including that they form monoidal double categories. We also establish some elementary properties of the novel structure they contain. Further, we give a model of the free cornering in terms of strong functors and strong natural transformations, inspired by the literature on computational effects.
A no-reference image quality assessment model based on neighborhood component analysis and Gaussian process

Authors:
raw_author_name: J. Rajevenceltha
raw_affiliation_string: Department of Electrical and Electronics Engineering and APPCAIR, Birla Institute of Technology and Science, Pilani, Dubai Campus 345055, United Arab Emirates
raw_author_name: Vilas H. Gaidhane
raw_affiliation_string: Department of Electrical and Electronics Engineering and APPCAIR, Birla Institute of Technology and Science, Pilani, Dubai Campus 345055, United Arab Emirates

Abstract: In this paper, a human visual system (HVS) based no-reference image quality assessment model (NR-IQA) is proposed. This is an objective NR-IQA model that uses perceptual features such as structure and orientation to compute the loss of naturalness in the image. An adaptive feature extraction process is modeled to capture the underlying significant features of the images along with the noise. Moreover, the optimal subset of features is derived using the neighborhood component analysis and further used as inputs to the Gaussian process for regression. The various experimentations are carried out on LIVE, TID2008, and TID2013 databases to test the effectiveness of the proposed approach. It is observed that the presented model exhibits a competitive performance in comparison with the existing IQA approaches. Moreover, the computation complexity and run-time show the effectuality of the proposed approach. Further, the experiments show that the predicted score matches with human perceptions. Thus, the proposed model is more accurate, less complex, independent of distortions, and well-suited for real-time applications.
Identification of the Original Images

Authors:
raw_author_name: Haoxian Song
raw_affiliation_string: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
raw_author_name: Zichi Wang
raw_affiliation_string: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
raw_author_name: Xinpeng Zhang
raw_affiliation_string: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China

Abstract: In social networks, people usually communicate online by posting and passing images, people will find that there are many similar or even visually identical images. When the images determine whether the published information is true or false, original images became crucial, and at the same time, deep learning excelled in image identification. Combining the original image identification and deep learning methods, this paper proposes the original image identification method based on the deep network models, aiming to use the deep network models to identify the original images between the original images and a bunch of variant images of the original images, and obtain satisfactory results. In this way, through the trained network models, the original images can be identified and can help the viewers to determine the correctness of the information. Experimental results verify the feasibility, effectiveness and security of the proposed method.
An efficient two-stage evolutionary algorithm for multi-robot task allocation in nuclear accident rescue scenario

Authors:
raw_author_name: Hongbin Ma
raw_affiliation_string: National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing, 100081, PR China; School of Automation, Beijing Institute of Technology, Beijing, 100081, PR China

Abstract: With the growing maturity of multi-robot system technology, its applications have expanded across various domains. This paper addresses the critical issue of task allocation in nuclear accident rescue scenario, which plays a pivotal role in the success of such operations. The problem is formulated as a multi-objective optimization problem, taking into account three key indicators: execution time, radiation accumulation, and waiting cost. To effectively tackle this problem, an two-stage evolutionary algorithm is proposed. Firstly, a solution encoding method and a crossover mutation method is devised tailored to the problem’s characteristics. Secondly, a two-stage search strategy is designed. In the first stage, a fixed population size and shift-based density estimation method are used to quickly converge the solution set to the Pareto front. The latter stage uses an infinite size population to find as many Pareto solutions as possible. Finally, a local search strategy is introduced to improve the quality of solution set. In the experimental section, our proposed method is compared with five state-of-the-art algorithms on nine instances of varying scales. Across five evaluation metrics, the proposed algorithm demonstrates competitive performance on all instances. These results underscore the efficacy and competitiveness of our approach in tackling the task allocation problem in multi-robot systems within nuclear accident rescue.
Multi-objective cost-aware bag-of-tasks scheduling optimization model for IoT applications running on heterogeneous fog environment

Authors:
raw_author_name: Seyyedamin Seifhosseini
raw_affiliation_string: Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
raw_author_name: Mirsaeid Hosseini Shirvani
raw_affiliation_string: Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran

