The governance technology of decentralized autonomous organizations (DAOs) provides an effective solution for solving existing organizational management issues. Governance mechanisms of DAOs are usually encoded in smart contracts in the form of rule sets and executed automatically. However, the mechanism’s logical flaws and code errors expose DAOs to unpredictable risks. Complex adaptive dynamics computer-aided design (CadCAD) can test the effectiveness of the mechanisms through simulation. Nonetheless, as DAOs are typical complex systems with social and engineering complexity, managing, controlling, and supervising their operation through traditional methods are difficult. The parallel intelligence theory based on artificial societies, computational experiments, and parallel execution (ACP) method provides an effective research framework and practical method for solving DAOs’ governance issues. Therefore, in this article, we propose a parallel mechanism verification method and execution system, namely, complex adaptive systems for computer-aided dynamic design (casCAD2) as an extension of cadCAD. Leveraging parallel intelligence and cyber–physical–social systems (CPSS), casCAD2 is capable of probing into the laws that govern system evolution within a simulated environment. It serves as a robust tool for verifying the efficacy of DAOs’ mechanisms and predicting their potential risks. We also build a parallel market-based anchoring mechanism (MAM) system to demonstrate how it can be used for DAOs’ mechanism verification. This study can provide a new research method and application system for DAOs’ effective governance.

The rise of Artificial Intelligence for Science (AI4S) has highlighted the importance and urgency of ensuring open-ness, fairness, impartiality, diversity, and sustainability in scientific systems. Existing scientific systems, referred to as Centralized Science (CeSci), are built on centralized organizational structures and top-down institutional frameworks, which are lagging behind the development and practical requirements of AI4S. To address these limitations, AI4S needs to embrace a new scientific organizational and operational paradigm, namely Decentralized Science (DeSci). It can provide strong support to AI4S via effectively addressing issues such as information silos, biases, unfair distribution, and monopolies and promoting multidisciplinary, interdisciplinary, and trans disciplinary cooperation in science. Based on these considerations, this paper presents the framework of AI4S based on DeSci and explores its potential application scenarios and research issues. The research can provide effective guidance for the development of scientific systems.

Decentralized autonomous organizations (DAOs) have become an indispensable part of digital infrastructure in recent years. The unique organizational characteristics and functional structure empower them to become an effective tool for solving corporate governance issues, including contract risks, principal-agent dilemmas, etc. However, DAOs themselves also face a variety of governance issues. On one hand, as a new economic organization model, the existing corporate governance theories and methods are no longer fully applicable to DAOs. On the other hand, unpredictable logic vulnerabilities and code loopholes in the governance mechanism might cause devastating damage to DAOs. The parallel intelligence theory based on the ACP method (i.e., artificial systems, computational experiments, and parallel execution) is an elegant research paradigm and a practical approach tailored to solving these challenges. As such, we propose a novel parallel governance framework for DAOs based on the parallel intelligence theory and further discuss its technical methodology and implementation model. Furthermore, we construct a parallel governance system for GnosisDAO and conduct computational experiments to validate the effectiveness of its governance mechanism. The experimental results not only confirm the defects of the GnosisDAO governance mechanism but also illustrate parallel governance as a useful research direction to solve existing governance problems of DAOs.

The new generation of digital intelligence technology enables knowledge creation, dissemination, and application to undergoing parallel changes. Scientific systems face an increasingly uncertain, diverse, and complex environment, making adopting multidisciplinary, interdisciplinary, and transdisciplinary approaches to research issues inevitable. Existing scientific systems follow linear value streams, leading to problems, such as inefficiency, unfairness, and knowledge monopoly. Decentralized science (DeSci) is a new scientific development paradigm based on Web3, Metaverses, and decentralized autonomous organizations and operations (DAOs) technologies, that can solve organizational and management problems in scientific systems through organizing, coordinating, and executing techniques. However, new economic theories and methods are still needed to effectively solve the problem of linear value flow in scientific systems. Metaeconomics based on the parallel intelligence theory, also known as decentralized economics (DeEco), provides a new approach and idea for redesigning the economic system of scientific markets. Thus, this article proposes a research framework and core mechanisms of DeSci MetaMarkets based on parallel economic theory to provide effective and practical methodologies for scientific system governance.

Empowered by blockchain and Web3 technologies, decentralized autonomous organizations (DAOs) are able to redefine resources, production relations, and organizational structures in a revolutionary manner. This article aims to reanalyze DAOs from the perspectives of organization and operation, and provide a more precise definition of DAOs as Decentralized Autonomous Organizations and Operations. Based on this, the fundamental principles and requirements of DAOs are explained, while the infrastructure based on cyber–physical–social system (CPSS) and parallel intelligence, as well as the supporting technologies, such as digital twins, metaverse, and Web3, are discussed. Besides, a five-layer intelligent architecture is presented, and the closed-loop equation and new function-oriented intelligent algorithms are also proposed. Moreover, the governance mechanisms from the individual, organizational and social perspectives are discussed, and the incentive mechanisms for the human, robot, and digital human are analyzed. This article can be regarded as a stepping stone for further research and developments of DAOs.

