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Symmetry
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Symmetry and Asymmetry in AI-Enabled Human-Centric Collaborative Computing
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Symmetry and Asymmetry in AI-Enabled Human-Centric Collaborative Computing

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 27220

Special Issue Editors

Prof. Dr. Lianyong Qi

E-Mail Website
Guest Editor
College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
Interests: explainable AI; recommendation systems
Special Issues, Collections and Topics in MDPI journals
Dr. Wajid Rafiq

E-Mail Website
Guest Editor
Department of Computer Sciences, University of Montreal, Montréal, QC, Canada
Interests: internet of things; blockchain; wireless networks
Dr. Wenwen Gong

E-Mail Website
Guest Editor
School of Information Science and Technology, Tsinghua University, Beijing, China
Interests: big data; AI; recommender systems
Dr. Maqbool Khan

E-Mail Website
Guest Editor
Software Competence Center Hagenberg, Hagenberg, Austria
Interests: big data analytics; cloud computing; predictive maintenance; explainable AI; knowledge graphs; data science and machine learning

Special Issue Information

Dear Colleagues,

Over the past few decades, the trajectory of daily human activities has become closely intertwined with cyberspace, resulting in a vast amount of human-centric digital information on an unprecedented scale. Human-Centric Collaborative Computing (HCCC) has emerged as a cross-disciplinary cutting-edge research domain enabling the effective integration of these various human-related computational elements, thus significantly benefiting the interactions and collaborations among the physical devices, cyberspace and human activity. The unprecedented volume of human-centric data generated by HCCC requires the support of powerful computing, raising a serious challenge in this field.

Recently, Artificial Intelligence (AI), such as Deep Learning (DL), has emerged as a key technologies in realizing intelligent digital information processing. Through AI-based HCCC techniques, end users’ sophisticated functional and nonfunctional requirements can be satisfied. However, since the proportion of data with different labels is often uneven, some researchers have incorporated symmetries into deep learning models and architectures to solve this issue while reducing the model’s complexity. Symmetry and asymmetry, as key structural properties of human-centric data, are often ignored by state-of-the-art HCCC studies. Furthermore, studies have found that learning is most efficient when these symmetries are compatible with those of the data distribution.

Therefore, the symmetry and asymmetry issues in AI-based methods deserve more attention, calling for efforts aimed at guaranteeing computing quality and achieving their full potential in HCCC applications. We invite both original research and reviews presenting recent results in a unified and systematic way.

Prof. Dr. Lianyong Qi
Dr. Wajid Rafiq
Dr. Wenwen Gong
Dr. Maqbool Khan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • symmetric learning frameworks
  • deep learning for symmetry
  • human-centric data management and balance/imbalance analysis
  • information diffusion and modelling in HCCC
  • deep learning for intelligent human computer interaction
  • symmetry/asymmetry network structure and community evolution analysis
  • human–cyber–physical interactions with symmetry/asymmetry
  • knowledge-driven human–computer interaction in cloud/edge
  • smart service quality optimization in HCCC
  • AI-enabled multi-agent systems in HCCC
  • AI-powered smart applications in HCCC

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Published Papers (8 papers)

