The evolution of modern mobile terminals, social networks, and other intelligent services makes everyone become a ubiquitous information perceiver, producer, and propagator. Also known as “social sensor” and “social IoT,” these individuals and communities generate a huge volume of social signals, which has shown prominent value for mining. These unstructured social signals provide a new perspective in the research of complex systems, which makes the traditional cyber–physical system (CPS)-oriented information computing sublimate to the cyber–physical–social system (CPSS)-oriented knowledge computing. However, there still exist great uncertainties, ambiguities, and complexities in modeling behaviors of social individuals or groups. Especially when we apply big-data-driven learning-based models in specific fields and scenarios, the lack of domain expert knowledge and characteristics of system uncertainty severely limits the performance and accuracy of these models. The introduction of fuzzy system modeling integrates data and knowledge in the social computing area, which has shown its unique advantages in solving the above issues and has drawn more attention to this topic. In this article, we conduct a review of recent advances in social computing with fuzzy technologies in CPSS. First, we briefly review the development of social computing, and analyze the characteristics and advantages of social computing through fuzzy methods. Second, we refine core fuzzy system methods for social computing and elaborate on existing fuzzy-technology-empowered social computing methodologies. As in a range of social spaces, we also review and analyze related advances in human-in-the-loop systems. We also reveal the trend of decentralized, autonomous, and organized computing in cyber–physical–social space with fuzzy-based methods and proposed a framework to categorize related studies in CPSS. Finally, we conclude the research trends and hotspots based on current studies, and discuss the challenges for future research directions.

Different from traditional source coding techniques, distributed source coding (DSC) techniques rely on independent encoding at the encoding end but joint decoding at the decoding end to compress image sources which exhibit correlation. Channel code-based DSC techniques compress correlated image sources by fully utilizing such correlation. However, in addition to this correlation information, the current symbol of most real image sources is correlated to the preceding or subsequent symbols. Such correlation information should also play an important role for improving the compression performance of channel code-based DSC schemes. To this end, we present an efficient message passing mechanism for LDPC Code-based DSC schemes (ELCDSC) to compress correlated image sources. By utilizing this message passing mechanism, we enable LDPC code-based DSC techniques to not only make full use of the inter-source correlation to assist compression, but also integrate the intra-source correlation in each message passing iteration to improve the compression performance. It is the first that enables LDPC code-based DSC techniques to achieve the utilization of both intra- and inter-source correlations in the message passing mechanism. Experimental results reveal that ELCDSC significantly enhances the compression ratio of correlated image sources, surpassing other DSC schemes.

Breast cancer (BC) seriously threatens women’s health. The establishment of a risk evaluation model cancer is conducive to early screening and prevention of BC. In this article, a new method of the evaluation and the framework of prevention for BC are proposed by synthesizing interval type-2 fuzzy sets (IT2 FSs), two-level fuzzy comprehensive evaluation, and parallel control (Artificial societies, Computational experiments, and Parallel execution, ACP). At the same time, 12 risk factors were selected as indicators to evaluate the risk level of BC, and then, according to the evaluation results, the correspondingly prevention strategies and intervention measures for different risk levels were discussed. Then, the prevention strategies are analyzed and selected in the computational experiment module of the artificial system, and the patient indexes are monitored in real time by the parallel mechanism between the actual and the artificial system, to feedback, adjust, and optimize the prevention scheme in time. This parallel BC prevention process can achieve dynamic closed-loop control effects. A new effective way for BC prevention was presented, which is of great significance for reducing its incidence rate and developing new medical means.

Welcome to the second issue of the IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (TCSS) of 2023. According to the latest update of CiteScoreTracker from Elsevier Scopus released on February 5, 2023, the CitesSore of TCSS has reached a historical high of 9.6. Many thanks to all for your great effort and support.

Neonatal pathological jaundice (NPJ) is easy to cause bilirubin encephalopathy, which has high mortality and sequelae rate. Therefore, accurate risk evaluation can help clinicians take appropriate measures to timely intervene in neonatal jaundice level and avoid complications. In this article, five indexes are extracted as the factor set for the risk evaluation of NPJ, and the diagnostic criteria are determined. Then, five index sets are described by interval type-2 fuzzy sets, and the corresponding membership functions and membership function figures are provided. The feasibility of interval type-2 fuzzy comprehensive evaluation in risk evaluation of NPJ is demonstrated through example, and the fuzzy rule bases of prevention and treatment for NPJ are constructed according to the results of risk evaluation. Finally, this article demonstrates that the proposed risk evaluation and treatment process of NPJ is actually a dynamic closed-loop control process, which is consistent with the clinical treatment process. This article provides a new solution for the aided diagnosis and decision-making treatment of NPJ, which is of great significance in reducing neonatal mortality and alleviating the pressure of medical staff.

Existing crowd counting designs usually exploit multi-branch structures to address the scale diversity problem. However, branches in these structures work in a competitive rather than collaborative way. In this paper, we focus on promoting collaboration between branches. Specifically, we propose an attention-guided collaborative counting module (AGCCM) comprising an attention-guided module (AGM) and a collaborative counting module (CCM). The CCM promotes collaboration among branches by recombining each branch's output into an independent count and joint counts with other branches. The AGM capturing the global attention map through a transformer structure with a pair of foreground-background related loss functions can distinguish the advantages of different branches. The loss functions do not require additional labels and crowd division. In addition, we design two kinds of bidirectional transformers (Bi-Transformers) to decouple the global attention to row attention and column attention. The proposed Bi-Transformers are able to reduce the computational complexity and handle images in any resolution without cropping the image into small patches. Extensive experiments on several public datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art crowd counting methods.