Abstract: Recently introduced by CISCO, the fog computing paradigm extends cloud-based computing services near the Internet of Things (IoT) devices to serve their applications, aiming to increase performance. This new computing paradigm tries to meet the requirements of emerging IoT applications, which mostly are geographically distributed and location-aware, low-cost, and delay-sensitive. One of the frequently applied applications is a bag-of-tasks (BoT) of IoT applications, each of which includes several independent tasks. However, inefficient task scheduling approaches for parallel executions may degrade the delivered quality of service (QoS) to users and also increase the providers’ total cost of ownership (TCO). The reliable execution commensurate with users’ service level agreement (SLA) and affordable services tailored to their budget at the same time is favorable for both users and fog service providers because the highly reliable execution is a reputational metric that increases the users’ trust and adherence. To this end, this paper formulates the task scheduling for the execution of BoT in IoT applications to a multi-objective optimization problem with execution time, costs and reliability perspectives. To measure total costs, both the execution cost and monetary cost models are presented. To measure the resource reliability, the new scheduling failure factor (SFF) metric is also presented, which is a kind of cost parameter. To solve this combinatorial NP-Hard problem, a multi-objective cost-aware discrete grey wolf optimization-based algorithm (MoDGWA) is proposed. To verify the performance of MoDGWA, diverse scenarios were conducted and the performance of the proposed algorithm was compared against other state-of-the-arts. The extensive simulation results prove the average dominance of MoDGWA, and it has 0.55 %, 7.28 %, 10.20 %, and 45.83 % reduction against other state-of-the-art in terms of makespan, ToC, TSFF, and the cost score respectively. Note that, the implementation of the MoDGWA is available on https://github.com/mirsaeid123/Mirsaeid.
Lens imaging opposition-based learning for differential evolution with cauchy perturbation

Authors:
raw_author_name: Hongrun Wu
raw_affiliation_string: Key Lab of Intelligent Optimization and Information Processing, Minnan Normal University, Zhangzhou, 363000, China; School of Physics and Information Engineering, Minnan Normal University, Zhangzhou, 363000, China
raw_author_name: Yingpin Chen
raw_affiliation_string: Key Lab of Intelligent Optimization and Information Processing, Minnan Normal University, Zhangzhou, 363000, China; School of Physics and Information Engineering, Minnan Normal University, Zhangzhou, 363000, China
raw_author_name: Xuewen Xia
raw_affiliation_string: Key Lab of Intelligent Optimization and Information Processing, Minnan Normal University, Zhangzhou, 363000, China; School of Physics and Information Engineering, Minnan Normal University, Zhangzhou, 363000, China

Abstract: Opposition-based learning (OBL) is an effective optimization strategy that enhances the performance of various global optimization algorithms. Among these algorithms, differential evolution (DE) based on OBL has received the attention of many researchers. However, OBL is a strategy with relative symmetry between the target point and the opposite point, which does not contribute significantly to the population in the later stage of the algorithm, and tends to disregard more meaningful points around the opposite points. Therefore, we propose a novel opposition-based differential evolution based on the principle of convex lens (LensOBLDE), which utilizes the distance relationship between the target point and the focal length of the lens, and dynamically adjusts the search radius by controlling the parameters, thus further improving the search ability. In addition, to avoid the algorithm from falling into a local optimum, a small-scale and low-probability cauchy perturbation strategy is applied to the elite individuals in the population, which ensures the exploration and exploitation of the algorithm. Finally, LensOBLDE evaluates the performance of several advanced algorithms on the CEC2017 benchmark suite and conducts a statistical test to evaluate the difference between algorithms. The results indicate that LensOBLDE outperforms several algorithms with a significant difference between them. This verifies the effectiveness of the strategies newly added in this study.
MIMR: Modality-Invariance Modeling and Refinement for unsupervised visible-infrared person re-identification

Authors:
raw_author_name: Zhiqi Pang
raw_affiliation_string: Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
raw_author_name: Chunyu Wang
raw_affiliation_string: Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
raw_author_name: Honghu Pan
raw_affiliation_string: School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China
raw_author_name: Lingling Zhao
raw_affiliation_string: Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
raw_author_name: Junjie Wang
raw_affiliation_string: School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
raw_author_name: Maozu Guo
raw_affiliation_string: Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Abstract: Compared to visible modality person re-identification that handles only the intra-modality discrepancy, visible-infrared person re-identification (VI-ReID) aims to achieve the match between visible modality and infrared modality. However, supervised VI-ReID methods are limited in flexibility due to their dependence on labeled data. Although several unsupervised VI-ReID methods have been developed, they usually ignore the impact of hard samples and noisy labels. In this paper, we propose the modality-invariance modeling and refinement (MIMR) framework for unsupervised VI-ReID. For the first issue, we first conduct cross-modality image translation, then enable the encoder to extract modality-invariant feature representation by aligning translated versions with original samples, and finally use cross-modality clustering and hard sample contrastive loss to handle the hard samples. For the second issue, we believe that ambiguous samples are usually assigned noisy labels, so we design ambiguity-oriented pseudo label refinement (APLR), which evaluates ambiguity from both the sample itself and the corresponding translated version, rather than discards hard samples as existing methods. Extensive experiments demonstrate that MIMR achieves superior performance compared to state-of-the-art unsupervised methods and even surpasses early supervised methods.
NQFL: Nonuniform Quantization for Communication Efficient Federated Learning