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Research

26 pages, 5290 KiB  
Article
Deep Residual Network with a CBAM Mechanism for the Recognition of Symmetric and Asymmetric Human Activity Using Wearable Sensors
by Sakorn Mekruksavanich and Anuchit Jitpattanakul
Symmetry 2024, 16(5), 554; https://doi.org/10.3390/sym16050554 - 3 May 2024
Cited by 7 | Viewed by 1908
Abstract
Wearable devices are paramount in health monitoring applications since they provide contextual information to identify and recognize human activities. Although sensor-based human activity recognition (HAR) has been thoroughly examined, prior studies have yet to definitively differentiate between symmetric and asymmetric motions. Determining these [...] Read more.
Wearable devices are paramount in health monitoring applications since they provide contextual information to identify and recognize human activities. Although sensor-based human activity recognition (HAR) has been thoroughly examined, prior studies have yet to definitively differentiate between symmetric and asymmetric motions. Determining these movement patterns might provide a more profound understanding of assessing physical activity. The main objective of this research is to investigate the use of wearable motion sensors and deep convolutional neural networks in the analysis of symmetric and asymmetric activities. This study provides a new approach for classifying symmetric and asymmetric motions using a deep residual network incorporating channel and spatial convolutional block attention modules (CBAMs). Two publicly accessible benchmark HAR datasets, which consist of inertial measurements obtained from wrist-worn sensors, are used to assess the model’s efficacy. The model we have presented is subjected to thorough examination and demonstrates exceptional accuracy on both datasets. The ablation experiment examination also demonstrates noteworthy contributions from the residual mappings and CBAMs. The significance of recognizing basic movement symmetries in increasing sensor-based activity identification utilizing wearable devices is shown by the enhanced accuracy and F1-score, especially in asymmetric activities. The technique under consideration can provide activity monitoring with enhanced accuracy and detail, offering prospective advantages in diverse domains like customized healthcare, fitness tracking, and rehabilitation progress evaluation. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in AI-Enabled Human-Centric Collaborative Computing)
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18 pages, 554 KiB  
Article
AI-Enabled Consensus Algorithm in Human-Centric Collaborative Computing for Internet of Vehicle
by Chenxi Sun, Danyang Li, Beilei Wang and Jie Song
Symmetry 2023, 15(6), 1264; https://doi.org/10.3390/sym15061264 - 15 Jun 2023
Cited by 4 | Viewed by 2217
Abstract
With the enhanced interoperability of information among vehicles, the demand for collaborative sharing among vehicles increases. Based on blockchain, the classical consensus algorithms in collaborative IoV (Internet of Vehicle), such as PoW (Proof of Work), PoS (Proof of Stake), and DPoS (Delegated Proof [...] Read more.
With the enhanced interoperability of information among vehicles, the demand for collaborative sharing among vehicles increases. Based on blockchain, the classical consensus algorithms in collaborative IoV (Internet of Vehicle), such as PoW (Proof of Work), PoS (Proof of Stake), and DPoS (Delegated Proof of Stake), only consider the node features, which is hard to adapt to the immediacy and flexibility of vehicles. On the other hand, classical consensus algorithms often require mass computing, which undoubtedly increases the communication overhead, resulting in the inability to achieve collaborative IoV under asymmetric networks. Therefore, proposing a low failure rate consensus algorithm that takes into account running time and energy consumption becomes a major challenge in IoV applications. This paper proposes an AI-enabled consensus algorithm with vehicle features, combining vehicle-based metrics and neural networks. First, we introduce vehicle-based metrics such as vehicle online time, performance, and behavior. Then, we propose an integral model and a hierarchical classification method, which combine with a BP neural network to obtain the optimal solution for interconnection. Among them, we also use Informer to predict the future online duration of vehicles, which effectively solves the situation that the primary node vehicle drops off in collaborative IoV. Finally, the experimentations show that the vehicle-based metrics eliminate the problem of the primary node vehicle being offline, which realizes the collaborative IoV considering vehicle features. Meanwhile, it reduces the vehicle network system delay and energy consumption. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in AI-Enabled Human-Centric Collaborative Computing)
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17 pages, 3785 KiB  
Article
A New Semantic Segmentation Method for Remote Sensing Images Integrating Coordinate Attention and SPD-Conv
by Zimeng Yang, Qiulan Wu, Feng Zhang, Xueshen Zhang, Xuefei Chen and Yue Gao
Symmetry 2023, 15(5), 1037; https://doi.org/10.3390/sym15051037 - 8 May 2023
Cited by 14 | Viewed by 3197
Abstract
Semantic segmentation is an important task for the interpretation of remote sensing images. Remote sensing images are large in size, contain substantial spatial semantic information, and generally exhibit strong symmetry, resulting in images exhibiting large intraclass variance and small interclass variance, thus leading [...] Read more.