Existing NAS methods for dense image prediction tasks usually compromise on restricted search space or search on proxy task to meet the achievable computational demands. To allow as wide as possible network architectures and avoid the gap between realistic and proxy setting, we propose a novel Densely Connected NAS (DCNAS) framework, which directly searches the optimal network structures for the multi-scale representations of visual information, over a large-scale target dataset without proxy. Specifically, by connecting cells with each other using learnable weights, we introduce a densely connected search space to cover an abundance of mainstream network designs. Moreover, by combining both path-level and channel-level sampling strategies, we design a fusion module and mixture layer to reduce the memory consumption of ample search space, hence favoring the proxyless searching. Compared with contemporary works, experiments reveal that the proxyless searching scheme is capable of bridging the gap between searching and training environments. Further, DCNAS achieves new state-of-the-art performances on public semantic image segmentation benchmarks, including 84.3% on Cityscapes, and 86.9% on PASCAL VOC 2012. We also retain leading performances when evaluating the architecture on the more challenging ADE20K and PASCAL-Context dataset.

Crowd counting has drawn more and more attention in computer vision. There are two mainstream approaches to deal with crowd counting tasks, regression and detection. Regression-based methods usually overestimate the count in sparse areas, while detection-based methods tend to underestimation in dense areas. In this paper, we propose a two-branch network combining regression and detection. We introduce the attention mechanism to make the network adaptively divide dense and sparse areas and employ appropriate methods on them respectively. The regression branch predicts density map in extremely dense areas. An improved detection network is applied to detect multi-scale heads in relatively sparse areas. Our method is able to obtain precise head bounding boxes in sparse areas with ensuring counting accuracy in dense areas. Experimental results show that our method achieves state-of-the-art on challenging public crowd counting datasets.

Crowd counting is a challenging problem due to the diverse crowd distribution and background interference. In this paper, we propose a new approach for head size estimation to reduce the impact of different crowd scale and background noise. Different from just using local information of distance between human heads, the global information of the people distribution in the whole image is also under consideration. We obey the order of far- to near-region (small to large) to spread head size, and ensure that the propagation is uninterrupted by inserting dummy head points. The estimated head size is further exploited, such as dividing the crowd into parts of different densities and generating a high-fidelity head mask. On the other hand, we design three different head mask usage mechanisms and the corresponding head masks to analyze where and which mask could lead to better background filtering1. Based on the learned masks, two competitive models are proposed which can perform robust crowd estimation against background noise and diverse crowd scale. We evaluate the proposed method on three public crowd counting datasets of ShanghaiTech [2], UCFQNRF [3] and UCFCC_50 [4]. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art crowd counting approaches.

In this paper, we present a HAnd Mesh Recovery (HAMR) framework to tackle the problem of reconstructing the full 3D mesh of a human hand from a single RGB image. In contrast to existing research on 2D or 3D hand pose estimation from RGB or/and depth image data, HAMR can provide a more expressive and useful mesh representation for monocular hand image understanding. In particular, the mesh representation is achieved by parameterizing a generic 3D hand model with shape and relative 3D joint angles. By utilizing this mesh representation, we can easily compute the 3D joint locations via linear interpolations between the vertexes of the mesh, while obtain the 2D joint locations with a projection of the 3D joints. To this end, a differentiable re-projection loss can be defined in terms of the derived representations and the ground-truth labels, thus making our framework end-to-end trainable. Qualitative experiments show that our framework is capable of recovering appealing 3D hand mesh even in the presence of severe occlusions. Quantitatively, our approach also outperforms the state-of-the-art methods for both 2D and 3D hand pose estimation from a monocular RGB image on several benchmark datasets.

In general, the evaluation for tourist attractive competency is always by means of natural language, owing to the difficulty for people to use conventional mathematics method to describe the evaluation modeling and their evaluation results. In this paper, discrete and partially connected type-2 fuzzy sets, fuzzy comprehension evaluation, are synthesized, and then discrete type-2 fuzzy comprehension valuation and partially connected type-2 fuzzy comprehension evaluation are presented to provide the analysis of evaluation and represent the results of different conditions. Finally, the method of evaluation for tourist attractive competency is given, and the results illustrate that the method can describe the otherness of evaluated data.

In China, the traffic police's micro-bo provides instant information for travelers and helps drivers to avoid congested roads. Management rules and laws, which are compatible with the traffic situation, help to maintain the road traffic order, improve traffic flow smoothness, and prevent traffic-related accidents. Formulating reasonable rules and laws is the key to traffic management. Furthermore, the analysis of traffic flow is good for the formulation of traffic laws and rules. In this paper, the congestion time, congested place, and congestion reason are analyzed on the traffic police's micro-bo, and the theories of linguistic dynamic systems based on multifactor time-varying universe and fuzzy comprehension evaluation are used to analyze traffic flow. In addition, dynamic fuzzy rules on time-varying universe are built to provide the corresponding traffic management rules. As an example, Shenzhen's traffic police micro-bo is used to study the information of traffic congestion, including jam session, congestion location and reasons, and disposal methods, and these results are presented by language form, i.e., keywords walls, and then the linguistic dynamic evolutionary process of working day congestion positions from January to April. Finally, the traffic flow during Labor Day is discussed. The analysis results show that jam sessions, congestion locations and reasons, and disposal methods are very different between working day congestion and holiday congestion.