Decentralized science (DeSci) is a hot topic emerging with the development of Web3 or Web3.0 and decentralized autonomous organizations (DAOs) and operations. DeSci fundamentally differs from the centralized science (CeSci) and Open Science (OS) movement built in the centralized way with centralized protocols. It changes the basic structure and legacy norms of current scientific systems via reshaping the cooperation mode, value system, and incentive mechanism. As such, it can provide a viable path for solving bottleneck problems in the development of science, such as oligarchy, silos, and so on, and make science more fair, free, responsible, and sensitive. However, DeSci itself still faces many challenges, including scaling, balancing the quality of participants, system suboptimal loops, lack of accountability mechanism, and so on. Taking these into consideration, this article presents a systematic introduction of DeSci, proposes a novel reference model with a six-layer architecture, addresses the potential applications, and also outlines the key research directions in this emerging field. This article is committed to providing helpful guidance and reference for future research efforts on DeSci.

Welcome to the third issue of IEEE Transactions on Computational Social Systems (TCSS) of 2022. According to the latest update of CiteScoreTracker from Elsevier Scopus released on April 6, 2022, the CitesSore of IEEE TCSS has reached a historical high of 8.4. Many thanks to all for your great effort and support.

Being a class of effective feature descriptors for action recognition, action representations based on skeleton sequences have yielded excellent recognition results. Most methods used to construct these action representations are based on the information from all the joint positions in actions. Unfortunately, some joints in the actions do not improve the accuracy of action recognition, and may even cause unnecessary inter-class errors. In this study, the authors propose a new method for action recognition by selecting active joints which are closely related to the movement of the body as the first step. Further, a skeleton is characterized as a set of its active-joint positions, and the set can be mapped to a point on pre-shape space to filter out the scale and translation variability. Then, a skeleton sequence (an action) can be regarded as points on the space. Because the timing-sequence relationship between skeletons is very valuable for action recognition, a tensor-based linear dynamical system (tLDS) is employed to model the temporal information of the action. To avoid using a finite-order sequence to estimate the infinite-order feature descriptor of a tLDS, the descriptor is mapped to a point on an infinite Grassmannian composed of the extended observability subspaces. The action is classified using sparse coding and dictionary learning (SCDL) on the infinite Grassmannian. Experimental results demonstrate that the recognition accuracies of the proposed method outperform state-of-the-art ones on four different action datasets.

Decentralized autonomy is a long-standing research topic in information sciences and social sciences. The self-organization phenomenon in natural ecosystems, the Cyber Movement Organizations (CMOs) on the Internet, and the Distributed Artificial Intelligence (DAI), and so on, can all be regarded as its early manifestations. In recent years, the rapid development of blockchain technology has spawned the emergence of the so-called Decentralized Autonomous Organization [DAO, sometimes labeled as Decentralized Autonomous Corporation (DAC)], which is a new organization form that the management and operational rules are typically encoded on blockchain in the form of smart contracts, and can autonomously operate without centralized control or third-party intervention. DAO is expected to overturn the traditional hierarchical management model and significantly reduce organizations’ costs on communication, management, and collaboration. However, DAO still faces many challenges, such as security and privacy issue, unclear legal status, and so on. In this article, we strive to present a systematic introduction of DAO, including its concept and characteristics, research framework, typical implementations, challenges, and future trends. Especially, a novel reference model for DAO which employs a five-layer architecture is proposed. This article is aimed at providing helpful guidance and reference for future research efforts.

In recent years, there has been renewed interest in developing methods for skeleton‐based human action recognition. In this study, the challenging problem of the similarity degree of skeleton‐based human postures is addressed. Human posture is described by screw motions between 3D rigid bodies, which can be seen as a relation matrix of 3D rigid bodies (RMRB3D). A linear subspace, a point of a Grassmannian manifold, is spanned by the orthonormal basis of matrix RMRB3D. A powerful way to compute the similarity degree between postures is researched to solve the geodesic distance between points on the Grassmannian manifold. Then representative postures are extracted through spectral clustering over representative postures. An action will be represented by a symbol sequence generated with a global linear eigenfunction constructed by spectral embedding. Finally, dynamic time warping and hidden Markov model (HMM) are used to classify these action sequences. The experimental evaluations of the proposed method on several challenging 3D action datasets show that the proposed approaches achieve promising results compared with other skeleton‐based human action recognition algorithms.

Predicting student learning outcomes is one of the prominent themes in Learning Analytics research. These studies varied to a significant extent in terms of the techniques being used, the contexts in which they were situated, and the consequent effectiveness of the prediction. This paper presented the preliminary results of a systematic review of studies in predictive learning analytics. With the goal to find out what methodologies work for what circumstances, this study will be able to facilitate future research in this area, contributing to relevant system developments that are of pedagogic values.

Human activity recognition has many valuable applications in computer vision. Unlike existing works, the challenging problem of the similarity degree of skeleton-based human postures is addressed. In this paper, the Relation Matrix of 3D Rigid Bodies (RMRB3D), which is a compact representation of postures, makes a powerful way to compute the similarity degree between postures. Then representative postures are built through Spectral Clustering (SC) on sample data and action sequences of discrete symbols will be generated according to a global linear eigenfunction constructed by Spectral Embedding (SE). Finally, action classifier can be modeled as temporal order by using Dynamic Time Warping (DTW) and Hidden Markov Model (HMM). The experimental evaluations of the proposed method on challenging 3D action datasets show that our approach achieves promising results.