Semantic segmentation is an important task for the interpretation of remote sensing images. Remote sensing images are large in size, contain substantial spatial semantic information, and generally exhibit strong symmetry, resulting in images exhibiting large intraclass variance and small interclass variance, thus leading to class imbalance and poor small-object segmentation. In this paper, we propose a new remote sensing image semantic segmentation network, called CAS-Net, which includes coordinate attention (CA) and SPD-Conv. In the model, we replace stepwise convolution with SPD-Conv convolution in the feature extraction network and add a pooling layer into the network to avoid the loss of detailed information, effectively improving the segmentation of small objects. The CA is introduced into the atrous spatial pyramid pooling (ASPP) module, thus improving the recognizability of classified objects and target localization accuracy in remote sensing images. Finally, the Dice coefficient was introduced into the cross-entropy loss function to maximize the gradient optimization of the model and solve the classification imbalance problem in the image. The proposed model is compared with several state-of-the-art models on the ISPRS Vaihingen dataset. The experimental results demonstrate that the proposed model significantly optimizes the segmentation effect of small objects in remote sensing images, effectively solves the problem of class imbalance in the dataset, and improves segmentation accuracy. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in AI-Enabled Human-Centric Collaborative Computing)
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13 pages, 320 KiB  
Article
A Levenberg-Marquardt Method for Tensor Approximation
by Jinyao Zhao, Xuejuan Zhang and Jinling Zhao
Symmetry 2023, 15(3), 694; https://doi.org/10.3390/sym15030694 - 10 Mar 2023
Cited by 3 | Viewed by 1979
Abstract
This paper presents a tensor approximation algorithm, based on the Levenberg–Marquardt method for the nonlinear least square problem, to decompose large-scale tensors into the sum of the products of vector groups of a given scale, or to obtain a low-rank tensor approximation without [...] Read more.
This paper presents a tensor approximation algorithm, based on the Levenberg–Marquardt method for the nonlinear least square problem, to decompose large-scale tensors into the sum of the products of vector groups of a given scale, or to obtain a low-rank tensor approximation without losing too much accuracy. An Armijo-like rule of inexact line search is also introduced into this algorithm. The result of the tensor decomposition is adjustable, which implies that the decomposition can be specified according to the users’ requirements. The convergence is proved, and numerical experiments show that it has some advantages over the classical Levenberg–Marquardt method. This algorithm is applicable to both symmetric and asymmetric tensors, and it is expected to play a role in the field of large-scale data compression storage and large-scale tensor approximation operations. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in AI-Enabled Human-Centric Collaborative Computing)
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30 pages, 1045 KiB  
Article
Collaborative Energy Price Computing Based on Sarima-Ann and Asymmetric Stackelberg Games
by Tiantian Zhang, Yongtang Wu, Yuling Chen, Tao Li and Xiaojun Ren
Symmetry 2023, 15(2), 443; https://doi.org/10.3390/sym15020443 - 7 Feb 2023
Cited by 1 | Viewed by 2237
Abstract
The energy trading problem in smart grids has been of great interest. In this paper, we focus on two problems: 1. Energy sellers’ inaccurate grasp of users’ real needs causes information asymmetry in transactions, making it difficult for energy sellers to develop more [...] Read more.
The energy trading problem in smart grids has been of great interest. In this paper, we focus on two problems: 1. Energy sellers’ inaccurate grasp of users’ real needs causes information asymmetry in transactions, making it difficult for energy sellers to develop more satisfactory pricing strategies for users based on those real needs. 2. The uneven variation of user demand causes the grid costs to increase. In this paper, we design a collaborative pricing strategy based on the seasonal autoregressive integrated moving average-artificial neural network (Sarima-Ann) and an asymmetric Stackelberg game. Specifically, we propose a dissatisfaction function for users and an incentive function for grid companies to construct a utility function for both parties, which introduces an incentive amount to achieve better results in equilibrating user demand while optimizing the transaction utility. In addition, we constructed a demand fluctuation function based on user demand data and introduced it into the game model to predict the demand by Sarima-Ann, which achieves better prediction accuracy. Finally, through simulation experiments, we demonstrate the effectiveness of our scheme in balancing demand and improving utility, and the superiority of our Sarima-Ann model in terms of forecasting accuracy. Specifically, the peak reduction can reach 94.1% and the total transaction utility increase can reach 4.6 × 107, and better results can be achieved by adjusting the incentive rate. Our Sarima-Ann model improves accuracy by 64.95% over Arima and 64.47% over Sarima under MAE metric evaluation, and also shows superior accuracy under other metrics evaluation. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in AI-Enabled Human-Centric Collaborative Computing)
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19 pages, 621 KiB  
Article
A Secure and Lightweight Multi-Party Private Intersection-Sum Scheme over a Symmetric Cryptosystem
by Junwei Zhang, Xin Kang, Yang Liu, Huawei Ma, Teng Li, Zhuo Ma and Sergey Gataullin
Symmetry 2023, 15(2), 319; https://doi.org/10.3390/sym15020319 - 23 Jan 2023
Cited by 7 | Viewed by 9335
Abstract
A private intersection-sum (PIS) scheme considers the private computing problem of how parties jointly compute the sum of associated values in the set intersection. In scenarios such as electronic voting, corporate credit investigation, and ad conversions, private data are held by different parties. [...] Read more.