Authors:
raw_author_name: Kaixuan Xie
raw_affiliation_string: China Mobile Research Institute, Beijing, China
raw_author_name: Yuheng Tu
raw_affiliation_string: School of Information Science and Engineering, Southeast University, Nanjing, China
raw_author_name: Tiecheng Song
raw_affiliation_string: School of Information Science and Engineering, Southeast University, Nanjing, China
raw_author_name: Yinfei Xu
raw_affiliation_string: School of Information Science and Engineering, Southeast University, Nanjing, China
raw_author_name: Jing Hu
raw_affiliation_string: School of Information Science and Engineering, Southeast University, Nanjing, China
raw_author_name: Lun Xin
raw_affiliation_string: China Mobile Research Institute, Beijing, China

Abstract: Federated learning (FL), as a potential machine learning framework for privacy preservation, has gained significant attention. However, the considerable communication overhead associated with FL remains a prominent challenge. To mitigate this issue, a nonuniform quantization scheme based on Lloyd-Max algorithm is introduced in this letter. By employing this approach, less communication resources are consumed to achieve the same performance. Through performance analysis and numerical simulations, we verify the convergence and effectiveness of the proposed algorithm. It demonstrates the potential of our approach in reducing communication overhead while maintaining reliable performance in FL systems.
Enhancing Gesture Recognition in Low-Light Conditions Using Laser-Induced Graphene Flexible Strain Sensors

Authors:
raw_author_name: Zhaohui Zhang
raw_affiliation_string: Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
raw_author_name: Xiaoyan Zhao
raw_affiliation_string: Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China

Abstract: Gesture recognition using machine learning methods is valuable in the development of healthcare and human-computer interaction, and typically relies on images or video. To improve recognition accuracy, these visual data can be combined with data from other sensors, but this approach, known as data fusion, is limited by the quality of sensor data and incompatible datasets. Here, we propose a flexible strain sensor based on laser-induced graphene technology, which is easy to manufacture and has superior performance. The sensor is attached to the finger and effectively captures finger bending information as an additional gesture information channel, constructing a bimodal gesture interaction interface for strain and vision. In particular, a deep convolutional neural network based on machine learning is used to acquire features in monocular camera image data. The data fusion method of feature layer fusion is selected to construct a bimodal gesture recognition system based on image data and strain sensor data. Experimental tests show that the strain and vision bimodal gesture interaction interface developed based on this fusion recognition model can still achieve more than 90% real-time gesture recognition accuracy under an illumination level of 20 lux, which effectively solves the problem of the traditional monocular vision’s gesture interaction interface with a significant decrease in recognition accuracy under dark light conditions, and also further expands the application of LIG flexible sensor devices in human-computer interaction system.
Enhancing Life Still Sketch Skills Through Virtual Reality Technology

Authors:
raw_author_name: Quan Wen
raw_affiliation_string: Sultan Idris University of Education, Malaysia
raw_author_name: Abdul Aziz Zalay
raw_affiliation_string: Sultan Idris University of Education, Malaysia
raw_author_name: Bin Huang
raw_affiliation_string: Mianyang Teachers' College, China

Abstract: This study addresses a significant gap in research concerning the utilization of virtual reality (VR) technology within higher education in China. Focusing on Mianyang Teachers' College in Sichuan, the research explores the potential integration of VR into the practice of life still sketching. Using a qualitative case study approach, ten experts, comprising five in life still sketching and five in VR technology, were interviewed. Thematic analysis of the data revealed that life still sketching is viewed as a highly subjective mode of artistic expression, emphasizing adherence to fundamental aesthetic principles. The study underscores the importance of factors such as the art's current status, exploratory nature, instructional objectives, and technical aspects for effective life still sketching. VR technology, with devices like PICO and QUEST2, emerges as a promising tool to enhance this artistic practice, offering innovative solutions to its challenges. This research contributes valuable insights to the intersection of VR and art education in the Chinese higher education context.