A private intersection-sum (PIS) scheme considers the private computing problem of how parties jointly compute the sum of associated values in the set intersection. In scenarios such as electronic voting, corporate credit investigation, and ad conversions, private data are held by different parties. However, despite two-party PIS being well-developed in many previous works, its extended version, multi-party PIS, has rarely been discussed thus far. This is because, depending on the existing works, directly initiating multiple two-party PIS instances is considered to be a straightforward way to achieve multi-party PIS; however, by using this approach, the intersection-sum results of the two parties and the data only belonging to the two-party intersection will be leaked. Therefore, achieving secure multi-party PIS is still a challenge. In this paper, we propose a secure and lightweight multi-party private intersection-sum scheme called SLMP-PIS. We maintain data privacy based on zero sharing and oblivious pseudorandom functions to compute the multi-party intersection and consider the privacy of associated values using arithmetic sharing and symmetric encryption. The security analysis results show that our protocol is proven to be secure in the standard semi-honest security model. In addition, the experiment results demonstrate that our scheme is efficient and feasible in practice. Specifically, when the number of participants is five, the efficiency can be increased by 22.98%. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in AI-Enabled Human-Centric Collaborative Computing)
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21 pages, 811 KiB  
Article
Extinction and Ergodic Stationary Distribution of COVID-19 Epidemic Model with Vaccination Effects
by Humera Batool, Weiyu Li and Zhonggui Sun
Symmetry 2023, 15(2), 285; https://doi.org/10.3390/sym15020285 - 19 Jan 2023
Cited by 3 | Viewed by 1699
Abstract
Human society always wants a safe environment from pollution and infectious diseases, such as COVID-19, etc. To control COVID-19, we have started the big effort for the discovery of a vaccination of COVID-19. Several biological problems have the aspects of symmetry, and this [...] Read more.
Human society always wants a safe environment from pollution and infectious diseases, such as COVID-19, etc. To control COVID-19, we have started the big effort for the discovery of a vaccination of COVID-19. Several biological problems have the aspects of symmetry, and this theory has many applications in explaining the dynamics of biological models. In this research article, we developed the stochastic COVID-19 mathematical model, along with the inclusion of a vaccination term, and studied the dynamics of the disease through the theory of symmetric dynamics and ergodic stationary distribution. The basic reproduction number is evaluated using the equilibrium points of the proposed model. For well-posedness, we also test the given problem for the existence and uniqueness of a non-negative solution. The necessary conditions for eradicating the disease are also analyzed along with the stationary distribution of the proposed model. For the verification of the obtained result, simulations of the model are performed. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in AI-Enabled Human-Centric Collaborative Computing)
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18 pages, 2017 KiB  
Article
Edge Computing-Based VANETs’ Anonymous Message Authentication
by Chengjun Yang, Jiansheng Peng, Yong Xu, Qingjin Wei, Ling Zhou and Yuna Tang
Symmetry 2022, 14(12), 2662; https://doi.org/10.3390/sym14122662 - 16 Dec 2022
Cited by 3 | Viewed by 2151
Abstract
Vehicular Ad-hoc Networks (VANETs) have high requirements for real-time data processing and security of message authentication. In order to solve the computing power asymmetry between vehicles and road side units (RSUs) in VANETs under high-density traffic, accelerate the processing speed of message authentication, [...] Read more.
Vehicular Ad-hoc Networks (VANETs) have high requirements for real-time data processing and security of message authentication. In order to solve the computing power asymmetry between vehicles and road side units (RSUs) in VANETs under high-density traffic, accelerate the processing speed of message authentication, and solve the problems of high computational overhead and long message authentication time caused by the use of bilinear pairing encryption technology in similar message-batch-authentication schemes, we propose introducing the concept of edge computing (EC) into VANETs and using idle nodes’ resources to assist the RSU in quickly authenticating messages to achieve computing power load balancing under multiple traffic flows. We propose introducing the idea of edge computing (EC) into VANETs and using idle nodes’ resources to assist RSUs in quickly authenticating messages. This scheme performs two identity-based message authentications based on the identity signature constructed by elliptic curve cryptography (ECC). One of them is the batch authentication of the vehicle sending messages by the RSU-authenticated vehicles with free resources, as temporary edge computing nodes (TENs), and the other is the authentication of the temporary TEN messages by the fixed-edge-node RSUs. The resources of the TEN are used to reduce the computational burden of RSUs and message authentication time, thereby improving the efficiency of system authentication of messages. We performed a security analysis of the scheme to prove its security properties and compared it with other schemes in terms of performance. The experimental results show that our scheme has a transmission overhead of 2400 bytes when there are four TENs, and the number of verification message requests reaches 20, which outperforms other methods. The gap will be more evident as the numbers of TEN and message verification requests increase. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in AI-Enabled Human-Centric Collaborative Computing)
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